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20th IEEE/ASME International conference on Mechatronic and Embedded systems and applications (MESA)

作者： Lowe, Jordon Kuru, Kaya Univ Cent Lancashire Sch Engn & Comp Preston Lancs England

ISBN: (纸本)9798331516246;9798331516239

The automation of vehicles is progressing through several automation levels, with the goal of reaching full autonomy, i.e. level 5, which involves no steering wheel, brakes, pedals, or windshield. This is achieved by the vehicle taking on an increasing number of autonomous decision-making tasks under the guidance of smart control systems that are instilled with Artificial Intelligence (AI) and equipped with advanced sensor and actuator technologies. Major vehicle companies are competing to build the most talented driver, i.e. AI-agent. In this report, how the intelligence of Self-Driving Vehicles (SDVs) is being built by the automotive industry for the efficient deployment of handover wheels is analysed and applications of machine intelligence for SDVs are implemented through video capturing using Deep Learning (DL). The results show that i) the use of DL techniques as well as reinforcement learning (RL) - Deep RL approaches - can contribute to the intelligence of SDVs significantly and ii) SDVs, equipped with advanced mechatronics systems, can be fully autonomous with the level-5 automation as they are trained appropriately with proper datasets.

关键词： Autonomous driving self-driving vehicles Autonomous vehicles driverless vehicles sensor fusion vehicle automation deep learning neural networks

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MoDD : A Model-Driven Framework For Data Collection In Drone-Based systems 50

MoDD : A Model-Driven Framework For Data Collection In Drone...

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50th Euromicro conference on Software Engineering and advanced applications

作者： Habouche, Manele Ait Kerboeuf, Mickael Guillou, Goulven Babau, Jean-Philippe Univ Bretagne Occidentale Brest France

ISBN: (纸本)9798350380279;9798350380262

Nowadays, Cyber-Physical systems (CPS), particularly drones, play a pivotal role in environmental research. Scientists depend on these platforms to monitor various sensor data and ensure comprehensive data archiving. However, despite their advantages, researchers encounter several challenges, including communication limitations and the complexity of setting up systems tailored to their needs. To address these issues, we propose MoDD, a model-driven data collection framework based on a customized publish/subscribe model. MoDD simplifies the development and configuration of data collection systems. It offers scientists a solution that meets their specific needs, allowing them to focus on high-level requirements while the framework manages the underlying complexities. We demonstrate the effectiveness of MoDD through practical evaluations on an actual Unmanned Surface Vehicle. Additionally, results show a 79% reduction in throughput (drone to base station link) compared to existing publish/subscribe systems.

关键词： model-driven engineering drone data communications architecture optimization

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Wireless sensor Nodes for Condition Based Maintenance in Railway systems

Wireless Sensor Nodes for Condition Based Maintenance in Rai...

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2024 IEEE sensors conference

作者： Kersten, Philipp Buzin, Johannes Hirsch, Soeren Brandenburg Univ Appl Sci Dept Engn Brandenburg Germany

Ensuring the reliability, safety, and efficiency of railway systems is increasingly critical in global transportation networks. This paper addresses the necessity for advanced monitoring systems by introducing a multi... 详细信息

ISBN: (纸本)9798350363524;9798350363517

关键词： Wireless sensor node railway monitoring

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How can sensor data accessibility be enhanced in IoT-based health monitoring systems? 19

How can sensor data accessibility be enhanced in IoT-based h...

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IEEE 19th conference on Industrial Electronics and applications (ICIEA)

作者： Wang, Ting Pileno, Adrian Amatriain Monacelli, Eric Univ Gustave Eiffel CNRS ESIEE Paris LIGM Marne La Vallee France Univ Publ Navarra ETSIIIT Navarra Spain Univ Versailles St Quentin En Yvelines LISV Velizy Villacoublay France

ISBN: (纸本)9798350360875;9798350360868

This paper outlines the development of an economical Internet of Things (IoT)-based real-time health monitoring system. The prototype integrates a microcontroller and various sensors to monitor essential health parameters, including temperature, heart rate (HR), electrocardiogram (ECG), blood oxygen saturation (SpO2), and blood pressure (BP). With a focus on enhancing the accessibility of sensor data, diverse access options were implemented using advanced technical methodologies. The collected sensor data was efficiently stored, uploaded to the cloud, and visualized through tools such as HTML, Blynk, PAX-DAQ, and PuTTY, enabling seamless access via mobile devices or laptops. The paper concludes with the presentation of measurement results and an initial analysis of the sensor data, affirming the practicality and effectiveness of the proposed methods.

关键词： IoT-based Health Monitoring microcontroller sensor data accessibility data visualization

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Recycled silicon solar cell-derived nanostructured p-black silicon device for high performance NO₂ gas sensor applications

RSC Applied Interfaces

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RSC Applied Interfaces 2024年第1期2卷 220-229页

作者： Aleem, Mahaboobbatcha Vishnuraj, Ramakrishnan Pullithadathil, Biji Nanosensors & Clean Energy Laboratory Department of Chemistry & Nanoscience and Technology PSG Institute of Advanced Studies Coimbatore 641 India Advanced Self-Powered Systems of Integrated Sensors and Technology Center Department of Electrical and Computer Engineering North Carolina State University Raleigh 27695 NC United States Center of Excellence in Advanced Materials and Green Technologies Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Chemical Engineering and Materials Science Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

Nitrogen dioxide (NO2) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards s...

Nitrogen dioxide (NO₂) is a toxic gas that can cause respiratory problems, and sensing its presence is crucial for environmental monitoring and industrial safety. This investigation presents a novel approach towards sensing NO₂ gas by utilizing partially completed/recycled silicon solar cells employing a metal-assisted etching process to fabricate a high-performance p-black-silicon based sensor. Structural and morphological analyses using X-ray diffraction patterns, Raman spectroscopy and cross sectional FESEM characterization confirm the integrity of the p-B-silicon sensor. By combining recycling techniques with advanced fabrication methods, the resulting sensor exhibits exceptional sensitivity, a low detection limit of 1 ppm, and rapid response times (12-14 s) when exposed to NO₂ gas concentrations ranging from 1 to 5 ppm. The enhanced sensitivity is attributed to the unique nanostructured comb-like morphology of the sensor material, which facilitates fast charge transport mechanisms, and a plausible sensing mechanism has been proposed and explained using a depletion model diagram and energy model diagram. This eco-friendly and cost-effective solution not only addresses electronic waste concerns but also highlights the potential of sustainable practices in scientific research. The findings emphasize on the importance of environmental consciousness and innovation, showcasing a promising future for gas sensing technology. By utilizing recycled materials and advanced fabrication techniques, this study contributes to the development of efficient, eco-friendly sensors for environmental monitoring applications, paving the way for a more sustainable and technologically advanced future in the field of gas sensors. © 2025 RSC.

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Design and Optimization of High-Performance Health Monitoring systems Using advanced sensor Networks 2

Design and Optimization of High-Performance Health Monitorin...

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2nd International conference on advanced Computing and Communication Technologies, ICACCTech 2024

作者： Mehta, Heena Singla, Mukesh Baba Mastnath University Dept. of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798331519056

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution. © 2024 IEEE.

关键词： Electronic health record

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Enhancing Road Safety: Leveraging CNN-LSTM and Bi-LSTM Models for advanced Driver Behavior Detection 2

Enhancing Road Safety: Leveraging CNN-LSTM and Bi-LSTM Model...

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2nd International conference of Intelligent Methods, systems and applications, IMSA 2024

作者： Diaaeldin, Ahmed Zaher, Moamen Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350362633

Recognizing the importance of driver behavior is essential for enhancing road safety and optimizing traffic management systems. This study employs advanced deep learning techniques, specifically CNN-LSTM and Bi-LSTM models, to refine the prediction of driver behaviors using sensor data from the Honda Research Institute Driving Dataset (HDD). Our approach integrates a robust dataset encompassing a broad spectrum of sensor inputs, from vehicle dynamics to driver operational parameters, propelling advancements in driver behavior detection. The methodologies utilized enable the discernment of subtle and complex driving patterns, contributing to the reduction of road safety hazards. Our findings indicate that these models significantly improve the detection of hazardous driving behaviors, surpassing previous state-of-the-art methodologies with notable gains in mean average precision (mAP). These advancements affirm the potential of deep learning technologies in crafting sophisticated predictive safety systems, paving the way for future innovations. © 2024 IEEE.

关键词： advanced traffic management systems

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Leveraging Memory and Attention in a Kinematically Aware Robot: An Ideomotor-Inspired Approach to Implicit Command Understanding from IMU sensor Data

Leveraging Memory and Attention in a Kinematically Aware Rob...

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IEEE/ASME International conference on advanced Intelligent Mechatronics (AIM)

作者： Guinot, Lena Matsumoto, Ryutaro Shota, Takahashi Iwata, Hiroyasu Waseda Univ Grad Sch Creat Sci & Engn Tokyo Japan Waseda Univ Fac Sci & Engn Tokyo Japan

ISBN: (纸本)9798350355376;9798350355369

As human-robot interaction (HRI) advances, the nuanced interpretation of implicit commands embedded in human gestures becomes paramount for fostering seamless collaboration. In this context, we present a novel machine learning algorithm designed to endow robots with the ability to decipher implicit commands from Inertial Measurement Unit (IMU) sensor data worn at specific locations on the human body. Our approach integrates memory and attention mechanisms inspired by ideomotor cues, allowing the robot to comprehend both temporal and spatial relationships within the sensor data. The attention mechanism operates bidirectionally, enhancing the system's awareness of the temporal sequence of human movements and the spatial interdependencies between sensor data across different body locations. This unique spatial attention enables the robot to understand the kinematic chain between joints during human motion, accommodating variations in sensor data arising from factors such as height differences and motion range capacity. Drawing on prior research in attention mechanisms, ideomotor cues, and memory augmentation, our algorithm represents a significant advancement in addressing the challenges of implicit command understanding in HRI. The proposed system's adaptability and nuanced comprehension of human gestures make it well-suited for diverse anatomies and movement patterns. Through comprehensive experiments, we demonstrate the effectiveness of our algorithm, paving the way for more intuitive and adaptable robotic systems in real-world applications.

关键词： Human robot interaction

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Exploring advanced Techniques for Security Testing of sensor Device Chips 2

Exploring Advanced Techniques for Security Testing of Sensor...

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2nd International conference on Inventive Computing and Informatics (ICICI)

作者： Li, Suye Jinan Vocat Coll Jinan 250000 Peoples R China

ISBN: (纸本)9798350373301;9798350373295

This study explores advanced techniques for security testing of sensor device chips, critical components in embedded systems and the Internet of Things (IoT). The study emphasizes comprehensive considerations including power management, communication protocol compatibility, and software/hardware interface stability. A review of existing literature highlights significant contributions to improving chip security, multi-factor authentication, data encryption, and intelligent security mechanisms. The proposed methodology focuses on improving hardware security, implementing multi-factor authentication, data encryption, and integrating intelligent security mechanisms for future sensor device chips. Detailed test procedures for sensor device chips are presented, including light source testing, position sensor chip testing, and monitoring system testing. The results contribute to the ongoing development of secure and efficient sensor device chips that are essential for various IoT applications.

关键词： sensor Device Chip Security Testing Techniques Security Analysis Chip-level Security Vulnerability Identification

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Poster Abstract: Tasking Heterogeneous sensor systems with LLMs 24

Poster Abstract: Tasking Heterogeneous Sensor Systems with L...

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22nd ACM conference on Embedded Networked sensor systems, SenSys 2024

作者： Liu, Kaiwei Yang, Bufang Xu, Lilin Guo, Yunqi Ling, Neiwen Zhao, Zhihe Xing, Guoliang Shuai, Xian Ren, Xiaozhe Jiang, Xin Yan, Zhenyu The Chinese University of Hong Kong Hong Kong Hong Kong Noah's Ark Lab Huawei Technologies Hong Kong Hong Kong

ISBN: (纸本)9798400706974

Despite the extensive use of sensors enabling intelligent applications, the complementary potential of co-existing sensor systems is often not fully utilized, limiting more advanced applications. This paper introduces a novel solution using Large Language Models (LLMs) to coordinate sensor systems for handling complex user queries. It defines a sensor language for sensor systems, including vocabulary set and grammar rules, analogous to natural language components, enabling LLMs to translate user intentions into sensor coordination plans. Preliminary results show that our approach significantly outperforms the existing solution at plan generation, execution and response generation stages. © 2024 Copyright held by the owner/author(s).

关键词： Context sensitive grammars

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