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检索条件"任意字段=Conference on Radar Sensor Technology and Data Visualization"

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UAV-Driven Surveillance: Security Enhancement with ML and Edge Detection Integrating LIDAR sensor for Trespassing and Area Mapping

UAV-Driven Surveillance: Security Enhancement with ML and Ed...

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International conference on Electronics, Materials Engineering and Nano-technology (IEMENTech)

作者： Sumit Saha Sujay Saha Susamoy Aich Nirlov Saha Debjit Roy Pratham Padala Shantanu Ghosh Samit Karmakar Department of Electronics and Communication Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This study investigates the integration of Unmanned Aerial Vehicles (UAVs) in security and surveillance systems to enhance countering trespassing and security measures. The study highlights the use of edge detection techniques, object detection, classification methods, and LIDAR data to improve UAV capabilities in monitoring and securing extensive and intricate areas. UAVs utilize edge detection to accurately identify boundaries, structures, and potential intruders, facilitating the definition of restricted zones and unauthorized activity detection. Furthermore, object detection and classification enable UAVs to distinguish between trespassers, vehicles, and other relevant objects, thereby enhancing decision-making and real-time alert systems. LIDAR data provides precise 3D reconstructions of the targeted region, aiding in both security monitoring and identifying environmental changes indicative of a breach. Machine learning models continuously improve detection accuracy and efficiency, allowing UAVs to adapt to dynamic environments with minimal human intervention. The integration of UAVs with these technologies offers a robust system for autonomous trespasser detection, area mapping, and rapid response to security threats. The findings underscore the scalability and efficiency of UAV-LIDAR integration as a solution for automated spatial analysis and strategic decision-making in remote, dynamic, and high-stakes environments.

关键词： Laser radar Surveillance Image edge detection Decision making Object detection Machine learning Autonomous aerial vehicles Robot sensing systems Security Vehicle dynamics

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An IoT Cloud Based Health Monitoring System for Remote Area Using Node MCU: E Health Checker

An IoT Cloud Based Health Monitoring System for Remote Area ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： B. Venkataramanaiah Sravan Kumar Chittimalla V Raveendra Reddy A. Vani Malokhat Saidmuratova Surayyo Khasanova P Vinayagam Department of ECE Vel Tech Rangarajan Dr.sagunthala R&D Institute of science and technology Chennai India Enliven Technologies Inc Texas United States Department of EEE Ramireddy Subbaramireddy Engineering College SPSR Nellore India Department of ECE Chaitanya Bharathi Institute of Technology Hyderabad India Department of Pedagogy and Psychology Urgench State University Urgench Uzbekistan Department of Psychology and Medicine Mamun University Khiva Uzbekistan Department of ECE Saveetha Engineering College Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research work introduces a real-time health monitoring system using IOT sensors and cloud integration for human health monitoring. The system leverages the Node MCU micro controller for its Wi-Fi capabilities., interfacing with sensors like DHT11 (for temperature and humidity)., HBR (for heart rate)., and SpO2 (for oxygen saturation). These sensors continuously gather health-related data., which is transmitted to the Thing Speak cloud platform for storage and analysis. The hardware setup involves connecting the Node MCU to the sensors for accurate data collection., while the firmware is programmed to handle real-time data acquisition and cloud communication. ThingSpeak is configured to receive and store the data, organizing it into custom channels for further analysis. Basic data processing algorithms are applied to filter out noise and detect trends in the collected health metrics. The system enables real-time visualization of health data and employs machine learning models to offer personalized healthcare insights. This framework facilitates proactive healthcare interventions., enhancing patient care and supporting chronic condition management. With continuous monitoring and cloud-based analysis, it provides a foundation for personalized treatment plans, improving the overall healthcare experience and people lives in rural areas will benefit.

关键词： Temperature sensors Cloud computing Machine learning algorithms Clouds Medical services Market research Real-time systems sensor systems sensors Monitoring

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Hi-ALPS - An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Driving

Hi-ALPS - An Experimental Robustness Quantification of Six L...

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Secure and Trustworthy Machine Learning (SaTML), IEEE conference on

作者： Alexandra Arzberger Ramin Tavakoli Kolagari Faculty of Computer Science Nuremberg Institute of Technology Nuremberg Bavaria Germany

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

Light Detection and Ranging (LiDAR) is an essential sensor technology for autonomous driving as it can capture high-resolution 3D data. As 3D object detection systems (OD) can interpret such point cloud data, they play a key role in the driving decisions of autonomous vehicles. Consequently, such 3D OD must be robust against all types of perturbations and must there-fore be extensively tested. One approach is the use of adversarial examples, which are small, sometimes sophisticated perturbations in the input data that change, i.e., falsify, the prediction of the OD. These perturbations are carefully designed based on the weaknesses of the OD. The robustness of the OD cannot be quantified with adversarial examples in general, because if the OD is vulnerable to a given attack, it is unclear whether this is due to the robustness of the OD or whether the attack algorithm produces particularly strong adversarial examples. The contribution of this work is Hi-ALPS-Hierarchical Adversarial-example-based LiDAR Perturbation Level System, where higher robustness of the OD is required to withstand the perturbations as the perturbation levels increase. In doing so, the Hi-ALPS levels successively implement a heuristic followed by established adversarial example approaches. In a series of comprehensive experiments using Hi-ALPS, we quantify the robustness of six state-of-the-art 3D OD under different types of perturbations. The results of the experiments show that none of the OD is robust against all Hi-ALPS levels; an important factor for the ranking is that human observers can still correctly recognize the perturbed objects, as the respective perturbations are small. To increase the robustness of the OD, we discuss the applicability of state-of-the-art countermeasures. In addition, we derive further suggestions for countermeasures based on our experimental results.

关键词： Training Point cloud compression Three-dimensional displays Laser radar Perturbation methods Object detection Machine learning Observers Robustness Autonomous vehicles

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Intelligent Eye Strain Relief Glasses using IoT and Naive Bayes Solutions for Digital Wellness

Intelligent Eye Strain Relief Glasses using IoT and Naive Ba...

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Emerging Systems and Intelligent Computing (ESIC), International conference on

作者： Thanalakshmi S G Geethamahalakshmi H. Azath Prabu K T Kumanan C. Srinivasan Department of Management Studies Gojan School of Business and Technology Redhills Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gojan School of Business and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Dr MGR Educational and Research Institute Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Eye strain and its effects on general health have become more pressing issues due to the pervasive nature of digital devices in modern life. This work offers a new approach by combining the Internet of Things (IoT) with Naive Bayes classification for smart eyewear that reduces eye strain. The proposed system aims to reduce digital eye strain by adapting the lens's characteristics in real-time to user behavior and ambient factors. Using the IoT architecture, data like screen time, distance from the device, and ambient lighting conditions may be captured in real-time when users interact with digital displays. Using Naive Bayes classification, this data is processed to identify probable causes of eye strain and suggest suitable eyewear changes to reduce pain. The proposed system provides a preventative method of fostering digital well-being using software and hardware components, such as sensors, microcontrollers, and machine learning algorithms. These smart glasses provide hope for a future free of the negative impacts of digital device use on eye health by making personal recommendations based on each user's unique requirements.

关键词： visualization Personal digital devices Real-time systems Software sensor systems Naive Bayes methods Internet of Things Strain Smart glasses Lenses

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Solid-State vs. Mechanical LiDAR for Land Vehicle Navigation: A Comparative Study Using LiDAR Odometry

Solid-State vs. Mechanical LiDAR for Land Vehicle Navigation...

引用

Annual IEEE Systems conference

作者： Eslam Mounier Mohamed Elsayed Michael Korenberg Aboelmagd Noureldin Queen's University Kingston ON Canada Ain Shams University Cairo Egypt Royal Military College Queen's University Kingston ON Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Reliable and accurate positioning is a fundamental requirement for ensuring the safe and efficient operation of autonomous vehicles (AVs), especially in dynamic and complex environments. With its active sensing capability, light detection and ranging (LiDAR) technology offers high accuracy, long-range coverage, and reliable 3D representation of the surrounding environment. These attributes have established LiDAR as an essential technology for improving the overall robustness of autonomous driving and facilitating significant advancements in navigation state estimation research. The growing popularity of LiDAR technology has driven continuous innovations aimed at enhancing quality and reducing costs. This progress has led to the emergence of new types, such as solid-state LiDARs (SSLs), which are gaining widespread adoption due to their reliability and cost-effectiveness. In this paper, we present a comprehensive analysis of the key differences between conventional mechanical LiDAR and SSL technologies. Furthermore, we assess their respective performance using various state-of-the-art LiDAR odometry (LO) algorithms. Our comparative analysis is supported by road test experiments conducted using a multi-sensor platform equipped with both SSL and mechanical LiDAR, which simultaneously collect point cloud data during typical land vehicle urban driving dynamics. Our findings offer valuable insights into the merits and limitations of these technologies within the context of resilient positioning and navigation in complex urban environments.

关键词： Point cloud compression Laser radar Accuracy Navigation Heuristic algorithms Urban areas Land vehicles Odometry Vehicle dynamics Autonomous vehicles

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A Hybrid Deep Learning Model for Optimizing Electric Vehicle Battery and Navigation Systems

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

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Machine Learning and Autonomous Systems (ICMLAS), International conference on

作者： R. Arul Jose P. Ebby Darney R. Santhana Krishnan J. Relin Francis Raj D. Jansi Rani P. Sundaravadivel Department of Electrical and Electronics Engineering Rajarajeswari College of Engineering Bengaluru India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency.

关键词： Deep learning Training Computational modeling Battery management systems Real-time systems data models Batteries State of charge Optimization Long short term memory

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Compact Feature Representation in Bird View for V2X Communication-Efficient Collaborative Analysis

Compact Feature Representation in Bird View for V2X Communic...

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data Compression conference (DCC)

作者： Linfeng Zheng Peilin Chen Shiqi Wang Dapeng Wu City University of Hong Kong

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

sensor data analysis is a crucial task for environmental cognition in smart traffic systems. Recently, vehicle-to-everything (V2X) collaborative analysis has leveraged intermediate feature communication between vehicles and infrastructure to achieve superior analysis performance compared to single-vehicle approaches. However, due to the limited bandwidth of V2X communication links, directly transmitting features can be inefficient, resulting in significant delays that are unacceptable for real-time decision-making. To address this challenge, we propose a compact feature representation method in the bird's eye view (BEV) space for communication-efficient collaborative analysis. As shown in Fig. 1, the proposed method can be viewed as a task-aware distributed coding approach with decoder side information. First, the ego vehicle and the networked infrastructure convert raw LiDAR data into BEV features using a shared PointPillars feature extractor. The infrastructure then applies the proposed BEV codec to transform these BEV features into a compact representation, encoding them into a binary bitstream through entropy coding based on the estimated distribution. The received features are subsequently warped and fused with the ego vehicle's features using a bidirectional attention fusion module, and processed by a single-shot detector to perform 3D object detection. Experimental results on the DAIR-V2X-C dataset demonstrate that the proposed framework achieves more than 1000 times compression compared to directly transmitting floating-point features, while maintaining high analysis performance in real-world V2X scenarios.

关键词： Three-dimensional displays Laser radar Collaboration Transforms Object detection Detectors Feature extraction Real-time systems Entropy coding Vehicle-to-everything

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Using Monotonic Neural Networks for Accurate and Efficient Passive Localization Performance Modeling

Using Monotonic Neural Networks for Accurate and Efficient P...

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IEEE conference on Wireless Communications and Networking

作者： Hanyue Guo Rui Zhou Wenqiang Put Ming-Yi You JunKun Yan Hangzhou Institute of Technology Xidian University Hangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Integrated Sensing and Communication (ISAC) systems are at the forefront of next-generation wireless technologies, enhancing high-precision target localization. Accurate prediction of localization performance is crucial for the design and optimization of these systems. Traditionally, the Cramer- Rao Lower Bound (CRLB) has been used as a theoretical benchmark for estimating localization errors, but it often does not reflect actual errors encountered in practice. The Monte Carlo simulation method, while accurate, is computationally intensive and less adaptable to varying parameters. To bridge this gap, we introduce LocNet-Mono, a novel approach based on monotonic neural networks, designed specifically for predicting localization errors. This approach maintains a consistent, monotonic relationship between input and output features, addressing the shortcomings of traditional methods. Our numerical experiments validate the high accuracy and efficiency of LocNet-Mono, confirming its potential as a superior tool for performance prediction in ISAC systems.

关键词： Location awareness Wireless communication Wireless sensor networks Monte Carlo methods Accuracy Neural networks Predictive models Integrated sensing and communication Numerical models Optimization

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Advanced Adaptive Traffic Management System for Urban Environments

Advanced Adaptive Traffic Management System for Urban Enviro...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： P. Sai Srujan Reddy B.U Naveen Raj Nikhil Tom Jose Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Viswa Vidhyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Urban traffic congestion is still a major problem that causes delays, collisions, and wasteful use of resources. Conventional fixed-schedule systems, which frequently exacerbate problems at junctions, U-turns, and pedestrian crossings, are insufficient for dynamic traffic situations. The Smart Dynamic Traffic Management System described in this work makes use of servo motors, proximity sensors and ultrasonic sensors, Arduino microcontrollers, and AI-powered cameras. To improve safety and streamline traffic flow, the system prioritizes emergency vehicles, controls U-turns, dynamically modifies traffic signals based on real-time data, and monitors road conditions. Scalability and adaptability for a range of urban scenarios are ensured by the integration of IoT technology and intelligent intercommunication between crossings. A visualization tool based on Pygame was used to simulate and assess the suggested solution. The findings showed that traffic flow had significantly improved, with an average increase in the exit-to-entry ratio of 30.71% for the three roadways. More specifically, traffic on Road A decreased by 15.28%, on Road B decreased by 31.25% and on Road C decreased by 45.61%. The outcomes confirm just how well the system works for reducing traffic and giving priorities to critical traffic situations, such as emergency response and turning. This system uniquely integrates real-time adaptive traffic control algorithms with IoT and machine learning to dynamically prioritize traffic flow and emergency vehicles, overcoming the limitations of existing static systems

关键词： Roads Scalability Machine learning Aerodynamics Real-time systems sensor systems Acoustics Internet of Things Vehicle dynamics Intelligent sensors

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IrriGaia: IoT-Integrated Robotic System for Comparative Analysis of Mist and Drip Irrigation

IrriGaia: IoT-Integrated Robotic System for Comparative Anal...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International conference on

作者： Stephanie Rose P. Miranda Angelo Regis M. Celestino Fritz Symon D. Blancaflor Glenda S. Guanzon Jopearlson Icy Ituriaga Adha Theza Naputol Computer Engineering Department College of Technology University of San Agustin Iloilo City Philippines Computer Engineering Department College of Technology Iloilo City Philippines

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

This study evaluates the performance of IoT-based mist and drip irrigation systems for Thai basil (Ocimum basilicum var. thyrsiflora) cultivation in a controlled indoor environment. We developed a prototype system that integrates ESP32 microcontrollers, capacitive soil moisture sensors, water flow sensors, and energy meters to automate irrigation scheduling and data collection. IoT integration facilitated real-time monitoring, automated control, optimized the irrigation schedules and resource management and enabled the remote visualization in the cloud dashboard. We found that the mist irrigation system demonstrated 33% less water consumption than the drip irrigation system but consumed 55% more energy due to continuous operation. Conversely, the drip irrigation system exhibited superior energy efficiency, making it more suitable for large-scale operations. These findings highlight the potential of IoT-enabled robotic irrigation systems to enhance sustainability and resource efficiency in agriculture. Researchers' future works will explore scalability to diverse crops and environments, as well as the integration of renewable energy sources.

关键词： Irrigation Schedules Renewable energy sources Service robots Soil moisture Robot sensing systems sensor systems Real-time systems Sustainable development Water resources

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