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

共 4468 条记录，以下是81-90 订阅

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Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Autonomous System

Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Auton...

引用

Electrical Automation and Artificial Intelligence (ICEAAI), International conference on

作者： Ariano Da Conceicao Lianfang Tian Qiliang Du Ling Yuan School of Automation Science and Engineering South China University of Technology Guangzhou China Research Institute of Modern Industrial Innovation South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education Guangzhou China Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot (2024KSYS004) Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Indoor mobile robots require reliable solutions for mapping, localization, and navigation tasks. This paper presents a mobile robot system that implements the ability to realize mapping using visual SLAM with ORB-SLAM3 and LiDAR-based mapping with Gmapping for indoor perception and navigation through a modular ROS architecture. The framework also implements autonomous navigation and an autonomous Rapidly-exploring Random Trees (RRT) exploration module with adaptive reset mechanisms. The system operates on a ROS-based platform equipped with an Intel RealSense D435i RGB-D camera and LDS-02 LiDAR scanner. Experimental validation in simulated indoor environments demonstrates the system's capabilities and the presented architecture achieves computational efficiency through selective utilization of mapping modules while maintaining mapping accuracy through complementary sensor modalities.

关键词： visualization Simultaneous localization and mapping Accuracy Laser radar Navigation Service robots Heuristic algorithms Computer architecture Mobile robots Autonomous robots

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Early Detection of Road Traffic Injuries with IoT sensor Networks and K-Nearest Neighbors Algorithm

Early Detection of Road Traffic Injuries with IoT Sensor Net...

引用

2025 International conference on Visual Analytics and data visualization, ICVADV 2025

作者： Jayanthi, L.N. Verma, S. Kishore Usha, R. Sekar, G. Lakshmi, V.T. Mohankumar, N. Saveetha University Department of Commerce Tamil Nadu Chennai India Department of Computer Science and Engineering Telangana Hyderabad India S. A. Engineering College Department of Mathematics Tamil Nadu Chennai India Vsb College of Engineering Technical Campus Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Department of Mathematics Tamil Nadu Salem India Nagpur Campus Pune India

ISBN: (纸本)9798331521394

Injuries sustained in road accidents are a major worldwide public health concern, highlighting the need for effective early detection systems. By combining the K-Nearest Neighbors (KNN) algorithm with Internet of Things (IoT) sensor networks, this research provides a novel approach to the early identification of road traffic injuries. Critical characteristics like vehicle speed, acceleration, and closeness may be continually monitored in real-time by installing IoT sensors in vehicles and road infrastructure. The data is further processed using the KNN algorithm, which may detect trends that might point to the incidence of traffic injuries. This preventative method allows for prompt action, which may lessen the impact of injuries and save lives. One of the many benefits of the proposed system is its quick detection and reaction time, which helps improve road safety and public health. Results from experimental tests show that the suggested method may reliably identify and forecast injuries sustained in traffic accidents. The significance of using IoT technology and machine learning algorithms for preventing and mitigating injuries in transportation systems is highlighted in this study, which shows the evolution of road safety early warning systems. © 2025 IEEE.

关键词： k-nearest neighbors

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A Comprehensive Review on LiDAR Based 3D Deep Learning Object Detection Algorithms

A Comprehensive Review on LiDAR Based 3D Deep Learning Objec...

引用

International conference on Computer, Electrical & Communication Engineering (ICCECE)

作者： Pooja Goswami Ram Vaishnav Tanishq Anand Prateek Dayal Central research Laboratory Bharat Electronics Limited Ghaziabad INDIA

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Recent advancements in autonomous vehicles and robotics have underscored the importance of robust 3D object detection(3DOD) systems. Among various sensor modalities, 3D LiDAR stands out for its ability to provide dense and accurate point cloud data(cloud of points signifying the reflected rays from the surface of objects present in the environment of the sensor), crucial for detecting and localizing objects in 3D space. This paper surveys the state-of-the-art (SOTA) deep learning models specifically tailored for 3D object detection for 3D LiDAR data. We classify these models based on their foundational architectural principles and methodologies, including voxel-based, point-based, and range-image-based *** Key datasets, evaluation metrics and considerations for real-time application are discussed, highlighting advancements and ongoing challenges in the field. Finally,this review seeks to guide future research towards enhancing the accuracy, speed and robustness of 3D object detection systems utilizing 3D LiDAR technology.

关键词： Deep learning Three-dimensional displays Laser radar Accuracy Reviews Computational modeling Object detection Robot sensing systems data models Real-time systems

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Greenhouse Environment Monitoring Using Raspberry Pi

Greenhouse Environment Monitoring Using Raspberry Pi

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Visual Analytics and data visualization (ICVADV), International conference on

作者： Ritam Biswas Aman Pandey Suganiya Murugan SRM Institute of Science And Technology Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

With the increasing emphasis on sustainable agriculture, there is a need to stress on technological advancement which can optimize the growth of plants under controlled conditions. We are proposing here an automated greenhouse monitoring system having a low cost which makes sure that parameters like temperature, humidity, soil moisture and the light intensity are constantly supervised by the Automatic Control. The specific system described in this paper includes several sensors and is a completely automated, cost-effective Raspberry Pi Microcontroller. Use the Internet (IoT) of things to provide remote monitoring and management so that users can access live data so that they can adjust anywhere. All agricultural processes are dealt with responsible management due to automation so there is a practical move towards targeted assistance. The greenhouse design which was drawn looking at the working life was aimed at suiting different climatic conditions and sustaining the various culture cycles. This is meant to encourage sustainable farming by providing a cheap and simple solution that improves greenhouse management. The system achieves high accuracy in monitoring key parameters, with temperature and humidity measurements accurate up to 98%, while keeping constant real-time visuals on the parameters. This stable environment positively impacts plant productivity while promoting responsible resource conservation. The results demonstrate the practical potential of IoT in agriculture, highlighting its application in enhancing productivity and sustainability.

关键词： Temperature sensors Temperature measurement Productivity Green products Soil moisture sensor systems Real-time systems sensors Monitoring Farming

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Charging Station Locator and Battery Monitoring System for Electric Vehicle

Charging Station Locator and Battery Monitoring System for E...

引用

Advances in Renewable Energy & Electric Vehicles (AREEV), International conference on

作者： Kandasamy V Ramprabu J Bagawathy V Manjula R Gowtham Sharma SKS KUMARAGURU COLLEGE OF TECHNOLOGY SARAVANAMPATTI COIMBATORE

ISBN: (数字)9798331529574

ISBN: (纸本)9798331529581

In order to reduce range anxiety in electric vehicles (EVs), this research proposes an integrated system for real-time battery monitoring and charging station identification. The system uses Coulomb counting to determine the battery's state of charge (SoC) with great accuracy and GPS technology to track the location of the vehicle. The NodeMCU, also called the ESP8266, a Wi-Fi-enabled microcontroller, analyses voltage and current sensor data to provide ongoing battery health monitoring. Drivers can find nearby charging stations and plan their journeys more efficiently according to a simple user interface that combines charging station data with MATLAB-based visualisations. This method ensures dependability and user ease by fusing cloud-based analytics with hardware efficiency. The system promotes broad acceptance of electric mobility by filling in gaps in the current EV infrastructure and improving the overall EV driving experience.

关键词： Cloud computing data visualization Voltage Charging stations Real-time systems Batteries State of charge MATLAB Monitoring Global Positioning System

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Machine Learning-Driven Hybrid Adaptive Beamforming for Efficient Interference Mitigation in Dense Wireless sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

引用

2025 International conference on Visual Analytics and data visualization, ICVADV 2025

作者： Loganathan, P. Priyeshkumar, A.T. Kodeeswari, K. Reena, K Jose Poonguzhali, I. Balaji, M. Department of Electronics and Communication Engineering Namakkal India Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India Department of Electronics and Communication Engineering Erode India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India Vivekananda College of Technology for Women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798331521394

The Adaptive Beamforming Algorithm (ABA) represents a promising approach to suppress interference in dense wireless sensor networks (WSNs), enhancing signal fidelity and network throughput. In dense WSNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array Signal Processing (AASP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (SINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant SINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WSNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(SINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency. © 2025 IEEE.

关键词： Beamforming

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Novel Multi-Scan LiDAR System for Enhanced Shadow Removal

Novel Multi-Scan LiDAR System for Enhanced Shadow Removal

引用

IEEE International conference on Consumer Electronics (ICCE)

作者： Jung-Min Kim Dae Yu Kim Dept. of Electrical and Computer Engineering Inha university Incheon Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In autonomous driving, sensor data is critical for ensuring safety in dynamic road environments. The conventional LiDAR systems face challenges when obstacles create shadow zones leading to incomplete mapping and potential detection failures. This paper presents a novel LiDAR system employing beam-splitting technology to minimize shadow regions in 3D mapping. Through multi-angle scanning, the proposed system effectively eliminates shadow regions, ensuring continuous detection in complex driving environments. Prototype testing demonstrates a reduction in shadow-induced detection errors achieving measurement accuracy within 3%. The research highlights the viability of this technology for safer autonomous driving systems.

关键词： Laser radar Accuracy Three-dimensional displays Pedestrians Roads Measurement uncertainty Prototypes Safety Autonomous vehicles Faces

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Improving Mall Traffic Flow with IoT and Reinforcement Learning Techniques in Footfall Analytics

Improving Mall Traffic Flow with IoT and Reinforcement Learn...

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Visual Analytics and data visualization (ICVADV), International conference on

作者： S. Muthumarilakshmi A. Sairam N. Mohankumar D. Hemalatha S. Velmurugan T. R. GaneshBabu Department of CSE Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Artificial Intelligence & Data Science Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering T.J.S. Engineering College Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

An innovative method for improving mall traffic flow utilizing IoT devices and reinforcement learning (RL) methods in footfall analytics is presented. Inefficiencies, congestion, and less-than-ideal consumer experiences are common outcomes of the conventional wisdom about managing mall foot traffic. Our proposed system uses Internet of Things (IoT) sensors to track shoppers' flow and density in real time. This data is then input into an RL model to optimize and predict traffic flow patterns. Aiming to enhance the shopping experience and distribute foot traffic more equitably, the system automatically adjusts parameters, including digital signage, escalator functioning, and store promotions. Simulation and pilot deployment results show substantial improvement in lowering congestion and boosting mall visitors' time spent there. By integrating IoT with powerful machine learning (ML) algorithms, this integrated technique demonstrates how smarter and more responsive retail environments may be created. IoT and RL improves mall traffic flow by analyzing footfall trends, optimizing routing, mitigating congestion, and enhancing customer experience.

关键词： Technological innovation Smart cities Visual analytics Scalability Reinforcement learning Routing Real-time systems sensor systems Internet of Things Intelligent sensors

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Detecting the Unseen: Exploiting radar-Sonar sensor Fusion for Visual Detection of Low-Profile Naval Drones 16th

Detecting the Unseen: Exploiting Radar-Sonar Sensor Fusion ...

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16th International conference on ICT Innovations, ICT Innovations 2024

作者： Longo, Giacomo Cantelli-Forti, Alessandro Russo, Enrico Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Genoa Italy Parma Italy

ISBN: (纸本)9783031861611

Recent advancements in naval technology have introduced agile and stealthy anti-ship drones. Characterized by rapid movements and compact above-water hulls, these drones often exhibit small radar cross sections (RCS), making them difficult to detect using traditional methods. To counter this emerging challenge, this research explores the integration of radar and sonar sensors, harnessing their complementary strengths to counter such threats via enhanced visualization capabilities. We illustrate how combining measurements from both radars and sonars, despite their different operational ranges and angular coverages, can significantly increase the surveillance effectiveness beyond their individual contributions. The proposed approach is expected to enhance the accuracy and reliability of visual detection systems, enabling better detection and tracking capabilities for these elusive naval drones. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drones radar detection sensor fusion Sonar

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Advancing NICU Monitoring with IoT and Real-Time AI Analytics

Advancing NICU Monitoring with IoT and Real-Time AI Analytic...

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Visual Analytics and data visualization (ICVADV), International conference on

作者： N. Vini Antony Grace S Balakrishna Reddy D Sahaya Lenin N. Mohankumar L.M. Merlin Livingston M. Rajmohan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering (Data Science) CVR College of Engineering Hyderabad Telangana India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering DMI College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In neonatal intensive care units (NICUs), the prompt and accurate observation of sick and premature infants is of the most critical significance. Using fuzzy logic and the Internet of Things (IoT), this research demonstrates an innovative method for NICU monitoring. Our system constantly records data on critical factors, including temperature, oxygen saturation, and heart rate, via an IoT sensor network. To facilitate proactive management of newborn health, this data is analysed using Artificial Intelligence (AI) algorithms based on fuzzy logic. The results of this processing include predictive analytics and real-time insights. To provide more adaptable and precise decision-making assistance, fuzzy logic is used to deal with physiological data's inherent uncertainties and unpredictability. The proposed approach aims to have a more responsive NICU staff, better patient outcomes, and fewer critical events. This integrated strategy is useful for improving NICU operations and providing the best care for premature newborns via clinical trials and simulations.

关键词： Fuzzy logic Pediatrics Uncertainty Visual analytics Decision making Real-time systems Internet of Things Artificial intelligence Predictive analytics Monitoring

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