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Among today's bustling lifestyles, the demand for autonomous, durable, and low-maintenance healthcare systems has surged, surpassing that of earlier periods. Nanostructured and environmentally friendly materials employed in nanogenerator technology offer a novel avenue for biomedical applications by harnessing biomechanical energy. Triboelectric Nanogenerators (TENGs) have emerged as comprehensive solutions, furnishing self-sustaining, eco-conscious, and compact devices. Recognizing the immense potential of TENGs, this paper presents a comprehensive overview of its motion detection. Our analysis delves into the versatility of TENG-based motion detection systems, providing wearable, user-friendly solutions powered by human motion. Recent advancements in triboelectric devices are cataloged, elucidating their structural intricacies, capabilities, performance metrics, and future prospects. In addition, the article also outlines the applications of different TENGs in motion monitoring, including contact, non-contact, and single-electrode mode. The evolution of intelligent wearable technologies has extended our capacities in communication, healthcare, and various other domains beyond our biological limits. Apart from the Internet of Things, the concept of Internet of bodies or beings is poised for rapid advancement, promising further transformation of our lifestyles. Conclusively, we present insights into forthcoming opportunities and plausible strategies to address anticipated hurdles. [ABSTRACT FROM AUTHOR]

Stroke represents a severe, widespread, and widely acknowledged health crisis on both national and international levels. It is one of the most prevalent life-threatening conditions. Despite impressive advances in treating stroke, in addition to a need for effective patient care services, many sufferers still rely solely on physical interventions. The present paper describes and explains the use of a newly designed gadget for stroke survivors who cannot move their fingers. This is a sophisticated mobile device that enables stroke patients to regain their muscle memory and thus their ability to perform repetitive actions by continuing to tighten and stretch their muscles without the intervention of a physiotherapist. Gamification methodology is used to encourage patients to become involved in the process of rehabilitation. The device also has sensors that take information and transmit it to an app through an ESP32 connection. This enables physicians to view glove usage information remotely and keep track of an individual patient's health. Communication between app and glove is facilitated by a broker in the Amazon Web Service IoT. With the robotic glove presented here, the recovery rate is found to be 90.23% over four weeks' duration, which represents a significant improvement compared with existing hospital-based rehabilitation techniques. HIGHLIGHTS: 1. New design gadget for stroke survivors to tighten and stretch muscles 2. Rehabilitation unit enables physicians to view glove usage information remotely 3. The recovery rate is improved in terms of weeks for the patient [ABSTRACT FROM AUTHOR]

The emergence of 5G represents a pivotal step in merging mobile communication networks with the Industrial Internet of Things. Despite the numerous advantages of 5G, the presence of unknown obstacles can adversely affect user signals. Although mitigating signal pressures can be achieved by increasing base station density, it often involves bulky equipment and high costs. To address this, we propose a deep learning-based method for controlling tunable transmissive metasurfaces and validate their scattering control capabilities in the presence of obstacles. By constructing a network model to analyze the mapping relationship between metasurface arrays and far-field scattering, rapid control of scattering characteristics is achieved. AI-driven high-performance tunable metasurfaces exhibit vast potential applications in intelligent communication, offering a universal solution for intelligent control in complex signal environments. [ABSTRACT FROM AUTHOR]

The main goal of this work is to design an optimized sensor-fault identification and diagnostic system for the Internet of Things (IoT) and Cyber-Physical Systems (CPS). The challenge is to accomplish this task within the sensors' limited computing, memory, and energy capabilities. More importantly, identifying errors is time-sensitive, even though the diagnosis does not have to be made quickly. This project aims to provide an enhanced sensor-fault detection and diagnostic system for the IoT and CPS with constrained energy, memory, and computation resources. The system's goals are to promptly detect defects, lessen the computing burden on sensors, and enhance the recall and accuracy of fault detection. This study used a hybrid approach that combined principal component analysis, autoencoder, and gated recurrent unit to create an optimal sensor-fault detection and diagnostic system. There were 1001 sensor readings in the dataset; 112 were defective, while the remaining 888 were normal. The investigation showed that the suggested method, which detected faults with an accuracy of 95% and recall of 92%, achieved high accuracy and recall in recognizing defects in IoT and CPS. With significantly shorter processing times, the system's potential to reduce computational strain on sensors was also proved. The findings of this study indicate that the suggested optimized sensor-fault detection and diagnosis system successfully detects faults in IoT and CPS with limited computation, memory, and energy resources. The system's ability to reduce the computing burden on sensors while improving accuracy and recall makes it an appealing choice for industrial and commercial applications. [ABSTRACT FROM AUTHOR]

Monitoring the health of the elderly using wearable sensors and Internet of Things (IoT) devices necessitates the collection of data from various sources, thereby increasing the volume of data to be gathered at the monitoring center. As previously elucidated, the system exhibits high accuracy in measuring variables such as blood oxygen saturation (BOS) and heart rate (HR), as well as demonstrating proficient accuracy in implementing fuzzy rules to detect the "stable" or "unstable" condition of elderly patients. Therefore, by integrating wearable sensors and IoT devices into the elderly health monitoring system, we can enhance the quality of monitoring and provide more timely healthcare solutions tailored to the elderly population's needs. The integration of fuzzy rules and IoT technology into the elderly health monitoring system offers an efficient and effective solution for continuous health surveillance. In this research, we integrated fuzzy rules and IoT technology into the elderly health monitoring system using parameters such as BOS and HR, through the integration of MAX30100 sensors and ESP32 microprocessors. The implementation of fuzzy rules resulted in nine rules indicating whether an elderly patient is in a "stable" or "unstable" condition. The methodology involved (1) component preparation, (2) device accuracy analysis, (3) fuzzy rule development, (4) Android-based mobile application development, (5) fuzzy rule accuracy analysis, and (6) cost analysis. Experimental results indicated a device accuracy of 98.89% in measuring BOS and HR variables compared to medical devices and a fuzzy rule accuracy of 96% in detecting whether the patient's condition is "stable" or "unstable." Based on the experimental findings, the elderly health monitoring system provides a user-friendly, precise, and cost-effective solution to enhance the quality of life for the elderly population. [ABSTRACT FROM AUTHOR]

Early fire identification is critical to saving lives and property. One viable strategy for improving fire detection systems' efficacy and precision is the use of Internet of Things (IoT) technology. This article details research into the conception and development of an IoT, ESP32-based, Arduino MEGA-connected smart pre-alert detection system for fires. Because of its flexible nature, the Arduino microcontroller can easily exchange data with various sensors for real-time monitoring and analysis. This study surveys the existing literature to provide insight into many approaches to early fire detection (forecasting), such as sensor fusion methods and the use of the intelligence concept as a pre-predictor. The research highlights the need to set up sophisticated fire monitoring and alarm systems and to develop predictive models based on sensor data. This study illustrates how embedded systems via IoT technology and the ESP32 architecture may improve the efficiency and dependability of very early fire detection systems. This research adds to the expanding corpus of information that may be used to improve fire safety in contemporary settings. The proposal's sensors include an Arduino MEGA, an ESP32, a MAX6675 module, an MQ-Smoke, and an MQ-Natural Gas for collecting data, which are then processed using Cloud Firebase and a sleek mobile interface that can be reached overseas at any time. [ABSTRACT FROM AUTHOR]

Magnetic sensors are used in many technologies and industries, such as medicine, telecommunications, robotics, the Internet of Things, etc. The sensitivity of these magnetic sensors is a key aspect, as it determines their precision. In this article, we investigate how a thin windmill-like ferromagnetic system can hugely concentrate a magnetic field at its core. A magnetic sensor combined with such a device enhances its sensitivity by a large factor. We describe the different effects that provide this enhancement: the thickness of the device and its unique windmill-like geometry. An expression for the magnetic field in its core is introduced and verified using finite-element calculations. The results show that a high magnetic field concentration is achieved for a low thickness-diameter ratio of the device. Proof-of-concept experiments further demonstrate the significant concentration of the magnetic field when the thickness-diameter ratio is low, reaching levels up to 150 times stronger than the applied field. [ABSTRACT FROM AUTHOR]

To meet the demands of high-frequency wireless communications and Internet of Things (IoT) applications, modern integrated circuit (IC) packages should support operating frequencies in the GHz range and be implemented on fine substrate structures. Glass has many advantages as an interposer material for three-dimensional IC (3D-IC) designs, including tunable electrical and mechanical properties, amenability to large-scale processing, and high optical transparency in the visible range. Moreover, glass is easily processed to produce the through-glass vias (TGVs) required to realize high-frequency circuit designs and microelectromechanical systems devices. The present study proposes a method for patterning TGVs on glass substrates via a nonlinear multiphoton-assisted modification process performed using single-pulse irradiation by a 1030-nm picosecond laser. A theoretical model is additionally proposed to describe the glass substrate modification mechanism induced by the nonlinear multiphoton excitation effect. The feasibility of the proposed method is demonstrated by patterning a TGV array with a high aspect ratio of 1:10 and a taper angle of ∼2° on a Corning SGW3 glass substrate. [ABSTRACT FROM AUTHOR]

True random number generator (TRNG) is a critical component in hardware security that is increasingly important in the era of mobile computing and internet of things. A memristor is a promising candidate to construct a TRNG due to its intrinsic variability of switching behavior and high-density integration. Here, we present a 1T1R oscillation structure with a TiN/NbOx/Pt memristor for constructing a rate-adjustable TRNG. The randomness of the oscillation comes from the stochastic switch latency of the memristor. The experimental results demonstrate that the oscillation rate can be modulated by changing the gate bias of the transistor in series. Furthermore, a TRNG circuit with adjustable rates is constructed based on the 1T1R oscillation, and the simulated results revealed that the proposed TRNG output can pass eight NIST tests, indicating the true randomness of the circuit. Finally, the randomness in the memristor, the adjustable rate of the 1T1R oscillation, and the NIST test result of RNG are proved in turn. These results demonstrate the feasibility of the circuit, which can minimize the power consumption by adjusting the random number generation rate to correct the frequency drift caused by extrinsic factors, such as environment temperature and humidity. [ABSTRACT FROM AUTHOR]