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Edge Intelligence Enabled Secured Digital Transformation in Internet of Autonomous Vehicles

Edge Intelligence Enabled Secured Digital Transformation in ...

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Annual Information Technology, Electromechanical Engineering and Microelectronics conference (IEMECON)

作者： Shagun Singh Dasawat Sachin Sharma Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Graphic Era Hill University Dehradun India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

As the Internet of Things (IoT) scene advances, autonomous vehicles (AVs) are rising as a significant application space with transformative potential. In any case, the integration of AVs into existing advanced biological systems postures special challenges, especially with respect to information security, security, and real-time decision-making. This paper presents a comprehensive system for leveraging edge insights to address these challenges and encourage the secure digital transformation of AV networks. Our system consolidates progressed edge computing capabilities to empower real-time handling and examination of sensor information created by AVs, minimizing idleness and improving decision-making productivity. Moreover, we propose novel security instruments custom fitted to the particular prerequisites of AV environments, counting confirmation, encryption, and irregularity location conventions. Through an arrangement of recreations and tests, we illustrate the adequacy of our approach in defending AV systems against different cyber threats whereas optimizing execution and asset utilization. Moreover, we talk about potential applications and suggestions of our system in progressing the broader field of independent frameworks and IoT advances.

关键词： Productivity Instruments Digital transformation Decision making Transportation Real-time systems Security Internet of Things Autonomous vehicles Edge computing

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Enhanced Smart-Shoe System for Gait Analysis with Real-Time Monitoring and Optimized Sampling

Enhanced Smart-Shoe System for Gait Analysis with Real-Time ...

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IEEE India conference (INDICON)

作者： Madhavan Bharanidivya Samiappan Dhanalakshmi Department of Electronics and Communication Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Gait analysis plays a vital role in medical diagnostics, with applications spanning treatment, rehabilitation, therapy, and physical training. However, conventional gait analysis typically requires a specialized laboratory setting, limiting accessibility and failing to capture natural gait patterns. In this study, we introduce an innovative approach to enhance the accuracy and efficiency of gait monitoring systems, particularly for knee osteoarthritis (KOA) detection. Our innovation, the Smart Insole, is a wearable device designed to overcome the limitations of traditional inertial measurement unit (IMU) methods. It offers superior feature extraction, real-time visualization, and advanced age-group classification. The device, which resembles an insole, is engineered to monitor pressure and inertial data from both feet, providing a discreet, lightweight, and user-friendly option for continuous gait analysis in everyday environments. we have developed a smartphone-based graphical user interface that displays real-time sensor data via a Wi-Fi module. The device's performance has been rigorously validated through a series of experiments and user trials, where it was benchmarked against traditional gait analysis techniques. Our evaluation also focused on the Smart Insole's comfort and usability for regular wear, particularly for KOA detection. Using sensor data and a deep learning algorithm, our method achieved an accuracy of 94.74% in disease prediction.

关键词： Knee Accuracy Inertial sensors Data visualization Real-time systems Osteoarthritis Biomedical monitoring Gait recognition Monitoring Diseases

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23.1 A 7.9fJ/Conversion-Step and 37.12aFrms Pipelined-SAR Capacitance-to-Digital Converter with kT/C Noise Cancellation and Incomplete-Settling-Based Correlated Level Shifting

23.1 A 7.9fJ/Conversion-Step and 37.12aFrms Pipelined-SAR Ca...

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IEEE International conference on Solid-State Circuits (ISSCC)

作者： Jihang Gao Linxiao Shen Heyi Li Siyuan Ye Jie Li Xinhang Xu Jiajia Cui Yunhung Gao Ru Huang Le Ye Peking University Beijing China Advanced Institute of Information Technology of Peking University Hangzhou China

In IoT sensor applications, capacitive sensors are widely used to convert various capacitances into digital signals, and the demand for power-efficient high-resolution capacitance-to-digital converters (CDCs) is on the rise. In a high-resolution CDC, kT/C noise, due to the sampling process, becomes dominant in discrete-time systems [1–3], especially in the sub-fF sensor fields. $\Delta \Sigma$ architectures averaging kT/C noise with a high oversampling rate require extended conversion time (~ms) and degraded power efficiency (~pJ/***). The CT $\Delta\Sigma$ CDC implemented in [4] eliminates the sampling-induced kT/C noise, but it loses frequency scalability and relies heavily on static high-gain analog components.

关键词： Discrete-time systems Scalability Capacitance Noise cancellation Frequency conversion Capacitive sensors

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UKF-Based Multi-sensor Data Processing Algorithm for SLAM

UKF-Based Multi-sensor Data Processing Algorithm for SLAM

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IEEE International conference on Control Science and systems Engineering (CCSSE)

作者： Kun Xu Boheng Zhang Jiangang Li Guizhong Wang Mingjian Sun School of Information Science and Engineering Harbin Institute of Technology Weihai Weihai China Department of Control Science and Engineering Harbin Institute of Technology Harbin China Woan Technology (Shenzhen) Co. Ltd. Shenzhen China Hebei Key Laboratory of Grid Advanced Measurement and Monitor Jinyanhagong Industrial Technology Research Institute Zhangjiakou China School of Information Science and Engineering Harbin Institute of Technology Harbin China

Simultaneous localization and mapping (SLAM) algorithm is widely used in unmanned and robotic applications. SLAM usually uses sensor data, which are from the LiDAR, the inertial measurement unit (IMU) and the wheeled odometry, to build maps of the surrounding environment. However, the motion distortion of LiDAR data and the error accumulation of wheeled odometry can affect the accuracy of the front-end odometry of the SLAM algorithm, which can lead to serious errors in the maps created. Therefore, in order to solve the problem of poor map construction results of SLAM algorithm due to various types of sensor errors. we propose the traceless Kalman filtering based (UKF-based) multi-sensor data processing algorithm. Firstly, the proposed algorithm uses the UKF to fuse IMU and wheeled odometry data to reduce the cumulative error of wheeled odometry. And then the algorithm uses IMU and corrected wheeled odometry data and combines with the motion states of the robot to solve the motion distortion problem of LiDAR data. Finally, we verified the effectiveness of the proposed algorithm for wheeled odometry data correction in Gazebo simulation environment and real environment experiments, and compared the mapping effectiveness of SLAM by using the original data and the data processed by our algorithm. The experimental results show that the map construction effect of SLAM algorithm is significantly improved when using the sensor data processed by the algorithm. Experimental validation on the robot shows that the proposed algorithm has universal applicability and good compatibility.

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Automated Control System Strategies to Ensure Safety of PEM Fuel Cells Using Kalman Filters 7th

Automated Control System Strategies to Ensure Safety of PEM ...

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7th International conference and Exhibition on Sustainable Energy and advanced Material, ICE-SEAM 2021

作者： Veettil, Yadu Krishna Morassery Rakshit, Shantam Schopen, Oliver Kemper, Hans Esch, Thomas Shabani, Bahman FH Aachen University of Applied Sciences Aachen Germany RMIT University Melbourne Australia

ISBN: (纸本)9789811931789

Having well-defined control strategies for fuel cells, that can efficiently detect errors and take corrective action is critically important for safety in all applications, and especially so in aviation. The algorithms not only ensure operator safety by monitoring the fuel cell and connected components, but also contribute to extending the health of the fuel cell, its durability and safe operation over its lifetime. While sensors are used to provide peripheral data surrounding the fuel cell, the internal states of the fuel cell cannot be directly measured. To overcome this restriction, Kalman Filter has been implemented as an internal state observer. Other safety conditions are evaluated using real-time data from every connected sensor and corrective actions automatically take place to ensure safety. The algorithms discussed in this paper have been validated thorough Model-in-the-Loop (MiL) tests as well as practical validation at a dedicated test bench. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Control systems

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Improving Coastal and Port Management in Smart Cities with UAVs and Deep Learning

Improving Coastal and Port Management in Smart Cities with U...

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Mediterranean Smart Cities conference (MSCC)

作者： Murat Bakirci Irem Bayraktar Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin Turkey

ISBN: (数字)9798350374001

ISBN: (纸本)9798350374018

Efficient coast and harbor management is integral to the vitality, sustainability, and resilience of smart cities. With bustling harbors serving as vital hubs of commerce, trade, and tourism, optimizing port operations is paramount for economic growth and prosperity. Smart technologies play a pivotal role in this optimization, leveraging advanced sensor networks, real-time monitoring systems, and predictive analytics to enhance safety, mitigate environmental risks, and improve overall efficiency. Additionally, smart coastal management strategies focus on preserving ecosystems, mitigating climate change impacts, and safeguarding against natural disasters. Aerial imagery, facilitated by Unmanned Aerial Vehicles (UAVs) equipped with high-resolution cameras and sensors, provides comprehensive insights into coastal dynamics, harbor operations, and environmental conditions. These images enable efficient monitoring of coastal areas, ports, and harbors, capturing crucial information for informed decision-making in coastal management and port operations. Object detection, particularly in ship detection, stands as a transformative technology for enhancing coastal and harbor management within smart cities. Leveraging advanced algorithms and high-resolution aerial imagery, ship detection systems offer real-time monitoring crucial for optimizing maritime operations and ensuring port security. Object detection algorithms, particularly Faster R-CNN, have shown promise in accurately detecting ships in aerial imagery, offering valuable insights for harbor planning and infrastructure development. This study focuses on utilizing the Faster R-CNN detection algorithm for ship detection in coastal and harbor environments, highlighting its potential to bolster security applications and contribute to the resilience of smart city infrastructure. Through rigorous evaluation and optimization, this research aims to enhance the effectiveness of ship detection systems in safeguarding coastal and

关键词： Visualization Smart cities Heuristic algorithms Sea measurements Seaports Security Marine vehicles Vehicle dynamics Optimization Resilience

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Touch Panel-Based Industrial Device Management System for Persons with Disabilities

Touch Panel-Based Industrial Device Management System for Pe...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： S. Jaganathan Muralidharan V Harish K Ajay Kumar M Gokul R Department of Electrical and Electronics Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The incorporation of touch screens in the control and monitoring of Industrial systems has substantially improved operational efficiency and user adaptability. This paper describes, developing and implementing an affordable scalable industrial device management system based on the ATmega328P microcontroller and a 7 inch DWIN touchscreen. The system allows real-time interactions with industrial devices such as sensors, actuators, and controllers, enabling powerful real-time monitoring and intuitive control of complicated processes. The ATmega328P is used as the CPU of the system and is the interfacing unit with industrial sensors for temperature, humidity, and pressure data, thereby allowing high precision real time data collection. Monitoring and controlling operations are performed from an advanced graphical user interface designed for the DWIN touch display. The GUI is designed to include customizable widgets such as active buttons, data panels, and gauges, enhancing user interaction. The communication between the microcontroller and the display is realized by robust serial communication and ensures an easy and reliable transfer of control and monitoring information. The system is intended to be modular, scalable and flexible for various industrial applications. By focusing on low-cost system implementation and including user-friendly design elements and real-time access to data, the solution makes a promising impact on the operational inclusiveness of the system particularly for people with physical disabilities. This work emphasizes the game-changing prospects of modern industrial automation and control systems through the integration of simplified hardware and complex software systems.

关键词： Temperature sensors Industries Automation Microcontrollers Assistive technologies Touch sensitive screens Real-time systems sensor systems sensors Graphical user interfaces

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An End-to-End ConvLSTM-based Method for Point Cloud Streaming Compression

An End-to-End ConvLSTM-based Method for Point Cloud Streamin...

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International conference on advanced Robotics and Mechatronics (ICARM)

作者： Junhua Long Mingjian Feng Boyang Li Yehua Ling Chenghao Wu Kai Huang Mingyue Cui School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangxi Transportation Science and Technology Group Co. Ltd Nanning China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

The processing and transmission of point cloud frame sequences is an important part of the applications of 3D LiDAR. However, due to the disorderliness and irregularity of the huge amount of point cloud data collected by 3D LiDAR sensor, finding an effective method to compress the point cloud data to a small volume is an urgent problem. In this paper, we propose spatio-temporal features sensitive neural network SPCCNet with an encoder-decoder structure to compress point cloud streams. To reduce information loss in point cloud preprocessing, we propose a unique convolution method on point sets. The curvature and density information are introduced to SPCCNet to enhance the raw point cloud data. Besides, the designed ConvLSTMIm and a Squeeze-andExcitation (SE) Block are embedded to help SPCCNet learn the effective features of point cloud sequences. Experimental results show that compared with other methods, our SPCCNet can compress point cloud data with a higher compression ratio at an acceptable noise level.

关键词： Point cloud compression Three-dimensional displays Laser radar Mechatronics Convolution Neural networks Logic gates Robot sensing systems Streams Noise level

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Monolithic Fabrication and Surface Metalization of Single-Crystal Suspended Sub-Micron Si Nanowires with 3D MEMS Architectures

Monolithic Fabrication and Surface Metalization of Single-Cr...

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International conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

作者： Basit Ali Mehdi Bostan Shirin Sina Zare Pakzad Umut Kerimzade B. Erdem Alaca Dept. of Mechanical Engineering Koç University Istanbul Türkiye Dept. of Mechanical Engineering Koç University Nanofabrication and Nanocharacterization Center for Scientific and Technological Advanced Research Koç University Istanbul Türkiye Dept. of Mechanical Engineering Koç University Nanofabrication and Nanocharacterization Center for Scientific and Technological Advanced Research Surface Science and Technology Center Koç University Istanbul Türkiye

ISBN: (数字)9798350362107

ISBN: (纸本)9798350362114

Thanks to their superior mechanical, electrical, and optical characteristics, silicon nanowires attract much attention in applications ranging from biochemical sensors and inertial measurement devices to energy storage systems. This study introduces a new method for the scalable production and monolithic integration of sub-micron silicon nanowires with microelectromechanical systems. In-plane (width) and out-of-plane (thickness) critical dimensions of 600 nm and 300 nm, respectively, are demonstrated for sub-micron silicon nanowires with an etch depth of up to 50 μm. Finally, the use of stencil lithography is also demonstrated by selective coating of sub-micron silicon nanowires with a gold layer. Having moderately larger dimensions compared to silicon nanowires, their sub-micron counterparts proved to cut fabrication costs and relieve fabrication-related challenges, as they improve yield and repeatability while they impart added functionality to electromechanical sensors, whose further miniaturization heavily relies on such multiscale processes.

关键词： Micromechanical devices Three-dimensional displays Surface resistance Lithography Monolithic integrated circuits Optical variables measurement sensor phenomena and characterization Silicon Nanowires Surface treatment

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SmartPosture: Real-Time Posture Detection and Feedback System Integrating IoT and Machine Learning

SmartPosture: Real-Time Posture Detection and Feedback Syste...

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AIML-applications for Engineering & Technology (ICAET), International conference on

作者： Milind Rane Shrihari Mohole Yash Pahade Gokul Kurandale Aditya Munjale E&TC VIT Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Traditional posture monitoring methods often rely on physical correction, lacking advanced technologies for real-time detection and feedback, which can result in delayed interventions. This research presents a machine learning framework designed to address this deficiency by giving instantaneous posture detection and feedback. The framework categorizes user posture into “Good” or “Bad” classifications, achieving an accuracy rate of 75.52%, by the use of Convolutional Neural Networks (CNNs) along with TensorFlow. Trained on a 2,000-image Yoga Poses Dataset and fine-tuned over 50 epochs, the model achieved optimal performance. The system integrates IoT and machine learning, providing recommendations for posture -related advice through a medical chatbot powered by techniques of NLP like Langchain and Hugging Face embeddings. Key hardware components include the flexible sensor, LCD display, vibration motor, and ESP8266 Wi-Fi module, which offer real-time alerts and monitoring. The web application, built using Flask and Streamlit, allows users to track posture data and receive corrective prompts. This innovative approach not only enhances posture monitoring but also advances smart healthcare, offering a continuously learning and adaptable solution relevant to real-world scenarios.

关键词： Vibrations Machine learning Medical services Chatbots Motors Real-time systems Liquid crystal displays Convolutional neural networks Monitoring Wireless fidelity

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