AI Sensors and Transducers

Topic Information

Dear Colleagues,

This Topic was created in collaboration with the 2nd International Conference on AI Sensors and Transducers (AIS 2025, https://sciforum.net/event/AIS2025), which will be held in Kuala Lumpur, Malaysia, from 29 July to 3 August 2025. Conference participants of AIS 2025 are cordially invited to contribute a full manuscript to this Topic and will receive a 20% discount on the Article Processing Charge.

In addition to the conference papers, we also welcome regular submissions. We are delighted to invite contributions at the forefront of sensors, sensing technology, artificial intelligence (AI) sensors and AI-enhanced sensing systems, as well as transducers and AI-enhanced transducers. This event promises to be an exciting platform for exploring cutting-edge advances and fostering collaboration in the rapidly evolving field of AI, sensors, and transducers.

Key areas of focus for this conference include, but are not limited to, the following:

• Sensing Systems Enhanced by AI;
• Computer Vision;
• AI Sensors Based on Vision and Voice Data;
• Data Fusion and Processing with Sensor Applications;
• Sensor Modality/Sensing System Enhanced by Multi-Modality Deep Learning;
• Data Mining for Sensor Applications;
• Edge Computing Technologies;
• Neuromorphic Computing;
• Computing in Memory;
• In Sensor Computing;
• MEMS Sensors Enhanced by Edge Computing;
• CMOS MEMS/NEMS and AI Sensors;
• Hardware for Enabling Artificial Intelligence of Things (AIoT) Systems;
• Heterogeneous Integration for AIoT Systems;
• Chiplet and System-in-Package (SiP) for AIoT-on-a-Chip;
• Autonomous AIoT Systems;
• Cutting-Edge AI Sensor Technologies;
• Generative AI Technologies;
• Photonics AI Accelerator;
• Innovative AI Sensor Applications Across Industries;
• Physical Sensors and MEMS;
• Chemical Sensors;
• Medical Sensors;
• Gas Sensors;
• Ultrasound Sensors;
• Optical Sensors, CMOS Image Sensors, and IR Focal Plane Array;
• Magnetic Sensors and Spintronics;
• Biosensors and Bioelectronics;
• Wearable Sensors;
• Implantable Sensors, Neural Interfaces, and Electroceuticals;
• Sensors for Intelligent Robots;
• Sensors for Drones;
• Self-powered Sensors;
• Zero-biased Sensors and Science;
• Nanomaterials, Nanosensors, and NEMS;
• Si Photonics (SiPh) for Sensing;
• Nanophotonics and metalenses for Sensing;
• Metasurface and Mid-IR Sensing;
• Metamaterials and THz Sensing;
• Spectroscopy and Sensing Technology;
• Internet of Things (IoT) and Sensor Networks;
• Energy Harvesting for IoT Sensors;
• Remote Sensors, Data Acquisition, and Processing;
• Integration of Actuators with Sensors for Smart System.

Dr. Chengkuo Lee
Prof. Dr. Toshihiro Itoh
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI Sensors aisens	-	-	2025	15.0 days *	CHF 1000	Submit
Chemosensors chemosensors	3.7	7.3	2013	20.5 Days	CHF 2000	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Micromachines micromachines	3.0	6.0	2010	17.2 Days	CHF 2100	Submit
Robotics robotics	3.3	7.7	2012	21.8 Days	CHF 1800	Submit

* Median value for all MDPI journals in the first half of 2025.

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Published Papers (3 papers)

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All AI Sensors Chemosensors Sensors Micromachines Robotics

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33 pages, 6643 KB  

Open AccessArticle

Smart Water Management: An Energetically Autonomous IoT-Based Application for Pressure and Flow Monitoring in Water Distribution Systems

by Jonatha B. Silva, Lucas D. de Oliveira, Rafael M. Duarte, Cícero de Rocha Souto and Juan M. M. Villanueva

Sensors 2025, 25(23), 7227; https://doi.org/10.3390/s25237227 - 26 Nov 2025

Viewed by 642

Abstract

The distribution of water in urban areas involves several challenges, such as maintaining pipelines, controlling pressure and flow, and monitoring water quality. In particular, the measurement of the flow rate and pressure in pipelines is essential for optimizing water distribution in cities. In [...] Read more.

The distribution of water in urban areas involves several challenges, such as maintaining pipelines, controlling pressure and flow, and monitoring water quality. In particular, the measurement of the flow rate and pressure in pipelines is essential for optimizing water distribution in cities. In recent decades, new technologies have been used to address these challenges, such as hydraulic modeling systems with software, smart sensors, and automated control systems. Among the new possibilities, the use of wireless sensor networks has been highlighted. In this sense, IoT-based nodes have been proposed as a low-cost alternative, with the ability to communicate over the Internet with low energy consumption. Thus, this work describes the necessary steps, challenges, and solutions for the development of an autonomous IoT node applicable to monitoring pressure and flow in a water supply network. In the second part of the work, the data collected by the IoT nodes was processed to eliminate outliers and used to train a model based on artificial neural networks that are capable of predicting the flow in the system under monitoring. The results show that, based on the data measured by the proposed IoT node, it is possible to predict the flow in distribution systems operating in real time. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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32 pages, 4544 KB  

Open AccessFeature PaperReview

A Review of Non-Invasive Continuous Blood Pressure Measurement: From Flexible Sensing to Intelligent Modeling

by Zhan Shen, Jian Li, Hao Hu, Chentao Du, Xiaorong Ding, Tingrui Pan and Xinge Yu

AI Sens. 2025, 1(2), 8; https://doi.org/10.3390/aisens1020008 - 7 Nov 2025

Cited by 1 | Viewed by 2112

Abstract

Accurate and continuous, non-invasive blood pressure (BP) monitoring plays a vital role in the long-term management of cardiovascular diseases. Advances in wearable and flexible sensing technologies have facilitated the transition of non-invasive BP monitoring from clinical settings to ambulatory home environments. However, the [...] Read more.

Accurate and continuous, non-invasive blood pressure (BP) monitoring plays a vital role in the long-term management of cardiovascular diseases. Advances in wearable and flexible sensing technologies have facilitated the transition of non-invasive BP monitoring from clinical settings to ambulatory home environments. However, the measurement consistency and algorithm adaptability of existing devices have not yet reached the level required for routine clinical practice. To address these limitations, comprehensive innovations have been made in material development, sensor design, and algorithm optimization. This review examines the evolution of non-invasive continuous BP measurement, highlighting cutting-edge advances in flexible electronic devices and BP estimation algorithms. First, we introduce measurement principles, sensing devices and limitations of traditional non-invasive BP measurement, including arterial tonometry, arterial volume clamp, and ultrasound-based methods. Subsequently, we review the pulse wave analysis-based BP estimation methods from two perspectives: flexible sensors based on optical, mechanical, and electrical principles, and estimation models that use physiological features or raw waveforms as input. Finally, we conclude the existing challenges and future development directions of flexible electronic technology and intelligent estimation algorithms for non-invasive continuous BP measurement. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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23 pages, 2058 KB  

Open AccessArticle

Inductive Displacement Sensor Operating in an LC Oscillator System Under High Pressure Conditions—Basic Design Principles

by Janusz Nurkowski and Andrzej Nowakowski

Sensors 2025, 25(19), 6078; https://doi.org/10.3390/s25196078 - 2 Oct 2025

Viewed by 631

Abstract

The paper presents some design principles of an inductive displacement transducer for measuring the displacement of rock specimens under high hydrostatic pressure. It consists of a single-layer, coreless solenoid mounted directly onto the specimen and connected to an LC oscillator located outside the [...] Read more.

The paper presents some design principles of an inductive displacement transducer for measuring the displacement of rock specimens under high hydrostatic pressure. It consists of a single-layer, coreless solenoid mounted directly onto the specimen and connected to an LC oscillator located outside the pressure chamber, in which it serves as the inductive component. The specimen’s deformation changes the coil’s length and inductance, thereby altering the oscillator’s resonant frequency. Paired with a reference coil, the system achieves strain resolution of ~100 nm at pressures exceeding 400 MPa. Sensor design challenges include both electrical parameters (inductance and resistance of the sensor, capacitance of the resonant circuit) and mechanical parameters (number and diameter of coil turns, their positional stability, wire diameter). The basic requirement is to achieve stable oscillations (i.e., a high Q-factor of the resonant circuit) while maintaining maximum sensor sensitivity. Miniaturization of the sensor and minimizing the tensile force at its mounting points on the specimen are also essential. Improvement of certain sensor parameters often leads to the degradation of others; therefore, the design requires a compromise depending on the specific measurement conditions. This article presents the mathematical interdependencies among key sensor parameters, facilitating optimized sensor design. Full article

(This article belongs to the Topic AI Sensors and Transducers)

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