Search Results (1,056)

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

With the development of industrial intelligence, traditional material handling systems suffer from insufficient flexibility, low functional integration, and weak fire safety response. To solve these problems, this paper designs an Arduino-based multifunctional intelligent material handling crane system with integrated fire safety protection. The system adopts a modular multi-sensor fusion architecture, realizing environmental perception, automatic path planning, and dual fire safety protection (smoke alarm + automatic fire extinguishing). Experiments were carried out in a laboratory-controlled environment with the system in as the benchmark; the results show that the operation efficiency of object handling is improved by 29.6%. This prototype system provides an experimental reference for the intelligent and safe upgrading of small and medium-sized warehousing material handling equipment. All experiments were completed in a controlled laboratory environment. Full article

Hidden waterlogging of subsurface soils may develop without clear external signs, while still deteriorating the hydro-physical state of foundation soils. This proof-of-concept study demonstrates a compact monitoring and interpretation workflow for identifying such zones through moisture profiling and subsequent engineering interpretation using the liquidity index (${\mathrm{I}}_{\mathrm{L}}$) for cohesive soils and the saturation ratio (${\mathrm{S}}_{\mathrm{r}}$) for non-cohesive soils. The developed prototype comprises a modular immersion probe, Arduino-based transmitter and receiver units, 433 MHz ASK wireless communication, and data logging. Using geotechnical survey data from a representative site in Astana, a baseline hydro-physical state and an intentionally constructed synthetic risk-waterlogging scenario were analyzed through vertical profiles and horizontal interpolation maps. Under the baseline state, moisture content varied mainly from about 6 to 23%, while most ${\mathrm{I}}_{\mathrm{L}}$ and ${\mathrm{S}}_{\mathrm{r}}$ values remained within the normal zone. In the synthetic scenario, the response was much stronger in cohesive soils, where ${\mathrm{I}}_{\mathrm{L}}$ increased from about −0.55 to 1.8, whereas ${\mathrm{S}}_{\mathrm{r}}$ in non-cohesive soils changed only slightly. The Welch’s t-test indicated significant scenario-related changes for ${\mathrm{I}}_{\mathrm{L}}$ (p-value of 1.095 × 10⁻¹⁹) but not for ${\mathrm{S}}_{\mathrm{r}}$ (p-value of 0.147). The results show the methodological potential of the proposed workflow for engineeringly interpretable zoning of hidden waterlogging; however, site-specific calibration, metrological characterization, and field validation are still required before practical deployment. Full article

An automated air-conditioner cleaning system was developed as a retrofit solution for conventional split-type units to reduce residual moisture in the evaporator section and suppress post-shutdown microbial accumulation. The system was integrated with an 18,000 BTU h⁻¹ air-conditioner and implemented using an Arduino-based closed-loop control platform with temperature and relative humidity monitoring. After shutdown, the indoor fan was operated under low-, medium-, or high-speed conditions to remove retained moisture from the cooling coil. System performance was evaluated in an 18 m³ test room through measurements of electrical consumption, operating cost, relative humidity, and microbial contamination in room air and on the evaporator coil before and after system installation. Low-speed operation showed the lowest current demand, power consumption, and electricity cost, with corresponding values of 0.36 ± 0.01 A, 79.2 ± 0.8 W, and 0.47 THB per 150 min. Post-shutdown humidity reduction was achieved under all tested conditions, while the high-speed mode provided the fastest drying response, reducing relative humidity to approximately 60% within 120 min. In the room air, the greatest reduction in airborne fungi after shutdown was observed at low speed, whereas the greatest reduction in airborne bacteria was observed at medium speed. On the evaporator coil, the strongest bacterial suppression was obtained at low speed, where the bacterial count after 24 h decreased from 633.33 ± 34.27 CFUs before installation to below the detection limit after installation. These results indicate that the proposed system reduced moisture retention and microbial contamination with minimal energy consumption. 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

Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Its technical contribution lies in the integration of non-intrusive virtual ignition, filtered GPS-based odometry, configurable MicroSD-based persistence, and progressive SMS alert logic into a low-cost aftermarket system for conventional vehicles without OBD-II dependence. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer and 100% effectiveness in sending alerts under the tested conditions (20/20 events; exact 95% binomial confidence interval: 83.2–100.0%). These results provide strong evidence of technical feasibility for the proposed architecture under the tested conditions in a representative single-vehicle proof-of-concept, while broader cross-vehicle validation remains necessary before generalizing the system to the wider diversity of aging fleets. 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

Robotic gait rehabilitation can increase training intensity and repeatability, yet many systems still rely on fixed assistance or pre-programmed trajectories and provide limited support for functional transitions such as sit-to-stand and stand-to-sit. This study presents RoboREHAB as a system-level integration effort that combines variable-gravity assistance through joint-level torque compensation, a redesigned leg assembly intended to improve knee kinematics, and motion-capture-informed reinforcement learning as a preliminary personalization layer for trajectory tracking. The manuscript defines a unified architecture with explicit module interfaces and signal flow and distinguishes simulation-based evaluation, benchtop prototype evidence, and future validation steps to maintain traceability and align claims with the current level of validation. Simulation-based evaluation under the present configuration indicated reductions in gait trajectory error (66%), balance recovery time (55%), stride deviation (72%), and joint torque variability (66%) relative to a fixed-gravity, predefined-trajectory baseline under matched simulated scenario definitions, together with trajectory tracking within an approximately 5% error margin for the redesigned assembly relative to motion-capture references. Benchtop prototype demonstrations support the subsystem-level feasibility of sensor-driven variable-gravity control and user-adjustable assistance scaling using an embedded control stack. Overall, the present evidence supports the feasibility of the integrated RoboREHAB architecture and defines a staged validation pathway toward future hardware-in-the-loop testing, instrumented full-scale evaluation, and eventual human-subject studies. Full article

Biogas is a renewable energy resource that is not only economical but also fulfills the criteria of net-zero carbon emissions. This is highly favorable for agriculture-based developing countries with an abundance of animal and agricultural waste that can be effectively utilized for biogas production. A dual-stage reactor was designed and built to investigate the optimal conditions during the different seasons of winter and summer for mesophilic biogas production utilizing cow manure from local dairy farms. During the experiments, the pH was continuously monitored and automatically controlled between 6.8 and 7.2 over a period of fifteen days for each experiment using an Arduino Mega controller. The weight ratio (r_w) of cow manure slurry was varied from 50% to 80%, and the optimal condition was found to be 70%, irrespective of the seasonal variations. However, the statistical analysis suggests that the optimal weight ratio is 66% for both seasons. A maximum reaction yield of 87% was achieved at a r_w value of 60% during the summer, with an expected yield of over 95% at a r_w value of 70% if similar extreme environmental conditions occur. Employing this apparatus for biogas production requires significant electrical energy to drive the stirrer and pumps, suggesting the use of a conventional underground setup for biogas production, integrated with an automatic pH control module. 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

Coconut milk-based Filipino foods provide a favorable environment for microbial growth and are highly susceptible to spoilage. Traditionally, spoilage in such foods has been assessed through subjective sensory evaluation, a method that often lacks consistency and accuracy. The present study introduces an electronic nose system employing Support Vector Machine (SVM) algorithms to objectively and quantitatively determine spoilage in coconut milk-based Filipino foods, including Bicol Express, Ginataang Langka, Laing, Bilo-bilo, Maja Blanca, and Ginumis. The developed system integrates six MQ gas sensors connected to an Arduino Nano and a Raspberry Pi 4B to detect and process volatile organic compounds emitted from the foods. The SVM algorithm was selected for its effectiveness in high-dimensional spaces and its ability to construct a binary classifier capable of distinguishing between spoiled and fresh samples. Dimensionality reduction in sensor data was achieved using Principal Component Analysis, which further enhanced classifier performance. System evaluation results demonstrated a high classification accuracy of approximately 98.95%, indicating the robustness of the proposed approach. The utilization of this technology offers significant benefits, not only for individuals with impaired olfactory function but also for the food industry, providing a reliable tool for food quality control and safety. Moreover, the outcomes suggest broader applicability to other perishable food products, with potential contributions to improved global food safety and storage practices. Full article

A non-invasive blood glucose monitoring system was developed in this study using near-infrared spectroscopy and an Arduino platform. Reflected signals from near-infrared-focused emissions to a user’s finger are captured via an infrared-tuned photodiode, digitally processed, and displayed on an Android-based application with logging, reminders, and cloud synchronization. Calibrated testing with 20 participants (10 diabetics and 10 non-diabetics) revealed that in the measurement of diabetics, the non-fasting readings showed high average accuracy (99.89%). Non-diabetic trials also demonstrated strong measurement acuity (92.18%), with improved accuracy in non-fasting measurements. The device demonstrates feasibility for affordable, portable, and cloud-connected smart non-invasive glucose tracking. Full article

Portable devices are powered in direct current (DC) or by batteries (primary battery), accumulators (secondary battery), and now supercapacitors, which can also be used for energy storage. The European Portable Battery Association states that approximately 239,000 tons of batteries were placed on the market in the European Economic Area (EEA) plus Switzerland in 2022. Even if they were all disposed of correctly respecting the 3R paradigm (Reduce, Reuse and Recycle), non-rechargeable batteries create an environmental problem because they do not discharge completely with an obvious waste of energy. Secondary batteries and supercapacitors can be recharged because they use reversible chemical/physical processes while primary batteries cannot be recharged because they are based on irreversible redox reactions; nevertheless, it is possible to try to recover their residual charge if this is higher than a threshold beyond which the reactions can be reversible. The most used batteries are alkaline zinc/manganese dioxide and they are non-rechargeable; an inappropriate recharge attempt can lead to serious harm to the operator and the environment. This paper describes a simple Arduino-based circuit and the protocol to measure and graph the residual charge of an alkaline battery in order to establish if it can be recharged. The circuit, design, the Arduino Uno R3 sketch (i.e., microprocessor software) and the full protocol are here presented under the open source license (Copyright Creative Commons Public license, CC BY-NC-ND 4.0 EN) so that they could become a pilot system and then a commercial product. The residual charge of 158 batteries, obtained after discharging those that, by eye, appeared damaged, was measured. Results evidenced that 49% of batteries had a residual voltage, under low load, between 1.2 and 1.6 V, making them good candidates for a recharge attempt. 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

Honey is one of the most valued natural food products, yet it remains highly vulnerable to fraud through mislabeling and adulteration, practices that mislead consumers and compromise food safety. We develop a low-cost and portable electronic nose (e-nose) system for classifying locally available honey brands in the Philippines. The system integrates an array of eight MQ gas sensors to detect volatile organic compounds (VOCs), with an Arduino Mega 2560 handling data acquisition and a Raspberry Pi 5 executing data processing and classification. An Extreme Gradient-Boosted Decision Tree (XGBoost) algorithm was applied to analyze the VOC profiles of three honey brands, each with 38 samples, resulting in a total dataset of 114 samples. The dataset was divided into training, testing, and validation sets to assess the system’s classifying and predictive performance, with accuracy evaluated using a 3 × 3 confusion matrix. The results showed that the system effectively distinguished between honey brands, achieving a validation accuracy of 87.50%, corresponding to 21 out of 24 correctly identified validation trials. Full article