Search Results (228)

Surface electromyography (sEMG) signals acquired with low-cost sensors tend to exhibit variable degradation that can compromise the reliability of myoelectric control systems outside controlled conditions. This work presents an adaptive processing pipeline for sEMG signals composed of three chained stages: a motor-intent classifier based on dilated temporal convolutional networks, whose function is to determine whether a signal window contains muscle activity associated with a voluntary gesture; a dual-output quality assessor that estimates a continuous score and a binary acceptability label, aimed at deciding whether the signal can be used directly or requires intervention; and a convolutional autoencoder that recovers the morphology of degraded windows before they are used in prosthetic control. The decision policy for reconstruction operates on two independent thresholds and classifies each window into one of three states: signal discard, direct acceptance, or active restoration. The models within the proposed architecture are trained on the public NinaPro DB1, DB3, and DB10 datasets and validated without recalibration on signals recorded from two participants with transradial amputation over three to four weekly sessions. The results show that the pipeline correctly handles signal profiles with opposing characteristics. Inference latency remained below 5 ms at the 50th percentile across all scenarios, consistent with real-time operation. All inference was executed on a host computer; a prototype using an Arduino UNO R4 WiFi as a peripheral interface was built to display the pipeline’s decisions on physical hardware and to confirm that the serial-communication link does not introduce additional latency, not to perform on-board inference. A downstream evaluation further showed that, while restoration improved signal-level fidelity metrics, it did not translate into improved motor-intent classification accuracy relative to using the degraded signal directly, a limitation discussed explicitly in this work. Full article

BOOLE is a four-face educational platform integrating analog, combinational-logic, and sequential-logic activities with optional AI-assisted component identification and datasheet support. The system was developed through requirements translation, circuit simulation, two-layer PCB design, mechanical review, fabrication, assembly, functional verification, and iterative refinement. A Raspberry Pi 5, Camera Module 3 NoIR, and touchscreen support image capture and local interaction, while an Arduino Mega provides deterministic control of the physical learning faces. Segmented power energizes only the selected face and activity, and removable boards improve maintenance and fault isolation. Hardware demonstrations reproduced the intended voltage-divider, diode threshold/polarity, counter, and sequential-logic states. Ten one-versus-rest classifiers were fine-tuned from a pretrained ViT-Base model using 2000 original photographs, with 200 images for each of ten categories. The dataset was partitioned class-wise into mutually exclusive 80/10/10 training, validation, and final-test sets before augmentation, which was applied only to training data. Final-test accuracy ranged from 91.0% to 99.5%, with precision, recall, F1-score, specificity, balanced accuracy, and confusion matrices also evaluated. A 73-student pilot produced 89–96% positive (Yes) responses across six binary survey items, providing preliminary evidence of learner-perceived effectiveness, engagement, usability, and theory-to-practice support. Overall, BOOLE demonstrates a feasible, serviceable architecture for progressive electronics education. Full article

Prolonged sitting and poor posture are linked to musculoskeletal discomfort, higher spinal loading, and lower study and work efficiency. An Arduino-based distributed system combining a multi-sensor was developed for low-cost, camera-free sitting posture monitoring. It comprises a wearable sensing board (WSB), a main control board (MCB), and a host computer. The WSB measures trunk inclination—that is, the forward pitch and lateral roll of the upper trunk relative to the upright reference—using an ADXL345 acceleration sensor, whereas the MCB measures the user-to-desk distance using a US-100 ultrasonic ranging unit; NRF24L01 Wireless Communication Units connect them. Rule-based thresholds classify six states: “normal”, “slouching”, “leaning left”, “leaning right”, “too close”, and “too far”. The Sound Audio Unit and Liquid Crystal Display Unit provide local voice alerts and visual feedback. Using a 4G Unit, the MCB uploads user ID, timestamp, ambient temperature, distance, and posture state to a cloud platform. Cloud-generated text files support host retrieval and display, with accounts for two users and one administrator. The system can determine sitting-distance states within a range of 40–2000 mm and output trunk inclination information over a range of 0–90°. Under the current test conditions, the wireless communication distance between the MCB and WSB exceeds 3 m. In addition, the auditory reminder, time and temperature display, and PC-side data retrieval functions all operate as intended. With a total hardware cost of USD 18.39, the system provides a viable prototype for low-cost, camera-free sitting posture monitoring and remote data management in educational and home settings. Full article

As AI research advances, various performance optimization techniques have been studied for TinyML models on microcontrollers with very limited resources. In TinyML frameworks such as TFLM (TensorFlow Lite Micro) and NNOM (Neural Network on Microcontrollers), their execution engines mainly depend on CMSIS-NN for inference acceleration, which is an ARM’s back-end library to execute optimized kernel functions for performance optimization. But we often find that CMSIS-NN is not invincible for inference time optimization on TinyML. In this paper, we examine TinyML frameworks and their CMSIS-NN libraries and consider how to improve CMSIS-NN in terms of runtime. Then, we propose the D2I technique to reduce the overhead of memory operations that occur while performing the Im2col procedure within the convolution function, which takes most of the inference time in CMSIS-NN. The proposed technique creates a necessary index table, finds the location of the input with the corresponding index, and performs direct operations between filters and inputs. Thus, it can quite mitigate data copy operations in Im2col with a small additional amount of memory compared to Im2col. In extensive experiments using an Arduino nano 33 BLE board with Cortex-M4 and an STM32F746G-DISCO board with Cortex-M7, D2I was found to achieve about 16.3% and 14.5% inference time improvements against the Im2col in TFLM’s and NNOM’s CMSIS-NNs, respectively, for the SqueezeNet model. And the additional memory usage was shown to be identically 11.52 kB. Full article

Programmable gas flowmeters with remote reading and control are increasingly needed in the era of automation and AI. However, commercially available options remain expensive, hindering their adoption. We therefore developed a cost-effective programmable flowmeter as a compact device based on a low-cost glass rotameter. This system consists of a reading unit (webcam + rotameter), a control unit (development board + stepper motor-actuated valve) using an open-loop pre-calibrated step-to-flow matrix, and a terminal interface. The side-view imaging of the glass rotameter avoids occlusion of the rotor by the scale, enabling reliable rotor identification using well-established algorithms. The flow rate is derived by mapping the normalized rotor position to the scale instead of recognizing the scale markings or numerals. Two terminal configurations are offered: USB-connected (PC + MATLAB) and embedded (Raspberry Pi + Python). The USB-connected configuration uses direct serial communication between MATLAB and Arduino for stepper motor step control, ensuring fast response and good compatibility. The prototype retains manual control and direct eye reading. Experiments demonstrated strong programmability, fast response (0.2 s), high accuracy (mean error < 3%), and stable reading (fluctuation < 0.5%). This cost-effective yet programmable gas flowmeter is expected to benefit gas sensor development and accelerate automation in fluid-related fields. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Kimchi fermentation involves dynamic physicochemical and microbial changes; however, conventional monitoring methods are generally dependent on intermittent measurements, resulting in limitations in the real-time detection of abnormal fermentation. In this study, a Wireless Sensor Network (WSN)-based Fermentation Monitoring System (WFMS) and a Long Short-Term Memory (LSTM)-based Anomaly Detection System (LADS) were developed to continuously monitor internal pressure changes during kimchi fermentation. Kimchi samples were prepared under normal fermentation conditions (CON) and glucose-added conditions (GLU-6). Pressure data were collected at 10 min intervals using 15 psi and 30 psi pressure sensors connected to an Arduino Nano 33 IoT board and were transmitted to the ThingSpeak platform. During the fermentation period, pressure data were collected stably, while the external temperature was maintained at approximately 25 °C. Both CON and GLU-6 samples exhibited a rapid increase in internal pressure during the early fermentation stage, followed by a gradual decrease. However, relatively larger pressure fluctuations were observed in the middle and late fermentation stages of the GLU-6 samples. An LSTM autoencoder model trained using CON data established a reconstruction error-based threshold of 0.0025 and successfully detected anomalies in the GLU-6 samples. Anomalies were mainly identified during the initial fermentation stage and between fermentation days 2 and 4. These results demonstrate that pressure-based real-time monitoring combined with LSTM autoencoder analysis can be effectively applied for the non-destructive tracking of kimchi fermentation and the early detection of abnormal fermentation patterns. Full article

Wearable technologies offer practical opportunities for assistive communication and educational support in introductory sign language learning. This paper presents an IoT-enabled smart glove for deterministic static sign language recognition over a bounded vocabulary of 15 isolated static gestures, comprising digits (0–9) and five vowel handshapes (A, E, I, O, U). The system is intended for foundational static gesture and posture practice and is not designed or validated for dynamic gestures, coarticulated signing, continuous sign language recognition, or sentence-level translation. The prototype integrates five 2.2-inch (55.9 mm) resistive flex sensors and an MPU6050 3-axis accelerometer, performs acquisition, exponential moving average filtering, user-specific calibration, normalization, and deterministic classification on a NodeMCU ESP32 board, and transmits selected processed variables to Arduino Cloud through MQTT for remote monitoring. A 10 s calibration routine maps user-specific open-hand and closed-fist responses into normalized flex-sensor ranges, allowing the same deterministic rule structure to operate across participants without model retraining. Experimental evaluation with 10 healthy adult participants aged 20–41 years (mean age: 27 years), all familiar with sign language and all providing written informed consent, produced a balanced dataset of 1500 labeled steady-state sensor vectors. The class-averaged recognition rate was 92.8%, and leave-one-subject-out validation produced a subject-wise accuracy of $92.80\pm 2.03\%$, with individual participant accuracies ranging from 90.00% to 96.00%. The local embedded processing pipeline required less than 2 ms per cycle, the complete path including MQTT visualization produced approximately 150 ms end-to-end latency, and the device operated for up to 14 h using a 3.7 V, 1000 mAh Li-Po battery. The results indicate that calibrated deterministic sensor fusion can provide a low-cost, low-latency, edge-executed solution for bounded static sign-language gesture learning tasks while maintaining stable short-term subject-wise performance under controlled experimental conditions. Full article

At present, although there are many SCARA manipulator solutions with vertical lifting functionality, they generally suffer from high maintenance costs and complex structures. Moreover, systematic performance evaluations based on international standards are lacking, leading to unclear critical performance boundaries such as accuracy and payload in practical applications. To address these issues, this paper designs and manufactures a low-cost SCARA manipulator for educational and research demonstrations as well as light-duty electronic parts assembly scenarios. A “leadscrew + stepper motor” scheme is adopted for vertical lifting, and an Arduino Mega 2560 development board serves as the core controller, significantly reducing system cost. A three-dimensional model is established using SolidWorks 2022, and kinematic simulations are carried out with MATLAB 2024a to preliminarily verify the feasibility of the mechanism. Subsequently, a physical prototype is built and experimental tests are conducted in accordance with the ISO 9283 standard. The experimental results show that the repeatability of the manipulator is controlled within the range of 0.05–0.3 mm, the path deviation caused by vibration lies between −0.52 mm and 0.3 mm, and the maximum payload capacity is 3.91 N. These experimental data can serve as a benchmark for the design and performance comparison of similar low-cost manipulators. Full article

This paper presents a hybrid methodology for the creation of educational mobile robots, combining the efforts of developers in design, construction, and instrumentation with the use of “Vibe Coding” as an alternative programming approach. To achieve this objective, the methodology integrates electronics and algorithmic thinking to enable adaptive behavior across three operating modes for robotics competitions. The mobile robot features a compact and modular architecture (430 g, 22 cm length × 14 cm width) with support components manufactured using 3D printing. Instrumentation included an Arduino Uno^® development board, a Syb-170^® proto shield, a buzzer, an HC-SR04^® ultrasonic sensor, an SG90 RC^® servomotor, a SSD1315 display, three TCRT5000^® reflective optical sensors, two DC motors with integrated 48:1 gearboxes, an L298N motor driver, and two 18650^® rechargeable lithium-ion batteries. Programming and algorithmic implementation were carried out using Vibe Coding, leveraging its intuitive environment to accelerate the development of three independent operating modes: (1) line follower on a racetrack, (2) obstacle avoidance with various objects, and (3) Bluetooth control via the free MIT Application Inventor. The mobile robot successfully demonstrated all three tasks, validating its suitability for educational and competitive purposes. Furthermore, its architecture supports AI-assisted decision-making through Vibe Coding, enabling dynamic responses to environmental disturbances. The multimodal configuration enhances navigation by correcting trajectory deviations, thereby improving robustness, adaptability, and overall functionality. Full article

This article describes a portable Arduino-based prototype for the recording and analysis of electroencephalogram (EEG) signals associated with anxiety situations. The system’s main aim is to enable the user to self-detect stress and take self-regulating/relaxing actions in real time before stress escalates. The recorded EEG signals are first processed in the analog domain (including amplification and noise reduction) and then, by using an Arduino Uno board, they are converted into digital format and transmitted through either a wired or wireless connection to a computer to be depicted in both the time and the frequency domains by means of an open-source software. During the performed tests, the system successfully showed visible changes in the alpha and beta brain signals corresponding to the states of resting, induced stress, and the subsequent self-regulation/relaxation process. The proposed prototype (though non-clinical in its present form) has the merits of relatively low cost, easy self-use (outside clinical environments), and real-time EEG signal depiction, and, apart from enabling the user to self-detect and self-monitor stress, it can also be used for educational and/or research purposes. 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

This paper presents the design, construction, and calibration of a modular low-cost 3D printer based on open-source technologies, developed as part of an academic research project. The printer utilises fused filament fabrication (FFF) and is built using locally available materials and components, including a T-slot aluminium frame, NEMA 23 stepper motors, and an Arduino Mega 2560 with RAMPS 1.4 control board. The system integrates Marlin firmware and CURA slicing software, enabling autonomous operation via an LCD panel and encoder interface. A detailed methodology is provided for mechanical assembly, electronic integration, firmware configuration, and calibration procedures. Special attention is given to the challenges encountered during the initial testing phase, including filament feeding issues, thermal inconsistencies, and mechanical misalignments. Solutions such as replacing inadequate components (e.g., fibreglass bushings with PTFE), adjusting spring tension, and refining firmware parameters are discussed. The results demonstrate successful printing of complex geometries after iterative calibration, validating the printer’s performance and replicability. This work contributes to the democratisation of additive manufacturing by offering a replicable, open-source solution for educational and prototyping purposes. The findings are relevant to machine design, automation, and robotics communities seeking practical insights into low-cost fabrication systems. Full article

In emergency scenarios where visibility is compromised, rapid and accurate object detection becomes critical. This study addresses this challenge by proposing an IoT-enabled robotic solution capable of operating in low-visibility environments, with a focus on supporting search and rescue missions through autonomous sensing and real-time data communication. This research presents the development and implementation of an IoT-based sensorized system designed to detect objects in low-visibility environments. The system aims to enhance search and rescue operations by identifying potential human presence in areas with limited access due to smoke, darkness, or hazardous conditions. The platform integrates distance sensors, a thermal camera (AMG8833), a PIR motion sensor, and wireless communication through the Arduino MKR1000 and ESP32-CAM boards. The mobile robot is equipped with obstacle avoidance, person detection, and IoT communication modules, allowing data to be sent to the cloud via ThingSpeak and enabling remote commands through TalkBack. A structured methodology was followed, including technology selection, hardware/software design, and testing under various lighting and opacity conditions. Experimental results showed the effectiveness of the system in identifying obstacles and detecting heat signatures representing human body, with optimal performance observed at a 15 cm detection threshold. The system demonstrated robust operation in simulated rescue environments, providing real-time data transmission and remote-control capabilities. Full article

Resource-constrained sensor nodes in Internet-of-Things (IoT) and embedded sensing applications frequently rely on low-cost microcontrollers, where even basic algorithmic choices directly impact latency, energy consumption, and memory footprint. This study evaluates six sorting algorithms—Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, and Heap Sort—in the restricted environment that microcontrollers provide. Three Arduino boards were used: Arduino Uno, Arduino Leonardo, and Arduino Mega 2560. Each algorithm was implemented in its unoptimized form and tested on datasets of increasing size, emulating buffered time-series sensor readings in random, ascending, and descending order. Execution time, number of write operations, and memory usage were measured. The tests show clear distinctions between the slower $O\left(n^2\right)$ algorithms and the more efficient $O(nlogn)$ algorithms. For random inputs of $n=1000$ elements, Bubble Sort required 1,958,193.75 $\mathsf{\mu }$son average, whereas Quick Sort completed it in 54,260.50 $\mathsf{\mu }$s and Heap Sort in 92,429.00 $\mathsf{\mu }$s, i.e., speedups of more than one order of magnitude compared to the quadratic baseline. These gains, however, come with very different memory footprints. Merge Sort kept the runtime below 100,000 $\mathsf{\mu }$s at $n=1000$ but required approximately 2023 bytes of additional static random-access memory (SRAM), effectively exhausting the 2 kB SRAM of the Arduino Uno. QuickSort used approximately 311 bytes of extra SRAM and failed to process larger ascending and descending datasets on the more constrained boards due to its recursive pattern and stack usage. Heap Sort offered the best overall trade-off: it successfully executed all tested sizes up to the SRAM limit of each board while using only about 12–13 bytes of additional SRAM and keeping the runtime below 100,000 $\mathsf{\mu }$s for $n=1000$. The results provide practical guidelines for selecting sorting algorithms on 8-bit AVR Arduino-class microcontrollers, which are widely used as simple sensing and prototyping nodes operating under strict RAM, program-memory, and energy constraints. Full article