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

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Six-Port Based Multitone and Low-Power radar System for Waveguide Measurements in Smart Factories

Six-Port Based Multitone and Low-Power Radar System for Wave...

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European Microwave conference

作者： Benedict Scheiner Fabian Lurz Fabian Michler Sarah Linz Robert Weigel Alexander Koelpin Institute for Electronics Engineering Friedrich-Alexander University of Erlangen-Nuremberg Erlangen Germany Chair for Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany

Smart Factories require new sensor systems to collect important data and to link different parts of a factory together in order to control it automatically. In this paper a low-power and low-cost radar system with a power consumption of 1.5 mW at a sampling rate of 10 measurements per second is presented. By using a multitone approach, the unambiguous range can be enhanced significantly and the system is made more robust against influences from harsh industrial environments. It can be used for applications as tank level measurements or piston monitoring in waveguides and is designed for the 24 GHz Industrial, Scientific and Medical (ISM) band.

关键词： Baseband Phase locked loops radar measurements Power demand Frequency measurement Microcontrollers

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Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing

Parallel Neural Computing for Scene Understanding from LiDAR...

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Intelligent Systems and Embedded Design (ISED), International conference on

作者： Suwesh Prasad Sah Suchismita Chinara Dept. Computer Science and Engineering National Institute of Technology Rourkela India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Autonomous driving in high-speed racing, as opposed to urban environments, presents significant challenges in scene understanding due to rapid changes in the track environment. Traditional sequential network approaches may struggle to meet the real-time knowledge and decision-making demands of an autonomous agent covering large displacements in a short time. This paper proposes a novel baseline architecture for developing sophisticated models capable of true hardware-enabled parallelism, achieving neural processing speeds that mirror the agent's high velocity. The proposed model (Parallel Perception Network (PPN)) consists of two independent neural networks, segmentation and reconstruction networks, running parallelly on separate accelerated hardware. The model takes raw 3D point cloud data from the LiDAR sensor as input and converts it into a 2D Bird's Eye View Map on both devices. Each network independently extracts its input features along space and time dimensions and produces outputs parallelly. The proposed method's model is trained on a system with two NVIDIA T4 GPUs, using a combination of loss functions, including edge preservation, and demonstrates a 2x speedup in model inference time compared to a sequential configuration. Implementation is available at: github/ParallelPerceptionNetwork. Learned parameters of the trained networks are provided at: huggingface/ParallelPercentionNetwork.

关键词： Point cloud compression Solid modeling Laser radar Three-dimensional displays Computational modeling Neural networks Urban areas Computer architecture Parallel processing Feature extraction

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Body Area Network Design for Spacewalk Nonverbal Communication in Extreme Conditions

Body Area Network Design for Spacewalk Nonverbal Communicati...

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IEEE International conference on Wireless for Space and Extreme Environments (WiSEE)

作者： Thuy T Pham Veronica B H Nguyen Philip H.W. Leong FEIT UTS Sydney NSW Australia School of Electrical & Data Engineering University of Technology Sydney Sydney Australia Chatswood High School Sydney Australia School of Electrical & Computer Engineering The University of Sydney Sydney Australia

ISBN: (数字)9798350351118

ISBN: (纸本)9798350351125

Human research in space must address a multitude of hazards during long exploration journeys. Feasible schemes for search and rescue in the absence of conventional means of communications are crucial for spacewalks or during times of unexpected electromagnetic instability. To prepare for advancements in wearable sensors and space-based edge computing, we propose a new communication method that is real-time, directly perceptible to humans, ultra-low in computing cost, and robust against electromagnetic interference. This work suggests a new research direction for the design of ad-hoc direct communication protocols to enhance survivability under such extreme environmental conditions. Visual cues for signing will be analysed to identify relevant pose landmarks, and are recognised via a body area network using wearable sensors in space. The sensors support real-time applications with low computing costs and without electromagnetic interference. We demonstrate preliminary results from initial blocks of the proposed framework. Our work may lead to new developments in spacesuit design and new search and rescue practices. It may also help unify current high-variant sign languages across countries and narrow the gap between spoken and unspoken interactions.

关键词： Wireless communication Wireless sensor networks visualization Sign language Costs Electromagnetic interference Body area networks Real-time systems Ad hoc networks Wearable sensors

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iMap: A Crowdsensing Based System for Outdoor Radio Signal Strength Map

iMap: A Crowdsensing Based System for Outdoor Radio Signal S...

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IEEE International conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiaochen Fan Panlong Yang Chaocan Xiang Lei Shi College of Communication Engineering PLA University of Science and Technology Logistic Engineering University Chongqing China

ISBN: (纸本)9781509032068

With the ubiquity of mobile appliances, efficient wireless access has become an urgent need for mobile users. Efficient wireless access requires accurate radio signal maps. Unfortunately, most of solutions could not be applied to large-scale urban space. Crowdsensing schemes could leverage the smartphone to sense WiFi signals, but could not provide accurate map because of the intolerable noises in mobile devices. Even worse, device heterogeneity makes the noise taming more intractable. Thus how to build accurate radio signal map with heterogeneous devices, especially in mobile crowdsensing is a major challenge. In this work, we build an accurate WiFi signal in geographical domain with iMap, a crowdsensing system leveraging heterogeneous smart devices to tame device noise ambiently in large-scale urban space. First of all, we deployed our system in typical urban area, and collect the real trace data with heterogeneous smart phones. After that, we make a synthetic analysis over the collected data, and find that, (1) although the noise is intolerable, it could be fitted to a statistical model fairly well, (2) although the noise model varies across smartphone types, the relative value between smart phones could be modeled. Leveraging this fundamental observation, we construct and implement an accurate RSS map with model-driven approach for taming heterogeneous noise, and geo-tagged measurements could be tamed for accurate RSS map. Experimental evaluations further validate our design.

关键词： IEEE 802.11 Standard Mobile communication sensors Servers Wireless sensor networks Wireless communication data visualization

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An Universal data Fusion Method for Velocity Measurements of Multi-sensor

An Universal Data Fusion Method for Velocity Measurements of...

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2012 IEEE 14th International conference on Communication technology

作者： Xu Can Li Zhi The Academy of Equipment

ISBN: (纸本)9781467321006

In multi-sensor system,Doppler velocity can be obtained directly from sensor such as radar or sonar while velocity parameters in Cartesian coordinates can only be obtained from filtering *** order to acquire higher velocity precision,an universal data fusion is *** a new location algorithm,JPDA (Joint Probability Density Algorithm) is extended to estimate target velocity when Doppler velocity and Cartesian velocity can be obtained ***,two velocity probability density functions are derived from Doppler measurements and velocity in Cartesian coordinates ***,joint velocity probability density is constructed and JPDA is *** an extension of JPDA,the new method proposed here could estimate target velocity with higher precise and there is no limitation on sensor type or *** results verify the feasibility of the method.

关键词： joint probability density maximum likelihood Doppler parameters.

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Modified CNN VoxNet Based Depthwise Separable Convolution for Voxel-Driven Body Orientation Classification

Modified CNN VoxNet Based Depthwise Separable Convolution fo...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Moch. Iskandar Riansyah Oddy Virgantara Putra Ardyono Priyadi Tri Arief Sardjono Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Telkom University Department of Informatics Universitas Darussalam Gontor Ponorogo Indonesia Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Biomedical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

In this research, LiDAR sensor technology introduces a new representation of point cloud data for tasks in 3D object recognition. Point clouds provide rich information that can be utilized to predict the orientation pose of the human body. The orientation of the human body is crucial for various needs such as autonomous vehicles, robotics, and human activity monitoring. However, the inherently unstructured nature and permutation invariance of point cloud data pose significant challenges in accurately predicting human body orientation using 3D data. In this paper, we propose a solution by introducing a volumetric method based on CNN to effectively recognize human body ori-entation. A novel approach is proposed by embedding depthwise sparse convolution into 2D CNN to process voxel data of human body orientation. We evaluate the performance using KITTI data divided into four orientation classes: east, west, north, and south. Additionally, we compare it with other architectures using the ModelNet10 and ModelNet40 datasets. The comparison with other architectures shows superior performance with accuracies of 84.31 % for the KITTI dataset, 86.67 % for ModelNet10, and 75.20 % for ModelNet40. This evidence strongly supports the effectiveness of our proposed method, highlighting its potential in enhancing 3D object recognition systems significantly.

关键词： Point cloud compression Measurement Three-dimensional displays Laser radar Convolution Imaging Robot sensing systems Object recognition Monitoring Autonomous vehicles

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DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR

DeepLiDARFlow: A Deep Learning Architecture For Scene Flow E...

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2020 IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Rishav Rishav Ramy Battrawy René Schuster Oliver Wasenmüller Didier Stricker German Research Center for Artificial Intelligence - DFKI Kaisers-lautern Germany Birla Institute of Technology and Science - BITS Pilani Pilani India University of Applied Sciences Mannheim Mannheim Germany University of Kaiserslautern - TUK Kaiserslautern Germany

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Scene flow is the dense 3D reconstruction of motion and geometry of a scene. Most state-of-the-art methods use a pair of stereo images as input for full scene reconstruction. These methods depend a lot on the quality of the RGB images and perform poorly in regions with reflective objects, shadows, ill-conditioned light environment and so on. LiDAR measurements are much less sensitive to the aforementioned conditions but LiDAR features are in general unsuitable for matching tasks due to their sparse nature. Hence, using both LiDAR and RGB can potentially overcome the individual disadvantages of each sensor by mutual improvement and yield robust features which can improve the matching process. In this paper, we present DeepLiDARFlow, a novel deep learning architecture which fuses high level RGB and LiDAR features at multiple scales in a monocular setup to predict dense scene flow. Its performance is much better in the critical regions where image-only and LiDAR-only methods are inaccurate. We verify our DeepLiDARFlow using the established data sets KITTI and FlyingThings3D and we show strong robustness compared to several state-of-the-art methods which used other input modalities. The code of our paper is available at https://***/dfki-av/DeepLiDARFlow.

关键词： Deep learning Laser radar Three-dimensional displays Estimation Robot sensing systems Robustness Task analysis

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Fall Detection for Elderly People using LiDAR sensor

Fall Detection for Elderly People using LiDAR Sensor

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Artificial Intelligence For Internet of Things (AIIoT), International conference on

作者： A.V. Viswa V Dharma Pravardhana Senthil Kumar Thangavel G. Jeyakumar Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Tamil Nadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Understanding the prevalence and severity of accidental falls among the elderly is crucial. Even for those living independently, falls are frequent and can lead to severe injuries, often being a primary cause of elderly mortality. To address this need, the development of a comprehensive platform becomes essential, capable of continuously monitoring individuals’ activity status for timely detection of falls and other unexpected behaviors. Real-time monitoring is vital for providing swift and efficient early warnings during a fall. Conventional methods of monitoring, such as manual surveillance, are resource-intensive and may lack the immediacy required to respond effectively to fall incidents. Therefore, to overcome these limitations, innovative solutions have been introduced. Implementing an automated system can significantly reduce the time and energy expended on monitoring while ensuring a prompt and proactive response to potential fall events. Despite advancements in fall detection research, many existing studies rely on wearable devices primarily within home settings. Extending the scope of these technologies to diverse environments could result in more inclusive and versatile fall detection systems, enhancing elderly safety across various settings. Conventional fall detection systems frequently encounter privacy concerns when employing RGB camera-based monitoring due to their intrusive nature. To tackle this issue, alternative non-intrusive methods such as LiDAR technology have been introduced. LiDAR proves to be effective in detecting falls while also upholding privacy, rendering it a promising option for enhancing geriatric safety.

关键词： Technological innovation Privacy Cloud computing Laser radar Accuracy data models Safety

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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data Driven Control and Learning Systems (DDCLS)

作者： Yanchao Zhang Hao Geng Bin Liu Yinlong Zhang Xudong Yuan Sichao Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China Guangzhou Institute of Industrial Intelligence Guangzhou China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation.

关键词： Point cloud compression visualization Laser radar Simultaneous localization and mapping conferences Robot vision systems Measurement uncertainty

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A FULLY AUTOMATIC REGISTRATION METHOD FOR LASER SCANNER data

A FULLY AUTOMATIC REGISTRATION METHOD FOR LASER SCANNER DATA

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IET International radar conference

作者： K. Al-Manasir Division of Engineering Nottingham University Ningbo China

ISBN: (纸本)9781629931340

At the present time there is a considerable amount of development interest in utilising laser scanner (LS) data for 3D reconstruction and modelling. LS technology produces point clouds of very high density in a very short time. When surveying an object of complex geometry, multiple LS scans are typically required to generate an occlusion-free 3D model. The principal task associated with building 3D models from laser scanner data in such cases is to transform the scan data from the sensor's local coordinate system into a uniform Cartesian reference datum. This paper presents a fully automatic registration procedure for 3D data whereby the transformation parameters of one of the 3D data sets with respect to a reference data set are determined by minimizing the sum of the squares of the coordinates different between the surfaces. The proposed method provides a simultaneous registration of arbitrary 3D surfaces, as well as statistical tools for the analysis of the quality of the registration results. Test results obtained with the proposed approach are presented to highlight their efficiency and accuracy.

关键词： 3D surface registration terrestrial laser scanner registration 3D point clouds

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