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15th German Microwave Conference (GeMiC)

作者： Iwamatsu, Shuya Headland, Daniel Mohammad, Israa Fujita, Masayuki Stoehr, Andreas Univ Duisburg Essen Dept Optoelect Duisburg Germany Univ Carlos III Madrid Optoelect & Laser Technol Grp Madrid Spain Osaka Univ Grad Sch Engn Sci Toyonaka Osaka Japan

ISBN: (纸本)9783982039732

Terahertz (THz) time- domain spectroscopy (TDS) technology has greatly advanced various THz applications ranging from fundamental science to industrial non-destructive imaging. However, traditional THz-TDS systems rely on complex setups with femtosecond pulse lasers and bulky free-space optical components, hindering their miniaturization. A more practical approach introduces THz frequency domain imaging using semiconductor laser diodes, which replace femtosecond laser and facilitate integration into photonic integrated circuit (PIC) chips. Despite its promise, addressing real-world imaging applications remains challenging due to the need for free-space optics, which also negatively impact resolution due to the diffraction limit. To overcome this limitation, this study explores the use of silicon (Si) microphotonics, focusing on an integrated Y-junction that operates as an interferometer. The Y-junction essentially replaces all free-space components, paving the way towards compact, monolithically integrated all-silicon structures that span just similar to 1 cm. We demonstrate near-field imaging at 325 GHz employing the Y-junction. The experiments achieve a 500-mu m spatial resolution and a 100-mu m depth resolution, showcasing potential for handheld THz near-field imaging sensors.

关键词： chip-scale terahertz imaging integrated photonics interferometer silicon Y-junction

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WEARABLE sensor-BASED FATIGUE CLASSIFICATION UNDER DIVERSE THERMAL CONDITIONS

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JOURNAL OF INFORMATION TECHNOLOGY IN CONSTRUCTION 2025年 30卷 875-902页

作者： Khan, Muhammad Anjum, Sharjeel Ibrahim, Abdullahi Nnaji, Chukwuma Aryal, Ashrant Koh, Amanda S. Texas A&M Univ Dept Construct Sci Safety Innovat Integrat Res SIIR Lab 574 Ross St College Stn TX 77840 USA Texas A&M Univ Dept Construct Sci Human Ctr Intelligent Built Environm HIBE Lab 574 Ross St College Stn TX 77840 USA Univ Alabama Dept Chem & Biol Engn 7th Ave Tuscaloosa AL 35487 USA

Fatigue induced by physical exertion and environmental stress remains a critical safety concern in construction and other physically demanding industries. This paper investigates whether integrating wearable sensor data (EMG, HR, HRV) and thermal conditions (hot, room, cold) can improve real-time fatigue prediction. Physiological signals were collected using wearable sensors, processed through noise filtering and feature extraction, and classified using Random Forest Classifier and Extreme Gradient Boosting algorithms. The models demonstrated high predictive accuracy, achieving 80% for continuous fatigue levels and over 90% for categorical fatigue classes. These findings are particularly valuable for construction safety managers, occupational health researchers, and technology developers seeking proactive fatigue management solutions. Future research should focus on field validation of wearable systems and integration with site management platforms such as BIM for broader industry adoption.

关键词： Health and Safety Physical exertion Fatigue monitoring Wearable sensor Machine learning

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integration of Biomechanical sensor in Spinal Brace for Posture Detection and Correction

Integration of Biomechanical Sensor in Spinal Brace for Post...

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Artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： C. Sivamani Santhosh M Vasanth M Arulmozhi K Abinaya V Dept of Biomedical Engineering KIT-Kalagnarkarunanidhi Institute Of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Over the past decade, 60% of people have been affected by spinal curvature at some point in their lives. It is a disease of the spine bones such as kyphosis, lordosis, and scoliosis that affects humans and occurs most commonly in adults, although the cause of most childhood cases is unknown. Everyone has some degree of curvature, but when it exceeds about certain degrees, it causes turbulence and difficulty breathing. It is diagnosed by methods such as X-rays and other measurements. It is treated using a variety of techniques, such as Back support requires less adjustment and is more comfortable. This may cause back pain, injury, and ventilation insufficiency for the person wearing the brace. The main goal of our research is to develop a spinal brace with circuits to detect the biomechanical properties of the spine in order to develop innovative and improved functions. The proposed system used for detection spinal curvature angle using biomechanical sensors like IMU and flex sensors. The sensor data is processed by NodeMCU so that the data can be compared with real-time data. When a difference is detected, a signal is passed to a vibrator and it begin alert. Furthermore, the system enhances the automatic correction with pressure cuff mechanism. The developed system overcome the disadvantages of traditional construction methods and proposes a model with high flexibility, strength, durability, and breathability. The system is resulting in less pain, better ventilation, and increased comfort and adjustability. The conclusion marks that the system has better efficiency and effectiveness in usage, which made bridge between gap of clinical and remote assessment

关键词： Biomechanics Pain Scoliosis X-rays Back Ventilation sensor systems Real-time systems Biosensors Intelligent sensors

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Polymer-encapsulated flexible strain sensors to monitor scapular movement: a pilot study

Polymer-encapsulated flexible strain sensors to monitor scap...

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IEEE International Workshop on Metrology for Industry 4.0 & IoT (IEEE MetroInd4.0 and IoT)

作者： Carnevale, Arianna Di Tocco, Joshua Massaroni, Carlo Longo, Umile Giuseppe Schena, Emiliano Denaro, Vincenzo Univ Campus Biomed Roma Unit Measurements & Biomed Instrumentat Dept Orthopaed & Trauma Surg Rome Italy Univ Campus Biomed Roma Unit Measurements & Biomed Instrumentat Rome Italy Univ Campus Biomed Roma Dept Orthopaed & Trauma Surg Rome Italy

ISBN: (纸本)9781665419802

Strain sensors based on conductive and flexible textiles are gaining great relevance in research applications, rehabilitation, and sports medicine for monitoring human joints movements, thanks to their flexibility, negligible weight, and easy integration into substrates and wearable systems. The purpose of this work was twofold: i) to carry out the static characterization and to analyze the hysteresis of two textile-based strain sensors integrated into a polymeric matrix, and ii) to evaluate the feasibility of these sensors in monitoring scapular movements during bilateral arms abduction-adduction and flexion-extension. Results showed a comparable range of resistance variation at 0-20% strain during static characterization, with a mean absolute gauge factor of 2.65 (sensor A) and 2.12 (sensor B). The maximum hysteresis error was always lower than 31% for sensor A and lower than 27% for sensor B. Both sensors showed promising results in monitoring scapular motion during the two selected single-plane upper limb movements.

关键词： conductive textile strain sensor wearables joint monitoring shoulder joint scapula

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Molybdenum Disulfide Membrane-based Ultrasensitive Temperature sensor 20

Molybdenum Disulfide Membrane-based Ultrasensitive Temperatu...

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20th IEEE sensors Conference

作者： Arora, Nishta Naik, Akshay Indian Inst Sci Ctr Nano Sci & Engn Bengaluru India

ISBN: (纸本)9781728195018

We report an ultrathin molybdenum disulfide (MoS2) membrane-based temperature sensor with sensitivity in the sub-Kelvin range. The resonant frequency of ultrathin MoS2 resonators is extremely sensitive to the intrinsic strain of the membrane. We utilize the resonant frequency shift caused due to change in strain to demonstrate the potential of these devices for ultrasensitive temperature sensing. The temperature coefficient of frequency (TCf) of the MoS2 drum resonator is estimated to be -283 ppm/K. We also report the first study on the frequency stability of MoS2 resonators at room temperature. The Allan deviation of these resonators is similar to 5 x 10(-5) at an integration time of 1 second. This study estimates the ultimate temperature detection limit of MoS2 membrane-based device.

关键词： two-dimensional materials MoS2 intrinsic strain frequency stability Allan deviation

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Augmented Reality-assisted Battery-less Microwave-based sensors for Smart Health Monitoring of Coatings

Augmented Reality-assisted Battery-less Microwave-based Sens...

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IEEE/MTT-S International Microwave Symposium (IMS)

作者： Balasubramanian, Vishal Zarifi, Mohammad H. Univ British Columbia OMEGA Lab Vancouver BC Canada

ISBN: (纸本)9798350375053;9798350375046

Rapid infrastructure expansion and growing emphasis on smart sustainable cities, demand the need for reliable, real-time coating health monitoring (CHM) techniques to ensure mechanical stability and prevent safety hazards. This work presents a microwave-based CHM system consisting of a passive array of split ring resonator (SRR) sensor integrated with augmented reality, for real-time non-destructive coating damage inspection and enhanced visualization capabilities. The developed system operates by monitoring the variations in the resonant response of the SRR array, caused by the loss in coating thickness due to operational and environmental wear. The SRR array is layered with 0.3 mm thick film-like polyethylene-based coating and gradually eroded. The system demonstrates a resonant frequency increase of similar to 331 MHz upon the erosive wear of the coating. The integration of augmented reality with the developed system provides a real-time and intuitive visualization of the system response and its corresponding damage assessment, providing a significant low-cost solution in areas that are traditionally challenging to monitor. The developed system promises the potential of microwave-based sensing integrated with augmented reality-based visualization, in out-of-sight monitoring for applications including pipelines, aircraft, bridges, and naval infrastructure.

关键词： Split Ring Resonators Microwave NDT Structural Health Monitoring Corrosion Detection Augmented Reality Remote Sensing Frequency Selective Surfaces

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Enhanced Real-Time Analysis and Anomaly Detection in Smart Health Monitoring Systems through integration of Deep Learning Algorithm with IoT-Edge Computing

Enhanced Real-Time Analysis and Anomaly Detection in Smart H...

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Computing and Data Science (ICCDS), International Conference on

作者： Antony Pradeesh D N P Subiramaniyam Department of Electronics and Communication Systems Nehru Arts and Science College Coimbatore Tamilnadu India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

In this research, the proposed system HealthEdge, a new IoT-based intelligent health monitoring framework based on edge computing and a diverse sensor array that includes breathing frequency (RR), pulse rate (PR), oxygen saturation level in blood (SpO2), galvanic skin response (GSR), body temperature and hemoencephalography as vital signs sensors, as well as a formaldehyde sensor to gauge environmental influence on health. HealthEdge utilizes a powerful Deep Convolutional Neural Network (DCNN) to perform and analyze the data in real-time at the edge, ensuring ultra-low latency and amplified privacy. The model is capable of identifying health anomalies with a high accuracy of 97% which is demonstrating its potential in real-time health observation and anomaly detection. The potential role of environmental quality measurements in particular formaldehyde levels, in imposing on health conditions, thereby spreading the purview of the monitoring system beyond the traditional set of parameters have been explored. The integration of edge computing not only addresses the challenges that conventional systems face in maintaining a balance between privacy concerns and processing speed, it is also a state-of-the-art technology which enables HealthEdge to become the new norm for complete and real-time health monitoring solutions.

关键词： Deep learning Temperature sensors Data privacy Accuracy Smart healthcare Real-time systems Monitoring

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A Novel IoT System for Diesel Level Monitoring

A Novel IoT System for Diesel Level Monitoring

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Shilpa Sondkar Paras Bhosale Omkar Patil Poonam Nyahalde Department of Instrumentation Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

There is a need to monitor the fuel level in diesel generator systems to ensure an uninterrupted power supply. Recent techniques such as ultrasonic sensors and float-based systems have faced challenges including high costs, mechanical wear, and limited accuracy in varying environmental conditions. Additionally, integrating these systems with real-time communication networks and mobile interfaces presents significant difficulties. This research study proposes a novel IoT system to monitor diesel levels in a diesel generator tank. The proposed work focuses on developing a wireless diesel tank monitoring system utilizing the WebSocket protocol for real-time communication between fuel sensors and a mobile app in remote locations. The mobile app, accessible via Wi-Fi networks, offers a simple interface for users to log in, view current diesel levels of different tanks, and access consumption statistics. Diesel levels are presented as a percentage, converted from voltage measurements. The system sends notifications if levels fall below predetermined thresholds. One challenge faced in this system is the need to cut the length of the sensor to fit the tank, which affects the sensor's accuracy. Despite this, the integration of the capacitive fuel level sensor, ESP8266 micro controller, and WebSocket communication enables a seamless and robust solution for diesel level monitoring. A potential divider circuit was used for mapping the sensor output to the on-chip ADC of the ESP8266 micro controller. This efficient and user-friendly solution aims to optimize diesel usage and facilitate timely refills in remote areas. This paper contributes to the field of remote monitoring systems by offering a practical and scalable solution for diesel level monitoring in diverse applications.

关键词： Industries Accuracy Power supplies sensor systems Real-time systems Generators sensors

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Implementation of Cross Layer Design with Localization Techniques in Wireless sensor Networks Using Deep Learning

Implementation of Cross Layer Design with Localization Techn...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： S. Sreethar Regulavalasa Satya S Srinivas C. Chanakyan S. Prasath G. Senthilkumar Natrayan L School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering(AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai Tamil Nadu India Department of Mechanical Engineering MVJ College of Engineering Bengaluru Karnataka India Department of Mechanical Engineering Mohamed Sathak A J college of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

This study addresses the integration of wireless sensor networks, machine learning models, and go-layer architecture to embellish industrial processes in small-scale manufacturing. A strong monitoring infrastructure is produced by the incorporation of various sensors, proximity, temperature, pressure, vibration, UWB, and RFID. Cross-layer arrangement, along with localization methodologies, gives spatial context to sensor information, enhancing safety, predictive maintenance, and asset monitoring. K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), Artificial Neural Networks (ANN), and Decision Trees (DT) act as predictive models, exhibiting outstanding overall performance across accuracy, precision, keep in mind, and F1 Score criteria. Practical deployment in a small-scale firm, displaying CNC machines and forging stations, illustrates the machine's versatility. Results suggest the models' success in forecasting replies relying completely on dynamic sensor data, allowing actual-time selection-making. The system's usefulness supports enhanced safety measures, simpler maintenance processes, and optimal asset management. In addition to offering practical insights to organisations navigating the challenges of Industry 4.0, this research adds to the theoretical framework of move-layer layout and machine learning integration. The suggested system emerges as a complete solution for intelligent operational improvement and performance in modern day industrial settings.

关键词： Temperature sensors Location awareness Wireless sensor networks Recurrent neural networks Layout Artificial neural networks Nearest neighbor methods

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Intelligent Path Planning and Machine Learning integration for Autonomous Firefighting Robots: A Novel Approach for Efficient Fire Suppression

Intelligent Path Planning and Machine Learning Integration f...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Sankaranarayanan, Prashanth Suresh, Karthik Sarvesh Kanna, A. Vishnu, A. Karthikeyan, A. School of Electrical Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325188

The fire-fighting robot is specifically designed to operate in high-risk environments, such as factories or chemical plants, where fires can occur. Its primary goal is to detect and extinguish fires effectively while minimizing the risk to human lives. Equipped with advanced technology, the robot features three flame sensors and a controller for detecting fires in its immediate surroundings. With a robust chassis housing four motors and wheels, it can maneuver through tight spaces with ease. The robot's arsenal includes a servo motor, L293D motor driver module, mini DC submersible pump, and a small breadboard for precise control. To suppress the fire, it carries a container for water or other extinguishing agents. Additionally, it is integrated with an HP W-200 web cam module, enabling remote monitoring of the fire's intensity and the presence of humans in the vicinity. The robot effectively clears a path by extinguishing the fire ahead and displays a path, making it safe for fire-fighters to reach and rescue any potential victims. © 2023 IEEE.

关键词： chassis controller motor safety sensor victim

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