Search Results (1,532)

Replacing mechanical utility meters with digital ones is crucial due to the numerous benefits they offer, including increased time resolution in measuring consumption, remote monitoring capabilities for operational efficiency, real-time data for informed decision-making, support for time-of-use billing, and integration with smart grids, leading to enhanced customer service, reduced energy waste, and progress towards environmental sustainability goals. However, the cost associated with replacing mechanical meters with their digital counterparts is a key factor contributing to the relatively slow roll-out of such devices. In this paper, we present a low-cost and power-efficient solution for retrofitting the existing metering infrastructure, based on state-of-the-art communication and artificial intelligence technologies. The edge device we developed contains a camera for capturing images of a dial meter, a 32-bit microcontroller capable of running the digit recognition algorithm, and an NB-IoT module with (E)GPRS fallback, which enables nearly ubiquitous connectivity even in difficult radio conditions. Our digit recognition methodology, based on the on-device training and inference, augmented with federated learning, achieves a high level of accuracy (97.01%) while minimizing the energy consumption and associated communication overhead (87 $\mathsf{\mu }$Wh per day on average). Full article

In everyday life, people often stand up and sit down. Unlike young, able-bodied individuals, older adults and those with disabilities usually stand up or sit down slowly, often pausing during the transition. It is crucial to design interfaces that accommodate these movements. Additionally, in public settings, protecting personal information is essential. Addressing these considerations, this paper presents a distance-based representation scheme for the motions of standing up and sitting down. This proposed scheme identifies both standing and sitting positions, as well as the transition process between these two states. Our scheme is based solely on the variations in distance between a sensor and the surfaces of the human body during these movements. Specifically, the proposed solution relies on distance as input, allowing for the use of a proximity sensor without the need for cameras or additional wearable sensor attachments. A single microcontroller is adequate for this purpose. Our contribution highlights that using a proximity sensor broadens the applicability of the approach while ensuring that personal information remains secure. Additionally, the scheme alleviates users’ mental burden, particularly regarding privacy concerns. Extensive experiments were performed on 58 subjects, including 19 people over the age of 70, to verify the effectiveness of the proposed solution, and the results are described in detail. Full article

With improvements in medical environments and the widespread use of smartphones, interest in wearable biosensors for continuous body monitoring is growing. We developed a wearable multiplexed bio-sensing system that non-invasively monitors body fluids and integrates with a smartphone application. The system includes sensors, readout circuits, and a microcontroller unit (MCU) for signal processing and wireless communication. Potentiometric and amperometric measurement methods were used, with calibration capabilities added to ensure accurate readings of analyte concentrations and temperature. Laser-induced graphene (LIG)-based sensors for glucose, lactate, Na⁺, K⁺, and temperature were developed for fast, cost-effective production. The LIG electrode’s 3D porous structure provided an active surface area 16 times larger than its apparent area, resulting in enhanced sensor performance. The glucose and lactate sensors exhibited high sensitivity (168.15 and 872.08 μAmM⁻¹cm⁻², respectively) and low detection limits (0.191 and 0.167 μM, respectively). The Na⁺ and K⁺ sensors demonstrated sensitivities of 65.26 and 62.19 mVdec⁻¹, respectively, in a concentration range of 0.01–100 mM. Temperature sensors showed an average rate of resistance change per °C of 0.25%/°C, within a temperature range of 20–40 °C, providing accurate body temperature monitoring. Full article

Nowadays, standalone microgrids that make use of renewable energy sources have gained great interest. They provide a viable solution for rural electrification and decrease the burden on the utility grid. However, because standalone microgrids are nonlinear and time-varying, controlling and managing their energy can be difficult. A fractional-order proportional integral (FOPI) controller was proposed in this study to enhance a standalone microgrid’s energy management and performance. An ultra-capacitor (UC) and a battery, called a hybrid energy storage scheme, were employed as the microgrid’s energy storage system. The microgrid was primarily powered by solar and wind power. To achieve optimal performance, the FOPI’s parameters were ideally generated using the gorilla troop optimization (GTO) technique. The FOPI controller’s performance was contrasted with a conventional PI controller in terms of variations in load power, wind speed, and solar insolation. The microgrid was modeled and simulated using MATLAB/Simulink software R2023a 23.1. The results indicate that, in comparison to the traditional PI controller, the proposed FOPI controller significantly improved the microgrid’s transient performance. The load voltage and frequency were maintained constant against the least amount of disturbance despite variations in wind speed, photovoltaic intensity, and load power. In contrast, the storage battery precisely stores and releases energy to counteract variations in wind and photovoltaic power. The outcomes validate that in the presence of the UC, the microgrid performance is improved. However, the improvement is very close to that gained when using the proposed controller without UC. Hence, the proposed controller can reduce the cost, weight, and space of the system. Moreover, a Hardware-in-the-Loop (HIL) emulator was implemented using a C2000™ microcontroller LaunchPad™ TMS320F28379D kit (Texas Instruments, Dallas, TX, USA) to evaluate the proposed system and validate the simulation results. Full article

Cardiovascular diseases pose a significant global health threat, with atrial fibrillation representing a critical precursor to more severe heart conditions. In this work, a multimodality-based deep learning model has been developed for diagnosing atrial fibrillation using an embedded system consisting of a Raspberry Pi 4B, an ESP8266 microcontroller, and an AD8232 single-lead ECG sensor to capture real-time ECG data. Our approach leverages a deep learning model that is capable of distinguishing atrial fibrillation from normal ECG signals. The proposed method involves real-time ECG signal acquisition and employs a multimodal model trained on the PTB-XL dataset. This model utilizes a multi-step approach combining a CNN–bidirectional LSTM for numerical ECG series tabular data and VGG16 for image-based ECG representations. A fusion layer is incorporated into the multimodal CNN-BiLSTM + VGG16 model to enhance atrial fibrillation detection, achieving state-of-the-art results with a precision of 94.07% and an F1 score of 0.94. This study demonstrates the efficacy of a multimodal approach in improving the real-time diagnosis of cardiovascular diseases. Furthermore, for edge devices, we have distilled knowledge to train a smaller student model, CNN-BiLSTM, using a larger CNN-BiLSTM model as a teacher, which achieves an accuracy of 83.21% with 0.85 s detection latency. Our work represents a significant advancement towards efficient and preventative cardiovascular health management. Full article

Building Information Modelling (BIM) integration with the Internet of Things (IoT) is progressing. The high level of BIM maturity involves using sensor data to manage processes or objects. The article presents the process of creating a telemetry connection between the BIM model and a Radio-Frequency Identification (RFID) sensor in the context of gaining access to various parts of a building. The process of creating a connection using an experimental set based on a microcontroller board for RFID reader support is described. The set was programmed using multiple programming languages and artificial intelligence. The article presents a unique process of connecting an RFID reader with BIM using a simple model that can be replicated in other contexts (e.g., gaining access to different parts of a construction site). Previous research shows that the unidirectional connection of IoT sensors with BIM models is not difficult. Instead, real-time bidirectional and stable connection (telemetry) is problematic. The authors undertook to fill this research gap using a proprietary IoT kit, programming sequence, and lightweight communication protocol. The paper makes a significant contribution to the discussion and understanding of BIM-IoT technology integration. The article also includes the limitations and possibilities for further development of such a connection. Full article

The adoption of Internet of Things (IoT) technology for transformer condition monitoring is increasingly replacing traditional methods. This systematic review aims to evaluate the existing research on IoT frameworks used in transformer condition monitoring, providing insights into their effectiveness and research trends. This review seeks to identify the leading IoT frameworks employed in transformer condition monitoring; analyze the key research objectives, methods, and outcomes; and assess the global research distribution and technological tools used in this field. A systematic literature review was conducted by searching published databases using keywords related to “Internet of Things”, “transformers”, “condition monitoring”, and “fault diagnosis”. The search spanned publications released between 2014 and 2024, yielding 262 articles. Of these, 120 met the predefined review criteria and were included for further analysis. This review found that Arduino boards are the most used microcontrollers for monitoring and analyzing transformer operational parameters, with Arduino IDE 1.8 being the predominant software for programming. The primary research focus in the reviewed literature is the identification of transformer faults. The geographical distribution of research contributions shows that India leads with 65% of the studies, followed by China (11%) and Pakistan (5%). The findings indicate a strong global interest in developing IoT-based transformer condition monitoring systems, particularly in India. This review highlights the potential of IoT technologies to enhance transformer monitoring and diagnostics. The insights gained from this review can guide future research and the development of more advanced IoT frameworks for transformer condition monitoring. Full article

This paper presents a real-time wearable system designed to assist Parkinson’s disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide ‘on demand’ vibratory stimulation to patients. This paper examines the system’s ability to operate with minimal latency, achieving an average detection delay of just 261 milliseconds and a freezing of gait detection accuracy of 95.1%. While patients received on-demand stimulation, the system’s effectiveness was assessed by decreasing the average duration of freezing of gait episodes by 45%. These preliminarily results underscore the potential of personalized, real-time feedback systems in enhancing the quality of life and rehabilitation outcomes for patients with movement disorders. Full article

Research shows that treadmill shock-absorbing devices can reduce the impact of ground reaction forces on the knee and ankle joints during running. Most existing treadmills use fixed or passive shock absorption, meaning their shock-absorbing systems do not actively adjust to changes in ground reaction forces (GRFs). Methods: This study establishes a mathematical model integrating human motion biomechanics and treadmill running surfaces, analyzing the relationships between various parameters affecting the system. Ultimately, an optimal shock-absorbing treadmill control system is designed, utilizing a microcontroller as the main control unit, airbags for shock absorption, and a widely used foot pressure testing system. Objective: The goal is to more effectively prevent running injuries caused by excessive foot pressure. Compared to conventional shock absorption systems, this design features an active multilevel adjustment function with higher precision in regulation. Results: The experimental results demonstrate that the ground reaction force (GRF) generated by the optimal shock-absorbing treadmill control system is reduced by up to 10% compared to that of a conventional shock-absorbing treadmill. Conclusions: This leads to a smaller impact force on the knees due to foot pressure, resulting in better injury prevention outcomes. Full article

The COVID-19 epidemic has raised awareness of exactly how crucial it is to continuously observe issues and diagnose respiratory problems early. Although the respiratory system is the primary objective of the disease’s acute phase, subsequent complications of SARS-CoV-2 infection might trigger enduring respiratory problems and symptoms, according to new research. These signs and symptoms, which collectively inflict considerable strain on healthcare systems and people’s quality of life, comprise, but are not restricted to, congestion, shortage of breath, tightness in the chest, and a decrease in lung function. Wearable technology offers a promising remedy to this persistent issue by offering continuous respiratory parameter monitoring, facilitating the early control and intervention of post-COVID-19 issues with respiration. In an effort to enhance patient outcomes and reduce expenses related to healthcare, this paper examines the possibility of using wearable technology to provide remote surveillance and the early diagnosis of respiratory problems in individuals suffering from COVID-19. In this work, a fog computing-based cost-effective smart health monitoring device is proposed for infectious disease applications. Further, the proposed device consists of three different biosensor modules, namely a MAX90614 infrared temperature sensor, a MAX30100 pulse oximeter, and a microphone sensor. All these sensor modules are connected to a fog computing device, namely a Raspberry PI microcontroller. Also, three different sensor modules were integrated with the Raspberry PI microcontroller and individuals’ physiological parameters, such as oxygen saturation (SPO2), heartbeat rate, and cough sounds, were recorded by the computing device. Additionally, a convolutional neural network (CNN)-based deep learning algorithm was coded inside the Raspberry PI and was trained with normal and COVID-19 cough sounds from the KAGGLE database. This work appears to be of high clinical significance since the developed fog computing-based smart health monitoring device is capable of identifying the presence of infectious disease through individual physiological parameters. Full article

During this research, an automatic monitoring system was developed to monitor the working parameters in a solar power plant consisting of two flexible silicon modules. The first stage of the monitoring system relies on a microcontroller, which collects data from wattmeter modules made using a microcontroller. This tier also includes DC/DC converter and RS232-TCP converter modules for data transfer. The second stage, the industrial PLC, receives data from the first stage and transmits them to the PC, where the information is stored and the processes are visualized on the HMI screen. During this study, the charging process was analyzed using PWM- and MPPT-type charging controllers, as well as the power supply of Fito LED strips for lighting plants. Using the created monitoring system, the parameters of the solar power plant with flexible PV modules were monitored. This study compared PWM and MPPT battery charging methods, finding that MPPT is more efficient, especially under unstable solar conditions. MPPT technology optimizes energy usage more efficiently, resulting in faster battery charging compared to PWM technology. Full article

Over the past decade, wearable medical devices (WMDs) have become the norm for continuous health monitoring, enabling real-time vital sign analysis and preventive healthcare. These battery-powered devices face computational power, size, and energy resource constraints. Traditionally, low-power microcontrollers (MCUs) and application-specific integrated circuits (ASICs) have been used for their energy efficiency. However, the increasing demand for multi-modal sensors and artificial intelligence (AI) requires more computational power than MCUs, and rapidly evolving AI asks for more flexibility, which ASICs lack. Field-programmable gate arrays (FPGAs), which are more efficient than MCUs and more flexible than ASICs, offer a potential solution when optimized for energy consumption. By combining real-time reconfigurability with intelligent energy optimization strategies, FPGAs can provide energy-efficient solutions for handling multimodal sensors and evolving AI requirements. This paper reviews low-power strategies toward FPGA-based WMD for physiological monitoring. It examines low-power FPGA families, highlighting their potential in power-sensitive applications. Future research directions are suggested, including exploring underutilized optimizations like sleep mode, voltage scaling, partial reconfiguration, and compressed learning and investigating underexplored flash and hybrid-based FPGAs. Overall, it provides guidelines for designing energy-efficient FPGA-based WMDs. Full article

In order to address the problem of a high miss-seeding rate in mechanized potato planting work, a novel reseeding device is designed and analyzed. Based on dynamic and kinematic principles, the seed potato’s motion analysis model in the seed preparation process was constructed. The analysis results indicate that the seed preparation performance is positively related to the seed preparation opening length l₁ and inclination angle of the seed-returning pipe θ. Then, the potato’s motion analysis model in the reseeding process was constructed. The analysis showed that the displacement of seeding potatoes in the horizontal direction d_s is influenced by the initial seeding potato’s speed $v_{0t}$, dropping height h_s, and the angle between the seeding pipe and the horizontal ground β_s. The horizontal moving distance x_r of the reseeding potatoes is influenced by the angle between the bottom of the reseeding pipe and horizontal ground β_s2, the distance from its centroid to the reseeding door d, and the dropping height of the potato h_r. The analysis results indicated that the reseeding potato can be effectively discharged into the furrow. Then, a prototype of a reseeding control system was constructed based on the STM32 microcontroller, electric pushers, and through-beam laser sensors. The simulation analysis was conducted to verify the theoretical analysis by using EDEM2020 software. The simulation results indicated that with the increase in the seeding chain speed, the seed preparation success rate initially increased slowly and then decreased gradually. The seed preparation performance can be increased by increasing the seed preparation opening length or decreasing the seed-returning pipe inclination angle. The impact on the successful seed preparation rate is ranked by significance as follows: seed preparation opening length > seed-returning pipe inclination angle > chain speed. Then, the prototype reseeding device and the corresponding seed metering device were manufactured and a series of bench tests and field tests were conducted. The bench test results showed an average successful seed preparation rate of 93.6%. The average qualified-seeding rate, miss-seeding rate, and multi-seeding rate in the field test were 89.6%, 2.46%, and 7.94%, respectively. This study can provide a theoretical reference for the design of potato reseeding devices. Full article

With advances in technology, the generation of electrical energy through the harvesting of energies dissipated in the form of mechanical vibration, known as power harvesting, has received increasing attention in recent decades. It is undoubtedly an interesting means to power systems with low energy consumption. This research aims to evaluate an energy generation system based on the piezoelectric effect activated by mechanical excitation and develop a system capable of powering devices and sensors for temperature monitoring in a practical situation, such as in an engine room, aiming to ensure its safe operation. Two transducers subjected to vibrational excitation were evaluated, and then an energy generation system using a buck DC-DC converter circuit was assessed. The transducer was connected to the input of the board, the microcontroller to the output, and the LM35 temperature sensor along with the battery was used to ensure the circuit’s autonomy. Additionally, the Attiny85 microcontroller was programmed to perform temperature monitoring tasks in a continuous low-energy-consumption mode. The obtained spectral analysis results showed a maximum generation power of 8.88 mW for the PZT-5H transducer and 3.3 mW for the P5-13B transducer. The use of LTC3588-1 increased the autonomy of the monitoring system by 64.3% and reduced the system’s usage time in cases of temperature anomalies by 50%. Full article

Current XR applications move beyond audiovisual information, with haptic feedback rapidly gaining ground. However, current haptic devices are still evolving and often struggle to combine key desired features in a balanced way. In this paper, we propose the development of a high-resolution haptic (HRH) system for perception enhancement, a wearable technology designed to augment extended reality (XR) experiences through precise and localized tactile feedback. The HRH system features a modular design with 58 individually addressable actuators, enabling intricate haptic interactions within a compact wearable form. Dual ESP32-S3 microcontrollers and a custom-designed system ensure robust processing and low-latency performance, crucial for real-time applications. Integration with the Unity game engine provides developers with a user-friendly and dynamic environment for accurate, simple control and customization. The modular design, utilizing a flexible PCB, supports a wide range of actuators, enhancing its versatility for various applications. A comparison of our proposed system with existing solutions indicates that the HRH system outperforms other devices by encapsulating several key features, including adjustability, affordability, modularity, and high-resolution feedback. The HRH system not only aims to advance the field of haptic feedback but also introduces an intuitive tool for exploring new methods of human–computer and XR interactions. Future work will focus on refining and exploring the haptic feedback communication methods used to convey information and expand the system’s applications. Full article