Search Results (245)

Edge-side acoustic monitoring enables animal sound recognition in remote environments, but microcontroller deployment remains constrained by feature extraction, numerical consistency, memory, latency, and energy consumption. This study presents a sensor-based tiny machine learning (TinyML) acoustic monitoring system on an Arduino Nano 33 BLE Sense Rev2 platform, integrating onboard pulse-density modulation (PDM) microphone acquisition, Mel-frequency cepstral coefficient (MFCC) feature extraction, deployment-side standardization, 8-bit integer (INT8) neural-network inference, and edge-side decision output. To reduce training-to-deployment feature drift, consistent frame parameters, mirrored C++ feature operators, and exported standardization parameters are used to align personal-computer-side and microcontroller-side feature representations. A source-isolated seven-class protocol was constructed for six target animal classes and one compound background-noise class. In the single-run baseline comparison, the proposed multilayer perceptron achieved 98.28% test accuracy and 97.21% test macro-F1, while the ten-seed stability analysis yielded 98.64% ± 0.26% test accuracy and 97.87% ± 0.38% test macro-F1. The deployed INT8 model occupied approximately 26.9 KB, with a post-window latency of about 303 ms. System-level input power was 0.783–0.825 W, corresponding to an estimated autonomy of 7.63–8.03 h under the reference battery setting. Full article

Microfinance institutions (MFIs) are vital in promoting financial inclusion for underserved populations. However, these institutions face growing security threats, including sophisticated burglary tactics like underground tunneling. In the Philippines, notable incidents, such as the “Termite Gang” heist in Marikina City and a mall robbery in Ozamiz, highlight the limitations of conventional security systems in addressing subterranean intrusions. This study addresses the gap in existing security technologies by developing a real-time detection system that integrates a vibration sensor, a Global System for Mobile Communications (GSM) module for sending real-time SMS alerts, an audible alarm, and a solar-powered backup system for continuous operation. The system was simulated in the electrical technology laboratory to enhance classroom learning. The system’s core is an Arduino Uno microcontroller that processes inputs from the SW-420 vibration sensor, activating alarms and triggering SMS notifications via the SIM900A module when it detects unusual vibrations. Simulations A, B, and C were conducted to evaluate the system’s response time, with results showing a progressive reduction in detection time from five seconds to one second, indicating improved calibration and system efficiency. These findings also support the existing literature on user interaction with vibration alerts, demonstrating high accuracy in interpreting haptic notifications and the cognitive trade-offs involved. The proposed solution offers a proactive, energy-resilient, and cost-effective security system specifically designed to address underground burglary attempts. It applies to MFIs, pawnshops, and other high-risk financial environments. Future research should explore the application of machine learning for adaptive threat detection, expand the system’s scalability, and integrate mobile applications to enable user customization and enhance alert management. Full article

Low-cost and portable analytical devices are increasingly relevant for decentralized measurements, in situ monitoring, and educational applications. This study presents the design, construction, and validation of a low-cost, portable colorimeter for the indirect determination of ethanol in aqueous solutions via dichromate oxidation. Built with accessible components, including an Arduino microcontroller, an RGB LED, and a light-dependent resistor (LDR) photodetector, the device provides a simple open-hardware platform for visible-range colorimetric measurements without the need for optical filters. Ethanol concentration is determined through oxidation in an acidic medium, generating an optical response proportional to the analyte concentration. Data processing is performed using open-source Python scripts combined with Gaussian fitting for signal extraction and calibration. The main novelty of the system lies in integrating simplified optical components, open-hardware architecture, and computational signal processing to obtain reliable analytical responses in a portable, accessible format. The device’s performance was compared with a commercial UV–Vis spectrophotometer, showing linear behavior over 0.5–1.5% (v/v) ethanol (R² = 0.99) and relative errors below 11% for beverage samples. These results demonstrate that the proposed system is a reliable and cost-effective alternative for rapid ethanol analysis in relatively simple alcoholic matrices. Full article

Efficient feed management is essential in aquaculture, especially for bottom-feeding species such as shrimp that require feed delivery at the tank bottom. Most commercial automated feeders are designed for surface-feeding fish and are unsuitable for benthic organisms, leading to feed waste and uneven distribution. This study developed and evaluated an automatic bottom feeder capable of dispensing sinking pellets directly to the substrate. The system integrated a 3D-printed auger for precise feed metering and a closed-type centrifugal impeller positioned at the water surface to achieve radial dispersion of feed. An Arduino Uno microcontroller operated the impeller speed (285.98–586.85 rpm), feed mass (95.23–285.68 g), and dispersion time (2–8 s). A Box–Behnken response surface methodology was used to analyze the influence of these parameters on the mean radius spread of feed, supported by image-based uniformity assessment using OpenCV. Results identified impeller speed as the most significant factor (p = 0.010), with optimal dispersion observed at moderate speeds and longer spread durations. The system demonstrated reliable mechanical performance and precise control, providing a novel, programmable solution for uniform feed delivery in shrimp aquaculture and a promising foundation for scalable, automated bottom-feeding technologies. Full article

In this paper, we advance the concept of an electronic controller for switching devices actuated by means of direct current (DC) electromagnets. Based on the method of controlling the supply voltage delivery and disconnection moment to the drive coil, it is feasible to control switching-on and switching-off operations of an electromagnetic (EM) circuit-breaker (CB). The developed control method, built upon an ATmega328P microcontroller and operating in the Arduino IDE 2.3.4 environment, minimizes the impact of CB moving part inertia and drive coil (de)energization time. As a result, it enables contacts to be made at the near-to-zero point of the voltage waveform and contacts to break at the near-to-zero point of the current waveform. Consequently, the implementation of the proposed synchronized switching (SS) method allows the minimization of overvoltages and overcurrents during switching operations. Through continuous monitoring of the drive coil supply source parameters, the developed electronic controller allows for minimizing the impact of potential voltage fluctuations on CB switching parameters. Extensive laboratory tests confirmed the effectiveness of the proposed controller and applied method for controlling various types and sizes of EM contactors and relays. Full article

Elevators exemplify mechatronics by integrating mechanical, electrical, and software systems. This paper discusses a four-story tabletop elevator model developed to demonstrate mechatronics and automation concepts in engineering education. The system utilized an Arduino MEGA microcontroller, 3D-printed components, an integrated servo motor, and standard electronics to replicate commercial elevator logic. The physical design features a ball screw linear actuator for vertical motion. It replicates dual-door systems with one door on the moving car and fixed doors at each floor that open simultaneously upon arrival. Development included designing the physical model, prototyping control algorithms, and integrating hardware and software. The model successfully demonstrated key functions: automatic dual-door operation, safety interlocks, smooth inter-floor motion, responsive floor-selection buttons with LED feedback, and efficient routing algorithms prioritizing requests based on current direction and location. Performance testing confirmed that the model accurately replicates modern elevator behavior and serves as an effective educational tool. Full article

Recent advances in edge computing and Tiny Machine Learning (TinyML) have enabled the deployment of artificial intelligence models directly on microcontrollers with extremely limited computational and memory resources. In this context, this work presents the design, implementation, and validation of a real-time cardiac arrhythmia classification system based on a quantized one-dimensional convolutional neural network (1D-CNN), deployed on an 8-bit Arduino UNO microcontroller. The proposed system integrates end-to-end processing, including ECG signal acquisition using a low-cost AD8232 analog front-end, signal preprocessing, heartbeat segmentation, classification, and real-time visualization on an OLED display. The model was trained and evaluated using the MIT-BIH Arrhythmia Database, considering a reduced three-class problem (Normal, Ventricular, and Supraventricular) to meet the constraints of ultra-low-cost hardware deployment. Under benchmark conditions, the quantized model achieved an accuracy of 97.6%, with a memory footprint below 24 KB and an average inference time of 200 ms per heartbeat, enabling real-time operation on a resource-constrained microcontroller. Real-time experiments were conducted using signals acquired from healthy volunteers to validate system functionality, although no annotated ground truth was available for these recordings, and therefore no diagnostic performance was derived from them. The results demonstrate the feasibility of deploying lightweight deep learning models on ultra-constrained embedded systems using the TinyML paradigm, implemented using TensorFlow 2.15 and TensorFlow Lite. This work should be interpreted as a proof-of-concept platform that highlights the trade-off between classification performance and hardware limitations, providing a foundation for future development of low-cost cardiac monitoring technologies in resource-limited environments. Full article

This study aims to develop and experimentally evaluate an artificial neural network-based temperature control strategy for hot-air carrot drying within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped with AM2302 (air temperature sensor), MLX90614 (infrared surface temperature sensor), and SHT35 (relative humidity sensor), an HX711 load cell, and a WS68 anemometer, with cloud communication provided by an ESP8266 module for remote monitoring via Wi-Fi. The neural controller, implemented using the Arduino Neurona library, regulates the dryer temperature in real time, enabling drying kinetics analysis under ANN-based thermal control to investigate its capability to maintain thermal stability. Three initial loads (2, 4, and 6 kg) were analyzed to determine the thermal efficiency. In the dehydration experiments, the 2 kg load reached a final moisture content of 10% in 4.4 h, consuming 1390 kJ with a thermal efficiency of 83%. The 4 kg load exhibited the best time–energy balance (6.6 h, 1850.0 kJ, 88%), while the 6 kg load achieved the highest efficiency (8.1 h, 2250.0 kJ, 91%). These results demonstrate the effectiveness of neural-network-based control implemented on low-cost microcontrollers to enhance thermal efficiency in food dehydration processes. Full article

Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in Gazipur, Bangladesh. The system combined ultrasonic water-level sensors, capacitive soil moisture sensors, an Arduino-based microcontroller, a GSM communication module, and solar-powered automatic control gates. Field performance was evaluated following a Randomized Complete Block Design (RCBD) under four irrigation treatments: IRRISAT, IRRI35, IRRI25, and continuous flooding (CF). The first three irrigation treatments were operated using scheduled daily decision windows, in which irrigation actions were automatically triggered based on predefined schedules and sensor threshold values. In IRRISAT, irrigation started when soil moisture dropped slightly below saturation and stopped at a ponding depth of 5 cm, while IRRI35 and IRRI25 were triggered at volumetric soil water contents of 35% and 25%, respectively, with the same upper cutoff of 5 cm ponding depth; CF served as the control. The IRRI35 treatment achieved a high grain yield (7.76 t ha⁻¹) while reducing water use by 28% and energy consumption by 37% compared to CF. Water use efficiency was considerably higher under IRRI35 (9.4 kg ha⁻¹ mm⁻¹) than under CF (6.7 kg ha⁻¹ mm⁻¹). The automated system proved to be reliable and precise in scheduled irrigation control, significantly reducing water use and labor requirements. The findings suggest that large-scale adoption of the system under real-world cultivation conditions could reduce irrigation energy needs and contribute to sustainable water governance in rice production. Full article

Educational robotics (ER) and robotics competitions offer an effective context for developing STEM (Science, Technology, Engineering, and Mathematics) competencies, technical skills, and soft skills in engineering degrees. However, current platforms reveal a pedagogical and technical gap: closed commercial systems restrict access to hardware, while open solutions frequently lack a robust and structured architecture for educational settings. Moreover, in both cases, many platforms do not achieve the hardware requirements of the most demanding competitions. To address this issue, the present article presents the design, implementation, and validation of EASYbot, a modular open-hardware robotics platform based on Arduino. The system integrates a microcontroller, a dual USB–battery power supply, high-performance motor power stages, and a plug-and-play interface for input/output and communication peripherals, enabling its use in several competition categories such as mini-sumo or maze robots. The platform is complemented by a state-based programming model and supports libraries that facilitate a learning assessment. The platform provides a scalable ecosystem, enabling students to progress from initial prototyping to optimised hardware control. The validation process encompasses a range of assessments, including technical tests, usability, and adoption evaluation through surveys. Full article

Fire outbreaks, whether caused naturally or unintentionally, pose serious threats to safety, especially in household environments such as kitchens. Common triggers include overheated personal devices, electrical malfunctions, and unattended cooking appliances. This study aims to develop and enhance an automated fire suppression system designed specifically for residential kitchen settings. The system integrates multiple sensors, photoelectric, ionization, and flame detectors, paired with an Arduino microcontroller to ensure accurate detection and timely activation of a servo mechanism that triggers either a Class A or Class K fire extinguisher. Through controlled testing using both solid and liquid combustible materials, we examined key variables, including sensor placement, height, and nozzle angle. The results from 15 trials per session revealed a correlation coefficient exceeding 0.90 between detection time and distance and the significance level of an analysis of variance of less than 0.05, indicating that increased distance significantly affects response time. The percent error remained below 6.7% across all tests, with strong correlations above 0.8 between combustible material type and the corresponding extinguisher class. This research contributes to the advancement of intelligent fire suppression systems by enhancing detection accuracy, reducing false triggers, and optimizing efficient sensor configurations for residential safety. Full article

The increasing global demand for plant-based meat alternatives, driven by concerns for environmental sustainability, animal welfare, and health, has led to a growing need for reliable food authentication methods. Animal-based and plant-based meat products are visually similar, which poses a challenge for consumers to distinguish them. We developed an electronic nose (e-nose) system with an array of MQ gas sensors (MQ-2, MQ-3, MQ-7, MQ-135, MQ-136, MQ-138), an Arduino MEGA microcontroller, and an LCD for displaying results. A fuzzy logic algorithm was implemented to process sensor data and enable decision-making through membership functions and IF-THEN rule evaluation to classify meat products as either animal meat or plant-based meat. The system performance was validated with 20 independent test samples. Determination accuracy for both categories, as well as the overall accuracy, was assessed using a confusion matrix. The findings demonstrate that the e-nose system can reliably distinguish between animal-based and plant-based meat products. Full article

Environmental monitoring is essential for smart agriculture, renewable energy assessment, and climate-aware farm management. However, deploying autonomous sensing platforms in rural environments remains challenging because of energy constraints, communication reliability, and real-time processing requirements. This paper presents a modular, solar-powered environmental monitoring platform integrating LTE-M communication and TinyML-enabled edge sensing. The proposed system adopts a dual-microcontroller architecture that combines an Arduino Nano 33 BLE for real-time sensor acquisition and edge processing with an Arduino MKR NB 1500 dedicated to low-power wide-area communication. The platform integrates temperature, humidity, atmospheric pressure, rainfall, wind, and light sensors within a scalable framework. Two monitoring stations were deployed in rural regions of Romania to evaluate communication robustness, sensing stability, and energy autonomy. Field results demonstrated reliable LTE-M connectivity (4306 received signal strength indicator [RSSI] samples; mean $-75.51$ dBm) and strong agreement with a regional weather station, with mean deviations of −0.71 °C (temperature), $4.98\%$ (humidity), and a stable pressure offset of $-9.58$ hPa attributable to altitude differences. Despite a total system cost of €315, the platform achieved measurement performance comparable to that of professional meteorological stations while maintaining long-term solar-powered operation. The proposed architecture provides a scalable and cost-effective solution for distributed smart agriculture and environmental monitoring applications. Full article

The growing reliance on renewable energy sources, particularly solar photovoltaics (PVs), requires intelligent management strategies to address challenges of intermittency, storage, and efficiency in autonomous microgrids. This review investigates IoT-based solutions for energy optimization, focusing on hardware platforms, communication protocols, and intelligent control strategies that enhance the reliability and autonomy of PV-powered systems. This review follows a structured methodological protocol including predefined research questions, database selection, screening criteria, and systematic categorization of studies of IoT-enabled solar microgrid applications, relying on peer-reviewed journal articles, reputable conference proceedings, and scholarly works published between 2020 and 2025. The focus centers on microcontroller-based platforms (e.g., Arduino, ESP32, NodeMCU, TTGO LoRa32) and Single-Board Computers (SBCs) (e.g., Raspberry Pi), alongside the integration of optimization algorithms with Machine Learning (ML) and Neural Network (NN) approaches. Results highlight that lightweight microcontrollers offer cost-effective monitoring, ESP32 and NodeMCU balance real-time analytics with energy efficiency, Raspberry Pi supports edge-level AI processing, and LoRa enables scalable long-range communication for remote PV systems. Furthermore, optimization algorithms (PSO, WOA-SA) and neural models (ANN, LSTM, CNN–LSTM) are explored as methods to improve forecasting accuracy, fault detection, and demand-side management. Conclusions indicate that IoT-based architectures significantly improve energy efficiency, support predictive maintenance, and enable scalable deployment of autonomous solar microgrids. The study emphasizes the necessity of hybrid IoT architectures, combining edge and cloud intelligence, to balance computational complexity, power constraints, and cybersecurity requirements. These findings provide practical insights into designing robust, cost-effective, and scalable IoT-enabled PV microgrids that contribute to decentralized and sustainable energy transitions. Full article

In a globalized world where data play a critical role in system operation, process automation, and decision-making, the development of real-time control systems is essential, as it enables operators and supervisors to monitor the current status of a process based on its physical variables. Consequently, a wide range of software and hardware platforms is currently available for implementing real-time control systems, including Arduino, ESP32, and PIC microcontrollers. However, these platforms lack sufficiently robust hardware features for closed-loop control applications, as they were primarily designed for general-purpose use. To address the limitations of conventional embedded systems, this paper presents a novel approach for the implementation of digital controllers using Texas Instruments embedded systems applied to experimental plants designed with different control strategies. The proposed contribution focuses on the development of an experimental framework that integrates multi-platform programming, automatic code generation, and the use of dedicated real-time control modules, such as the Control Law Accelerator available in the LAUNCHXL-F28379D LaunchPad embedded system. The results highlight the capability of Texas Instruments microcontrollers to execute real-time control loops applied to different physical systems and operating under various control parameters. In conclusion, the findings demonstrate that Texas Instruments embedded systems equipped with advanced microcontroller architectures represent a promising alternative not only for scalable control applications but also for industrial-level control system development. Full article