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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21-30 订阅

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On Integrating Robotic data with GIS Tools in a Cloud Environment (Application Paper)

On Integrating Robotic Data with GIS Tools in a Cloud Enviro...

引用

Workshops of the EDBT/ICDT 2025 Joint conference, EDBT/ICDT-WS 2025

作者： Wrembel, Robert Kasprzyk, Jean-Paul Billen, Roland Bimonte, Sandro d'Orazio, Laurent Sacharidis, Dimitris Skrzypczyński, Piotr Université de Liège Liège Belgium INRAE Clermont-Ferrand France Université de Rennes Rennes France Université Libre de Bruxelles Bruxelles Belgium Poznan University of Technology Poznań Poland

Merging robotic technologies, sensor networks, and Geographic Information Systems (GIS) offers significant potential across various domains, including agriculture and urban planning. However, a critical challenge lies in the lack of interoperability between data generated by these technologies and existing GIS tools. The EU-funded GIS4IoRT project addresses this gap by developing a plug-and-play and cloud-based middleware. This middleware facilitates seamless integration and visualization of multi-dimensional and multi-modal data within GIS environments. Key GIS4IoRT components include: a middleware architecture, a scalable cloud-based infrastructure, real-time robot querying capabilities, data quality assurance, spatio-temporal query support within the cloud, integration with GIS tools, and adherence to relevant standards. The middleware supports diverse data types, including LiDaR, imagery, and sensor data. This paper (1) presents an initial data integration architecture specifically designed for the sustainable architecture domain, (2) outlines the challenges encountered in designing such an architecture, and (3) explores novel data processing paradigms enabled by the architecture. © 2025 Copyright © 2025 for this paper by its authors.

关键词： data integration geographical information system images LiDaR robots sensors sustainable agriculture

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Perception System for Autonomous Vehicles: Object Detection, Motion Tracking, and Future Position Prediction

Perception System for Autonomous Vehicles: Object Detection,...

引用

IEEE International conference on Industrial technology (ICIT)

作者： Gustavo Andreas Günther Pedro Henrique Almeida da Cruz Joáo Pedro Barcelos de Assis Carlos N. Silla Marcelo Eduardo Pellenz Marco Antonio Simões Teixeira PPGia-PUCPR Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Paraná Brazil Halmstad University Halmstad Sweden

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents a multi-sensor fusion approach for detecting, clustering, and predicting the motion of dynamic objects by integrating camera and LiDAR data. The framework combines YOLO-based visual segmentation with LiDAR point clouds to enable accurate spatial localization and motion tracking. Ego-motion compensation is achieved using IMU and velocity data, while trajectory prediction is enhanced through centroidbased orientation estimation. Detected objects are segmented into coherent clusters using DBSCAN, facilitating visualization and motion forecasting. The framework was validated on a simulated KITTI dataset, demonstrating its effectiveness in dynamic object detection and trajectory prediction.

关键词： Point cloud compression visualization Laser radar Accuracy Motion segmentation Dynamics Object detection Cameras Trajectory Vehicle dynamics

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SurroundSense: Event-Driven User Personalization with Ambient Context Using Ultra-Wideband radar 22

SurroundSense: Event-Driven User Personalization with Ambien...

引用

22nd IEEE Consumer Communications and Networking conference, CCNC 2025

作者： Ranjan, Prajwal Ppallan, Jamsheed Manja Damam, Yellappa Badiger, Sakshi Madhan Raj, K. Mangla, Raghav Thakur, Rajip Kim, Chiho Samsung R&d Institute India-Bangalore (SRI-B) Connectivity R&d India Samsung Electronics Connectivity R&d Group Suwon South Korea

ISBN: (纸本)9798331508050

In this paper, we propose SurroundSense, a novel event-driven framework that generates contextual information based on the user's surroundings using the Impulse Radio Ultra-Wideband (IR-UWB) radar integrated into smartphones. By leveraging the channel impulse response (CIR) data obtained from the IR-UWB radar, SurroundSense constructs a detailed contextual understanding of the user's environment, allowing for tailored recommendations based on the surrounding conditions. The system processes the CIR data and combines it with acoustic sensing information, specifically noise levels and ambient light information to generate the surrounding information. Further-more, utilizing the IMU sensors enables an expanded field-of-view (FOV), providing a comprehensive 360° environmental awareness. This enhanced surrounding information enhances the accuracy and pertinence of personalized recommendations. Our approach showcases the potential of incorporating IR-UWB technology into personalization algorithms, ultimately improving user experiences and facilitating adaptive, context-aware applications. © 2025 IEEE.

关键词： Channel Impulse Response (CIR) Contextual Information Impulse Radio Ultra-Wideband (IR-UWB) radar Personalized Recommendations sensor Fusion

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19th Global conference on Sustainable Manufacturing, GCSM 2023

19th Global Conference on Sustainable Manufacturing, GCSM 20...

引用

19th Global conference on Sustainable Manufacturing, GCSM 2023

ISBN: (纸本)9783031774287

The proceedings contain 95 papers. The special focus in this conference is on Sustainable Manufacturing. The topics include: Ecosystem Collaboration for Sustainability: Learnings from the Food System;business Model Design in Context of Circular Economy;From Talk to Action: A Diagnosis of Drivers of Sustainable Logistics Practices in LATAM, a Collaborative Study Between Industry and Academia;aligning Strategic Priorities in Manufacturing and Sustainable Operational Practices in Firms: Exploration and Evidence in North-East Colombia;towards Circular Business Models in the Punching Industry: Leveraging Smart sensor technology for Sustainable Manufacturing Processes;challenges of Low-Carbon Transition of Manufacturing Industry in Developing Countries and Research on the Construction of Green Manufacturing System in China;risk Assessment for Small Manufacturing Businesses in Soweto;criteria-Driven Comparison of Hydrogen, Charcoal and Liquefied Petroleum Gas as Cooking Fuels Based on a Systematic Literature Review;bridging the Educational Gap in Circular Design and Engineering—An Educational Concept and Case Study from Austria;concept of an Integrated Information and Education Platform for Sustainable Development;innovating Blended Learning Model for Professional Education in Manufacturing;work Engineering for Sustainability: Required Education in Engineering;developing a Framework for Sustainable Education in a Production Engineering Study Program;Identification of Lightweighting Potentials Towards More Sustainable Products via the Functional Lifecycle Energy Analysis (FLCEA);sustainability data Map: Framework for data-Based Product Carbon Footprinting of Technical Products;Life Cycle Assessment (LCA) of a Circular Saw Blade for Wood Processing;a Systems-Based Framework for Product Circularity Assessment;technology radar for a Sustainable Biological Transformation in the Manufacturing Industry.

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Real-Time 3D LiDAR Point Cloud data Acquisition & Generation on Jetson

Real-Time 3D LiDAR Point Cloud Data Acquisition & Generation...

引用

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

作者： Tanishq Anand Pooja Goswami Shrajan Jain Central Research Laboratory Bharat Electronics Ltd. Ghaziabad Uttar Pradesh India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

This paper presents a novel algorithm for real-time data acquisition, processing, and visualization of 3D point cloud data generated by the VLP-32C LiDAR sensor. The proposed method efficiently captures raw UDP packets transmitted by the LiDAR, decodes the data to generate a comprehensive 360degree point cloud representation, and visualizes it in real-time on NVIDIA Jetson boards. Leveraging the computational capabilities of the Jetson platform, the algorithm ensures low-latency performance, making it suitable for applications in autonomous systems, robotics, and real-time mapping.

关键词： Point cloud compression Laser radar Three-dimensional displays Simultaneous localization and mapping Autonomous systems Scalability data acquisition data visualization Real-time systems Low latency communication

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International conference on Power Engineering and Intelligent Systems, PEIS 2024

International Conference on Power Engineering and Intelligen...

引用

International conference on Power Engineering and Intelligent Systems, PEIS 2024

ISBN: (纸本)9789819767090

The proceedings contain 41 papers. The special focus in this conference is on Power Engineering and Intelligent Systems. The topics include: Smart Seating: The Smart Seating Solution for Crowded Public Transit;comparative Study of Coil Shapes for Electric Vehicle Resonant Wireless Power Transfer System;A Case Study of Solar Photovoltaic and Biomass-Based Hybrid Power System in Educational Institutes in Delhi-NCR;comparative Analysis of Water Bodies Segmentation Techniques;load Frequency Control in Hybrid Integrated Power Systems with Redox Flow Batteries Using Dual-Mode Gain Scheduling Method;enhanced Discrete Cosine Transform Image Compression for Ultra-High-Resolution Imagery;Simulation and Analysis of 6T SRAM Cell in NGSpice: Exploring Performance and Stability;design of Double Precision Floating Point Comparator for Multiple Numbers;secured Regulatory data Compliance for the Pharmaceutical Sector Through Blockchain;Optimizing IoT-Based Quantum Wireless sensor Networks Using NM-TEEN Fusion of Energy Efficiency and Systematic Governance;optimal Sizing and Allocation of Multiple Distributed Generation;high-Gain X-Band Patch Antenna for Spaceborne Synthetic Aperture radar Applications;protection of Series Compensated Networks Using Different Techniques: An Overview;a Robotic Way to Investigate Unapproachable Places and Inaccessible Substances Using e-skin;a Review of Chatbot technology: Its Design and Implementation;exploring the Microcosm: A Comprehensive Survey of Micro sensor Applications Across Multidisciplinary Research;stress Classification Using Machine Learning Techniques: Comparative Study;normalized Mutual Information-Driven Feature Extraction Method for Big data Analytics;modeling of a Novel Correlation-Weighted Elman Neural Network for Building Automation System;Application FOPID for PLL Design in Grid Integration;Impact of Inter-Next Generation Node B (gNB) Distance on End-User Applications in 5G Networks.

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MMA-Net: Multi-Modal Attention Network for 2-D Object Detection in Autonomous Driving

MMA-Net: Multi-Modal Attention Network for 2-D Object Detect...

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abhilash Gaur Shubh Goel Kanishk Goel Seshan Srirangarajan Po-Hsuan Tseng Kai-Ten Feng IIT Delhi-NYCU Taiwan Joint Doctoral Program Bharti School of Telecommunication Technology and Management Indian Institute of Technology Delhi (IIT Delhi) New Delhi India International College of Semiconductor Technology National Yang Ming Chiao Tung University (NYCU) Hsinchu Taiwan Department of Electrical Engineering IIT Delhi New Delhi India Department of Electrical Engineering Bharti School of Telecommunication Technology and Management IIT Delhi New Delhi India Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electronics and Electrical Engineering NYCU Hsinchu Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multimodal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator.

关键词： Laser radar Fuses Multimodal sensors radar detection Object detection Signal processing Cameras Speech processing Autonomous vehicles Meteorology

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Fast Dynamic Calibration of Millimeter-Wave radar External Parameters: Research and Applications

Fast Dynamic Calibration of Millimeter-Wave Radar External P...

引用

sensors and Information technology (ICSI), International conference on

作者： Jie Yang Xiaohui Li Xianjin He Qin Xia Juncheng Huang China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With the rapid development of intelligent connected technologies, the application of millimeter-wave radar in roadside perception systems has become increasingly widespread [1], [2]. Known for its excellent anti-interference capabilities and resistance to adverse weather conditions, millimeter-wave radar has become a key sensor in these systems [3]. However, the accuracy of radar data is still limited by the calibration of the coordinate system, especially in dynamic environments [4]. Traditional calibration methods rely on manual adjustments, which are inefficient [5]. This paper proposes an automated dynamic millimeter-wave radar calibration method based on deep reinforcement learning, combined with multi-sensor data fusion, to address the high-precision calibration issue between the radar coordinate system and the world coordinate system in dynamic environments. Experiments on open roads show that both the new method and the method incorporating deep reinforcement learning (DQN) significantly improve calibration performance. The calibration accuracy increased by 11.11% and 48.15%, respectively, compared to traditional methods, while the calibration speed was also greatly improved. The proposed method effectively reduces human intervention, lowers system deployment costs, and demonstrates strong scalability, making it highly valuable for practical applications in intelligent connected vehicles and roadside perception systems.

关键词： Accuracy Roads Manuals Millimeter wave radar sensor fusion Deep reinforcement learning Calibration Trajectory Vehicle dynamics Optimization

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Design of an Ultrasonic radar Ranging System with Low Power Consumption and High Accuracy

Design of an Ultrasonic Radar Ranging System with Low Power ...

引用

sensors and Information technology (ICSI), International conference on

作者： Zhenghao Huang Yuchen Zhao Xinyu Jia College of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Reversing radar is an electronic device that can detect obstacles behind the vehicle when reversing, and it is highly practical and innovative to study low-cost radar ranging devices in close-range environments. In this paper, an ultrasonic radar ranging system based on Arduino is developed independently. The system utilizes an ultrasonic sensor as its core component, employing ultrasonic transmission and reception to measure the distance between the sensor and an obstruction. To enhance the user experience, the design integrates an OLED screen, sound, and light alarms to achieve real-time visualization and dynamic reminders of the obstacle position. The core hardware module encompasses signal amplification, detector rectification, and sensor components, which collectively enhance the stability and precision of the received signal. The software algorithm is engineered to optimize these functions. The core hardware module includes signal amplification, detector rectification, and sensor components. These components work together to optimize the stability and accuracy of the received signal. The software algorithm enhances the ranging accuracy and environmental adaptability through dynamic correction and environmental temperature monitoring. After experimental verification, the system can stably achieve a range of 5 cm to 155 cm with an error control of ±1 cm. Additionally, the system can dynamically adjust the number of transmitting waves to achieve low power consumption, extend the detection range, and reduce the blind zone. The system has been demonstrated to meet the requirements of accurate ranging in a close range environment. It has been shown to have low power consumption and high accuracy, as well as excellent practicality and innovation. In sum, the system provides a low-cost and high-accuracy system. Furthermore, the system has been demonstrated to meet the requirements of accurate ranging in a close range environment, with low power consumption and high ac

关键词： Accuracy Power demand Heuristic algorithms Software algorithms radar Distance measurement Acoustics Hardware Software Thermal stability

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A Flexible data Set for radar-Based Gesture Recognition

A Flexible Data Set for Radar-Based Gesture Recognition

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German Microwave conference

作者： Theresa Antes Elizabeth Bekker Akanksha Bhutani Thomas Zwick Institute of Radio Frequency Engineering and Electronics Karlsruhe Institute of Technology Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

Gesture recognition is a simple and intuitive way of human-machine interaction that is increasingly used to control devices in a variety of applications. Often, the gestures are captured by a radar sensor due to its flexible operation, robustness to challenging lighting conditions, and high privacy capability. Nonetheless, radar-based data sets to train classification structures are still rare. A broad and flexible data set for radar-based gesture recognition of 7 gestures using a fast chirp frequency-modulated continuous wave (FC-FMCW) radar is presented and made publicly available. A descriptive demonstrator is built up and trained to showcase possible usage.

关键词： Hands Human computer interaction Privacy Lighting Gesture recognition radar Robustness Recording sensors Usability

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