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IoT Based Control Networks and Intelligent systems (ICICNIS), International conference on

作者： Balamanikandan A Siddavatam Lakshmi Neeraja Thathireddy Venkata Kishore Udatha Deepika Settipalli Prathyusha Venkatachalam K Electronics and communication engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Electronics and communication engineering Audisankara College of Engineering & Technology Gudur India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research presents a real-time vital sign monitoring system utilizing IoT and wearable technology aiming to provide continuous health monitoring. The system is developed using an ESP32 microcontroller unit (MCU) integrated with various biomedical and environmental sensors including an ECG sensor, temperature sensor, humidity sensor, gyroscope sensor, SPO2 sensor, and heartbeat sensor. A 16x2 LCD provides local feedback, while a buzzer alerts users to abnormal conditions. To facilitate remote monitoring, NODEMCU (ESP8266) serves as a communication gateway, connecting the system to the IoT platform UBIDOTS. Through UBIDOTS, the system transmits vital data to cloud storage in real-time, enabling healthcare professionals and caretakers to monitor patients remotely. This solution is designed to support the early detection of critical health issues, improve patient outcomes, and enhance accessibility to health monitoring through scalable IoT solutions. Experimental results indicate that the proposed system effectively monitors vital signs, sending alerts and updating data in real-time demonstrating its potential in wearable health monitoring applications.

关键词： Temperature sensors Temperature measurement Microcontrollers Medical services sensor systems Real-time systems sensors Biosensors Biomedical monitoring Monitoring

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Human Motion Tracking: A Comparative Analysis of Recent Advancements

Human Motion Tracking: A Comparative Analysis of Recent Adva...

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International conference on advanced Robotics and Intelligent systems (ARIS)

作者： Yan-Ru Lee Jin-Siang Shaw Fu-Shin Lee Department of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan Department of Mechanical Engineering National Taipei University of Technology Taipei Taiwan Department of Smart Living Huafan University New Taipei City Taiwan

ISBN: (数字)9798350362572

ISBN: (纸本)9798350362589

This paper explores the advancements in human motion-tracking technology within the past three years, analyzing research that presents novel methods for capturing and analyzing human movement. We examine the methodologies employed by each approach, including sensor-based systems, radar technology, Wi-Fi signal utilization, lightweight networks for real-time action recognition, and a system for joint camera localization and human motion tracking. This paper compares each surveyed research's advantages, disadvantages, and limitations, highlighting their practical implications and potential applications. Finally, this review discusses the future directions and challenges within human motion tracking.

关键词： Simultaneous localization and mapping Tracking Signal processing algorithms Radar Radar tracking Cameras Real-time systems

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advanced Modeling of Sodium-Ion Batteries for Electric Vehicles: Enabling Wireless Communication and Intelligent Transportation systems

Advanced Modeling of Sodium-Ion Batteries for Electric Vehic...

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Wireless Africa conference (WAC)

作者： Khanyisile Masemola Cuthbert Nyamupangedengu David G. Dorrell School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Department of Mechanical & Material Engineering University of Turku Turku Finland

ISBN: (数字)9798331517588

ISBN: (纸本)9798331517595

Sodium-ion batteries (SIBs) have emerged as a viable substitute for lithium-ion batteries (LIBs) in electric vehicles (EVs) due to the growing need for sustainable energy solutions. A comprehensive modeling framework for SIBs designed especially for electric vehicle applications is presented in this paper, with a focus on how these batteries integrate with wireless communication and intelligent transportation systems (ITS). The thermal and electrochemical models of SIBs, their possible use in vehicle-to-everything (V2X) systems, and the wireless state-of-charge (SoC) monitoring via real-time communication protocols are investigated. Simulation results demonstrated the efficiency of SIBs in vehicular applications, their compatibility with wireless data transfer protocols, and their contribution to energy optimization in ITS. The paper concludes with insights into the future of SIBs in EVs and wireless transportation ecosystems.

关键词： Wireless sensor networks Protocols Simulation Transportation Real-time systems Batteries Dedicated short range communication Vehicle-to-everything Optimization Testing

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A Novel Design of Thin Flexible Force Myography sensor Using Weaved Optical Fiber: A Proof-of-Concept Study

A Novel Design of Thin Flexible Force Myography Sensor Using...

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International conference on Ubiquitous Robots (UR)

作者： Chongyoung Chung Heeju Mun S. Farokh Atashzar Ki-Uk Kyung Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Mechanical and Aerospace Engineering New York University (NYU) New York NY USA Department of Electrical and Computer Engineering Mechanical and Aerospace Engineering and Biomedical Engineering New York University (NYU) New York NY USA NYU WIRELESS Center and NYU CUSP Brooklyn NY USA Mechanical Engineering Department Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Electrical and Computer Engineering New York University (NYU) New York NY USA

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

Motion recognition and tracking is one of the crucial features for intuitive and direct human-robot interaction (HRI), prompting researchers to explore various sensor types. In this paper, we proposed a sleeve-type force myography (FMG) system using new type of thin and flexible FMG sensor utilizing weaved optical fiber. This study introduces a novel design for a thin and flexible FMG sensor using weaved plastic optical fibers for motion recognition and tracking. The proposed sensor demonstrates a compact form factor (15 mm width, 25 mm height, and 2 mm thickness) with high flexibility, making it suitable for embedding in clothing without causing discomfort. Evaluations confirm its high sensitivity, wide force sensing range (>10 N). Accuracy of the estimating force using the proposed sensor was approximately 99.17% or higher and the response time of 85 ms ensures its effectiveness in real-time applications, emphasizing its potential for applications like prosthetics and virtual reality (VR) interactions. To conduct the proof of concept for the FMG sensor, elbow flexion angle estimation was performed focusing solely on the bicep muscle, and high-precision flexion angle tracking was achieved with 94.27% of correlation coefficient. Overall, the proposed FMG sensor presents a promising solution for intuitive and accurate motion recognition in various HRI applications.

关键词： Accuracy Sensitivity Tracking Force Human-robot interaction Virtual reality Robot sensing systems

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Fiber Bragg Grating Array-based Belts for Respiratory Activity Monitoring: Respiratory Signal Analysis

Fiber Bragg Grating Array-based Belts for Respiratory Activi...

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International conference on Electromagnetics in advanced applications (ICEAA)

作者： Manish Mishra Prasant Kumar Sahu School of Electrical Sciences Indian Institute of Technology Bhubaneswar Odisha India

ISBN: (数字)9798350360974

ISBN: (纸本)9798350360981

This study focuses on the application of Fiber Bragg Grating Arrays (FBGA) for monitoring respiratory activity. Respiratory belts were placed on the chest, and upper and lower abdomen, and data was acquired in standing, sitting, and supine position for varied breathing patterns i.e. Normal, Accelerated, and Apnea. In the study, wavelets were utilized as compared to conventional filter-based techniques and Daubechies has been observed to be more reliable as a minimal amount of RMSE and PRD were reported. Coherence index values above threshold values ($\gt 0.5$) were reported majorly for all postures indicating the similarity of frequency components contained within array data. Limitations of conventional techniques for monitoring respiratory activity and variations observed in breathing patterns are also discussed in the conducted study.

关键词： Location awareness Coherence Belts Fiber gratings sensor systems and applications Arrays Reliability Signal analysis Monitoring Signal to noise ratio

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Comparative Analysis of Power Consumption in Electric and Autonomous Vehicles

Comparative Analysis of Power Consumption in Electric and Au...

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Mechanical Engineering and Robotics Research (ICMERR), International conference on

作者： Fay Alzahrani Dhay Alzahrani Omar D. Mohammed Jamal F. Nayfeh Mechanical Engineering Department Prince Mohammad bin Fahd University PMU Khobar Saudi Arabia

ISBN: (数字)9798331509262

ISBN: (纸本)9798331509279

Electric vehicles (EVs) and autonomous vehicles (AVs) are subject to increasing demands for sustainable transportation. In the current paper, a model design is developed that can fit both EV and AV applications. A comparative analysis is studied for power consumption between EVs and AVs, focusing on how the integration of autonomous systems influences energy efficiency. While both EVs and AVs contribute to sustainable transportation, the power demands of AVs are heightened due to the additional computational and sensor systems required for real-time decision-making and navigation. The study evaluates power consumption across several factors, including propulsion, sensor and onboard computation, and HVAC systems, under similar driving conditions. Results indicate that even when Avs and EVs have the same weight, AVs consume more power due to their advanced sensor suites and computational systems, leading to noticeable differences in power consumption across both urban and highway driving scenarios. The findings highlight the impact of automation on vehicle efficiency and provide insights into future design considerations for energy-efficient autonomous transportation. This paper aims to bridge existing gaps in the literature by offering a direct comparison of power usage between EVs and AVs, ultimately contributing to the development of more sustainable vehicle technologies.

关键词： Power demand HVAC Navigation Transportation Focusing Propulsion Robot sensing systems Electric vehicles Energy efficiency Real-time systems

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Stone Soup open source framework for tracking and state estimation: enhancements and applications 31

Stone Soup open source framework for tracking and state esti...

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conference on Signal Processing, sensor/Information Fusion, and Target Recognition XXXI

作者： Barr, Jordi Harrald, Oliver Hiscocks, Steven Perree, Nicola Pritchett, Henry Vidal, Sebastien Wright, James Carniglia, Peter Hunter, Emily Kirkland, David Raval, Divy Zheng, Siyuan Young, Anne Balaji, Bhashyam Maskell, Simon Hernandez, Marcel Vladimirov, Lyudmil Def Sci & Technol Lab London England Def Res & Dev Canada Toronto ON Canada Univ Liverpool Liverpool Merseyside England

ISBN: (数字)9781510651210

ISBN: (纸本)9781510651210;9781510651203

The tracking and state estimation community is broad, with diverse interests. These range from algorithmic research and development, applications to solve specific problems, to systems integration. Yet until recently, in contrast to similar communities, few tools for common development and testing were widespread. This was the motivation for the development of Stone Soup - the open source tracking and state estimation framework. The goal of Stone Soup is to conceive the solution of any tracking problem as a machine. This machine is built from components of varying degrees of sophistication for a particular purpose. The encapsulated nature and modularity of these components allow efficiency and reuse. Metrics give confidence in evaluation. The open nature of the code promotes collaboration. In April 2019, the Stone Soup initial beta version (v0.1b) was released, and though development continues apace, the framework is stable, versioned and subject to review. In this paper, we summarise the key features of and enhancements to Stone Soup - much advanced since the original beta release - and highlight several uses to which Stone Soup has been applied. These include a drone data fusion challenge, sensor management, target classification, and multi-object tracking in video using TensorFlow object detection. We also detail introductory and tutorial information of interest to a new user.

关键词： open source tracking state estimation drone tracking data fusion sensor management classification video tracking

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Enhancing Security Features in Wireless sensor Networks using AI based Deep Learning Methods

Enhancing Security Features in Wireless Sensor Networks usin...

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Distributed systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Muhammad Puzhakkalaveettil M. Praveena Department of Computer Science Dr.S.N.S Rajalakshmi College of Arts and Science Coimbatore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Wireless sensor networks (WSNs) are crucial for a wide range of applications. However, their open architecture and limited resources make them vulnerable to intrusion. Improve the security of WSNs by incorporating advanced deep learning methods, including RNNs, Autoencoders, and Convolutional Neural Networks (CNNs). When it comes to identifying intrusions, CNNs need to analyze spatial patterns in data, while RNNs are responsible for managing temporal relationships in sequential data. Autoencoders are commonly used in anomaly detection to identify typical data patterns. By training on preprocessed sensor data, these deep learning models ensure network availability and data integrity. They enable the immediate detection and handling of potential dangers. Evaluation metrics denote that in simulated assault scenarios, CNNs, RNNs, and autoencoders have achieved impressive accuracy rates of 95.2%, 97.8%, and 97.5%, respectively which showcases their effectiveness when compared to previous techniques. The proposed design emphasizes adaptability and resilience, offering WSNs robust protection against emerging cyber threats.

关键词： Deep learning Wireless sensor networks Adaptation models Accuracy Autoencoders Real-time systems Convolutional neural networks Protection Anomaly detection Resilience

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A High Range-to-Resolution Multiaxis μForce and Torque Sensing Platform

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2021年第4期26卷 1837-1845页

作者： Tiwari, Bhawnath Blot, Melissa Laurent, Guillaume J. Agnus, Joel Sandoz, Patrick Lutz, Philippe Clevy, Cedric Univ Bourgogne Franche Comte CNRS FEMTO ST Inst F-25000 Besancon France

This article presents a novel multiaxis mu Force sensor with a range-to-resolution ratio of 55 000, which makes the sensing device highly useful for wide range of applications. The presented device relies on a sensing strategy where a 2-D encoded micropattern is encrypted to the mobile part of the compliant platform. This encoded pattern allows multiaxis visual sensing of displacements over a long range, whereas Fourier computations ensure subpixel interpolation leading to the high resolution. The device is fabricated on a silicon wafer by means of clean room technology to comply with both precision encoding requirements and reliable mechanical behavior. This article presents the design, modeling, and fabrication as well as the experimental validation of the multiaxis mu Force-Torque sensor. A modeling strategy is proposed for an effective estimation of force/torque with its capability to adapt nonlinear stiffness evolution during the external loading. Experimentation demonstrates a sensing resolution of 2 mu N over a validated range of 110 mN. The proposed sensor measures the force along the two planar axes, and also the torque along the axis orthogonal to the plane, thus, validating its axial decoupling capabilities.

关键词： Force measurement force sensors instrumentation and measurement materials testing microsensors nanopositioning optoelectronic and photonic sensors periodic structures sensor systems and applications

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Neural Network Technology of Mine Recognition Based on Data From Magnetic Field sensors

Neural Network Technology of Mine Recognition Based on Data ...

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IEEE International Workshop on Intelligent Data Acquisition and advanced Computing systems: Technology and applications

作者： Roman Peleshchak Vasyl Lytvyn Ivan Peleshchak Serhii Voloshyn Department of Information Systems and Networks Lviv Polytechnic National University Lviv Ukraine

Mine detection is an ongoing and growing problem that affects many people around the world due to the enormous danger that mines pose to humans. Mine detection occurs using various methods that use active sensors, including ultra-wideband (UWB) radar. Although active landmine sensors are often used for performance reasons, they can cause unintended landmine detonations because they operate on transmitted and reflected electromagnetic signals. Another way to detect mines without detonating mechanisms is the use of passive detectors. In this article, an optimal neural network structure with Adam's optimization algorithm is developed for effective mine classification based on magnetic field sensor data. This approach made it possible to increase the accuracy of mine detection to 99.23 percent.

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