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Open AccessEditorial

Preface: Proceedings of the 12th International Electronic Conference on Sensors and Applications

by Stefano Mariani, Stefan Bosse, Francisco Falcone and Jean-Marc Laheurte

Eng. Proc. 2025, 118(1), 102; https://doi.org/10.3390/engproc2025118102 - 2 Feb 2026

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Abstract

This issue of Engineering Proceedings gathers the papers presented at the 12th International Electronic Conference on Sensors and Applications (ECSA-12), held online from 12 to 14 November 2025, through the sciforum [...] Full article

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1 pages, 140 KB

Open AccessEditorial

ECSA-12: Statement of Peer-Review

by Stefano Mariani, Stefan Bosse, Francisco Falcone and Jean-Marc Laheurte

Eng. Proc. 2025, 118(1), 103; https://doi.org/10.3390/engproc2025118103 - 2 Feb 2026

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Abstract

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...] Full article

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Open AccessProceeding Paper

CMOS-Compatible Narrow Bandpass MIM Metamaterial Absorbers for Spectrally Selective LWIR Thermal Sensors

by Moshe Avraham, Mikhail Klinov and Yael Nemirovsky

Eng. Proc. 2025, 118(1), 1; https://doi.org/10.3390/ECSA-12-26501 - 7 Nov 2025

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Abstract

The growing demand for compact, low-power infrared (IR) sensors necessitates advanced solutions for on-chip spectral selectivity, particularly for integration with Thermal Metal-Oxide-Semiconductor (TMOS) devices. This paper investigates the design and analysis of CMOS-compatible metal–insulator–metal (MIM) metamaterial absorbers tailored for selective absorption in the [...] Read more.

The growing demand for compact, low-power infrared (IR) sensors necessitates advanced solutions for on-chip spectral selectivity, particularly for integration with Thermal Metal-Oxide-Semiconductor (TMOS) devices. This paper investigates the design and analysis of CMOS-compatible metal–insulator–metal (MIM) metamaterial absorbers tailored for selective absorption in the long-wave infrared (LWIR) region. We present a design methodology utilizing an equivalent-circuit model, which provides intuitive physical insight into the absorption mechanism and significantly reduces computational costs compared to full-wave electromagnetic simulations. An important rule in this design methodology is demonstrating how the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and, critically, by optimizing the dielectric substrate’s refractive index and thickness, which assist in designing small period MIM absorber units which are important in infrared thermal sensor pixels. Our results demonstrate that the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and by optimizing the dielectric substrate’s refractive index and thickness. Specifically, the selection of silicon as the dielectric material, owing to its high refractive index and low losses, facilitates compact designs with high-quality factors. The transmission line model provides intuitive insight into how near-perfect absorption is achieved when the absorber’s input impedance matches the free-space impedance. This work presents a new approach for the methodology of designing MIM absorbers in the mid-infrared and long-wave infrared (LWIR) regions, utilizing the intuitive insights provided by equivalent circuit modeling. This study validates a highly efficient design approach for high-performance, spectrally selective MIM absorbers for LWIR radiation, paving the way for their monolithic integration with TMOS sensors to enable miniaturized, cost-effective, and functionally enhanced IR sensing systems. Full article

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8 pages, 2126 KB

Open AccessProceeding Paper

Scalable Sewer Fault Detection and Condition Assessment Using Embedded Machine Vision

by Timothy Malche

Eng. Proc. 2025, 118(1), 2; https://doi.org/10.3390/ECSA-12-26508 - 7 Nov 2025

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Abstract

Municipal sewer networks span across large areas in cities around the world and require regular inspection to identify structural failures, blockages, and other issues that pose public health risks. Traditional inspection methods rely on remote-controlled robotic cameras or CCTV surveys performed by skilled [...] Read more.

Municipal sewer networks span across large areas in cities around the world and require regular inspection to identify structural failures, blockages, and other issues that pose public health risks. Traditional inspection methods rely on remote-controlled robotic cameras or CCTV surveys performed by skilled inspectors. These processes are time-consuming, expensive, and often inconsistent; for example, the United States alone has more than 1.2 million miles of underground sewer pipes, and up to 75,000 failures are reported annually. Manual CCTV inspections can only cover a small fraction of the network each year, resulting in delayed discovery of defects and costly repairs. To address these limitations, this paper proposes a scalable and low-power fault detection system that integrates embedded machine vision and Tiny Machine Learning (TinyML) on resource-constrained microcontrollers. The system uses transfer learning to train a lightweight TinyML model for defect classification using a dataset of sewer pipe images and deploys the model on battery-powered devices. Each device captures images inside the pipe, performs on-device inference to detect cracks, intrusions, debris, and other anomalies, and communicates inference results over a long-range LoRa radio link. The experimental results demonstrate that the proposed system achieves 94% detection accuracy with sub-hundred-millisecond inference time and operates for extended periods on battery power. The research contributes a template for autonomous, scalable, and cost-effective sewer condition assessment that can help municipalities prioritize maintenance and prevent catastrophic failures. Full article

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10 pages, 3249 KB

Open AccessProceeding Paper

A TinyML Wearable System for Real-Time Cardio-Exercise Tracking

by Timothy Malche

Eng. Proc. 2025, 118(1), 3; https://doi.org/10.3390/ECSA-12-26554 - 7 Nov 2025

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Abstract

Cardiovascular exercise strengthens the heart and improves circulation, but most people struggle to fit regular workouts into their day. Short bursts of vigorous activity, sometimes called exercise snacks, can raise the heart rate and deliver meaningful health benefits. Accurate, real-time monitoring of cardio-exercises [...] Read more.

Cardiovascular exercise strengthens the heart and improves circulation, but most people struggle to fit regular workouts into their day. Short bursts of vigorous activity, sometimes called exercise snacks, can raise the heart rate and deliver meaningful health benefits. Accurate, real-time monitoring of cardio-exercises is essential to ensure that these workouts meet recommended intensity and rest guidelines. This paper proposes a Tiny Machine Learning (TinyML) wearable system that tracks the duration and type of common cardio-exercises in real time. A compact device containing a six-axis inertial measurement unit (IMU) is worn on the arm. The device streams accelerometer data to an on-device neural network model, which classifies exercises such as jumping jacks, squat jumps and jogging in place and resting states. The TinyML model is trained with labelled motion data and deployed on a microcontroller using quantization to meet memory and latency constraints. Preliminary tests with ten participants show that the system correctly recognizes the targeted exercises with around 95% accuracy and an average F1 score of 0.93 while maintaining inference latency below 100 ms and a memory footprint under 60 KB. By prompting users to alternate 30–60 s of high-intensity exercise with rest periods, the device can structure effective interval routines. This work demonstrates how TinyML can enable low-cost, low-power wearables for personalized cardiovascular exercise monitoring. Full article

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11 pages, 2173 KB

Open AccessProceeding Paper

Optimized Electrode Configurations for Multi-Parameter Detection in Microfluidic Impedance Cytometry

by Shengzhi Ji, Huancheng Zhang, Zhiyang Hu and Tieying Xu

Eng. Proc. 2025, 118(1), 4; https://doi.org/10.3390/ECSA-12-26486 - 7 Nov 2025

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Abstract

Microfluidic impedance cytometry enables label-free and real-time single-cell analysis by detecting changes in electrical impedance as cells traverse microchannels. Electrode configuration plays a critical role in determining detection sensitivity, signal quality, and spatial resolution. In this study, finite element simulations were conducted to [...] Read more.

Microfluidic impedance cytometry enables label-free and real-time single-cell analysis by detecting changes in electrical impedance as cells traverse microchannels. Electrode configuration plays a critical role in determining detection sensitivity, signal quality, and spatial resolution. In this study, finite element simulations were conducted to model the impedance response of mammalian red blood cells under various electrode designs, including coplanar, parallel, tilted, and parabolic configurations, as well as electrode layouts coupled with flow velocity. A multiphysics simulation model was established to analyze the effects of geometric parameters on electric field distribution and impedance response. The results demonstrate that optimized electrode arrangements significantly enhance detection performance and enable multi-parameter analysis. Furthermore, the influence of flow dynamics and dielectric properties on impedance signals is explored. These findings provide both theoretical and experimental guidance for the development of high-efficiency, integrated impedance cytometry platforms, contributing to the advancement of microfluidic systems in biomedical diagnostics and single-cell characterization. Full article

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12 pages, 3006 KB

Open AccessProceeding Paper

Development and Testing of a Low-Cost, Trackable Portable Sensor Node for Ambient Monitoring in Automated Laboratories

by Mohammed Faeik Ruzaij Al-Okby, Thomas Roddelkopf, Vahid Hassani and Kerstin Thurow

Eng. Proc. 2025, 118(1), 5; https://doi.org/10.3390/ECSA-12-26601 - 7 Nov 2025

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In automated laboratories, ambient monitoring and precise object tracking are essential for safety and system reliability. In this paper, we present the development and evaluation of a low-cost, portable sensor node for environmental sensing and ultrawideband (UWB) based localization. The sensor node integrates [...] Read more.

In automated laboratories, ambient monitoring and precise object tracking are essential for safety and system reliability. In this paper, we present the development and evaluation of a low-cost, portable sensor node for environmental sensing and ultrawideband (UWB) based localization. The sensor node integrates a set of commercial gas sensors for measuring environmental parameters and an ultra-wideband unit for object tracking. The device has an IoT microcontroller that can efficiently process the data from both environmental sensors and the location information from the UWB module and transmit it wirelessly to the cloud/monitoring server via Wi-Fi user datagram protocol (UDP). A custom Python application was developed for real-time monitoring, implementing trilateration and least-squares algorithms for accurate indoor positioning. Experimental results showed a location accuracy better than 50 cm under line-of-sight conditions. Full article

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9 pages, 1671 KB

Open AccessProceeding Paper

An Explorative Evaluation of Using Smartwatches to Track Athletes in Marathon Events

by Dominik Hochreiter

Eng. Proc. 2025, 118(1), 6; https://doi.org/10.3390/ECSA-12-26553 - 7 Nov 2025

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Abstract

Accurate and continuous tracking of athletes is essential to meet the infotainment demands and health and safety requirements of major marathon events. However, the current ability to track individual athletes or groups at mass sporting events is severely limited by the weight, size [...] Read more.

Accurate and continuous tracking of athletes is essential to meet the infotainment demands and health and safety requirements of major marathon events. However, the current ability to track individual athletes or groups at mass sporting events is severely limited by the weight, size and cost of the equipment required. In marathons, Radio Frequency Identification (RFID) technology is typically used for timing but can only provide accurate tracking at widely spaced intervals, relying on heuristic and interpolation algorithms to estimate runners’ positions between measurement points. Alternative IOT solutions, such as Low Power Wide Area Network (LWPAN), have limitations in terms of range and require dedicated infrastructure and regulation. Therefore, we analyzed the potential use of smartwatches as accurate and continuous tracking devices for athletes, assessing battery consumption during tracking and standby drain, achievable GPS tracking accuracy and the update rate of data transfer from the device in urban environments. The 4G LTE battery drain is different from non-urban areas. Analysis of standby usage is necessary as devices need to conserve power for tracking. We programmed an application that allowed us to control the modalities of acquisition and transmission intervals, integrating advanced logging and statistics at runtime, and evaluated the achievable results in major marathon events. Our empirical evaluation at the Frankfurt, Athens and Vienna marathons with three different types of smartwatch tracking platforms showed the validity of this approach, while respecting some necessary limitations of the tracking settings. Median battery drain was 5.3%/h in standby before race start (σ 1.5) and 16.5%/h in tracking mode (σ 3.29), with an actual update rate varying between 19 and 57 s on Wear OS devices. The average GPS offset to the track was 4.5 m (σ 8.7). Future work will focus on integrating these consumer devices with existing time and tracking infrastructure. Full article

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6 pages, 1220 KB

Open AccessProceeding Paper

Ensemble Learning-Assisted Spectroelectrochemical Sensing Platform for Detection of Fluoride in Water

by Sagar Rana and Sudeshna Bagchi

Eng. Proc. 2025, 118(1), 7; https://doi.org/10.3390/ECSA-12-26585 - 7 Nov 2025

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Abstract

Fluoride is a crucial inorganic anion found in drinking water, which may pose serious health hazards to human health if consumed in excess amounts. The quantification of fluoride in drinking water with high sensitivity, selectivity, and cross-sensitivity is critical. Given these factors, the [...] Read more.

Fluoride is a crucial inorganic anion found in drinking water, which may pose serious health hazards to human health if consumed in excess amounts. The quantification of fluoride in drinking water with high sensitivity, selectivity, and cross-sensitivity is critical. Given these factors, the present work proposes a spectroelectrochemical sensing platform for fluoride sensing using 5,10,15,20-tetraphenyl-21H,23H-porphine iron (III) chloride (FeTPP), and tetrabutylammonium perchlorate (TBAP) as the electrolyte. The proposed spectroelectrochemistry (SEC) is a hybrid platform that concurrently provides spectroscopic and electrochemical information about a system susceptible to oxidation and reduction. An ensemble–based multivariate prediction model was developed to simultaneously analyze electrochemical and spectroscopic data to predict fluoride concentration with enhanced reliability and precision. The prediction model provided promising results with a coefficient of determination of 0.9923 ± 0.0063 and a MSE of 0.369 ± 0.0596. These encouraging results demonstrate the promising performance of the proposed spectroelectrochemical platform in complex real-world applications. Full article

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9 pages, 2784 KB

Open AccessProceeding Paper

YOLOv8-Based Autonomous Ball Detection and Tracking for Rotorcraft UAVs Using Onboard Vision Sensors

by Oumaima Gharsa, Mostefa Mohamed Touba, Mohamed Boumehraz, Nesrine Abderrahmani, Imam Barket Ghiloubi and Abdel Hakim Drid

Eng. Proc. 2025, 118(1), 8; https://doi.org/10.3390/ECSA-12-26570 - 7 Nov 2025

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Abstract

Drones equipped with onboard cameras offer promising potential for modern digital media and remote sensing applications. However, effectively tracking moving objects in real time remains a significant challenge. Aerial footage captured by drones often includes complex scenes with dynamic elements such as people, [...] Read more.

Drones equipped with onboard cameras offer promising potential for modern digital media and remote sensing applications. However, effectively tracking moving objects in real time remains a significant challenge. Aerial footage captured by drones often includes complex scenes with dynamic elements such as people, vehicles, and animals. These scenarios may involve large-scale changes in viewing angles, occlusions, and multiple object crossings occurring simultaneously, all of which complicate accurate object detection and tracking. This paper presents an autonomous tracking system that leverages the YOLOv8 algorithm combined with a re-detection mechanism, enabling a quadrotor to effectively detect and track moving objects using only an onboard camera. To regulate the drone’s motion, a PID controller is employed, operating based on the target’s position within the image frame. The proposed system functions independently of external infrastructure such as motion capture systems or GPS. By integrating both positional and appearance-based cues, the system demonstrates high robustness, particularly in challenging environments involving complex scenes and target occlusions. The performance of the optimized controllers was assessed through extensive real-world testing involving various trajectory scenarios to evaluate the system’s effectiveness. Results confirmed consistent and accurate detection and tracking of moving objects across all test cases. Furthermore, the system exhibited robustness against noise, light reflections, and illumination interference, ensuring stable object tracking even when implemented on low-cost computing platforms. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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13 pages, 4225 KB

Open AccessProceeding Paper

Development of an Autonomous Unmanned Ground Vehicle for Artistic Landscaping

by Rowida Meligy, Anton R. Ahmad, Nariman E. Elbaly, Arafa S. Sobh and Sherif A. Elatraby

Eng. Proc. 2025, 118(1), 9; https://doi.org/10.3390/ECSA-12-26509 - 7 Nov 2025

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Abstract

As cities strive to become more sustainable and livable in the age of smart urban development, there is a tendency toward urban landscaping concepts that combine ecological benefits and esthetic appeal. Within this context, artistic landscaping, the deliberate spatial arrangement of plant species [...] Read more.

As cities strive to become more sustainable and livable in the age of smart urban development, there is a tendency toward urban landscaping concepts that combine ecological benefits and esthetic appeal. Within this context, artistic landscaping, the deliberate spatial arrangement of plant species to create visual compositions, has emerged as a valuable aspect of modern urban green infrastructure. While cutting-edge Unmanned Ground Vehicle (UGV) development has primarily focused on large-scale precision agriculture, its potential for artistic and small-scale urban landscaping remains unexplored. Furthermore, integrating Internet of Things (IoT) technology into UGVs for autonomous seeding presents an interesting research point. Addressing these challenges, this paper introduces a compact design of an IoT-enabled UGV specifically for artistic landscaping applications. The system includes an effective full seeding mechanism with dedicated modules for soil digging, sowing, water spraying, and backfilling. These operational modules are coordinated using a microcontroller-based control system to ensure reliability and repeatability. Additionally, in this study, a web-based interface has been developed to support both autonomous and manual operation modes, allowing users to customize path planning for geometric seeding patterns as well as real-time monitoring. A fully functional prototype was built and tested under controlled conditions to confirm the core modules’ effectiveness. This development provides a practical solution for supporting the realization of smart and sustainable cities. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 1281 KB

Open AccessProceeding Paper

Development of a New 3-Axis Force Sensor for Measuring Cutting Forces in a Lathe Machine

by Anton R. Ahmad and Syed Humayoon Shah

Eng. Proc. 2025, 118(1), 10; https://doi.org/10.3390/ECSA-12-26575 - 7 Nov 2025

Cited by 1 | Viewed by 494

Abstract

Despite the digital era and the emergence of simplified production paradigms enabled by digital twin technology, traditional manufacturing processes like lathe machining are invaluable because of their ability to meet various and multiple needs reliably and flexibly. To combine these processes into smart [...] Read more.

Despite the digital era and the emergence of simplified production paradigms enabled by digital twin technology, traditional manufacturing processes like lathe machining are invaluable because of their ability to meet various and multiple needs reliably and flexibly. To combine these processes into smart monitoring and control systems, including tool wear detection and fault diagnosis, precise multi-axis force measurement is necessary. Single-axis sensors are insufficient to measure the entire dynamics of cutting interactions. A new idea based on strain gauge technology for a self-decoupled three-axis force sensor is proposed in this paper, which aims to measure cutting forces and vibration in a lathe operation. During the cutting process, the sensor is designed to independently recognize the onset of forces at three orthogonal axes to enhance real-time process monitoring capabilities. The Timoshenko beam theory is used to design the mechanical structure, where sensitivity could be improved with minimal crosstalk. Finite element analysis (FEA) simulations were conducted to evaluate the sensor’s performance, stress distribution, modal assessments, and interference error. The interference error of 0.31 percent indicated by the results of the simulation is extremely low, indicating a successful decoupling of the force components. These encouraging simulation results indicate that there is a high possibility of applying the proposed sensor design to intelligent manufacturing systems. It presents an initial point of departure into the embodiment of sophisticated monitoring platforms for conventional machining processes, eliminating the discontinuity between old and new smart manufacturing systems. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 7618 KB

Open AccessProceeding Paper

Human-Centered Interfaces for a Shipyard 5.0 Cognitive Cyber–Physical System

by Diego Ramil-López, Esteban López-Lodeiro, Javier Vilar-Martínez, Tiago M. Fernández-Caramés and Paula Fraga-Lamas

Eng. Proc. 2025, 118(1), 11; https://doi.org/10.3390/ECSA-12-26611 - 7 Nov 2025

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Abstract

Industry 5.0 represents the next stage in the industrial evolution, with a growing impact in the shipbuilding sector. In response to its challenges, Navantia, a leading international player in the field, is transforming its shipyards towards the creation of a Shipyard 5.0 through [...] Read more.

Industry 5.0 represents the next stage in the industrial evolution, with a growing impact in the shipbuilding sector. In response to its challenges, Navantia, a leading international player in the field, is transforming its shipyards towards the creation of a Shipyard 5.0 through the implementation of digital technologies that enable human-centered, resilient and sustainable processes. This approach gives rise to Cognitive Cyber-Physical Systems (CCPS) in which the system can learn and where the generated data are integrated into a digital platform that supports operators in decision-making. In this scenario, different smart elements (e.g., IoT-based tows, trucks) are used to transport key components of a ship like pipes or steel plates, which are present in a large number, representing a strategic opportunity to enhance traceability in shipbuilding operations. The accurate tracking of these elements, from manufacturing to assembly, helps to improve operational efficiency and enhances safety within the shipyard environment. Considering the previous context, this paper describes a CCPS that enables tracking and real-time data visualization through portable interfaces adapted to the shipyard operator needs. Following the Industry 5.0 foundations, the presented solution is focused in providing human-centric interfaces, tackling issues like information overload, poor visual organization and accessibility of the control panels. Thus, to address such issues, an iterative human-centered redesign process was performed. This approach incorporated hands-on testing with operators at each development stage and implemented specific adjustments to improve interface clarity and reaction speed. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 454 KB

Open AccessProceeding Paper

Wireless Soil Health Beacons: An Intelligent Sensor-Based System for Real-Time Monitoring in Precision Agriculture

by Vijayalakshmi Subramanian, Alwin Joseph, Durgadevi Paramasivam, Akilan Tamilselvan and Mahesh Kumar Thangavel

Eng. Proc. 2025, 118(1), 12; https://doi.org/10.3390/ECSA-12-26539 - 7 Nov 2025

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Abstract

Precision agriculture is a modern technology that focuses on the crop by meeting the specific needs of the field. This research presents the Wireless Soil Health Beacons design that can be used in precision agriculture to enhance the production and real-time monitoring of [...] Read more.

Precision agriculture is a modern technology that focuses on the crop by meeting the specific needs of the field. This research presents the Wireless Soil Health Beacons design that can be used in precision agriculture to enhance the production and real-time monitoring of the soil and field parameters. The proposed system integrates bio and physical sensors into an IoT-enabled Wireless Soil Health Beacons (WSHB) to provide detailed and real-time soil health parameters. The beacons are compact and are powered by solar, which is weather-resistant and interconnected via wireless nodes. A set of beacons will be implanted to capture biological and environmental data. The biosensor module detects key soil microbiological parameters such as nitrogen-fixing microbial activity, soil pathogen presence, and general microbial population shifts indicative of soil fertility and disease conditions. The physical sensor module continuously measures soil moisture levels, temperature, and salinity. The data is passed from the nodes to a processing module, which collects and analyses the critical parameters directly related to plant growth, water management, and fertiliser optimisation. A mobile interface assists the farmers and stakeholders with the required information, such as field maps, real-time soil health indicators, and critical alerts related to drought, salinity stress, or pathogen hotspots. The proposed system forms as a multi-dimensional soil profiling tool capable of supporting precision agriculture. Most existing soil monitoring systems rely on environmental parameters, while the proposed system allows the continuous tracking of ecological and microbial dynamics in the area. The mesh network architecture helps the system to be redundant and enhances the outcomes. The proposed system helps with sustainable agriculture and improves the yields with minimal environmental degradation, enabling an adaptive and precise farm management system. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 1392 KB

Open AccessProceeding Paper

Design and Implementation of a Wi-Fi-Enabled BMS for Real-Time LiFePO₄ Cell Monitoring

by Ioannis Christakis, Vasilios A. Orfanos, Chariton Christoforidis and Dimitrios Rimpas

Eng. Proc. 2025, 118(1), 13; https://doi.org/10.3390/ECSA-12-26613 - 7 Nov 2025

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Abstract

This paper presents the design and implementation of a custom-built LiFePO₄ battery monitoring system that offers real-time visibility into the status of individual battery cells. The system is based on a Battery Management System (BMS) architecture and is implemented by measuring the [...] Read more.

This paper presents the design and implementation of a custom-built LiFePO₄ battery monitoring system that offers real-time visibility into the status of individual battery cells. The system is based on a Battery Management System (BMS) architecture and is implemented by measuring the voltage, current, and temperature of each cell in a multi-cell pack. These key parameters are essential for ensuring safe operation, prolonging battery life, and optimizing energy usage in off-grid or mobile power systems. The system architecture is based on an ESP32 microcontroller that interfaces with INA219 and DS18B20 sensors to continuously measure individual cell voltage, current, and temperature. Data are transmitted wirelessly via Wi-Fi to a remote time-series database for centralized storage, analysis, and visualization. Experimental validation, conducted over a 15-day period, demonstrated stable system performance and reliable data transmission. Analytically, the findings indicate that utilizing an advanced smart charger for precise cell balancing and improving the physical layout for cooling led to superior thermal performance. Even when load current nearly tripled to 110 mA, the system maintained a stable cell operating temperature range of 29.8 °C to 30.3 °C. This result confirms significantly reduced cell stress compared to previous iterations, which is critical for enhancing battery health and lifespan. The application of this project aimed to demonstrate how a combination of open hardware components and lightweight network protocols can be used to create a robust, cost-effective battery monitoring solution suitable for integration into smart energy systems or remote IoT infrastructures. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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16 pages, 2240 KB

Open AccessProceeding Paper

Impact of Electrical Noise on the Accuracy of Resistive Sensor Measurements Using Sensor-to-Microcontroller Direct Interface

by Marco Grossi and Martin Omaña

Eng. Proc. 2025, 118(1), 14; https://doi.org/10.3390/ECSA-12-26551 - 7 Nov 2025

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Abstract

Wireless sensor networks (WSNs) implemented in the paradigm of the Internet of Things (IoT) are characterized by a large number of distributed sensor nodes that make measurements in-the-field and communicate with other sensor nodes and servers in the cloud by means of wireless [...] Read more.

Wireless sensor networks (WSNs) implemented in the paradigm of the Internet of Things (IoT) are characterized by a large number of distributed sensor nodes that make measurements in-the-field and communicate with other sensor nodes and servers in the cloud by means of wireless technology. Sensor-to-microcontroller direct interface (SMDI) is a technique used for the measurement of resistive sensors without the use of an ADC. In SMDI-based measurements, the sensor is directly interfaced with the digital input–output pins of the general-purpose input–output (GPIO) interface of microcontrollers and FPGAs. Compared with the measurements performed with an ADC, SMDI is characterized by lower cost and lower power consumption. In this paper, the impact of noise on the accuracy of resistive sensor measurements using SMDI is investigated. This study was carried out by LTSpice electrical-level simulations and validated by preliminary experimental measurements, where a set of resistances in the range from 100 Ω to 10 kΩ were measured by SMDI under different levels of noise. For each operative condition, the simulations were also carried out in the case of measurements performed with a 12-bit ADC, and the achieved accuracy for the measured resistances was compared with the results achieved by SMDI. The results have shown that noise can seriously impact the measured accuracy of resistive sensors by SMDI and, unlike the ADC measurements, the accuracy cannot be improved by averaging on multiple measurements. A mitigation strategy to estimate the noise level and to improve the measurement accuracy of resistive sensors by SMDI was also proposed. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 1807 KB

Open AccessProceeding Paper

Development of Microfluidic Organ-on-a-Chip Systems Dedicated to the Analysis of Cell Morphology

by Junwei Wang and Tieying Xu

Eng. Proc. 2025, 118(1), 15; https://doi.org/10.3390/ECSA-12-26485 - 7 Nov 2025

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Abstract

Traditional medical techniques are constrained by macro-scale detection methods, making it difficult to capture dynamic changes at the cellular level. The miniaturization and high-throughput capabilities of integrated circuit technology enable precise manipulation and real-time monitoring of biological processes. In this study, COMSOL Multiphysics [...] Read more.

Traditional medical techniques are constrained by macro-scale detection methods, making it difficult to capture dynamic changes at the cellular level. The miniaturization and high-throughput capabilities of integrated circuit technology enable precise manipulation and real-time monitoring of biological processes. In this study, COMSOL Multiphysics 6.3 software was used to model electrode units, simulating the interaction between cells and their biological environment. From the perspective of electrode arrays, the influence of varying electrode-cell contact areas on electrical signals was investigated, and the structure and layout of the microelectrode array (MEA) were optimized. The research explored the relationship between cellular activity and electrical properties, as well as the effect of cellular activity on membrane permeability. Simulation results demonstrated that larger electrode coverage areas improve potential distribution. The intact phospholipid bilayer and functional membrane proteins of living cells create a significant current-blocking effect, with impedance values reaching 10⁵–10⁶ Ω·cm². In contrast, apoptotic or necrotic cells exhibit structural damage and ion channel inactivation, leading to significantly enhanced membrane permeability, with impedance decreasing by 1–2 orders of magnitude. Further simulations involved modeling microfluidic channels to study cellular behavior within them. Frequency response analysis and Bode plots revealed that impedance differences between low and high frequencies could distinguish living cells (higher impedance) from apoptotic cells (lower impedance). Therefore, Bode plot analysis can assess membrane permeability and infer cellular health or apoptotic state. Additionally, this study examined micro-nanofabrication techniques, particularly the lift-off process for microelectrode fabrication, and optimized photoresist selection in photolithography. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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7 pages, 1443 KB

Open AccessProceeding Paper

Nanostructured Copper Oxide Materials for Photocatalysis and Sensors

by Natalia Chirkunova, Amani Azaizia, Semen Domarev and Maksim Dorogov

Eng. Proc. 2025, 118(1), 16; https://doi.org/10.3390/ECSA-12-26496 - 7 Nov 2025

Cited by 1 | Viewed by 453

Abstract

This study investigates the influence of morphology on the photocatalytic and gas-sensing properties of copper oxide (CuO) nanomaterials, comparing spherical nanoparticles (NPs) and nanowhiskers (NWs). CuO NWs were synthesized via thermal oxidation of electrodeposited copper, exhibiting a polycrystalline structure with an average diameter [...] Read more.

This study investigates the influence of morphology on the photocatalytic and gas-sensing properties of copper oxide (CuO) nanomaterials, comparing spherical nanoparticles (NPs) and nanowhiskers (NWs). CuO NWs were synthesized via thermal oxidation of electrodeposited copper, exhibiting a polycrystalline structure with an average diameter of 69 nm, while NPs were obtained by ball-milling NWs, resulting in spherical particles (227 nm). Photocatalytic tests using methylene blue degradation under UV and visible light revealed that NWs exhibited superior properties. Kinetic analysis indicated pseudo-first-order behavior under visible light, while UV-driven reactions deviated due to surface-limited processes. In gas-sensing experiments, CuO NPs demonstrated higher sensitivity to acetone than NWs. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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5 pages, 980 KB

Open AccessProceeding Paper

Sensitive Electrochemical Detection of the Nitrite Ion Using an ISEM-3 Graphite Electrode and Comparison with Other Carbon-Containing Materials

by Irina Kuznetsova, Olesya Polyakova, Olga Lebedeva, Dmitry Kultin and Leonid Kustov

Eng. Proc. 2025, 118(1), 17; https://doi.org/10.3390/ECSA-12-26487 - 7 Nov 2025

Viewed by 352

Abstract

The need for an accurate, rapid, and affordable method for determining nitrite ions arises from their toxic effects on humans at elevated levels in wastewater and drinking water. The electrochemical determination is faster, cheaper, and less labor-intensive. It is based on the study [...] Read more.

The need for an accurate, rapid, and affordable method for determining nitrite ions arises from their toxic effects on humans at elevated levels in wastewater and drinking water. The electrochemical determination is faster, cheaper, and less labor-intensive. It is based on the study of the electrochemical oxidation of NO₂⁻ ions at different carbon electrodes. In this work, it was established that the cyclic voltammograms for the ISEM-3 graphite electrode have an excellent limit of detection for nitrite ions: 5 × 10⁻⁶ M at pH 3, which makes it possible to determine the NO₂⁻ content below the maximum permissible concentration (6.5 × 10⁻⁵ M) in water. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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6 pages, 1125 KB

Open AccessProceeding Paper

Towards Low-Cost Magnetic Resonance Relaxometry

by Kerry Worton, Robert H. Morris, Nicasio R. Geraldi and Michael I. Newton

Eng. Proc. 2025, 118(1), 18; https://doi.org/10.3390/ECSA-12-26500 - 7 Nov 2025

Viewed by 309

Abstract

Magnetic Resonance Relaxometry is a powerful technique that reveals a sample’s molecular dynamics thanks to the dependence of the T₁ relaxation time on field strength. With applications in protein research, food systems, material development, and environmental science, relaxometry measurements are typically undertaken [...] Read more.

Magnetic Resonance Relaxometry is a powerful technique that reveals a sample’s molecular dynamics thanks to the dependence of the T₁ relaxation time on field strength. With applications in protein research, food systems, material development, and environmental science, relaxometry measurements are typically undertaken using a technique known as fast field cycling, where T₁ is measured at a range of detection fields. However, the sample experiences relaxation in a variable field without the challenges associated with retuning a probe to each of the necessary frequencies of interest. This technique is limited by a maximum relaxation time, since the measurement and relaxation fields are typically applied using a fluid-cooled electromagnet, which will ultimately overheat for very long experimental times. In this work, we propose an alternative approach to permit measurements of samples with inherently long T₁ values. We utilise a broadband spectrometer alongside a solenoid transmit-receive coil and custom tuning and matching boards, whilst two sets of magnets are moved around the coil, to achieve a range of different fields. By collecting a reduced number of points and utilising this method, we show it is still possible to make useful measurements on samples at a range of frequencies, which has great potential in quality assurance applications. We find a similar trend for food samples of corn oil, while manganese chloride, a common contrast agent, has more than a 100% difference when compared to traditional fast field cycling measurements. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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14 pages, 4601 KB

Open AccessProceeding Paper

Enhancing Rain Sensor Sensitivity Using a Nylon Mesh Overlay: A Low-Cost and Practical Solution

by Ioannis Christakis

Eng. Proc. 2025, 118(1), 19; https://doi.org/10.3390/ECSA-12-26548 - 7 Nov 2025

Viewed by 561

Abstract

Monitoring humidity is essential for the protection and long-term preservation of historical monuments and cultural heritage structures, particularly those made of stone, marble, or iron. Excess moisture can accelerate material degradation and compromise structural integrity. This paper presents an alternative, low-cost method for [...] Read more.

Monitoring humidity is essential for the protection and long-term preservation of historical monuments and cultural heritage structures, particularly those made of stone, marble, or iron. Excess moisture can accelerate material degradation and compromise structural integrity. This paper presents an alternative, low-cost method for enhancing the sensitivity of a raindrop sensor, aiming to detect micro-droplets such as early morning dew—an important factor in environmental monitoring around such sensitive sites. The proposed method involves covering the sensor’s surface with a fine nylon mesh, such as a stocking, which allows tiny water droplets to accumulate and spread more effectively across the sensor. This modification improves the electrical conductivity between the copper tracks when droplets are present, enabling the sensor to detect moisture levels that would otherwise go unnoticed. Experimental results demonstrate that the modified sensor performs significantly better than the original, unaltered version, offering greater sensitivity and consistency in its readings. The sensor responds more reliably to low volumes of moisture without requiring internal changes to its circuitry, making it both practical and cost-effective. The outcomes of this work are encouraging, suggesting that this approach is suitable for moisture detection in both research and real-world conservation scenarios. It provides a simple and scalable solution for integrating humidity monitoring into broader environmental sensing systems. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 2199 KB

Open AccessProceeding Paper

Prototyping LoRaWAN-Based Mobile Air Quality Monitoring System for Public Health and Safety

by Tanzila, Sundus Ali, Muhammad Imran Aslam, Irfan Ahmed and Ayesha Ahmed

Eng. Proc. 2025, 118(1), 20; https://doi.org/10.3390/ECSA-12-26510 - 7 Nov 2025

Viewed by 906

Abstract

In this paper, we present the design, prototyping, and working of a cost-effective, energy-efficient, and scalable air quality monitoring system (AQMS), enabled by a Low-power, long-Range Wide-Area Network (LoRaWAN), an Internet of Things (IoT) technology designed to provide connectivity for massive machine-type communication [...] Read more.

In this paper, we present the design, prototyping, and working of a cost-effective, energy-efficient, and scalable air quality monitoring system (AQMS), enabled by a Low-power, long-Range Wide-Area Network (LoRaWAN), an Internet of Things (IoT) technology designed to provide connectivity for massive machine-type communication applications. The growing threat of air pollution necessitates outdoor and mobile environmental monitoring systems to provide real-time, location-specific data, which is unfortunately not possible using fixed monitoring devices. For our AQMS, we have developed two custom-built sensor nodes. The first node is equipped with a Nucleo-WL55JC1 microcontroller and sensors to measure temperature, humidity, and carbon dioxide (CO₂), while the other node is equipped with an Arduino MKR WAN 1310 controller with sensors to measure carbon monoxide (CO), ammonia (NH₃), and particulate matter (PM2.5 and PM10). These sensor nodes connect to a WisGate Edge LoRaWAN gateway, which aggregates and forwards the sensor data to The Things Network (TTN) for processing and cloud storage. The final visualization is handled via the Ubidots IoT platform, allowing for real-time visualization of environmental data. Besides environmental data, we were able to acquire a received signal strength indicator, signal-to-noise ratio, as well as a frame counter, which shows the number of packets received by the gateway. We performed laboratory testing, which confirmed reliable communication, with a packet delivery rate of 98% and a minimal average latency of 2.5 s. Both nodes operated efficiently on battery power, with the Nucleo-WL55JC1 consuming an average of 20 mA in active mode, while the Arduino MKR WAN 1310 operated at 15 mA. These values ensured extended operation for remote deployment. The system’s low power consumption and modular architecture make it viable for smart city applications and large-scale deployments in resource-constrained areas. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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18 pages, 2606 KB

Open AccessProceeding Paper

Smart IoT-Based COVID-19 Vaccine Supply Chain, Monitoring, and Control System

by Sani Abba and Itse Nyam Musa

Eng. Proc. 2025, 118(1), 21; https://doi.org/10.3390/ECSA-12-26526 - 7 Nov 2025

Viewed by 927

Abstract

This research paper presents a smart IoT-based COVID-19 vaccine supply chain, monitoring, and control system. This proposed system is designed to efficiently and effectively monitor COVID-19 vaccine storage sites by tracking their temperature, humidity, quantity, and location on a map across various supply [...] Read more.

This research paper presents a smart IoT-based COVID-19 vaccine supply chain, monitoring, and control system. This proposed system is designed to efficiently and effectively monitor COVID-19 vaccine storage sites by tracking their temperature, humidity, quantity, and location on a map across various supply chain categories. It ultimately aims to monitor and control temperatures outside the range at the tracked location. The approach utilized temperature, humidity, and ultrasonic sensors, a GPS module, a Wi-Fi module, and an Arduino Uno microcontroller. The system was designed and implemented using Arduino and Proteus integrated design environments (IDEs) and coded using the embedded C/C++ programming language. A real-life working system prototype was designed and implemented. The measured sensor readings can be viewed via a computer system capability or any mobile device, such as an Android phone, iPhone, iPad, or laptop, with the aid of a cloud-based platform, namely, Thingspeak.com. The experimentally measured sensor readings are stored in a data log file for subsequent download and analysis whenever the need arises. The data aggregation and analytics are coded using MATLAB and viewed as charts, and the location map of vaccine carrier coordinates is sent to the web cloud for tracking. An alarm message is sent to the monitoring and control system if an unfavorable vaccine environment exists in either the store or the carrier container. A suitable sensor-based interface architecture and web portal are provided, allowing health practitioners to remotely monitor the vaccine supply chain system. This method encourages health workers by reducing the high levels of supervision required by vaccine supervisors to ensure the smooth supply of vaccines to vaccine collection centers, by using a wireless sensor network and IoT technology. Experimental results from the implemented system prototype demonstrated the benefits of the proposed approach and its possible real-life health monitoring applications. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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7 pages, 4694 KB

Open AccessProceeding Paper

Performance of Bloch-like Surface Wave Refractometers Based on Laterally Polished Photonic Crystal Fibers with Single-Layer Coatings: From Nanolayer to Nanostrip

by Esteban Gonzalez-Valencia, Natalia Carolina Lara-Davila, Jorge Andres Montoya-Cardona, Nelson Gomez-Cardona and Pedro Torres

Eng. Proc. 2025, 118(1), 22; https://doi.org/10.3390/ECSA-12-26492 - 7 Nov 2025

Viewed by 224

Abstract

Bloch-like surface waves (BLSWs) are electromagnetic waves generated at the interface between a dielectric medium and a photonic crystal. BLSWs have significant potential for sensing applications, since their electromagnetic fields are tightly confined near the interface, reaching comparable sensitivities to those of surface [...] Read more.

Bloch-like surface waves (BLSWs) are electromagnetic waves generated at the interface between a dielectric medium and a photonic crystal. BLSWs have significant potential for sensing applications, since their electromagnetic fields are tightly confined near the interface, reaching comparable sensitivities to those of surface plasmon polariton (SPP)-based devices, but with higher figures of merit (FOM). This work explores a sensor based on BLSW at the interface formed by a TiO₂ thin film deposited on the flat surface of a laterally polished photonic crystal fiber (PCF). The performance of the sensor is studied when the TiO₂ film is partially removed, transforming the nanolayer into a nanostrip. The results of this study contribute to the optimization of the sensing performance of the proposed structure. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 4817 KB

Open AccessProceeding Paper

Highly Sensitive Voltammetric Sensor for Acid Yellow 3 Based on Cerium and Tin Dioxide Nanoparticles

by Guzel Ziyatdinova

Eng. Proc. 2025, 118(1), 23; https://doi.org/10.3390/ECSA-12-26488 - 7 Nov 2025

Viewed by 288

Abstract

A novel highly sensitive voltammetric sensor based on a glassy carbon electrode (GCE) modified with a mixture of cerium and tin dioxide nanoparticles (NPs) as a sensing layer was developed. Surfactants of various nature (anionic sodium dodecyl sulfate, cationic N-cetylpyridinium bromide, and [...] Read more.

A novel highly sensitive voltammetric sensor based on a glassy carbon electrode (GCE) modified with a mixture of cerium and tin dioxide nanoparticles (NPs) as a sensing layer was developed. Surfactants of various nature (anionic sodium dodecyl sulfate, cationic N-cetylpyridinium bromide, and non-ionic Triton X-100, Brij^® 35, and Tween-80) were used as dispersive agents for NPs. Complete suppression and a significant decrease in the dye oxidation peak occurred in the case of Tween-80 and sodium dodecyl sulfate, respectively. CeO₂–SnO₂ NPs in Brij^® 35 gave the best response to Acid Yellow 3 caused by its adsorption at the electrode surface. Linear dynamic ranges of 0.50–7.5 and 7.5–25 mg L⁻¹ with a detection limit of 0.13 mg L⁻¹ of Acid Yellow 3 were achieved using differential pulse mode in Britton–Robinson buffer pH 5.0. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 1541 KB

Open AccessProceeding Paper

Enhancing Fire Alarm Systems Using Edge Machine Learning for Smoke Classification and False Alarm Reduction

by Abdulrhman Alshaya and Abdullah Almutairi

Eng. Proc. 2025, 118(1), 24; https://doi.org/10.3390/ECSA-12-26524 - 7 Nov 2025

Viewed by 594

Abstract

Traditional fire alarm systems use smoke sensors to monitor the concentration of smoke particles in the air. If the concentration exceeds a certain threshold, an alarm signal is triggered. However, this detection process could lead to false fire alarms, causing unnecessary evacuations and [...] Read more.

Traditional fire alarm systems use smoke sensors to monitor the concentration of smoke particles in the air. If the concentration exceeds a certain threshold, an alarm signal is triggered. However, this detection process could lead to false fire alarms, causing unnecessary evacuations and panic among residents. False alarms may result from activities such as smoking in non-smoking areas, burning Oud, or cooking smoke. In this study, a deep neural network (DNN) model was trained to classify three types of smokes that were Oud, cigarette, and burning tissue smokes. The offline prediction accuracy of this model was 97.5%. The size of the model after converting it to TensorFlow lite was 4.7 Kbytes. It can also be converted to a tiny model to deploy it on microcontroller. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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13 pages, 2917 KB

Open AccessProceeding Paper

Application of a Low-Cost Electronic Nose to Monitoring of Soft Fruits Spoilage

by Tomasz Grzywacz, Krzysztof Brzeziński, Piotr Sochacki, Rafał Tarakowski, Miłosz Tkaczyk and Piotr Borowik

Eng. Proc. 2025, 118(1), 25; https://doi.org/10.3390/ECSA-12-26600 - 7 Nov 2025

Viewed by 509

Abstract

A new construction of a custom-made, low-cost, electronic-nose-applying eight-TGS-type gas sensors manufactured by Figaro Inc. was assembled. The gas sensors were used to collect response signals caused by changes in gas composition from clean air to the studied odor, to which the sensors [...] Read more.

A new construction of a custom-made, low-cost, electronic-nose-applying eight-TGS-type gas sensors manufactured by Figaro Inc. was assembled. The gas sensors were used to collect response signals caused by changes in gas composition from clean air to the studied odor, to which the sensors were exposed. In addition, modulation of sensor heater temperature was implemented in order to register complementary information useful for differentiation between the studied odor categories. An automatic mechanism was to open the gas sensor chamber, allowing sensors exposure to the studied gas and cleaning of sensors in the condition of a closed chamber. Sensor cleaning was conducted by forcing a clean air current through the application of a pneumatic pump. Three-dimensional printing was used to manufacture the sensor chamber. The Raspberry PI microcomputer was used for control of the measurement procedure and data collection. The operation of the device could be controlled by a web-based interface from a connected laptop or smartphone. The device was applied to the monitoring of the development of spoilage of soft fruits like strawberries and raspberries. Periodic measurements were performed in an automatic manner. A dedicated system of separation of the measured sample from the gas sensor array, preventing heat flow, was designed. Technical challenges encountered during the measurement are presented. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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17 pages, 1458 KB

Open AccessProceeding Paper

Robust IMU Sensor Fusion via Schreiber’s Nonlinear Filtering Approach

by Samir Rasulov, Ahmet Mehmet Karadeniz and Péter Bakucz

Eng. Proc. 2025, 118(1), 26; https://doi.org/10.3390/ECSA-12-26586 - 7 Nov 2025

Cited by 1 | Viewed by 602

Abstract

This study introduces a hybrid sensor fusion approach that integrates Schreiber’s nonlinear filter with traditional filtering methods to enhance the performance of IMU-based systems in autonomous vehicles. As autonomous vehicles grow more dependent on Inertial Measurement Unit (IMU) data for real-time stability and [...] Read more.

This study introduces a hybrid sensor fusion approach that integrates Schreiber’s nonlinear filter with traditional filtering methods to enhance the performance of IMU-based systems in autonomous vehicles. As autonomous vehicles grow more dependent on Inertial Measurement Unit (IMU) data for real-time stability and control, the need for resilient and accurate sensor fusion becomes critical. This research addresses that need by introducing a method capable of maintaining robustness under highly dynamic and uncertain conditions. Accelerometer and gyroscope data from an IMU are first fused using a complementary filter. The fused signals are then refined by phase-space reconstruction and local manifold projection, improving noise resilience and maintaining system dynamics. Two datasets are used to assess the methodology: one was collected indoors with a smartphone, and another was captured outdoors using a Bosch sensor in various environmental settings. The proposed method demonstrates superior noise reduction, greater resistance to outliers, and improved signal consistency compared to conventional complementary and Kalman filters. The findings demonstrate how chaos-based nonlinear filtering may improve the reliability of sensor fusion on a variety of sensing platforms in highly dynamic environments. Given the importance of IMU data for maintaining vehicle stability, this study seeks to support the development of more stable autonomous transportation systems. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 2177 KB

Open AccessProceeding Paper

Hand Gesture to Sound: A Real-Time DSP-Based Audio Modulation System for Assistive Interaction

by Laiba Khan, Hira Mariam, Marium Sajid, Aymen Khan and Zehra Fatima

Eng. Proc. 2025, 118(1), 27; https://doi.org/10.3390/ECSA-12-26516 - 7 Nov 2025

Viewed by 535

Abstract

This paper presents the design, development, and evaluation of an embedded hardware and digital signal processing (DSP)-based real-time gesture-controlled system. The system architecture utilizes an MPU6050 inertial measurement unit (IMU), Arduino Uno microcontroller, and Python-based audio interface to recognize and classify directional hand [...] Read more.

This paper presents the design, development, and evaluation of an embedded hardware and digital signal processing (DSP)-based real-time gesture-controlled system. The system architecture utilizes an MPU6050 inertial measurement unit (IMU), Arduino Uno microcontroller, and Python-based audio interface to recognize and classify directional hand gestures and transform them into auditory commands. Wrist tilts, i.e., left, right, forward, and backward, are recognized using a hybrid algorithm that uses thresholding, moving average filtering, and low-pass smoothing to remove sensor noise and transient errors. Hardware setup utilizes I²C-based sensor acquisition, onboard preprocessing on Arduino, and serial communication with a host computer running a Python script to trigger audio playing using the playsound library. Four gestures are programmed for basic needs: Hydration Request, Meal Support, Restroom Support, and Emergency Alarm. Experimental evaluation, conducted over more than 50 iterations per gesture in a controlled laboratory setup, resulted in a mean recognition rate of 92%, with system latency of 120–150 milliseconds. The approach has little calibration costs, is low-cost, and offers low-latency performance comparable to more advanced camera-based or machine learning-based methods, and is therefore suitable for portable assistive devices. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 485 KB

Open AccessProceeding Paper

IoT-Enabled Sensor Glove for Communication and Health Monitoring in Paralysed Patients

by Angshuman Khan, Uttam Narendra Thakur and Sikta Mandal

Eng. Proc. 2025, 118(1), 28; https://doi.org/10.3390/ECSA-12-26518 - 7 Nov 2025

Viewed by 773

Abstract

Due to their limited mobility and vocal limitations, paralysed individuals frequently struggle with communication and health monitoring. This work introduces an Internet of Things (IoT)-based system that combines continuous health monitoring with a sensor-based smart glove to enhance patient care. The glove detects [...] Read more.

Due to their limited mobility and vocal limitations, paralysed individuals frequently struggle with communication and health monitoring. This work introduces an Internet of Things (IoT)-based system that combines continuous health monitoring with a sensor-based smart glove to enhance patient care. The glove detects falls, sends emergency messages via hand gestures, and monitors vital indicators, including SpO₂, heart rate, and body temperature. The smart glove uses Arduino UNO (RoboCraze, Bengaluru, India) and ESP8266 (RoboCraze, Bengaluru, India) modules with MPU6050 (RoboCraze, Bengaluru, India), MAX30100 (RoboCraze, Bengaluru, India), LM35 (Bombay Electronics, Mumbai, India), and flex sensors for these functions. MPU6050 detects falls precisely, while MAX30100 and flex sensors measure gestures, SpO₂, heart rate, and body temperature. The flex sensor interprets hand motions as emergency alerts sent via Wi-Fi to a cloud platform for remote monitoring. The experimental results confirmed the superiority and validated the efficacy of the suggested module. Scalability, data logging, and real-time access are guaranteed by IoT integration. The actual test cases were predicted using a Support Vector Machine, achieving an average accuracy of 81.98%. The suggested module is affordable, non-invasive, easy to use, and appropriate for clinical and residential use. The system meets the essential needs of disabled people, enhancing both their quality of life and carer connectivity. Advanced machine learning for dynamic gesture detection and telemedicine integration is a potential future improvement. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 3087 KB

Open AccessProceeding Paper

Design of an X-Band TR Module Based on LTCC

by Qingqi Zou and Jie Cui

Eng. Proc. 2025, 118(1), 29; https://doi.org/10.3390/ECSA-12-26546 - 7 Nov 2025

Viewed by 881

Abstract

Phased array radar, with its electronic scanning, high reliability, and multifunctionality, has become a core equipment for unmanned aerial vehicle detection, modern air defense, meteorological monitoring, and satellite communication. The T/R module is the core equipment of the active phased array radar, and [...] Read more.

Phased array radar, with its electronic scanning, high reliability, and multifunctionality, has become a core equipment for unmanned aerial vehicle detection, modern air defense, meteorological monitoring, and satellite communication. The T/R module is the core equipment of the active phased array radar, and its performance largely determines the performance of the phased array. At the same time, the application scenario requires relatively high transmission gain and transmission power, so attention should be paid to its heating situation. In addition, the overall size requirements for components are gradually becoming stricter, and miniaturization has become a trend in the development of T/R modules. This paper presents a four-channel T/R module in an X-band based on LTCC technology. In order to reduce weight and have high-density electronic devices, this module uses the latest technologies such as low-temperature cofired ceramic substrate (LTCC), Monolithic Microwave Integrated Chip (MMIC), and the MIC assembly process, and is hermetically sealed. The transmission channel of this module has high gain and high power, and the RF signal is transmitted through an eight-layer LTCC board to reduce interference between adjacent signal transmission lines and reduce the module size at the same time. The method of dividing the transmission and reception channels using a metal shell frame reduces crosstalk between the input and output ports of the transmission channel. Good heat dissipation design ensures the thermal management of the module. The test results show that the size of the TR module is 70 mm × 55 mm × 10 mm, the transmission power is ≥39 dBm, the reception gain is >28 dB, and the noise figure is <3 dB. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 2595 KB

Open AccessProceeding Paper

Development of Cellular IoT-Based, Portable Outdoor Air Quality Monitoring System for Pollution Mapping

by Muhammad Abdullah, Ghada Noor, Sundus Ali, Irfan Ahmed and Muhammad Imran Aslam

Eng. Proc. 2025, 118(1), 30; https://doi.org/10.3390/ECSA-12-26529 - 7 Nov 2025

Viewed by 584

Abstract

In contrast to the existing Wi-Fi-based systems, we have developed a GSM-based, cellular-Internet of Things (C-IoT)-enabled, portable air quality monitoring system that collects critical air quality parameters through advanced sensors in addition to the location of the device using a Global Positioning System [...] Read more.

In contrast to the existing Wi-Fi-based systems, we have developed a GSM-based, cellular-Internet of Things (C-IoT)-enabled, portable air quality monitoring system that collects critical air quality parameters through advanced sensors in addition to the location of the device using a Global Positioning System (GPS). Our system utilizes sensors to monitor temperature, humidity, carbon dioxide, and Particulate Matter concentrations along with the location information. These sensors are integrated with an ESP32 microcontroller which is interfaced with the GPS module for location information as well as with the GSM module to transmit the sensor data and location information to a central IoT gateway using an existing cellular infrastructure. The developed C-IoT sensor node is powered through a portable power bank, allowing complete mobility of the developed sensor node within the cellular coverage in an entire city. The data collected through the mobile C-IoT system is displayed in a live dashboard as well as over a live map for location-aware air quality monitoring. As a pilot run, we collected localized environmental data through a developed node by moving around a pre-defined urban area to create a pollution map of said area. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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6 pages, 2167 KB

Open AccessProceeding Paper

Optical Chemosensory Studies of Novel Amphiphilic D-A-π-A Benzothiadiazoles for Cyanide Detection

by Mathilde L. Boland, Susana P. G. Costa and M. Manuela M. Raposo

Eng. Proc. 2025, 118(1), 31; https://doi.org/10.3390/ECSA-12-26493 - 7 Nov 2025

Viewed by 196

Abstract

Two positively charged amphiphilic benzothiadiazoles (2–3), functionalized with 2,3-dimethylbenzo[d]thiazol-3-ium and 2,3-dimethylnaphtho[2,1-d]thiazol-3-ium acceptor moieties, synthesized earlier in our research group, from 7-(4-methoxyphenyl)benzo[c][1,2,5]thiadiazole-4-carbaldehyde (1), were evaluated concerning their optical chemosensory capabilities towards different [...] Read more.

Two positively charged amphiphilic benzothiadiazoles (2–3), functionalized with 2,3-dimethylbenzo[d]thiazol-3-ium and 2,3-dimethylnaphtho[2,1-d]thiazol-3-ium acceptor moieties, synthesized earlier in our research group, from 7-(4-methoxyphenyl)benzo[c][1,2,5]thiadiazole-4-carbaldehyde (1), were evaluated concerning their optical chemosensory capabilities towards different anions in DMSO and in a DMSO/water (75:25) solution. Spectrophotometric and spectrofluorimetric titrations were performed, demonstrating that both compounds were highly sensitive to cyanide in DMSO. Compound 2 showed fluorescence quenching at 657 nm with 5 equivalents of CN⁻, while compound 3 displayed a decrease in absorption at 480 nm and emission at 666 nm with seven equivalents of CN⁻ in DMSO solution. Nevertheless, in the DMSO/water mixture, the sensitivity decreased, requiring 50–70 equivalents of cyanide for fluorescence quenching. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 1322 KB

Open AccessProceeding Paper

Computational Methodology for the Analysis of SMS Interferometric Structures as Potential Biosensors

by Natalia Carolina Lara-Davila, Jorge Andres Montoya-Cardona and Esteban Gonzalez-Valencia

Eng. Proc. 2025, 118(1), 32; https://doi.org/10.3390/ECSA-12-26491 - 7 Nov 2025

Viewed by 311

Abstract

Optical fiber sensors are recognized as a promising technology for detecting parameters such as temperature, biomolecules, and chemical substances. Among these, multimode interference (MMI) sensors stand out for their high sensitivity, ease of fabrication, and low cost. This work presents the design and [...] Read more.

Optical fiber sensors are recognized as a promising technology for detecting parameters such as temperature, biomolecules, and chemical substances. Among these, multimode interference (MMI) sensors stand out for their high sensitivity, ease of fabrication, and low cost. This work presents the design and analysis of an interferometric sensor based on a single-mode–multimode–single-mode (SMS) structure, in which the multimode section consists of a coreless fiber whose diameter was reduced from 125 µm to 20 µm. Numerical simulations using FIMMWAVE software were performed for external refractive indices (RIs) between 1.33 and 1.43, evaluating sensitivity in two spectral ranges (600–800 nm and 900–1100 nm) and achieving a maximum value of 918.21 nm/RIU for the smallest diameter. The influence of the MMF length on resonance peak position and spectral selectivity was also studied. Experimental validation was carried out with a 125 µm coreless MMF of ≈15 mm length, using solutions of different refractive indices. The experimental results confirmed the sensor’s effective RI response and demonstrated the feasibility of the proposed configuration as a basis for developing low-cost, high-precision optical biosensors. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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11 pages, 2779 KB

Open AccessProceeding Paper

Federated Edge Learning for Distributed Weed Detection in Precision Agriculture Using Multimodal Sensor Fusion

by Dasaradha Arangi and Neelamadhab Padhy

Eng. Proc. 2025, 118(1), 33; https://doi.org/10.3390/ECSA-12-26608 - 7 Nov 2025

Viewed by 532

Abstract

In this work, our goal is to develop a privacy-preserving distributed weed detection and management system. The proposed work integrates FEL (Federated Learning) and deep learning with multi-modal sensor fusion to enhance the model’s performance while minimising data transfer, latency, and energy consumption. [...] Read more.

In this work, our goal is to develop a privacy-preserving distributed weed detection and management system. The proposed work integrates FEL (Federated Learning) and deep learning with multi-modal sensor fusion to enhance the model’s performance while minimising data transfer, latency, and energy consumption. In this study, we used multimodal sensors, such as LiDAR (Light Detection and Ranging), RGB (Red–Green–Blue) cameras, multispectral imaging devices, and soil moisture sensors placed in controlled agricultural plots. Deep learning models, such as Convolutional Neural Networks (CNNs), LSTM–CNN hybrids, and Vision Transformers, were trained using standardized parameters. A proposed Federated CNN (FedCNN) was deployed across multiple edge devices, each locally trained on sensor data without exchanging raw data, using FedAvg and FedProx algorithms. The experimental work revealed that the model FedCNN performed well in comparison to other models and achieved the highest accuracy of 94.1%, precision of 94.3%, recall of 93.9%, F1-score of 94.1%, and AUC of 94.1% during hybrid fusion strategies. We compared the centralized and federated learning performance. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 955 KB

Open AccessProceeding Paper

Satellite-Based Crop Recognition Using Vision Transformer Models for Smart Agriculture

by Kusum Lata, Navneet Kaur and Simrandeep Singh

Eng. Proc. 2025, 118(1), 34; https://doi.org/10.3390/ECSA-12-26538 - 7 Nov 2025

Viewed by 797

Abstract

Precision agriculture is dependent on precise crop identification to maximize resource utilization and enhance yield forecasting. This paper investigates the use of Vision Transformers (ViTs) for crop classification from high-resolution satellite images. In contrast to traditional deep learning models, ViTs use self-attention mechanisms [...] Read more.

Precision agriculture is dependent on precise crop identification to maximize resource utilization and enhance yield forecasting. This paper investigates the use of Vision Transformers (ViTs) for crop classification from high-resolution satellite images. In contrast to traditional deep learning models, ViTs use self-attention mechanisms to capture intricate spatial relationships and improve feature representation. The envisioned framework combines preprocessed multispectral satellite imagery with a Vision Transformer model that is optimized to classify heterogeneous crop types more accurately. Experimental outcomes confirm that ViTs are superior to conventional Convolutional Neural Networks (CNNs) in processing big agricultural datasets, yielding better classification accuracy. The proposed model was tested on a multispectral satellite image from Sentinel-2 and Landsat-8. The results show that ViTs efficiently captured long-range dependencies and intricate spatial patterns and attained a high classification accuracy of 94.6% and a Cohen’s kappa coefficient of 0.91. The incorporation of multispectral characteristics like NDVI and EVI also improved model performance, allowing for improved discrimination between crops with comparable spectral signatures. The results highlight the applicability of Vision Transformers in remote sensing for sustainable and data-centric precision agriculture. Even with the improvements made in this study, issues like high computational expense, data annotation needs, and environmental fluctuations are still major hurdles to widespread deployment. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 1233 KB

Open AccessProceeding Paper

Exploring the Correlation Between Gaseous Emissions and Phenological Phases in Tomato Crops Through Machine Learning

by Emanuela Tavaglione, Melissa Tamisari, Francesco Tralli, Matteo Valt, Sandro Gherardi, Barbara Fabbri and Vincenzo Guidi

Eng. Proc. 2025, 118(1), 35; https://doi.org/10.3390/ECSA-12-26543 - 7 Nov 2025

Viewed by 206

Abstract

Nowadays, agriculture is facing significant challenges, including climate change. Precision agriculture might address these issues by optimizing resource use and promoting sustainability. In this work, a case study of tomato crop monitoring is presented, employing a large amount of gas sensor data collected [...] Read more.

Nowadays, agriculture is facing significant challenges, including climate change. Precision agriculture might address these issues by optimizing resource use and promoting sustainability. In this work, a case study of tomato crop monitoring is presented, employing a large amount of gas sensor data collected over three years (2020–2022) to develop models for phenological phase classification. A k-NN classifier achieved accuracies above 99% across multiple train/test splits, with AUC, sensitivity, specificity, precision, and F1-score above 98%. Results demonstrate the feasibility of low-computational-cost systems capable of real-time detection of the transition point between plants’ developmental stages. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 1087 KB

Open AccessProceeding Paper

A Three-Stage Transformer-Based Approach for Food Mass Estimation

by Sinda Besrour, Ghazal Rouhafzay and Jalila Jbilou

Eng. Proc. 2025, 118(1), 36; https://doi.org/10.3390/ECSA-12-26521 - 7 Nov 2025

Viewed by 453

Abstract

Accurate food mass estimation is a key component of automated calorie estimation tools, and there is growing interest in leveraging image analysis for this purpose due to its ease of use and scalability. However, current methods face important limitations. Some rely on 3D [...] Read more.

Accurate food mass estimation is a key component of automated calorie estimation tools, and there is growing interest in leveraging image analysis for this purpose due to its ease of use and scalability. However, current methods face important limitations. Some rely on 3D sensors for depth estimation, which are not widely accessible to all users, while others depend on camera intrinsic parameters to estimate volume, reducing their adaptability across different devices. Furthermore, AI-based approaches that bypass these parameters often struggle with generalizability when applied to images captured using diverse sensors or camera settings. To overcome these challenges, we introduce a three-stage, transformer-based method for estimating food mass from RGB images, balancing accuracy, computational efficiency, and scalability. The first stage applies the Segment Anything Model (SAM 2) to segment food items in images from the SUECFood dataset. Next, we use the Global-Local Path Network (GLPN) to perform monocular depth estimation (MDE) on the Nutrition5k dataset, inferring depth information from a single image. These outputs are then combined through alpha compositing to generate enhanced composite images with precise object boundaries. Finally, a Vision Transformer (ViT) model processes the composite images to estimate food mass by extracting relevant visual and spatial features. Our method achieves notable improvements in accuracy compared to previous approaches, with a mean squared error (MSE) of 5.61 and a mean absolute error (MAE) of 1.07. Notably, this pipeline does not require specialized hardware like depth sensors or multi-view imaging, making it well-suited for practical deployment. Future work will explore the integration of ingredient recognition to support a more comprehensive dietary assessment system. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 3748 KB

Open AccessProceeding Paper

Industry 4.0-Compliant IoT Supervisory System for Green Hydrogen Applications in Industrial and Domestic Sectors

by Francisco Javier Folgado, Pablo Millán, David Calderón, Isaías González, Antonio José Calderón and Manuel Calderón

Eng. Proc. 2025, 118(1), 37; https://doi.org/10.3390/ECSA-12-26610 - 7 Nov 2025

Cited by 1 | Viewed by 316

Abstract

In recent years, advancements in technologies related to hydrogen have facilitated the exploitation of this energy carrier in conjunction with renewable energies to meet the energy demands of diverse applications. This paper describes a pilot plant within the framework of a research and [...] Read more.

In recent years, advancements in technologies related to hydrogen have facilitated the exploitation of this energy carrier in conjunction with renewable energies to meet the energy demands of diverse applications. This paper describes a pilot plant within the framework of a research and development (R&D) project aimed at utilizing hydrogen in both industrial and domestic sectors. To this end, this facility comprises six subsystems. Initially, a photovoltaic (PV) generator consisting of 48 panels is employed to generate electrical current from solar radiation. This PV array powers a proton exchange membrane (PEM) electrolyzer, which is responsible for producing green hydrogen by means of water electrolysis. The produced hydrogen is subsequently stored in a bottling storage system for later use in a PEM fuel cell that reconverts it into electrical energy. Finally, a programmable electronic load is utilized to simulate the electrical consumption patterns of various profiles. These physical devices exchange operational data with an open source supervisory system integrated by a set of Industry 4.0 (I4.0) and Internet of Things (IoT)-framed environments. Initially, Node-RED acts as middleware, handling communications, and collecting and processing data from the pilot plant equipment. Subsequently, this information is stored in MariaDB, a structured relational database, enabling efficient querying and data management. Ultimately, the Grafana environment serves as a monitoring platform, displaying the stored data by means of graphical dashboards. The system deployed with such I4.0/IoT applications places a strong emphasis on the continuous monitoring of the power inverter that serves as the backbone of the pilot plant, both from an energy flow and communication standpoint. This device ensures the synchronization, conversion, and distribution of electrical energy while simultaneously standing as a primary data source for the supervisory system. The results presented in this article describe the design of the system and provide evidence of its successful implementation. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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11 pages, 1661 KB

Open AccessProceeding Paper

Adaptive Extended Kalman Filtering for Online Monitoring of Concrete Structures Subject to Impacts

by Shang-Jun Chen, Chuan-Chuan Hou and Stefano Mariani

Eng. Proc. 2025, 118(1), 38; https://doi.org/10.3390/ECSA-12-26587 - 7 Nov 2025

Viewed by 431

Abstract

Structures are susceptible to external impacts over the long term, resulting in various types of damage. An online, accurate assessment of the severity of damage is the basis for formulating subsequent maintenance and reinforcement plans. In this work, an online damage identification method [...] Read more.

Structures are susceptible to external impacts over the long term, resulting in various types of damage. An online, accurate assessment of the severity of damage is the basis for formulating subsequent maintenance and reinforcement plans. In this work, an online damage identification method based on the Adaptive Extended Kalman Filter (AEKF) is proposed. Initially, the vibration signals of a concrete-filled steel tubular (CFST) test structure subject to multiple lateral impacts are processed, and signals before and after damage inception are spliced to track damage evolution. Subsequently, the natural frequencies extracted from the signals before and after damage inception, along with the amplitude of the damage itself, are integrated into the state vector to build a nonlinear state transfer and observation model, allowing estimation of the dynamic flexural stiffness of the structure. To further improve the problem solution in the presence of signal losses due to sensor detachment or breakage, missing signals are reconstructed using the weighted matrix pencil (MP), thereby ensuring the continuity and stability of the AEKF filtering process. By comparing the results with the actual damage state, the proposed method is shown to effectively track the gradual reduction in flexural stiffness and to verify its feasibility for providing reliable support for online monitoring and damage assessment. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 5851 KB

Open AccessProceeding Paper

Tool Wear Assessment in Composite Helical Milling via Acoustic Emission Monitoring

by Tony Emerson Marim, Catherine Bezerra Markert, Marcio Marques da Silva, Alessandro Roger Rodrigues, Fabio Romano Lofrano Dotto and Pedro de Oliveira Conceição Junior

Eng. Proc. 2025, 118(1), 39; https://doi.org/10.3390/ECSA-12-26547 - 7 Nov 2025

Viewed by 295

Abstract

This study investigates the machining challenges of fiber-reinforced composite materials (FRCMs), focusing on carbon fiber-reinforced polymer (CFRP) plates, which exhibit high abrasiveness, delamination tendency, and accelerated tool wear. Two solid carbide helical end mills, designed for composite machining, were evaluated through helical interpolation [...] Read more.

This study investigates the machining challenges of fiber-reinforced composite materials (FRCMs), focusing on carbon fiber-reinforced polymer (CFRP) plates, which exhibit high abrasiveness, delamination tendency, and accelerated tool wear. Two solid carbide helical end mills, designed for composite machining, were evaluated through helical interpolation drilling. Acoustic emission signals were continuously acquired via a piezoelectric sensor during standardized cycles, and tool wear was assessed using confocal microscopy and a digital altimeter. Signal processing played a central role, combining energy-based metrics and damage indices to identify the onset of wear and early delamination, enhancing the understanding of tool degradation and improving machining reliability. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 2413 KB

Open AccessProceeding Paper

Enhanced Teleoperation for Manual Remote Driving: Extending ADAS Remote Control Towards Full Vehicle Operation

by İsa Karaböcek, Ege Özdemir and Batıkan Kavak

Eng. Proc. 2025, 118(1), 40; https://doi.org/10.3390/ECSA-12-26609 - 7 Nov 2025

Viewed by 625

Abstract

This study advances prior work on the remote control of Advanced Driver Assistance Systems (ADASs) by introducing a full manual teleoperation mode that enables remote control over both longitudinal and lateral vehicle dynamics via accelerator, brake, and steering inputs. The core contribution is [...] Read more.

This study advances prior work on the remote control of Advanced Driver Assistance Systems (ADASs) by introducing a full manual teleoperation mode that enables remote control over both longitudinal and lateral vehicle dynamics via accelerator, brake, and steering inputs. The core contribution is a flexible, dual-mode teleoperation architecture that allows seamless switching between assisted ADAS control and full manual operation, depending on driving context or system limitations. While teleoperation has been explored primarily for autonomous fallback or direct remote driving, few existing systems integrate dynamic mode-switching in a unified, real-time control framework. Our system leverages a wireless game controller and a Robot Operating System (ROS)-based vehicle software stack to translate remote human inputs into low-latency vehicle actions, supporting robust and adaptable remote driving. This design maintains a human-in-the-loop approach, offering improved responsiveness in complex environments, edge-case scenarios, or during autonomous system fallback. The proposed solution extends the applicability of teleoperation to a broader range of use cases, including remote assistance, fleet management, and emergency response. Its novelty lies in the integration of dual-mode teleoperation within a modular architecture, bridging the gap between ADAS-enhanced autonomy and full remote manual control. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 2023 KB

Open AccessProceeding Paper

Detection of Eccentricity in Conventional Grinding Wheels Using Acoustic Emission Signals and Counts Statistics During the Dressing Operation

by Matheus H. Pereira, Wenderson N. Lopes, Igor H. S. C. de Miranda, Renan O. A. Takeuchi and Breno O. Fernandez

Eng. Proc. 2025, 118(1), 41; https://doi.org/10.3390/ECSA-12-26614 - 7 Nov 2025

Viewed by 235

Abstract

This study proposes a novel approach to monitoring the grinding wheel during the dressing operation by using acoustic emission (AE) signals and the statistical Counts method. AE signals were acquired during the dressing passes and processed in MATLAB^®. The Counts matrices [...] Read more.

This study proposes a novel approach to monitoring the grinding wheel during the dressing operation by using acoustic emission (AE) signals and the statistical Counts method. AE signals were acquired during the dressing passes and processed in MATLAB^®. The Counts matrices were segmented according to the grinding wheel rotation, and the metric termed z-ratio, which combines mean and standard deviation statistics, was calculated for each subwindow. The vectors were then filtered, normalized, and represented in polar coordinates. The results demonstrate the method’s ability to track the evolution of dressing and detect grinding wheel eccentricity, offering a promising tool for the indirect monitoring of the surface conditions of the grinding tool during the dressing operation of conventional grinding wheels. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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5 pages, 1949 KB

Open AccessProceeding Paper

Gesture-Controlled Bionic Hand for Safe Handling of Biomedical Industrial Chemicals

by Sudarsun Gopinath, Glen Nitish, Daniel Ford, Thiyam Deepa Beeta and Shelishiyah Raymond

Eng. Proc. 2025, 118(1), 42; https://doi.org/10.3390/ECSA-12-26577 - 7 Nov 2025

Viewed by 360

Abstract

In pharmaceutical and biomedical industries, manual handling of dangerous chemicals is a leading cause of hazardous exposure to chemicals, toxic burning, and chemical contamination. To counteract these risks, we proposed a gesture-controlled bionic hand system to mimic human finger movements for safe and [...] Read more.

In pharmaceutical and biomedical industries, manual handling of dangerous chemicals is a leading cause of hazardous exposure to chemicals, toxic burning, and chemical contamination. To counteract these risks, we proposed a gesture-controlled bionic hand system to mimic human finger movements for safe and contactless chemical handling. This innovation system uses an ESP32 microcontroller to decode the hand gestures that are detected by the system using computer vision via an integrated camera. A PWM servo driver converts these movements to motor commands such that accurate movements of the fingers can be achieved. Teflon and other corrosion-proof materials are utilized in the 3D printing of the bionic hand in order to withstand corrosive conditions. This new, low-cost, and non-surgical approach replaces the EMG sensors, gives real-time control, and enhances industrial and laboratory process safety. The project is a major milestone in the application of robotics and AI for automation and risk reduction in dangerous environments. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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6 pages, 658 KB

Open AccessProceeding Paper

(Electro)catalytic and Sensing Properties of Redox-Active Nanoparticles with Peroxidase-like Activity

by Aleksandra A. Shneiderman, Elena S. Povaga, Maria A. Komkova and Arkady A. Karyakin

Eng. Proc. 2025, 118(1), 43; https://doi.org/10.3390/ECSA-12-26495 - 7 Nov 2025

Viewed by 321

Abstract

Herein we first attempt to compare the catalytic and electrocatalytic properties of the most commonly used peroxidase-mimicking nanozymes based on transition metal ions, including magnetite, cerium oxide, and Prussian Blue. For the nanomaterials under consideration, the catalytic rate constant for reducing substrate increases [...] Read more.

Herein we first attempt to compare the catalytic and electrocatalytic properties of the most commonly used peroxidase-mimicking nanozymes based on transition metal ions, including magnetite, cerium oxide, and Prussian Blue. For the nanomaterials under consideration, the catalytic rate constant for reducing substrate increases upon decreasing its redox potential and reaches its maximum value for Prussian Blue in the presence of ferrocyanide (k_cat = 3.8 s⁻¹ per single redox-active site). In addition to the highest k_cat, Prussian Blue nanoparticles in electrochemical sensors exhibit sensitivity to H₂O₂ more than three orders of magnitude higher than other nanomaterials. The sensing properties of the electrodes modified with Prussian Blue nanoparticles appear to be dependent on their diameter; particles with a diameter of 140 nm provide optimal sensitivity and lifespan of the corresponding sensor. The achieved exceptional (electro)catalytic properties of Prussian Blue nanoparticles open prospects for their application as universal labels for personal analyzers with either optical or electrochemical readout. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 1971 KB

Open AccessProceeding Paper

Design and Implementation of an IoT-Based Respiratory Motion Sensor

by Bardia Baraeinejad, Maryam Forouzesh, Saba Babaei, Yasin Naghshbandi, Yasaman Torabi and Shabnam Fazliani

Eng. Proc. 2025, 118(1), 44; https://doi.org/10.3390/ECSA-12-26582 - 7 Nov 2025

Viewed by 527

Abstract

In the last few decades, several wearable devices have been designed to monitor respiration rate to capture pulmonary signals with a higher accuracy and reduce patients’ discomfort during use. In this article, we present the design and implementation of a device for the [...] Read more.

In the last few decades, several wearable devices have been designed to monitor respiration rate to capture pulmonary signals with a higher accuracy and reduce patients’ discomfort during use. In this article, we present the design and implementation of a device for the real-time monitoring of respiratory system movements. When breathing, the circumference of the abdomen and thorax changes; therefore, we used a Force-Sensing Resistor (FSR) attached to a Printed Circuit Board (PCB) to measure this variation as the patient inhales and exhales. The mechanical strain this causes changes the FSR electrical resistance accordingly. Also, for streaming this variable resistance on an Internet of Things (IoT) platform, Bluetooth Low Energy (BLE) 5 is utilized due to its adequate throughput, high accessibility, and the possibility of power consumption reduction. In addition to the sensing mechanism, the device includes a compact, energy-efficient micro-controller and a three-axis accelerometer that captures body movement. Power is supplied by a rechargeable Lithium-ion Polymer (LiPo) battery, and energy usage is optimized using a buck converter. For comfort and usability, the enclosure was 3D printed using Stereolithography (SLA) technology to ensure a smooth, ergonomic shape. This setup allows the device to operate reliably over long periods without disturbing the user. Altogether, the design supports continuous respiratory tracking in both clinical and home settings, offering a practical, low-power, and portable solution. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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20 pages, 3729 KB

Open AccessProceeding Paper

A Smart Glove-Based System for Dynamic Sign Language Translation Using LSTM Networks

by Tabassum Kanwal, Saud Altaf, Rehan Mehmood Yousaf and Kashif Sattar

Eng. Proc. 2025, 118(1), 45; https://doi.org/10.3390/ECSA-12-26530 - 7 Nov 2025

Viewed by 1470

Abstract

This research presents a novel, real-time Pakistani Sign Language (PSL) recognition system utilizing a custom-designed sensory glove integrated with advanced machine learning techniques. The system aims to bridge communication gaps for individuals with hearing and speech impairments by translating hand gestures into readable [...] Read more.

This research presents a novel, real-time Pakistani Sign Language (PSL) recognition system utilizing a custom-designed sensory glove integrated with advanced machine learning techniques. The system aims to bridge communication gaps for individuals with hearing and speech impairments by translating hand gestures into readable text. At the core of this work is a smart glove engineered with five resistive flex sensors for precise finger flexion detection and a 9-DOF Inertial Measurement Unit (IMU) for capturing hand orientation and movement. The glove is powered by a compact microcontroller, which processes the analog and digital sensor inputs and transmits the data wirelessly to a host computer. A rechargeable 3.7 V Li-Po battery ensures portability, while a dynamic dataset comprising both static alphabet gestures and dynamic PSL phrases was recorded using this setup. The collected data was used to train two models: a Support Vector Machine with feature extraction (SVM-FE) and a Long Short-Term Memory (LSTM) deep learning network. The LSTM model outperformed traditional methods, achieving an accuracy of 98.6% in real-time gesture recognition. The proposed system demonstrates robust performance and offers practical applications in smart home interfaces, virtual and augmented reality, gaming, and assistive technologies. By combining ergonomic hardware with intelligent algorithms, this research takes a significant step toward inclusive communication and more natural human–machine interaction. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 616 KB

Open AccessProceeding Paper

Evaluating Voice Biomarkers and Deep Learning for Neurodevelopmental Disorder Screening in Real-World Conditions

by Hajarimino Rakotomanana and Ghazal Rouhafzay

Eng. Proc. 2025, 118(1), 46; https://doi.org/10.3390/ECSA-12-26523 - 7 Nov 2025

Viewed by 485

Abstract

Voice acoustics have been extensively investigated as potential non-invasive markers for Autism Spectrum Disorder (ASD). Although many studies report high accuracies, they typically rely on highly controlled clinical protocols that reduce linguistic variability. Their data is also recorded using specialized microphone arrays that [...] Read more.

Voice acoustics have been extensively investigated as potential non-invasive markers for Autism Spectrum Disorder (ASD). Although many studies report high accuracies, they typically rely on highly controlled clinical protocols that reduce linguistic variability. Their data is also recorded using specialized microphone arrays that ensure high-quality recordings. Such dependencies limit their applicability in real-world or in-home screening contexts. In this work, we explore an alternative approach designed to reflect the requirements of mobile-based applications that could assist parents in monitoring their children. We use an open-access dataset of naturalistic storytelling, extracting only the speech segments in which the child is speaking. We applied previously published ASD voice-analysis pipelines to this dataset, which yielded suboptimal performance under these less controlled conditions. We then introduce a deep learning-based method that learns discriminative representations directly from raw audio, eliminating the need for manual feature extraction while being more robust to environmental noise. This approach achieves an accuracy of up to 77% in classifying children with ASD, children with Attention Deficit Hyperactivity Disorder (ADHD), and neurotypical children. Frequency-band occlusion sensitivity analysis on the deep model revealed that ASD speech relied more heavily on the 2000–4000 Hz range, TD speech on both low (100–300 Hz) and high (4000–8000 Hz) bands, and ADHD speech on mid-frequency regions. These spectral patterns may help bring us closer to developing practical, accessible pre-screening tools for parents. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 1226 KB

Open AccessProceeding Paper

Hyperdimensional Computing for Lightweight Modal-Based Damage Classification in Concrete Structures

by Xiao-Ling Lin and Stefano Mariani

Eng. Proc. 2025, 118(1), 47; https://doi.org/10.3390/ECSA-12-26588 - 7 Nov 2025

Viewed by 232

Abstract

Structural Health Monitoring (SHM) systems increasingly require efficient and scalable methods for identifying structural damage under dynamic loading. Traditional learning-based SHM models often rely on high-dimensional features or deep architectures, which may be computationally intensive and difficult to deploy in real-time applications, especially [...] Read more.

Structural Health Monitoring (SHM) systems increasingly require efficient and scalable methods for identifying structural damage under dynamic loading. Traditional learning-based SHM models often rely on high-dimensional features or deep architectures, which may be computationally intensive and difficult to deploy in real-time applications, especially in scenarios with limited resources or bandwidth constraints. In this work, we propose a lightweight classification framework based on Hyperdimensional Computing (HDC) to detect structural damage using vibration-induced features, aiming to reduce complexity while maintaining detection performance. The proposed method encodes a rich feature set, including time-domain, frequency-domain, and autoregressive (AR) model features into high-dimensional binary vectors through a sliding window approach, capturing temporal variations and local patterns within the signal. A supervised HDC classifier is trained to distinguish between healthy and damaged structural states using these compact encodings. The framework enables fast learning and low memory usage, making it particularly suitable for edge-level SHM applications where real-time processing is required. To evaluate the feasibility and effectiveness of the proposed method, experiments are conducted on vibration data collected from controlled lateral impact tests on a concrete-filled steel tubular structure. The results validate the method ability to detect the damage-induced variations in modal frequencies and highlight its potential as a compact, robust, and efficient solution for future SHM systems based on modal data. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 2099 KB

Open AccessProceeding Paper

Printable Chemoresistive Sensor Based on PrFeTiO₅ Solid Solution for Acetone Detection

by Danial Ahmed, Elena Spagnoli, Adil Chakir, Maura Mancinelli, Matteo Ferroni, Boubker Mehdaoui, Abdeslam El Bouari and Barbara Fabbri

Eng. Proc. 2025, 118(1), 48; https://doi.org/10.3390/ECSA-12-26592 - 7 Nov 2025

Viewed by 248

Abstract

Acetone necessitates reliable detection for the sake of both industrial and environmental safety. Metal oxides are widely used as functional materials for the development of gas sensors because techniques like nanostructure modification, doping, and solid solution formation can enhance their sensitivity and selectivity [...] Read more.

Acetone necessitates reliable detection for the sake of both industrial and environmental safety. Metal oxides are widely used as functional materials for the development of gas sensors because techniques like nanostructure modification, doping, and solid solution formation can enhance their sensitivity and selectivity by tuning structural and electronic properties. This study developed PrFeTiO₅ nanostructures, synthesized via the solid-state reaction for acetone sensing. The sensor demonstrated a high response to acetone at an operating temperature of 400 °C, with a low influence of humidity, displaying outstanding selectivity towards acetaldehyde, NH₃, H₂, CO, and CO₂, making it suitable across various applications. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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22 pages, 2676 KB

Open AccessProceeding Paper

Development of Integrated Framework for Automated Construction Progress Sensing, Monitoring and Evaluation

by Mofiyinfoluwa Tobi Olowe and Michael Ayomoh

Eng. Proc. 2025, 118(1), 49; https://doi.org/10.3390/ECSA-12-26603 - 7 Nov 2025

Viewed by 581

Abstract

The construction industry is increasingly adopting digital technologies to enhance productivity and efficiency, in alignment with the principles of Construction 4.0 (C4). The progress and advances recorded thus far are largely due to advancements in cyber-physical systems (CPS), computational processing power, deep learning [...] Read more.

The construction industry is increasingly adopting digital technologies to enhance productivity and efficiency, in alignment with the principles of Construction 4.0 (C4). The progress and advances recorded thus far are largely due to advancements in cyber-physical systems (CPS), computational processing power, deep learning solutions, robotics, and other related technologies. However, a major challenge in this research space is the lack of an integrated solution for both the interior and exterior construction environments, which has led to fragmented data, hindering efficiency. Several researchers have proposed frameworks in recent years that focused on either indoor or outdoor construction environments; this approach has resulted in the creation of siloed information, to the detriment of the C4 ideals and principles. In this study, a comprehensive system architecture for raw data captured using sensors and other inputs to provide useful insight for the construction team and stakeholders was mapped out. This study presents an integrated framework of various technologies for both indoor and outdoor construction environments. The solution provided for localisation algorithms and technologies such as Simultaneous Localisation and Mapping (SLAM), odometry, and inertial measurement unit (IMU) devices. The unified 5-level Cyber-Physical Systems (CPS) architecture was used as the primary architecture, and it was compared with the IoT Architecture layers in terms of data analytics and management perspectives. The Digital Twin (DT), which sits at the cyber level of the architecture, warehouses and tracks in real-time the dynamic complexities of the construction site throughout the project life cycle, serving as the single source of truth for the project. This system architecture and framework presented in this research contributed towards advancing the field of construction automation by offering a scalable solution for efficient construction in project management. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 589 KB

Open AccessProceeding Paper

Defence Pal: A Prototype of Smart Wireless Robotic Sensing System for Landmine and Hazard Detection

by Uttam Narendra Thakur, Angshuman Khan and Sikta Mandal

Eng. Proc. 2025, 118(1), 50; https://doi.org/10.3390/ECSA-12-26578 - 7 Nov 2025

Viewed by 560

Abstract

Landmines remain a significant hazard in contemporary warfare and post-conflict areas, jeopardizing the safety of both civilians and military personnel. This work suggests “Defence Pal,” a cost-effective and portable robotic prototype for landmine detection and environmental monitoring. Its primary objective is to minimize [...] Read more.

Landmines remain a significant hazard in contemporary warfare and post-conflict areas, jeopardizing the safety of both civilians and military personnel. This work suggests “Defence Pal,” a cost-effective and portable robotic prototype for landmine detection and environmental monitoring. Its primary objective is to minimize human risk while improving detection speed and accuracy. The system consists of a wireless-controlled vehicle equipped with a metal detector, gas sensors, and obstacle avoidance features, enabling real-time terrain surveillance while ensuring operator safety. Built with components including a Flysky FS-i6 transmitter and receiver, the prototype was tested under hazardous conditions. It demonstrated reliable detection of buried metallic objects and dangerous gases such as methane and carbon dioxide. The autonomous response system halts the robot and activates visual and auditory alarms upon detecting threats. Our experiments achieved average detection accuracies of 83% for metallic objects and 87% for hazardous gases, validating their performance. These results highlight the practicality and effectiveness of Defence Pal compared to conventional manual detection methods. The results confirm that Defence Pal is a practical, scalable, and cost-effective alternative to traditional manual detection methods for improving landmine identification and environmental hazard monitoring. Therefore, the novelty of this work lies in a low-cost dual-sensing prototype that enables simultaneous detection of gas and metal, providing a practical alternative to conventional single-target, high-cost systems. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 859 KB

Open AccessProceeding Paper

Time-Frequency Analysis and Statistical Variation for Feature Extraction in the Dressing of Conventional Grinding Wheels

by Wenderson N. Lopes, Matheus H. Pereira, Breno O. Fernandez, Igor H. S. C. de Miranda and Renan O. A. Takeuchi

Eng. Proc. 2025, 118(1), 51; https://doi.org/10.3390/ECSA-12-26602 - 7 Nov 2025

Viewed by 240

Abstract

The study proposes a new methodology based on time-frequency analysis for the indirect monitoring of the dressing operation of conventional grinding wheels. Through a low-cost piezoelectric diaphragm (PZT), acoustic signals are captured during the process. The analysis is based on the coefficient of [...] Read more.

The study proposes a new methodology based on time-frequency analysis for the indirect monitoring of the dressing operation of conventional grinding wheels. Through a low-cost piezoelectric diaphragm (PZT), acoustic signals are captured during the process. The analysis is based on the coefficient of variation in the Short-Time Fourier Transform (STFT). The results indicate that the signal instability is high in the first passes but progressively decreases, reaching stability between passes 10 and 15. This suggests that the surface of the grinding wheel is regularized and ready for grinding. The methodology can serve as an objective indicator to assist the operator in interrupting the dressing process at the optimal moment, thereby optimizing grinding quality and reducing operational costs. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 2160 KB

Open AccessProceeding Paper

Digital Sensor-Aware Recommendation Systems: A Progressive Framework Using Agentic AI and Explainable Hybrid Techniques

by Bani Prasad Nayak, Neelamadhab Padhy and Rasmita Panigrahi

Eng. Proc. 2025, 118(1), 52; https://doi.org/10.3390/ECSA-12-26527 - 7 Nov 2025

Viewed by 339

Abstract

Currently, the recommendation system is a challenging task in the 21st centuries.The three main reasons and these are: the need for real-time user behavior analysis, the inability to explain why recommendations are made, and the difficulty handling new users/items. In this article, our [...] Read more.

Currently, the recommendation system is a challenging task in the 21st centuries.The three main reasons and these are: the need for real-time user behavior analysis, the inability to explain why recommendations are made, and the difficulty handling new users/items. In this article, our objective is to develop a hybrid recommendation system that solves the challenges of traditional approaches. Our framework combined real-time learning and agentic rules, as well as sensor compatibility, in a dynamic environment. We developed a novel framework called SAFIRE (Sensor-Aware Framework for Intelligent Recommendations and Explainable Hybrid Techniques), where the eight traditional algorithms (User-Based CF, Item-Based CF, KNNWithMeans, KNNBaseline, SVD, SVD++, NMF, and BaselineOnly), a hybrid ensemble, and Explainable AI are used to recommend it. Our experimental work reveals that the model of BaselineOnly (Baseline Estimation Algorithm) whose accuracy under 5-fold obtained is 0.5156, MAE of 0.34055. Similarly, under 10-fold cross-validation, the models’ performance reached to 0.51558,0.34069, respectively. It has been observed that the lowest MAE obtained in the 5 CV setting is 0.29913. The model NMF(Non-Negative Matrix Factorisation) achieved an MAE of 0.30144 under 10-fold CV. Apart from this, Memory-Based Collaborative Filtering models perform marginally better with 10-fold CV as compared to the 5-fold CV. Overall, the model-based methods—BaselineOnly, NMF, and SVD—show little variance between folds (mean difference < 0.003), suggesting that they hold steady across various cross-validation setups. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 1286 KB

Open AccessProceeding Paper

Quantitative Evaluation and Comparison of Motion Discrepancy Analysis Methods for Enhanced Trajectory Tracking in Mechatronic Systems

by Alberto Borboni, Roberto Pagani and Cinzia Amici

Eng. Proc. 2025, 118(1), 53; https://doi.org/10.3390/ECSA-12-26574 - 7 Nov 2025

Viewed by 341

Abstract

Pre-defined motion command profiles enable precise positioning and dynamic control in mechanical and mechatronic systems, maximizing efficiency and reliability. Real-world applications introduce dynamic factors like mechanical compliance, friction, and external disturbances that significantly impact system performance. Understanding these influences improves motion control strategy [...] Read more.

Pre-defined motion command profiles enable precise positioning and dynamic control in mechanical and mechatronic systems, maximizing efficiency and reliability. Real-world applications introduce dynamic factors like mechanical compliance, friction, and external disturbances that significantly impact system performance. Understanding these influences improves motion control strategy accuracy, robustness, and system stability. This study emphasizes the role of systematic and stochastic disturbances in improving motion control and accuracy. It introduces a structured method for evaluating system behavior under realistic operational conditions using advanced vibration analysis and spatio-temporal similarity measures. Using vibration indicators like amplitude, frequency content, phase relationships, crest factor, and acceleration root mean square (RMS) values, a comprehensive framework is created to quantify motion profile deviations. These indicators identify resonant frequencies, transient disturbances, and system inconsistencies, improving compensation strategies and predictive maintenance. A key contribution of this research is the comparison of quantification methods for motion precision and robustness integrating vibration diagnostics and advanced motion similarity analysis to improve motion control and assessment. Multi-faceted motion deviation characterization is achieved by combining displacement, velocity, and acceleration measurements with statistical and mathematical analysis. To assess motion consistency, spatio-temporal similarity measures like Dynamic Time Warping (DTW), Hausdorff distance, and discrete Fréchet distance capture spatial alignment and temporal progression. These measures allow a more nuanced evaluation of motion quality than traditional error metrics, especially in variable-speed dynamics, sampling rate inconsistencies, and complex motion patterns. Frequency-domain methods like FFT and wavelet transforms detect oscillatory behaviors to improve motion analysis reliability. The study uses spectral analysis and time–frequency domain techniques to detect motion inconsistencies that may cause mechanical wear, instability, or energy waste. Crest factor analysis and phase relationship assessment can also detect misalignment, structural resonance, and transient perturbations that conventional metrics miss. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 1019 KB

Open AccessProceeding Paper

Backstepping-Based Trajectory Control for a Three-Rotor UAV: A Nonlinear Approach for Stable and Precise Flight

by Imam Barket Ghiloubi, Marco Rinaldi, Yattou El Fadili and Oumaima Gharsa

Eng. Proc. 2025, 118(1), 54; https://doi.org/10.3390/ECSA-12-26573 - 7 Nov 2025

Viewed by 397

Abstract

Ensuring precise trajectory tracking and stability in unconventional UAVs is a critical challenge in aerial robotics. This paper investigates a three-rotor UAV with complex underactuated dynamics and develops a nonlinear backstepping controller. The UAV model highlights the essential role of onboard sensors, since [...] Read more.

Ensuring precise trajectory tracking and stability in unconventional UAVs is a critical challenge in aerial robotics. This paper investigates a three-rotor UAV with complex underactuated dynamics and develops a nonlinear backstepping controller. The UAV model highlights the essential role of onboard sensors, since position and angular velocity measurements are fundamental for feedback and must be continuously exploited by the control law. Using these sensor-based signals in simulation, the proposed controller achieves accurate trajectory tracking, fast convergence, and stable behavior. The study emphasizes that sensor integration is crucial for enabling reliable autonomous flight of unconventional UAVs. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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13 pages, 5587 KB

Open AccessProceeding Paper

Towards Autonomous Raised Bed Flower Pollination with IoT and Robotics

by Rusira Thamuditha Karunarathna, Chathupa Wickramarathne, Mohamed Akmal Mohamed Alavi, Chamath Shanaka Wickrama Arachchi, Kapila Dissanayaka, Bhagya Nathali Silva and Ruchire Eranga Wijesinghe

Eng. Proc. 2025, 118(1), 55; https://doi.org/10.3390/ECSA-12-26572 - 7 Nov 2025

Viewed by 484

Abstract

Strawberries, a high-value crop with growing demand, face increasing challenges from labour shortages, declining pollinator populations, and the limitations of inconsistent manual pollination. This paper presents an IoT-enabled robotic system designed to automate strawberry pollination in open-field raised-bed environments with minimal human intervention. [...] Read more.

Strawberries, a high-value crop with growing demand, face increasing challenges from labour shortages, declining pollinator populations, and the limitations of inconsistent manual pollination. This paper presents an IoT-enabled robotic system designed to automate strawberry pollination in open-field raised-bed environments with minimal human intervention. The system consists of a mobile rover equipped with an ESP32-CAM for image capture and a robotic arm mounted on an Arduino Uno, capable of controlled X, Y, and Z positioning to perform targeted pollination. Images of strawberry beds are transmitted to a locally deployed server, which uses a lightweight detection model to identify flowers. System components communicate asynchronously via HTTP and I2C protocols, and the onboard event-driven architecture enables responsive behaviour while minimizing RAM and power usage, which is an essential requirement for low-cost, field-deployable robotics. The server also manages multi-rover scheduling through a custom priority queue designed for low-end hardware. In controlled lo0ad tests, the scheduler improved average response time by 6.9% and handled 2.4% more requests compared to the default queueing system, while maintaining stability. Preliminary field tests demonstrate successful flower identification and reliable arm positioning under real-world conditions. Although full system yield measurements are ongoing, current results validate the core design’s functional feasibility. Unlike previous systems that focus on greenhouse deployments or simpler navigation approaches, this work emphasizes modularity, affordability, and adaptability for small and medium farms, particularly in resource-constrained agricultural regions such as Sri Lanka. This study presents a promising step toward autonomous and scalable pollination systems that integrate embedded systems, robotics, and IoT for practical use in precision agriculture. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 1001 KB

Open AccessProceeding Paper

Real-Time Air Quality and Weather Monitoring System Utilizing IoT for Sustainable Urban Development and Environmental Management

by Akash Ram Kondeti, Leelavathi Rudraksha, Silpa Chinnaiahgari and Anitha Bujunuru

Eng. Proc. 2025, 118(1), 56; https://doi.org/10.3390/ECSA-12-26599 - 7 Nov 2025

Viewed by 1219

Abstract

Environmental conditions like temperature, humidity, light, and gas levels directly affect human health, agriculture, and industrial processes. Monitoring these factors in real time is necessary for detecting dangerous situations early and making informed choices. This work presents a compact, mobile, IoT-enabled device that [...] Read more.

Environmental conditions like temperature, humidity, light, and gas levels directly affect human health, agriculture, and industrial processes. Monitoring these factors in real time is necessary for detecting dangerous situations early and making informed choices. This work presents a compact, mobile, IoT-enabled device that measures environmental data and sends it wirelessly for remote access. The system uses the ESP32 microcontroller, chosen for its low power use, built-in Wi-Fi, and ease of connecting with sensors and cloud services. Key sensors include the DHT22 for temperature and humidity, MQ135 for ammonia and gas detection, and an LDR for checking light intensity. An infrared (IR) sensor identifies obstacles, and a buzzer alerts users to dangerous conditions. The collected data appears on a 16X2 LCD for local monitoring. It is also transmitted to the ThingSpeak cloud platform for long-term storage and visualization. Users can view this data in real time through the Blynk mobile application, which also enables remote control of the device. The system is built for mobility. It operates with DC motors powered by an L298N motor driver. This lets it navigate different environments and collect data from various locations. This feature gives more flexibility and improves the system’s effectiveness compared to traditional stationary monitoring units. The innovative part of this project is the mix of real-time sensing, autonomous movement, and cloud connectivity in a low-cost, portable setup. The system was tested in controlled environments and consistently provided reliable readings. Its practical uses include smart agriculture, urban air quality monitoring, and industrial safety. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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11 pages, 726 KB

Open AccessProceeding Paper

Intelligent Chatbot System Design, Development, and Deployment for Client Queries: Efficient and Effective Perception and Cognition

by Tlou Sebola, Michael Ayomoh and Brain Ndlovu

Eng. Proc. 2025, 118(1), 57; https://doi.org/10.3390/ECSA-12-26595 - 17 Nov 2025

Viewed by 458

Abstract

The recent synergistic explosion of artificial intelligence and the world of machines, in a bid to make them smarter entities as a result of the fourth industrial revolution, has resulted in the concept of chatbots, which have evolved over the years and gained [...] Read more.

The recent synergistic explosion of artificial intelligence and the world of machines, in a bid to make them smarter entities as a result of the fourth industrial revolution, has resulted in the concept of chatbots, which have evolved over the years and gained heightened attention for the sustainability of most human corporations. Organisations are increasingly utilising chatbots to enhance customer engagement through the process of agent-based autonomous sensing, interaction, and enhanced service delivery. The current state of the art in chatbot technology is such that the system lacks the ability to conduct text-sensing in a bid to acquire new information or learn from the external world autonomously. This has limited the current chatbot systems to being system-controlled interactive agents, hence, strongly limiting their functionalities and posing a question on the purported intelligence. In this research, an integrated framework that combines the functionalities and capabilities of a chatbot and machine learning was developed. The integrated system was designed to accept new text queries from the external world and import them into the knowledge base using the SQL (Structured Query Language) syntax and MySQL workbench (version 8.0.44). The search engine and decision-making cluster was built in the Python (version 3.12.7) coding environment with the learning process, solution adaptation, and inference, anchored using a reinforcement machine learning approach. This mode of chatbot operation, with an interactive capacity, is known as the mixed controlled system mode, with a viable human–machine system interaction. The smart chatbot was assessed for efficacy using performance metrics (response time, accuracy) and user experience (usability, satisfaction). The analysis further revealed that several self-governed chatbots deployed in most corporate organisations are system-controlled and significantly constrained, hence lacking the ability to adapt or filter queries beyond their predefined databases when users employ diverse phrasing or alternative terms in their interactions. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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7 pages, 1099 KB

Open AccessProceeding Paper

Monitoring Femtosecond Laser Ablation Processes on Human Teeth Using FT-IR Spectroscopy

by Marianna Portaccio, Achu Purushothaman, Jijil J. J. Nivas, Salvatore Amoruso, Giovanni Maria Gaeta and Maria Lepore

Eng. Proc. 2025, 118(1), 58; https://doi.org/10.3390/ECSA-12-26505 - 7 Nov 2025

Viewed by 287

Abstract

In recent years, the laser-based ablation of damaged or undesired tooth material has emerged as a highly promising technique for improving dental cavity preparation. While X-ray diffraction and X-ray photoelectron spectroscopy are generally used to characterize the constituents of ablated surfaces, Fourier-transform infrared [...] Read more.

In recent years, the laser-based ablation of damaged or undesired tooth material has emerged as a highly promising technique for improving dental cavity preparation. While X-ray diffraction and X-ray photoelectron spectroscopy are generally used to characterize the constituents of ablated surfaces, Fourier-transform infrared (FT-IR) can also be employed for monitoring the changes induced by the femtosecond laser ablation process. In the present study, FT-IR spectroscopy has been adopted to characterize the changes induced in extracted human teeth. The laser ablation was performed in ambient air by using a femtosecond laser source at different fluences in the range of 0.7–1.5 J/cm² to produce regular lines on various samples. Micro-ATR spectroscopy was employed to examine laser-processed tooth disks. The spectra acquired from different samples reveal the contributions of the various dental components and provide insight into the effect of laser processing under different conditions. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 1320 KB

Open AccessProceeding Paper

Real-Time Sheath Damage Detection in Multicore Wire Production Using Laser-Diffused Reflection

by Nisala Damith, Aruna Kulasinghe, Teshan Gunasekara, Narmada Ranaweera, Nipun Shantha Kahatapitiya, Hansa Hettiarachchi, Udaya Wijenayake, Bhagya Nathali Silva, Mansik Jeon, Jeehyun Kim, Ruchire Eranga Wijesinghe and Nishan Dharmaweera

Eng. Proc. 2025, 118(1), 59; https://doi.org/10.3390/ECSA-12-26550 - 7 Nov 2025

Viewed by 387

Abstract

This study presents a real-time system for detecting sheath damage in multicore wire production, addressing the limitations of conventional high-voltage testing. After evaluating multiple sensing techniques, laser-diffused reflection emerged as the most reliable, non-intrusive, and effective for continuous monitoring. A calibrated system combining [...] Read more.

This study presents a real-time system for detecting sheath damage in multicore wire production, addressing the limitations of conventional high-voltage testing. After evaluating multiple sensing techniques, laser-diffused reflection emerged as the most reliable, non-intrusive, and effective for continuous monitoring. A calibrated system combining a laser source and photodetector was implemented and tested on a live production line, achieving 83.75% detection accuracy. The system maintained consistent performance across wire colors and insulation conditions. This advancement offers a safer, more efficient alternative for in-process quality control. Future work aims to enhance robustness and incorporate intelligent algorithms to further optimize detection accuracy. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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7 pages, 1549 KB

Open AccessProceeding Paper

Real-Time Surface Roughness Analysis in Milling Using Acoustic Emission Signals for Industry 4.0 Applications

by Paulo Vitor Pereira de Oliveira, Fernando Henrique Pimentel Rondon de Assis, Catherine Bezerra Markert, Pedro de Oliveira Conceição Junior, Alessandro Roger Rodrigues and Fabio Romano Lofrano Dotto

Eng. Proc. 2025, 118(1), 60; https://doi.org/10.3390/ECSA-12-26514 - 7 Nov 2025

Viewed by 262

Abstract

In the expansion of Industry 4.0, many automation processes are being enhanced as a means of accomplishing higher productivity goals. With the prospect of new achievable objectives, the demand for faster and more reliable resource processing methods has also increased. Similarly, machining processes [...] Read more.

In the expansion of Industry 4.0, many automation processes are being enhanced as a means of accomplishing higher productivity goals. With the prospect of new achievable objectives, the demand for faster and more reliable resource processing methods has also increased. Similarly, machining processes have also been improved with the development of IoT devices by streamlining operations, enabling predictive maintenance, and providing real-time data for better decision-making and collaborating with such productivity levels. For instance, in metal milling, IoT-based sensor techniques are being developed and proven efficient in increasing speed and reliability while reducing system invasiveness and complexity, which grants more profitability. The present paper proposes a real-time metal roughness average (Ra) analysis method based on Acoustic Emission (AE), which indirectly estimates roughness through signal processing and feature extraction of the AE signal through Power Spectral Density (PSD) evaluation. The experimental setting consists of a steel workpiece in which straight lines were milled with four distinct roughness levels (6 μm, 12 μm, 18 μm, and 24 μm, produced by defined milling parameters), and the method was able to estimate the Ra with an error under 7%. This work aims to contribute to the real-time monitoring of surface roughness in alignment with Industry 4.0 requirements by demonstrating the effectiveness of IoT-based solutions and the potential of Acoustic Emission in machinery sensing and process automation. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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14 pages, 1709 KB

Open AccessProceeding Paper

A Secure FPGA-Based IoT Gateway for Smart Home Automation Using PUF-Based Authentication

by Lopamudra Samal, Riya Kori and Kamalakanta Mahapatra

Eng. Proc. 2025, 118(1), 61; https://doi.org/10.3390/ECSA-12-26512 - 7 Nov 2025

Viewed by 758

Abstract

The fast expansion of the Internet of Things (IoT) has accelerated the advancement of smart home technologies. However, secure communication and access control remain significant challenges. This paper presents a fully implemented FPGA-based IoT gateway that utilizes the Zynq-7000 SoC, integrating sensing, processing, [...] Read more.

The fast expansion of the Internet of Things (IoT) has accelerated the advancement of smart home technologies. However, secure communication and access control remain significant challenges. This paper presents a fully implemented FPGA-based IoT gateway that utilizes the Zynq-7000 SoC, integrating sensing, processing, wireless communication, and hardware-level authentication. Analog temperature data from an LM35 sensor is digitized via a 12-bit XADC and transmitted over Wi-Fi using an ESP8266-01 module. An SPI-based OLED provides real-time feedback. To ensure device-level trust, an XOR-based Physically Unclonable Function (PUF) enables lightweight challenge–response authentication with over good uniqueness and a latency of under 10 ms. The system demonstrates ±0.5 °C sensing accuracy, <50 ms transmission delay, and low power consumption. It offers a scalable and secure platform suitable for real-time smart home and facility automation. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 1996 KB

Open AccessProceeding Paper

Wearable Sensors for Gynecological Health Monitoring with AI-Driven Approaches in Post-Hysterectomy Ovarian Function Assessment

by Gunavathi Ramasamy and Angelo P. Chery

Eng. Proc. 2025, 118(1), 62; https://doi.org/10.3390/ECSA-12-26564 - 7 Nov 2025

Viewed by 726

Abstract

A hysterectomy is a common surgery performed to remove a woman’s womb (uterus). Monitoring health after a hysterectomy is also extremely important, especially if the ovaries are still present. At that point, the functioning of the ovaries and their impact on a woman’s [...] Read more.

A hysterectomy is a common surgery performed to remove a woman’s womb (uterus). Monitoring health after a hysterectomy is also extremely important, especially if the ovaries are still present. At that point, the functioning of the ovaries and their impact on a woman’s metabolism or cardiovascular health are still in question, which is why we proposed this study. The combination of wearable sensors and Artificial Intelligence helps with post-hysterectomy health monitoring, especially for women who retain their ovaries. Ovarian function remains vital for hormone balance, cardiovascular health, and metabolic regularity. As traditional approaches lose effectiveness over time, this novel approach explores data collection and AI-driven analytics to address these challenges. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 1687 KB

Open AccessProceeding Paper

Architecture of a Piezoelectric Acoustic Detector for Applications in Tissue and Soft Material

by Raúl Alberto Reyes-Villagrana

Eng. Proc. 2025, 118(1), 63; https://doi.org/10.3390/ECSA-12-26606 - 7 Nov 2025

Viewed by 216

Abstract

There are various non-destructive techniques for determining the internal properties of materials in fluids, semi-solids, solids, and biological tissue. One of these techniques is low-intensity ultrasonic testing. In this proceeding paper, a study on the architecture of a piezoelectric acoustic detector (PAD) is [...] Read more.

There are various non-destructive techniques for determining the internal properties of materials in fluids, semi-solids, solids, and biological tissue. One of these techniques is low-intensity ultrasonic testing. In this proceeding paper, a study on the architecture of a piezoelectric acoustic detector (PAD) is presented, from which an analysis for the design, development, and construction of an acoustic wave detector in the ultrasonic spectrum has emerged. Its purpose is to be applied to soft matter and tissue. The 110 μm thick polyvinylidene fluoride (PVDF) piezoelectric element was used as the active element in the thickness mode configuration. Piezoelectric constitutive equations were applied to a one-dimensional model for the analysis. A cylindrical iron–nickel backing was used, and the parts were bonded with conductive silver epoxy glue. The results are presented. The equation for the output voltage of the piezoelectric acoustic detector is described. Functional testing of the PAD is demonstrated using the pulse-echo technique, in which an acoustic wave generator excites an ultrasonic immersion sensor in emission configuration and the DAP is connected to a digital oscilloscope to observe the received signal. Finally, pulsed photoacoustic spectroscopy was applied to a biological tissue emulator and yielded significant results in the detection of a ruby sphere embedded in the emulator. It is proposed to further investigation the DAP models in multilayer structural configurations to increase their sensitivity. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 3524 KB

Open AccessProceeding Paper

HMI Based on Industrial Operator Panels for Supervision of a Smart Microgrid Hybridized with Hydrogen

by David Calderón, Francisco Javier Folgado, Isaías González and Antonio José Calderón

Eng. Proc. 2025, 118(1), 64; https://doi.org/10.3390/ECSA-12-26598 - 7 Nov 2025

Viewed by 306

Abstract

This research is framed within a larger project, whose general objective is the implementation of a SMG (Smart Micro-Grid) for distributed generation from renewable energy sources, with hydrogen as a backup. This project in-corporates an energy management system to optimize the operation of [...] Read more.

This research is framed within a larger project, whose general objective is the implementation of a SMG (Smart Micro-Grid) for distributed generation from renewable energy sources, with hydrogen as a backup. This project in-corporates an energy management system to optimize the operation of each of the systems involved while ensuring energy demand. Additionally, a hydrogen management strategy is included to maximize performance in its production, storage, and consumption within the SMG. Specifically, this work focuses on the design and implementation of a monitoring and optimization system for a SMG composed of a photovoltaic generator, a short-term energy storage system using a lithium battery, and a system for the generation, storage, and use of hydrogen produced in a fuel cell. The objective is the development of an HMI (Human Machine Interface) based on a touch operator panel KTP700 by the manufacturer Siemens, which runs in parallel with the existing SCADA (Supervisory Control and Data Acquisition) application implemented using the graphical programming software LabVIEW. The purpose of this HMI is to complement the SCADA system in such a way that it allows for direct, simple, and immediate interaction with all the equipment that comprises the SMG. This will provide quick and secure access to the monitoring of relevant variables and the parameterization of the hydrogen generator. Furthermore, due to the robustness and reliability of the industrial operator panels, the aim is to establish a supervision system with continuous and permanent operation, similar to the industrial plants automation and management systems. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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14 pages, 927 KB

Open AccessProceeding Paper

Research on Intelligent Monitoring of Offshore Structure Damage Through the Integration of Multimodal Sensing and Edge Computing

by Keqi Yang, Kefan Yang, Shengqin Zeng, Yi Zhang and Dapeng Zhang

Eng. Proc. 2025, 118(1), 65; https://doi.org/10.3390/ECSA-12-26605 - 7 Nov 2025

Cited by 1 | Viewed by 392

Abstract

With the increasing demand for safety monitoring of offshore engineering structures, traditional single-modality sensing and centralized data processing models face challenges such as insufficient real-time performance and weak anti-interference abilities in complex marine environments. This research proposes an intelligent monitoring system based on [...] Read more.

With the increasing demand for safety monitoring of offshore engineering structures, traditional single-modality sensing and centralized data processing models face challenges such as insufficient real-time performance and weak anti-interference abilities in complex marine environments. This research proposes an intelligent monitoring system based on multimodal sensor fusion and edge computing, aiming to achieve high-precision real-time diagnosis of offshore structure damage. The research plans to construct multimodal sensors through sensors such as stress change sensors, vibration sensors, ultrasonic sensors, and fiber Bragg grating sensors. A distributed wireless sensor network will be adopted to realize the transmission of sensor data, reduce the complexity of wiring, and meet the requirements of high humidity and strong corrosion in the marine environment. At the edge computing layer, lightweight deep learning models (such as multi-branch Transformer) and D-S evidence theory fusion algorithms will be deployed to achieve real-time feature extraction of multi-source data and damage feature fusion, supporting the intelligent identification of typical damages such as cracks, corrosion, and deformation. Experiments will simulate the coupled working conditions of wave impact, seismic load, and corrosion to verify the real-time performance and accuracy of the system. The expected results can provide a low-latency and highly robust edge-intelligent solution for the health monitoring of offshore engineering structures and promote the deep integration of sensor networks and artificial intelligence in Industry 4.0 scenarios. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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16 pages, 5297 KB

Open AccessProceeding Paper

Forest Fire Monitoring from Unmanned Aerial Vehicles Using Deep Learning

by Christophe Graveline and Pierre Payeur

Eng. Proc. 2025, 118(1), 66; https://doi.org/10.3390/ECSA-12-26597 - 7 Nov 2025

Viewed by 545

Abstract

Forest fires pose a serious threat to the environment with the potential of causing ecological harm, financial losses, and human casualties. While research suggests that climate change will increase the frequency and severity of these fires, recent developments in deep learning and convolutional [...] Read more.

Forest fires pose a serious threat to the environment with the potential of causing ecological harm, financial losses, and human casualties. While research suggests that climate change will increase the frequency and severity of these fires, recent developments in deep learning and convolutional neural networks (CNN) have greatly enhanced fire detection techniques and capability. These models can be leveraged by unmanned aerial vehicles (UAVs) to automatically monitor burning areas. However, drones can carry only limited computational and power resources; therefore, on-board computing capabilities are constrained by hardware limitations. This work focuses on the design of segmentation models to identify and localize active burning areas from aerial RGB images processed on limited computing resources. To achieve this goal, the research compares the performance of different variants of the DeepLabv3 neural network model for fire segmentation when trained and tested with the FLAME dataset using a k-fold cross validation approach. Experimental results are compared with U-Net, a benchmark model used with the FLAME dataset, by implementing this model in the same codebase as the DeepLabv3 model. This work demonstrates that a refined version of DeepLabv3, with a MobileNetv2 backbone using pretrained layers and a simplified atrous spatial pyramid pooling (ASPP) module, yields a similar performance to U-Net, with a precision of 87.8% and a recall of 83.2%, while only requiring 20% of the number of parameters involved with the U-Net topology. This significantly reduces memory and power consumption, enabling longer UAV flight duration and reducing the processing overhead associated with sensor input, making it more suitable for deployment on unmanned aerial vehicles. The model’s compact architecture, implemented using TensorFlow and Keras for model design and training, along with OpenCV for image preprocessing, makes it portable and easy to integrate with edge devices such as NVIDIA Jetson boards. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 1987 KB

Open AccessProceeding Paper

A 13 G to 24.8 GHz Broadband Power Amplifier with 23% PAE for Sensor Applications

by Yubin Wu and Jie Cui

Eng. Proc. 2025, 118(1), 67; https://doi.org/10.3390/ECSA-12-26511 - 7 Nov 2025

Viewed by 193

Abstract

Millimeter-wave (mm-wave) radar has become a key technology in wireless sensor networks (WSNs) due to its high spatial resolution and penetration capability, enabling applications such as smart traffic control and non-contact health monitoring. Achieving fine-range resolution necessitates wide signal bandwidth, which places stringent [...] Read more.

Millimeter-wave (mm-wave) radar has become a key technology in wireless sensor networks (WSNs) due to its high spatial resolution and penetration capability, enabling applications such as smart traffic control and non-contact health monitoring. Achieving fine-range resolution necessitates wide signal bandwidth, which places stringent demands on power amplifier (PA) performance in terms of bandwidth, efficiency, and output power. Therefore the design of the power amplifier for WSN poses significant challenges. This paper presents a broadband mm-wave PA implemented in a 40 nm CMOS process, utilizing transformer-based power combining to enhance efficiency and bandwidth simultaneously, which can adequately meet the requirements of WSN systems. The PA achieves a 3 dB flat power bandwidth up to 62% from 13 to 24.8 GHz. At 19 GHz, it delivers a saturated output power (Psat) of 12.3 dBm, a 1 dB compression point (P1dB) of 10.15 dBm, and exhibits a peak power-added efficiency (PAE) of 23%, with 17.2% PAE at P1dB. The PA consumes 43 mW from a 1.1 V supply and occupies an active area of only 0.06 mm². These results validate the effectiveness of transformer-based combining for achieving compact, high-performance broadband PAs in CMOS, and demonstrate its suitability for mm-wave radar systems requiring high-range resolution. The amplifier provides a high stability, with output return losses better than −10 dB. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 985 KB

Open AccessProceeding Paper

Integer-PSO-Optimized Checkerboard Dual-Band Terahertz Metamaterial Absorber for Biomedical Sensing Applications

by Santosh Kumar Mishra, Sunil Kumar Mishra and Bhargav Appasani

Eng. Proc. 2025, 118(1), 68; https://doi.org/10.3390/ECSA-12-26497 - 7 Nov 2025

Viewed by 199

Abstract

This paper presents a checkerboard-patterned terahertz (THz) metamaterial absorber engineered for wide-band, dual-band absorption. The absorber consists of a gold metal layer patterned on a polyimide substrate, forming a unit cell structure with dimensions of 85 µm × 85 µm. At the core [...] Read more.

This paper presents a checkerboard-patterned terahertz (THz) metamaterial absorber engineered for wide-band, dual-band absorption. The absorber consists of a gold metal layer patterned on a polyimide substrate, forming a unit cell structure with dimensions of 85 µm × 85 µm. At the core of the design is a square metal patch of 67 µm × 67 µm, which is divided into a 5 × 5 grid of 25 smaller cells. An integer-coded Particle Swarm Optimization (PSO) algorithm is employed to generate the pattern, where an input value of ‘1’ retains the metal in a cell, and a ‘0’ results in the removal of metal from that cell, resulting in a digitally optimized checkerboard pattern. The substrate height is also optimized and fixed at 7 µm to enhance resonance characteristics. The PSO algorithm is run for 50 iterations, with the fitness function defined as the number of frequency points at which the absorption exceeds 90%. The finalized design achieves two distinct absorption peaks with high efficiency: 99.53% at 3.434 THz, with a 90% absorption bandwidth of 212 GHz; and 99.35% at 3.823 THz, with a bandwidth of 177 GHz. While the absorption performance is already significant, it can be further improved by increasing the number of PSO iterations, albeit at the cost of higher computational complexity. The proposed absorber demonstrates strong potential for biomedical sensing, as validated through its ability to differentiate between cancerous and non-cancerous breast and blood cells. This work paves the way for fully automated, algorithm-driven metamaterial design strategies in the THz regime, particularly for applications in non-invasive biomedical diagnostics. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 4651 KB

Open AccessProceeding Paper

Shape Signature Features of Healthy and Diseased Tomato Leaves Using Contour Metrics

by Jazzie R. Jao and Edgar A. Vallar

Eng. Proc. 2025, 118(1), 69; https://doi.org/10.3390/ECSA-12-26528 - 7 Nov 2025

Viewed by 404

Abstract

This study investigates the role of leaf shape in detecting disease in tomato plants, grounded in the observation that plant leaves often undergo structural changes in response to infection. Healthy and diseased tomato leaves are characterized by extracting shape signature features from images [...] Read more.

This study investigates the role of leaf shape in detecting disease in tomato plants, grounded in the observation that plant leaves often undergo structural changes in response to infection. Healthy and diseased tomato leaves are characterized by extracting shape signature features from images and analyzing their spectral characteristics. Leaf images were captured using a Sony ZV-E10 Mark II mirrorless camera equipped with a Sigma 16 mm f/1.4 DC DN lens. Each leaf was placed flat on a matte white surface under a controlled overhead photography setup. The camera was mounted at a fixed height on a tripod, and uniform illumination was achieved using two symmetrically positioned LED spotlight lamps, minimizing shadows and glare. The dataset comprises 200 samples: 100 healthy and 100 diseased tomato leaves, representing a range of morphological and pathological variations. Three primary shape metrics were extracted from the images to characterize the structural differences: (1) the Centroid Contour Distance measured the radial distances from the leaf centroid to its outer contour; (2) the Hausdorff Distance quantified the geometric dissimilarity between contours; and (3) the Dice Similarity Index assessed the degree of overlap. In addition, spectral characteristics were derived from the RGB channels: mean intensities of red, green, blue, and the Excess Green Index. Results show that both shape and spectral features are valuable for detecting plant diseases: PCA shows clustering patterns between the two classes of leaves, and correlation analysis highlights the relationship between several pairs of geometric and color features. In conclusion, shape is an essential aspect of plant health, as it reflects the structural changes that occur as a result of disease. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 2949 KB

Open AccessProceeding Paper

Early Detection of Volatile Tumor Biomarkers Using Chemoresistive Sensors and MEMS-Based Preconcentration: A Study on K562 Cell Line

by Melissa Tamisari, Elena Spagnoli, Maria Teresa Altieri, Michele Astolfi, Monica Borgatti, Giulia Breveglieri, Ivan Elmi, Vincenzo Guidi, Luca Masini, Arianna Rossi, Giuseppe Sabbioni, Emanuela Tavaglione, Stefano Zampolli and Barbara Fabbri

Eng. Proc. 2025, 118(1), 70; https://doi.org/10.3390/ECSA-12-26565 - 7 Nov 2025

Viewed by 289

Abstract

The analysis of volatile organic compounds emitted by cell cultures provides a non-invasive method for monitoring metabolic and oxidative stress states. However, detection is challenged by low volatile organic compound concentrations and high sample humidity. This study introduces an integrated system combining a [...] Read more.

The analysis of volatile organic compounds emitted by cell cultures provides a non-invasive method for monitoring metabolic and oxidative stress states. However, detection is challenged by low volatile organic compound concentrations and high sample humidity. This study introduces an integrated system combining a MEMS-based pre-concentrator with an array of n-type Metal-Oxide chemiresistive gas sensors to analyze emissions from the K562 leukemia cell line. The main goal is to distinguish cellular volatile organic compound signals from those of the culture medium. To achieve this, the pre-concentrator is used with different temperature-programmed desorption protocols to enhance signal intensity and improve discrimination performance. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 3474 KB

Open AccessProceeding Paper

A Wearable PPG Multi-Sensor for Measurement of Skin Humidity, Temperature, and Contact Pressure

by Jiří Přibil, Anna Přibilová and Tomáš Dermek

Eng. Proc. 2025, 118(1), 71; https://doi.org/10.3390/ECSA-12-26558 - 7 Nov 2025

Viewed by 521

Abstract

The aim of our work was to analyze the influence of changes in humidity and temperature on temporal features of sensed photoplethysmography (PPG) waves. This paper describes a special prototype of a wearable PPG multi-sensor with an integrated I2C humidity sensor and a [...] Read more.

The aim of our work was to analyze the influence of changes in humidity and temperature on temporal features of sensed photoplethysmography (PPG) waves. This paper describes a special prototype of a wearable PPG multi-sensor with an integrated I2C humidity sensor and a thermometer to carry out measurements at three skin moisture levels. This sensor is supplemented with a force-sensitive resistor for the measurement of the physical contact pressure between the measuring probe and the skin surface, which can be used to sense heart pulsation in the wrist radial artery. The experiments conducted show that the performed skin manipulation (skin drying, moistening) was always detectable; the PPG signal range is mainly affected, while changes in signal ripple and heart rate variance are smaller. The detailed analysis per hand and gender type yielded differences between male and female subjects, and the results for left and right hands differed less. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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6 pages, 784 KB

Open AccessProceeding Paper

Analysis of the Second Derivative of the PPG Signal as Indicator of Vascular Aging

by Gianluca Diana, Francesco Scardulla, Salvatore Pasta and Leonardo D’Acquisto

Eng. Proc. 2025, 118(1), 72; https://doi.org/10.3390/ECSA-12-26557 - 7 Nov 2025

Viewed by 567

Abstract

The second derivative of the photoplethysmographic signal presents five relevant points that provide information about the structural properties of arteries. This study investigates the ratio between the amplitude of the d wave (end of systole) and the a wave (beginning of systole) as [...] Read more.

The second derivative of the photoplethysmographic signal presents five relevant points that provide information about the structural properties of arteries. This study investigates the ratio between the amplitude of the d wave (end of systole) and the a wave (beginning of systole) as a potential indicator of vascular aging. The research combines an in vitro study on silicone models with different stiffness and an in vivo study on volunteers aged between 26 and 63 years. The results show a strong negative correlation between the d/a ratio and arterial stiffness, confirming the potential of this parameter as a noninvasive index for assessing vascular health status. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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11 pages, 1464 KB

Open AccessProceeding Paper

A Privacy-Preserving Health Monitoring Framework Using Federated Learning on Wearable Sensor Data

by Rasmita Panigrahi and Neelamadhab Padhy

Eng. Proc. 2025, 118(1), 73; https://doi.org/10.3390/ECSA-12-26567 - 7 Nov 2025

Viewed by 939

Abstract

Health monitoring systems play a crucial role in every life. In the 21st century, advanced technologies like wearable sensors have emerged and make healthcare better overall. These sensors collect massive amounts of data about our health over time in many dimensions. In this [...] Read more.

Health monitoring systems play a crucial role in every life. In the 21st century, advanced technologies like wearable sensors have emerged and make healthcare better overall. These sensors collect massive amounts of data about our health over time in many dimensions. In this paper, our objective is to develop and evaluate a machine learning-based clinical decision support system using wearable sensor data to accurately classify users’ physiological states and activity contexts. The most accurate and effective model is for identifying wearable sensor-based physiological signal classification. However, there are serious privacy and security issues with sending raw sensor data to centralized computers. We gathered the multivariate physiological and activity data from wearable technology, including smartwatches and fitness trackers, which make up the dataset. Physiological signals, including heart rate, resting heart rate, normalized heart rate, entropy of heart rate variability, and caloric expenditure, are all included in the dataset. Lying, sitting, self-paced walking, and running at different MET(Metabolic Equivalent of Task) levels are examples of activity context labels. To secure our data, we proposed an architecture based on federated learning that helps machine learning model training across several dispersed devices without exchanging raw data. In this study, we used eight classifiers, and these are XGBoost, RF, Extra Trees, LightGBM, CatBoost, Bagging, DT, and GB. It has been observed that XGBoost performs well in comparison to the other classifiers with an accuracy of 0.94, a precision of 0.90, a recall of 0.89, an F1-score of 0.90, and an AUC-ROC of 0.98. This study demonstrates the potential of wearable sensor data, combined with machine learning, for accurately classifying activity and physiological conditions. The ML boosting family, especially XGBoost, exhibited strong generalization across diverse signal inputs and activity contexts. These results suggest that explainable, non-invasive wearable analytics can support early detection and monitoring frameworks in personalized healthcare systems. The proposed federated learning framework effectively combines privacy-aware computation and accurate classification using wearable sensor data. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 1366 KB

Open AccessProceeding Paper

Energy Harvesting for a Microscale Biosensing Device via Piezoelectric Micromachined Ultrasonic Transducers

by Alexandru Paolo Mardare, Mamoun Morh and Aldo Ghisi

Eng. Proc. 2025, 118(1), 74; https://doi.org/10.3390/ECSA-12-26489 - 7 Nov 2025

Viewed by 266

Abstract

Microdevices with dimensions comparable to a blood cell, i.e., tens of micrometers, show great potential for use in the human body. They can be adopted to identify the source of diseases, track their evolution and enhance the effectiveness of therapies, significantly improving patients’ [...] Read more.

Microdevices with dimensions comparable to a blood cell, i.e., tens of micrometers, show great potential for use in the human body. They can be adopted to identify the source of diseases, track their evolution and enhance the effectiveness of therapies, significantly improving patients’ quality of life. A key challenge is how to power the devices, which should ideally be performed wirelessly from a remote source. Piezoelectric micromachined ultrasonic transducers (pMUTs) offer a solution thanks to their ability to generate and collect energy via acoustic waves. In this work, numerical simulations of transmitter pMUT arrays are performed with the aim of generating an acoustic wave synchronized with a single pMUT or pMUT array receiver. The latter is intended for insertion in the human body. The characteristics required to switch on and power nano-electronics, in terms of generated voltage and electrical power at the receiver, are studied in ballistic gel, a material that mimics human organs. The focus is on a bio-compatible material for the piezoelectric layer, i.e., aluminum nitride enriched with scandium. Coupled electromechanical and acoustic simulations show that, of the considered pMUT devices, an 8 × 8 transmitter array combined with a single-device receiver (with a 50 µm pitch) or a 2 × 2 receiver array provide alternative options, with each offering advantages in terms of voltage amplitude or power at a steady state. The overall dimensions of the receiver, at a maximum of only 100 × 100 µm², is compatible with a future proof-of-concept biosensing platform test chip. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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6 pages, 1855 KB

Open AccessProceeding Paper

Inkjet-Printed PEDOT:PSS Devices on Tattoo Paper for Transferable Epidermal Temperature Sensing and Heating Applications

by Apostolos Apostolakis, Dimitris Barmpakos, Fadi Jaber, Konstantinos Aidinis and Grigoris Kaltsas

Eng. Proc. 2025, 118(1), 75; https://doi.org/10.3390/ECSA-12-26561 - 7 Nov 2025

Viewed by 347

Abstract

Here, we report a facile technique for fabricating inkjet-printed PEDOT:PSS thermally active devices on commercial tattoo paper, subsequently transferred to Kapton substrate with pre-patterned copper tracks, to enable integration with other electronic systems. Printing parameters were investigated for consistent film quality. Electrical and [...] Read more.

Here, we report a facile technique for fabricating inkjet-printed PEDOT:PSS thermally active devices on commercial tattoo paper, subsequently transferred to Kapton substrate with pre-patterned copper tracks, to enable integration with other electronic systems. Printing parameters were investigated for consistent film quality. Electrical and thermal characterization confirmed stable ohmic behavior; after transfer, the device exhibited superior contact performance with lower measured electrical resistance. Temperature coefficient of resistance (TCR) of −0.0164 °C⁻¹ was measured, indicating the device’s capability for accurate temperature sensing. Additionally, a temperature exceeding 37 °C was achieved with a power consumption of approximately 50 mW. This work presents a robust method for passivating and transferring electronics for on-skin applications. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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21 pages, 3054 KB

Open AccessProceeding Paper

SOC Estimation-Based Battery Management System for Electric Bicycles: Design and Implementation

by Pranid Reddy, Bhanu Pratap Soni and Satyanand Singh

Eng. Proc. 2025, 118(1), 76; https://doi.org/10.3390/ECSA-12-26513 - 7 Nov 2025

Cited by 1 | Viewed by 1032

Abstract

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation due to their energy efficiency and zero-emission operation. However, challenges such as battery overcharging, overheating, and degradation from improper use can reduce battery lifespan and increase maintenance costs. To address these [...] Read more.

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation due to their energy efficiency and zero-emission operation. However, challenges such as battery overcharging, overheating, and degradation from improper use can reduce battery lifespan and increase maintenance costs. To address these issues, this paper presents the design and implementation of a Battery Management System (BMS) tailored for E-Bike applications, with a focus on enhancing safety, reliability, and performance. The proposed BMS includes core functionalities such as State of Charge (SOC) estimation, temperature monitoring, and under-voltage and overcharge protection. Different approaches, including open-circuit voltage (OCV), Coulomb counting (CC), and Kalman filter techniques are employed to improve SOC estimation accuracy. The circuit for CC-based BMS was first simulated using Proteus, and system behavior was modeled in MATLAB Simulink is used to validate design assumptions before hardware implementation. An Arduino Uno microcontroller was used to control the system, interfacing with an LM35 temperature sensor, a voltage divider, and an ACS712 current sensor. The BMS controls battery charging based on SOC levels and activates a cooling fan when the battery temperature exceeds 45 °C. It disconnects the charger at 100% SOC and triggers a beep alarm when the SOC falls below 40%. An external charger and regenerative charging from four electrodynamometers on the bicycle chain recharge the battery when the SOC drops below 20%, provided the load is disconnected. Measurement results closely matched simulation data, with the MATLAB model showing 44% SOC after 3 h, compared to the actual real-time 45.85%. The system accurately tracked charging/discharging patterns, validating its effectiveness. This compact and cost-effective BMS design ensures safe operation, improves battery longevity, and supports broader adoption of E-Bikes as an eco-friendly transportation solution. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 1348 KB

Open AccessProceeding Paper

IoT-Enabled Soil and Crop Monitoring System Using Low-Cost Smart Sensors for Precision Agriculture

by Thriumbiga Srinivasan Kalaivani, Thishalini Kamireddy and Saranya Govindakumar

Eng. Proc. 2025, 118(1), 77; https://doi.org/10.3390/ECSA-12-26537 - 7 Nov 2025

Viewed by 2820

Abstract

A game-changing strategy for increasing crop productivity while preserving vital resources is precision agriculture. The development of cloud computing and the Internet of Things (IoT) has made it possible and efficient to monitor soil and environmental data in real time. In order to [...] Read more.

A game-changing strategy for increasing crop productivity while preserving vital resources is precision agriculture. The development of cloud computing and the Internet of Things (IoT) has made it possible and efficient to monitor soil and environmental data in real time. In order to monitor temperature, soil moisture, humidity, and light intensity, this work proposes an inexpensive, IoT-enabled smart agriculture system that uses low-cost sensors. The real-time data is wirelessly transmitted by an ESP32 edge computing device and stored and analyzed on cloud platforms like Firebase or ThingSpeak. A rule-based algorithm generates alerts when sensor values surpass predefined thresholds, enabling prompt and informed decision-making. Field experiments reveal that the proposed system is accurate, economical, and energy-efficient, making it ideal for automation and remote monitoring in precision agriculture. A user-friendly dashboard allows farmers to easily visualize data trends and receive timely notifications. The system supports scalability and can be adapted to different crop types and soil conditions with minimal effort. Moreover, by optimizing water and resource usage, the system contributes to sustainable farming practices and environmental conservation. This deployable solution offers a practical and affordable pathway for small- and medium-sized farmers to adopt smart agriculture technologies and improve crop yield outcomes efficiently. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 1707 KB

Open AccessProceeding Paper

A Patent Landscape Analysis of Textile Sensors for Muscular Activity Sensing of Stimulation

by Massimo Barbieri and Giuseppe Andreoni

Eng. Proc. 2025, 118(1), 78; https://doi.org/10.3390/ECSA-12-26559 - 7 Nov 2025

Viewed by 505

Abstract

In the era of smart garments, textile electrodes for electromyography (EMG) or functional electric stimulation (FES) represent a very interesting and promising area of development and exploitation. In this frame, we conducted a patent landscape analysis of textile solution for EMG sensing and [...] Read more.

In the era of smart garments, textile electrodes for electromyography (EMG) or functional electric stimulation (FES) represent a very interesting and promising area of development and exploitation. In this frame, we conducted a patent landscape analysis of textile solution for EMG sensing and FES actuation, using Espacenet as a reference database and Orbit Intelligent platform as a data analysis tool. The landscape analysis focused on the following aspects: filing trends, top applicants in this domain, main publication countries, forward citations, and collaborations between applicants. Following the screening process, a total of 97 patent families were subjected to subsequent analysis. China and the United States account for the majority of patents. The main applicants by volume of the topics studied are universities or research public entities. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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5 pages, 692 KB

Open AccessProceeding Paper

Innovations in Wearable Glucose Sensors and Integrated Systems for Personalized Type 1 Diabetes Management: Clinical Evidence and Patient Acceptance

by Anxo Carreira-Casais and Antia G. Pereira

Eng. Proc. 2025, 118(1), 79; https://doi.org/10.3390/ECSA-12-26569 - 7 Nov 2025

Viewed by 409

Abstract

Type 1 diabetes (T1D) management is increasingly enhanced by wearable glucose sensors (WGSs) integrated with artificial intelligence (AI) that combine multiple physiological parameters—such as heart rate, galvanic skin response, body temperature, and physical activity—to predict glucose fluctuations more accurately. Noninvasive sensor technologies, including [...] Read more.

Type 1 diabetes (T1D) management is increasingly enhanced by wearable glucose sensors (WGSs) integrated with artificial intelligence (AI) that combine multiple physiological parameters—such as heart rate, galvanic skin response, body temperature, and physical activity—to predict glucose fluctuations more accurately. Noninvasive sensor technologies, including optical and sweat-based methods, show promise in reducing patient discomfort but still require further clinical validation to confirm reliability. Recent clinical data demonstrate significant potential for these advanced WGS technologies, with substantial improvements in glycemic control and overall disease management reported among all surveyed patients. Insulin pumps integrated with continuous glucose monitoring form “artificial pancreas” systems that automatically adjust insulin delivery in real time, improving patient convenience and metabolic outcomes. Despite progress, challenges remain related to response latency, device interoperability, and adaptation to abrupt physiological changes. According to our results, patient acceptance of WGS-based treatments is high, with nearly all individuals willing to adopt these technologies. Initial reluctance is mostly observed during the first weeks, coinciding with the AI algorithm’s calibration and learning phase; however, adherence increases significantly once this period concludes. In conclusion, these integrated technologies represent a practical shift toward personalized, proactive T1D care. Their successful implementation depends on overcoming technical and ethical challenges while addressing psychological factors such as alert fatigue, particularly in vulnerable populations. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 219 KB

Open AccessProceeding Paper

Use of Machine Learning to Detect Dangerous Level of Coal Mine Methane (CMM) Concentrations During Underground Mining Operations

by Rubeshen Mooroogen and Michael K. Ayomoh

Eng. Proc. 2025, 118(1), 80; https://doi.org/10.3390/ECSA-12-26591 - 7 Nov 2025

Viewed by 434

Abstract

Underground coal mining is considered to be a highly dangerous activity and has been responsible for large amounts of accidents, causing the death of many mine workers. One of the factors responsible for the fatal aspect of underground coal mining is the presence [...] Read more.

Underground coal mining is considered to be a highly dangerous activity and has been responsible for large amounts of accidents, causing the death of many mine workers. One of the factors responsible for the fatal aspect of underground coal mining is the presence and accumulation of toxic gases during underground mining operations. This paper focused its investigation specifically on coal mine methane (CMM), which is released as a result of the extraction of coal and the disturbance inflicted to surrounding rock formations during deep mining operations. Methane is considered a highly dangerous gas as it holds the capacity to cause explosions due to its highly inflammable nature. It can also displace oxygen, which eventually leads to asphyxiation. This research was based on the use of machine learning models to successfully predict dangerous concentrations of methane over the authorized threshold. Those predictions were made from a dataset containing information on the temperature, airflow, humidity, pressure and methane concentration in an underground coal mine. The temperature, airflow, humidity and pressure measurements were recorded by a series of sensors, namely anemometers and component sensors THP2/93. Three machine learning classification models were implemented and compared, with the objective to find the best model to predict and detect dangerous levels of coal mine methane. The models that were investigated included naïve Bayes, logistic regression and artificial neural networks (ANNs). This paper concludes with an engineering decision matrix that illustrates the precision of these models in predicting and detecting dangerous levels of methane concentrations in underground mines. Furthermore, recommendations for capacity improvement towards successfully predicting and detecting dangerous levels of coal mine methane from an artificial intelligence’s perspective are provided. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

8 pages, 5739 KB

Open AccessProceeding Paper

Smart Cattle Behavior Sensing with Embedded Vision and TinyML at the Edge

by Jazzie R. Jao, Edgar A. Vallar and Ibrahim Hameed

Eng. Proc. 2025, 118(1), 81; https://doi.org/10.3390/ECSA-12-26519 - 7 Nov 2025

Viewed by 546

Abstract

Accurate real-time monitoring of cattle behavior is essential for enabling data-driven decision-making in precision livestock farming. However, existing monitoring solutions often rely on cloud-based processing or high-power hardware, which are impractical for deployment in remote or low-infrastructure agricultural environments. There is a critical [...] Read more.

Accurate real-time monitoring of cattle behavior is essential for enabling data-driven decision-making in precision livestock farming. However, existing monitoring solutions often rely on cloud-based processing or high-power hardware, which are impractical for deployment in remote or low-infrastructure agricultural environments. There is a critical need for low-cost, energy-efficient, and autonomous sensing systems capable of operating independently at the edge. This paper presents a compact, sensor-integrated system for real-time cattle behavior monitoring using an embedded vision sensor and a TinyML-based inference pipeline. The system is designed for low-power deployment in field conditions and integrates the OV2640 image sensor with the Sipeed Maixduino platform, which features the Kendryte K210 RISC-V processor and an on-chip neural network accelerator (KPU). The platform supports fully on-device classification of cattle postures using a quantized convolutional neural network trained on the publicly available cattle behavior dataset, covering standing and lying behavioral states. Sensor data is captured via the onboard camera and preprocessed in real time to meet model input specifications. The trained model is quantized and converted into a K210-compatible. kmodel using the NNCase toolchain, and deployed using MaixPy firmware. System performance was evaluated based on inference latency, classification accuracy, memory usage, and energy efficiency. Results demonstrate that the proposed TinyML-enabled system can accurately classify cattle behaviors in real time while operating within the constraints of a low-power, embedded platform, making it a viable solution for smart livestock monitoring in remote or under-resourced environments. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 4878 KB

Open AccessProceeding Paper

Study on AE-Based Tool Condition Monitoring in CFRP Milling Processes

by Vinicius Dias, Thiago Lopes, Marcio Silva, Alessandro Rodrigues, Fabio Dotto and Pedro Oliveira Conceição Junior

Eng. Proc. 2025, 118(1), 82; https://doi.org/10.3390/ECSA-12-26576 - 7 Nov 2025

Viewed by 235

Abstract

Industry 4.0, in its search for improvements to processes and efficient products, has increasingly invested in the use and development of high-performance materials for its production lines. This is exemplified by the introduction of CFRP in the aeronautical industry, since this composite material [...] Read more.

Industry 4.0, in its search for improvements to processes and efficient products, has increasingly invested in the use and development of high-performance materials for its production lines. This is exemplified by the introduction of CFRP in the aeronautical industry, since this composite material has reduced the weight of aircraft and improved their performance. For the construction of large structures, drilling processes are also necessary to fix parts. However, this machining process can cause failures in the structure as a whole. These structural failures occur due to fragmentation, tearing, or detachment of the matrix fiber, significantly reducing the quality and reliability of the final equipment. In this scenario, it is important to preventively detect these intrinsic production failures that lead to the condemnation of the final parts. One indirect detection method is acoustic emission. This work presents a feasibility study focused on the application of data-driven methods for delamination detection and tool wear monitoring in composite machining. A setup for a helical interpolation end-milling drilling process was performed under varying machining conditions, from mild to severe, on CFRP composite plates. Acoustic emission (AE) signals were acquired at each machining pass. The methodology involved selecting an optimal frequency band to obtain information about the wear of the drilling tool through RMS and power spectral density (PSD) analysis, followed by using correlation indices to characterize tool wear progression. The results demonstrate the potential of spectral and statistical techniques to support real-time monitoring and decision-making in advanced composite manufacturing. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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13 pages, 1685 KB

Open AccessProceeding Paper

Wrist Photoplethysmography Pulse Waves: Morphological Classes and Physiological Influences

by Adrian Dendorfer and Peter H. Charlton

Eng. Proc. 2025, 118(1), 83; https://doi.org/10.3390/ECSA-12-26556 - 7 Nov 2025

Viewed by 620

Abstract

Wearables such as smartwatches provide opportunity for large-scale cardiovascular health monitoring. Wearables often use photoplethysmography (PPG), an optical sensing technique, to measure the arterial pulse wave and derive insights into cardiovascular physiology. Whilst there has been much research into the shape and physiological [...] Read more.

Wearables such as smartwatches provide opportunity for large-scale cardiovascular health monitoring. Wearables often use photoplethysmography (PPG), an optical sensing technique, to measure the arterial pulse wave and derive insights into cardiovascular physiology. Whilst there has been much research into the shape and physiological determinants of the finger-PPG pulse wave, much less is known about the wrist-PPG pulse wave. The aim of this study was to describe the morphology of wrist-PPG pulse waves and compare them with finger-PPG pulse waves. We analyzed wrist-PPG recordings from 686 adults in the Aurora-BP dataset. Visual inspection of pulse wave shapes revealed five classes of PPG pulse waves, three of which were similar to those seen in finger-PPG pulse waves, and two of which were different. An algorithm was developed to automatically classify wrist-PPG pulse waves and revealed variability in pulse wave shape within and between subjects. A multivariable regression analysis of associations between subject metadata and two features of pulse wave shape indicated that wrist-PPG pulse wave shape is associated with heart rate, body size (body size index and height), and blood pressure. No significant associations with age were observed, in contrast to previous findings on finger-PPG pulse waves. The differences observed between wrist- and finger-PPG pulse wave shapes indicate a need for greater understanding of the physiological origins of the wrist-PPG pulse wave and for the adaptation of algorithms specifically for wrist-PPG analysis. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 1557 KB

Open AccessProceeding Paper

XAI-Interpreter: A Dual-Attention Framework for Transparent and Explainable Decision-Making in Autonomous Vehicles

by Candaş Ünal, Pelin Öksüz, Tolga Bodrumlu and Musa Yazar

Eng. Proc. 2025, 118(1), 84; https://doi.org/10.3390/ECSA-12-26531 - 7 Nov 2025

Viewed by 405

Abstract

Autonomous vehicles need to explain their actions to improve reliability and build user trust. This study focuses on enhancing the transparency and explainability of the decision-making process in such systems. A module named XAI-Interpreter is developed to identify and highlight the most influential [...] Read more.

Autonomous vehicles need to explain their actions to improve reliability and build user trust. This study focuses on enhancing the transparency and explainability of the decision-making process in such systems. A module named XAI-Interpreter is developed to identify and highlight the most influential factors in driving decisions. The module combines two complementary methods: Learned Attention Weights (LAW) and Object-Level Attention (OLA). In the LAW method, images captured from the ego vehicle’s front and rear cameras in the CARLA simulation environment are processed using the Faster R-CNN model for object detection. GRAD-CAM is then applied to generate visual attention heatmaps, showing which regions and objects in the images affect the model’s decisions. The OLA method analyzes nearby dynamic objects, such as other vehicles, based on their size, speed, position, and orientation relative to the ego vehicle. Each object receives a normalized attention score between 0 and 1, indicating its influence on the vehicle’s behavior. These scores can be used in downstream modules such as planning, control, and safety. The module is currently tested in simulation. Future work will involve deploying the system on real vehicles. By helping the vehicle focus on the most critical elements in its surroundings, the Explainable Artificial Intelligence (XAI)-Interpreter supports more transparent and explainable autonomous driving systems. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 1390 KB

Open AccessProceeding Paper

AI/ML-Enabled Internet of Medical Things (IoMT) for Personalized Cardiac Health Monitoring and Predictive Diagnostics

by Hira Mariam, Anushay Khan, Humna Nadeem and Barirah Khan

Eng. Proc. 2025, 118(1), 85; https://doi.org/10.3390/ECSA-12-26520 - 7 Nov 2025

Viewed by 661

Abstract

Cardiovascular diseases (CVDs) are a major cause of global mortality, underscoring the need for intelligent and accessible cardiac health monitoring. This paper proposes a non-wearable Internet-of-Medical-Things (IoMT) system combining real-time sensing, edge processing, and AI-driven diagnostics. Stationary sensors MAX30102 (heart rate, SpO₂ [...] Read more.

Cardiovascular diseases (CVDs) are a major cause of global mortality, underscoring the need for intelligent and accessible cardiac health monitoring. This paper proposes a non-wearable Internet-of-Medical-Things (IoMT) system combining real-time sensing, edge processing, and AI-driven diagnostics. Stationary sensors MAX30102 (heart rate, SpO₂) and AD8232 (ECG) interfaced with micro-controller (ESP8266), processes data locally and feeds into the machine learning models trained on UCI Cleveland dataset. Random Forest and XGBoost achieved over 80% accuracy in predicting early cardiac risk. A Flask-SQLite web application provides role-based doctor/patient access, and a Natural Language Processing (NLP)-based interactive chatbot offers personalized guidance. The system delivers scalable, real-time, edge-enabled cardiac diagnostics without relying on wearable devices. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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10 pages, 6439 KB

Open AccessProceeding Paper

Development of Breakout Boards for Wearable ECG Applications Based on the AD823X Microchip and the Arduino Platform

by Juan C. Delgado-Torres, Sonia D. Becerril-Zepeda, K. Iris Vargas-Patiño, Daniel Cuevas-González, Juan P. García-Vázquez, Eladio Altamira-Colado, O. E. Barreras and Roberto L. Avitia

Eng. Proc. 2025, 118(1), 86; https://doi.org/10.3390/ECSA-12-26566 - 7 Nov 2025

Viewed by 428

Abstract

The development of wearable devices continues to be a growing trend. The mobile health wearables market is extremely fast-moving, with wearable ECG designs demanding increasingly complex features from manufacturers, such as size reduction, high accuracy, low weight, power efficiency, and good signal quality. [...] Read more.

The development of wearable devices continues to be a growing trend. The mobile health wearables market is extremely fast-moving, with wearable ECG designs demanding increasingly complex features from manufacturers, such as size reduction, high accuracy, low weight, power efficiency, and good signal quality. The AD823X integrated circuits for ECG miniaturization, as an analog front-end (AFE), provide an amplified and filtered analog signal for subsequent digitization. The aim of this work is the development of expansion boards for portable ECG applications based on the AD823X microchip and the Arduino platform. This study includes three different circuit designs for specific ECG applications: cardiac monitor, ECG fitness, and Holter monitor. It also presents designs using both AD823X integrated circuits. After performing tests with analog stage, the Atmega328 microcontroller was used for the analog-to-digital conversion of the ECG signals, and a miniaturized custom breakout board was developed for each ECG application, incorporating a CSR BC417143 chip for Bluetooth connectivity. The digitized signals can be transmitted by serial cable, via Bluetooth to a PC, or to an Android smartphone system for visualization. Other performed tests included measuring the noise induced during the analog-to-digital conversion stage of the Atmega328 microcontroller. This work evaluated, compared, and determined the best of the applications proposed by the manufacturer of the AD8232X for a wearable ECG monitor, addressing the current needs of the devices and emerging trends in mobile health. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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9 pages, 2485 KB

Open AccessProceeding Paper

Study of the Influence of Silk Fibroin on 3D-Printed G/PLA Sensors for Biological Detection Applications

by Enzo Penati de Carvalho Nascimento, Guilherme Mendonça Roveri, André Capaldo Amaral, Fábio Romano Lofrano Dotto, Alessandro Roger Rodrigues and Pedro Oliveira Conceição Junior

Eng. Proc. 2025, 118(1), 87; https://doi.org/10.3390/ECSA-12-26484 - 7 Nov 2025

Viewed by 220

Abstract

The demand for low-cost, portable, and sensitive analytical devices has fueled the development of 3D-printed biosensors. This study evaluates the effect of silk fibroin incorporation on the electrical properties of graphite-PLA electrodes manufactured via 3D printing. Electrochemical Impedance Spectroscopy (EIS) method was utilized [...] Read more.

The demand for low-cost, portable, and sensitive analytical devices has fueled the development of 3D-printed biosensors. This study evaluates the effect of silk fibroin incorporation on the electrical properties of graphite-PLA electrodes manufactured via 3D printing. Electrochemical Impedance Spectroscopy (EIS) method was utilized to assess capacitive–resistive behavior under dry conditions, and with PBS buffer, at fibroin concentrations of 0%, 0.04%, 0.4%, and 4%. Fibroin modulated impedance magnitude values without a clear trend, indicating the presence of additional influencing factors. The results promote better understanding of biofunctionalization effects in 3D-printed electrodes and support their potential for biomedical, environmental, and industrial sensing applications. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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7 pages, 780 KB

Open AccessProceeding Paper

Systematic Analysis of Distribution Shifts in Cross-Subject Glucose Prediction Using Wearable Physiological Data

by Andrew Beten, Luna Lococco, Ayaan Baig and Thilini Karunarathna

Eng. Proc. 2025, 118(1), 88; https://doi.org/10.3390/ECSA-12-26583 - 7 Nov 2025

Cited by 1 | Viewed by 324

Abstract

Wearable sensors offer a promising platform for non-invasive glucose monitoring by indirectly predicting glucose levels from physiological signals. However, machine learning models trained on such data often suffer degraded performance when applied to new individuals due to distribution shifts in physiological patterns. This [...] Read more.

Wearable sensors offer a promising platform for non-invasive glucose monitoring by indirectly predicting glucose levels from physiological signals. However, machine learning models trained on such data often suffer degraded performance when applied to new individuals due to distribution shifts in physiological patterns. This study investigates how inter-subject distribution shifts impact the performance of glucose prediction models trained on wearable data. We utilized the BIGIDEAs dataset, which includes simultaneous recordings of glucose levels and multimodal physiological signals. Personalized XGBoost regression models were trained on data from 10 subjects and evaluated on 5 held-out subjects to assess cross-subject generalization. Distribution shifts in glucose profiles between training and test subjects were quantified using the Anderson–Darling (AD) statistic. The results showed that models trained on one individual performed poorly when tested on others. Repeated measures correlation analysis revealed significant positive correlations between the AD statistic and model performance metrics, including RMSE, NRMSE, and MARD. Our findings highlight the challenge of inter-individual generalization and the need for distribution-aware models. We propose personalized calibration and subject phenotyping as future directions to enhance model generalizability. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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11 pages, 2596 KB

Open AccessProceeding Paper

An Anemometer Integration in a Low-Cost Air Quality Sensor System: A Real-World Case Study

by Valerio Pfister, Mario Prato and Michele Penza

Eng. Proc. 2025, 118(1), 89; https://doi.org/10.3390/ECSA-12-26552 - 7 Nov 2025

Viewed by 383

Abstract

The field deployment of low-cost air quality sensor systems enables enhanced spatial resolution in air quality monitoring. Although these sensor systems cannot achieve the same accuracy as regulatory monitoring stations, they can attain acceptable levels of confidence and provide Indicative Measurements as regulated [...] Read more.

The field deployment of low-cost air quality sensor systems enables enhanced spatial resolution in air quality monitoring. Although these sensor systems cannot achieve the same accuracy as regulatory monitoring stations, they can attain acceptable levels of confidence and provide Indicative Measurements as regulated by Ambient Air Quality EU Directive. The integration of an anemometer into a system can provide additional information for the classification of the measurement area, the identification of potential sources of pollutant emissions, and the assessment of the device’s operating conditions during measurement. In this study, the measurement capabilities of an Airbox, a low-cost air quality sensor system, were extended through the integration of a DW6410 anemometer (Davis Instruments). The Airbox, designed to transmit data in real-time or near real-time to servers and IoT platforms, was deployed for a duration of 4 months, from October 2021 to February 2022, within the airport area of Grottaglie (Southern Italy). The anemometric measurements and particulate concentration data (PM_2.5 and PM₁₀, measured by NextPM sensor, Tera Sensor) were integrated and compared to meteorological open data and data from a regulatory regional air quality control network located in the area around the airport. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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5 pages, 420 KB

Open AccessProceeding Paper

Low-Cost IoT-Based Smart Grain Monitoring System for Sustainable Storage Management

by Saleimah Alyammahi, Aisha Alhmoudi, Maryam Alawadhi and Fatima Alqaydi

Eng. Proc. 2025, 118(1), 90; https://doi.org/10.3390/ECSA-12-26545 - 7 Nov 2025

Viewed by 1469

Abstract

Efficient grain storage is critical for ensuring food security, particularly in regions with hot and humid climates where environmental fluctuations can accelerate spoilage. This study presents the development of a low-cost, Arduino-based microcontroller platform Smart Grain Monitoring System designed to continuously monitor key [...] Read more.

Efficient grain storage is critical for ensuring food security, particularly in regions with hot and humid climates where environmental fluctuations can accelerate spoilage. This study presents the development of a low-cost, Arduino-based microcontroller platform Smart Grain Monitoring System designed to continuously monitor key storage parameters. The system integrates sensors to measure temperature, relative humidity, air quality, and the weight of stored grains—factors essential for the early detection of microbial activity, fermentation, or structural degradation. Data is transmitted wirelessly in real time to a mobile application via the Blynk Internet of Things (IoT) platform, allowing for remote access, alerts, and trend analysis. The system is designed to be affordable, scalable, and easy to deploy in agricultural settings with limited infrastructure. To enhance mechanical performance and usability, the sensor system is housed in a reflective glass silo enclosure that provides both thermal insulation and visual grain access. A three-dimensional computer-aided design (3D CAD)model was developed to optimize the placement of electronics and ensure structural integrity. Key features include custom mounts for sensors and electronics, a top lid for grain refill and hygiene, and a stable base for load cell installation. This integrated framework offers a reliable, real-time monitoring solution that supports proactive grain management and reduces post-harvest losses in rural storage environments. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 3845 KB

Open AccessProceeding Paper

Exploring the Application of UAV-Multispectral Sensors for Proximal Imaging of Agricultural Crops

by Tarun Teja Kondraju, Rabi N. Sahoo, Selvaprakash Ramalingam, Rajan G. Rejith, Amrita Bhandari, Rajeev Ranjan and Devanakonda Venkata Sai Chakradhar Reddy

Eng. Proc. 2025, 118(1), 91; https://doi.org/10.3390/ECSA-12-26542 - 7 Nov 2025

Viewed by 579

Abstract

UAV-mounted multispectral sensors are widely used to study crop health. Utilising the same cameras to capture close-up images of crops can significantly improve crop health evaluations through multispectral technology. Unlike RGB cameras that only detect visible light, these sensors can identify additional spectral [...] Read more.

UAV-mounted multispectral sensors are widely used to study crop health. Utilising the same cameras to capture close-up images of crops can significantly improve crop health evaluations through multispectral technology. Unlike RGB cameras that only detect visible light, these sensors can identify additional spectral bands in the red-edge and near-infrared (NIR) ranges. This enables early detection of diseases, pests, and deficiencies through the calculation of various spectral indices. In this work, the ability to use UAV-multispectral sensors for close-proximity imaging of crops was studied. Images of plants were taken with a Micasense Rededge-MX from top and side views at a distance of 1 m. The camera has five sensors that independently capture blue, green, red, red-edge, and NIR light. The slight misalignment of these sensors results in a shift in the swath. This shift needs to be corrected to create a proper layer stack that could allow for further processing. This research utilised the Oriented FAST and Rotated BRIEF (ORB) method to detect features in each image. Random sample consensus (RANSAC) was used for feature matching to find similar features in the slave images compared to the master image (indicated by the green band). Utilising homography to warp the slave images ensures their perfect alignment with the master image. After alignment, the images were stacked, and the alignment accuracy was visually checked using true colour composites. The side-view images of the plants were perfectly aligned, while the top-view images showed errors, particularly in the pixels far from the centre. This study demonstrates that UAV-mounted multispectral sensors can capture images of plants effectively, provided the plant is centred in the frame and occupies a smaller area within the image. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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7 pages, 3047 KB

Open AccessProceeding Paper

Autonomous Traffic-Monitoring Pedestrian Crossing Detection with Motion Sensors and Maintenance Decision Systems in Smart Cities Using YOLOV8

by Murugeswari Kandavel, Aanjankumar Sureshkumar and Rajesh Kumar Dhanaraj

Eng. Proc. 2025, 118(1), 92; https://doi.org/10.3390/ECSA-12-26515 - 7 Nov 2025

Viewed by 370

Abstract

The rapid expansion of urban infrastructure and the complexity of analysing sudden vehicle movements in traffic systems necessitate autonomous traffic monitoring solutions for intelligent, autonomous traffic management. However, traditional methods like manual monitoring and static rule-based detection often fail to meet the real-time [...] Read more.

The rapid expansion of urban infrastructure and the complexity of analysing sudden vehicle movements in traffic systems necessitate autonomous traffic monitoring solutions for intelligent, autonomous traffic management. However, traditional methods like manual monitoring and static rule-based detection often fail to meet the real-time requirements of a modern city, resulting in inefficient congestion management, pedestrian safety issues, and inadequate road maintenance. Conventional approaches are highly dependent on human intervention and predefined algorithms, and consequently cannot adapt to dynamic traffic and the unpredictable movement of pedestrians. With urban populations on the rise, there is an urgent need for artificial intelligence-driven solutions that can effectively process large volumes of real-time data to ease traffic management and decision-making. This study presents an AI-based traffic monitoring framework that integrates deep learning and natural language processing (NLP) models for improved traffic safety, anomaly detection, and infrastructure optimisation. The system comprises high-accuracy object detection with YOLOv8, adaptive pedestrian crossing recognition with Few-Shot Learning (FSL), and contextual analysis and real-time decision-making with LLaMA 3.2B. Through the use of these technologies, along with a BDD100K available dataset, the system achieves a high detection accuracy of 96%, a low inference time of 75.1 ms, and an improved adaptability compared to SOTA of 89%. The results indicate the suitability of AI-driven methods for thoughtful city planning and autonomous mobility, with the potential for AI-driven frameworks to improve urban traffic management by increasing its efficiency and safety. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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6 pages, 6872 KB

Open AccessProceeding Paper

Synchronization of High-Resolution Imageries Acquired by NOAA and SUOMI NFP Satellites for Active Fire Detection over Etna Volcano

by Hind Hallabia

Eng. Proc. 2025, 118(1), 93; https://doi.org/10.3390/ECSA-12-26503 - 7 Nov 2025

Viewed by 300

Abstract

Mount Etna, considered to be one of the world’s most active volcanoes, is located in Europe. In this study, we physically characterize and model the geohazard area recently caused by the active Etna volcano. An advanced image processing method is presented, in which [...] Read more.

Mount Etna, considered to be one of the world’s most active volcanoes, is located in Europe. In this study, we physically characterize and model the geohazard area recently caused by the active Etna volcano. An advanced image processing method is presented, in which the scene is acquired simultaneously by two high-resolution remote sensors, the NOAA and SUOMI NFP. The proposed experimental protocol for data visualization and analysis is as follows. First, the images are processed with the same spectral reflectance using VIIRS I-bands at 375 m spatial resolution. In more detail, the spectral signatures of pixels confirm the environmental changes according to color visualization coding. In this context, the widespread volcano clouds over Mount Etna are estimated approximately through a signal-processing measurement algorithm. Second, the images are acquired by two high-resolution sensors, which are the NOAA and SUOMI NFP, in the visible spectrum wavelength. The synchronization of both sensors provides more details about the area occupied by the volcano’s fires. A spectral wavelength analysis is presented in both cases: (1) non-synchronized (i.e., each sensor separately) and (2) synchronized (i.e., combination of two sensors). Third, the protocol of active fire detection applied to the geohazard of the Etna volcano is displayed: fire area detection and estimation, spectral measurement, synchronization of remote sensors, and assessment of the fire spread. Finally, the strengths and limitations of satellite-based active fire detection are presented with respect to the synchronization of different sensors. A theoretical and experimental study will be presented. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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14 pages, 943 KB

Open AccessProceeding Paper

Smart Gir Cow Disease Prediction and Support System Using Artificial Intelligence

by Arunagiri Vijayalakshmi, Pichai Shanmugavadivu and Vijayalakshmi Subramanian

Eng. Proc. 2025, 118(1), 94; https://doi.org/10.3390/ECSA-12-26568 - 7 Nov 2025

Viewed by 496

Abstract

The health and productivity of dairy cows are critical factors in sustainable livestock management. Along with the rapid rise in intelligence and technology, applying intelligence in livestock management helps in monitoring and provide precise and effective care for the cattle herd. This research [...] Read more.

The health and productivity of dairy cows are critical factors in sustainable livestock management. Along with the rapid rise in intelligence and technology, applying intelligence in livestock management helps in monitoring and provide precise and effective care for the cattle herd. This research designs an intelligent system that can assist the farmers and predict Gir cows’ diseases and a support system powered by Artificial Intelligence (AI). The proposed system integrates Internet of Things (IoT) and sensors to track and monitor critical health parameters of the Gir cow, which includes the step count, lying time, rumination time, heart rate, and various environmental factors contributing to the well-being of the cow. The data points that are gathered from the sensors is then processed and analysed using machine learning (ML) algorithms, including Random Forest (RF), decision tree (DT), Logistic Regression, K-Neighbours, and Support Vector Machine (SVM) to predict abnormalities including diseases such as lameness, mastitis, heat stress, and digestive problems. The AI techniques used in the system involve complex data processing and pattern recognition to identify early signs of diseases. The RF and DT ML models achieved the highest accuracy (100%), while SVM demonstrated robust performance with 94% accuracy. Integrating real-time monitoring with predictive analytics enables early detection of health issues, allowing timely interventions and improving overall herd management. The proposed system enhances cow welfare and optimises farm productivity but also has the potential to revolutionise the dairy industry. The complex intelligent system provides a reliable and efficient platform for disease prediction and herd management, and can significantly contribute to the sustainability and profitability of dairy farming, thereby shaping the future of the industry. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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8 pages, 7643 KB

Open AccessProceeding Paper

Non-Invasive Disease Stage Classification of Bitter Rot in Fruits Using Optical Coherence Tomography and Intensity-Based Image Analysis

by Amila Dhanushka Karunanayaka, Nipun Shantha Kahatapitiya, Nisala Damith, Jeehyun Kim, Mansik Jeon, Bhagya Nathali Silva, Ruchire Eranga Wijesinghe and Udaya Wijenayake

Eng. Proc. 2025, 118(1), 95; https://doi.org/10.3390/ECSA-12-26540 - 7 Nov 2025

Viewed by 263

Abstract

Plant disease has a tremendous impact on global food security, and bitter rot caused by Colletotrichum spp. is a greater challenge to post-harvest quality. Conventional diagnosis is precise but invasive and therefore inappropriate for real-time purposes. This study investigates optical coherence tomography (OCT) [...] Read more.

Plant disease has a tremendous impact on global food security, and bitter rot caused by Colletotrichum spp. is a greater challenge to post-harvest quality. Conventional diagnosis is precise but invasive and therefore inappropriate for real-time purposes. This study investigates optical coherence tomography (OCT) as a high-resolution, non-invasive imaging method to detect internal structural changes from disease progression. The developed OCT-based image analysis framework stages diseases by assessing morphological degradation. The discovery of unique oval-shaped internal features, invisible to other non-invasive methods, demonstrates OCT’s potential for early detection, accurate monitoring, and real-time application in precision agriculture. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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7 pages, 2076 KB

Open AccessProceeding Paper

Entropy Knows You’re Low: Wearable Signal Coupling Patterns Reveal Glucose State

by Salma Khurshe, Thilini Savindya Karunarathna, Cleber Franca Carvalho, Nhung Huyen Hoang and Zilu Liang

Eng. Proc. 2025, 118(1), 96; https://doi.org/10.3390/ECSA-12-26590 - 7 Nov 2025

Viewed by 194

Abstract

Wearable sensors enable continuous monitoring of physiological signals, offering opportunities for the early detection of metabolic dysfunction. In this study, we propose the use of cross-fuzzy entropy (X-FuzzEn) to quantify the dynamic coupling between wearable-derived time series, i.e., heart rate (HR), electrodermal activity [...] Read more.

Wearable sensors enable continuous monitoring of physiological signals, offering opportunities for the early detection of metabolic dysfunction. In this study, we propose the use of cross-fuzzy entropy (X-FuzzEn) to quantify the dynamic coupling between wearable-derived time series, i.e., heart rate (HR), electrodermal activity (EDA), and body acceleration (ACC), across four clinically relevant glucose ranges. Analysis revealed differences in signal coordination across both metabolic and demographic groups. Prediabetic individuals exhibited elevated X-FuzzEn between HR and EDA during hypoglycemia compared to normoglycemic individuals, indicating potential autonomic dysregulation. Males showed lower X-FuzzEn compared to females, indicating more coherent and adaptive autonomic regulation. A similar pattern was observed in HR–ACC coupling, with lower X-FuzzEn in males during hypoglycemia. These findings suggest that cross-fuzzy entropy may serve as a sensitive, non-invasive biomarker of physiological resilience and autonomic stability in response to metabolic stress. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 1561 KB

Open AccessProceeding Paper

Low-Cost Remote Sensing Module for Agriculture 4.0 Based on STM32

by Gustavo Gimenes, Wenderson Nascimento Lopes, Ronald José Contijo, Reinaldo Götz de Oliveira Junior and Renan de Oliveira Alves Takeuchi

Eng. Proc. 2025, 118(1), 97; https://doi.org/10.3390/ECSA-12-26544 - 7 Nov 2025

Viewed by 581

Abstract

Agriculture 4.0 integrates smart technologies to optimize agricultural management. This work proposes the development of a low-cost remote sensing module for small producers in the north of Paraná, Brazil, using the STM32F411CEU6 (STMicroelectronics, Geneva, Switzerland) microcontroller and the nRF24L01 (Nordic Semiconductor, Trondheim, Norway) [...] Read more.

Agriculture 4.0 integrates smart technologies to optimize agricultural management. This work proposes the development of a low-cost remote sensing module for small producers in the north of Paraná, Brazil, using the STM32F411CEU6 (STMicroelectronics, Geneva, Switzerland) microcontroller and the nRF24L01 (Nordic Semiconductor, Trondheim, Norway) + module for mesh communication. The system measures temperature, humidity, and pressure using DS18B20, BME280, and capacitive soil moisture sensors via Inter-Integrated Circuit (I²C), Serial Peripheral Interface (SPI), and Analog-to-Digital Converter (ADC). Powered by a solar cell and Lithium Polymer (Li-Po) battery, along with a charge controller, the module acts as a transceiver, sending data to a gateway where it can be stored and analyzed, democratizing access to technology and supporting decision-making in crop management. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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14 pages, 2487 KB

Open AccessProceeding Paper

Multi-Emitter Infrared Sensor System for Reliable Near-Field Object Positioning

by Eren Bülbül

Eng. Proc. 2025, 118(1), 98; https://doi.org/10.3390/ECSA-12-26549 - 7 Nov 2025

Viewed by 406

Abstract

Infrared (IR) proximity sensors measure distance using either time-of-flight (ToF) or reflection intensity methods. While the ToF offers greater precision, it requires costly, specialized components. Reflection-based sensors use simpler circuits, enabling lower-cost designs. This study presents a multi-emitter reflection intensity IR sensor as [...] Read more.

Infrared (IR) proximity sensors measure distance using either time-of-flight (ToF) or reflection intensity methods. While the ToF offers greater precision, it requires costly, specialized components. Reflection-based sensors use simpler circuits, enabling lower-cost designs. This study presents a multi-emitter reflection intensity IR sensor as an economic alternative to near-field object positioning. Six IR LEDs, sequentially driven, surround a central photodiode that captures backscattered signals. A machine learning pipeline estimates the object coordinates, cross-section, and height. Tested on 20 objects and 13,750 labeled data points, the system achieved a <1 cm mean positioning error, which is competitive with multi-zone ToF accuracy with a reduced cost. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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12 pages, 2624 KB

Open AccessProceeding Paper

Sensor Fusion of Doppler Microwave and Multizone ToF for Short-Range Dynamic Object Tracking

by Eren Bülbül and Umut Dolangac

Eng. Proc. 2025, 118(1), 99; https://doi.org/10.3390/ECSA-12-26534 - 7 Nov 2025

Viewed by 337

Abstract

We present a low-cost sensor-fusion system combining a 10.525 GHz CW Doppler microwave sensor with an 8 × 8 Time-of-Flight (ToF) infrared sensor for short-range object tracking. Data are acquired and processed in a sequential fusion pipeline: ToF-based Convolutional Neural Networks (CNNs) estimate [...] Read more.

We present a low-cost sensor-fusion system combining a 10.525 GHz CW Doppler microwave sensor with an 8 × 8 Time-of-Flight (ToF) infrared sensor for short-range object tracking. Data are acquired and processed in a sequential fusion pipeline: ToF-based Convolutional Neural Networks (CNNs) estimate object presence, coordinates, and cross-section, while Doppler histograms yield radial velocity; outputs are then fused at the decision level. A dataset of 31,367 frames was collected. The system tracks objects (≥35 cm²) at speeds up to 10 m/s within 5–250 cm, achieving 98% detection and 84% positioning accuracy. This approach offers radar-like capabilities at a reduced cost, enabling applications in industrial, and consumer-electronics domains. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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5 pages, 1936 KB

Open AccessProceeding Paper

Applications of Terahertz FMCW Radar Reflectometry with Plastic Waveguide

by Humberto Vazquez-Sanchez, Elodie Strupiechonski, Silvia Eugenia Cano-Rodríguez, Mauricio Torres, Mario Quiroz-Juarez and Jean-Paul Guillet

Eng. Proc. 2025, 118(1), 100; https://doi.org/10.3390/ECSA-12-26504 - 7 Nov 2025

Viewed by 241

Abstract

This paper presents a compact 122 GHz Terahertz FMCW radar using a plastic hollow-core dielectric waveguide for non-destructive testing. The guided approach simplifies the system, avoiding complex free-space optics and alignment, while improving the signal-to-noise ratio by isolating endpoint reflections from internal ones. [...] Read more.

This paper presents a compact 122 GHz Terahertz FMCW radar using a plastic hollow-core dielectric waveguide for non-destructive testing. The guided approach simplifies the system, avoiding complex free-space optics and alignment, while improving the signal-to-noise ratio by isolating endpoint reflections from internal ones. Various configurations, including solid immersion lenses, enhance spatial resolution and imaging capabilities. Experiments combine 3D electromagnetic simulations and raster scanning to image fine details and detect subsurface defects. Applications span aerospace, automotive, and art conservation. Results demonstrate that the guided FMCW radar is a cost-effective, portable, and reliable alternative to traditional free-space setups, enabling broader, practical implementation across industries. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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14 pages, 1077 KB

Open AccessProceeding Paper

Enhanced Gait Recognition for Person Identification Using Spatio-Temporal Features and an Attention-Based Deep Learning Model

by Kollaparampil Thomas Thomas and Kaimadathil Pushpangadan Pushpalatha

Eng. Proc. 2025, 118(1), 101; https://doi.org/10.3390/ECSA-12-26532 - 7 Nov 2025

Viewed by 361

Abstract

Human gait has proven to be one of the standard biometrics for human identification. It is a non-invasive biometric method that uses human walking patterns specific to each human being. In most of the traditional methods, we use handcrafted features of simple convolutional [...] Read more.

Human gait has proven to be one of the standard biometrics for human identification. It is a non-invasive biometric method that uses human walking patterns specific to each human being. In most of the traditional methods, we use handcrafted features of simple convolutional models for gait analysis in human identification. Here, we may face challenges addressing complex temporal dependencies in gait sequences. This study proposes a novel deep learning framework that applies multi-feature input representations. It combines Gait Energy Images (GEIs), Frame Difference Gait Images (FDGIs), and Histogram of Oriented Gradients (HOG) features. This is proposed for enhancing the accuracy of human identification. The proposed work implements a CNN-based feature extractor with an attention mechanism for gait recognition. The model is trained and validated on a labeled dataset, showcasing its ability to learn discriminative gait representations with improved accuracy. The proposed pipeline of activities includes preprocessing and converting gait sequences into frames, organizing them using folder-based numerical extraction, followed by the training of an attention-enhanced convolutional network. The proposed model was found to perform better than existing methods on public datasets and works well even with different camera angles and clothing styles. Full article

(This article belongs to the Proceedings of The 12th International Electronic Conference on Sensors and Applications)

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Eng. Proc., 2025, ECSA-12

The 12th International Electronic Conference on Sensors and Applications

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