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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications VI"

共 306 条记录，以下是21-30 订阅

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Neural Architecture Search for Automotive Grid fusion Networks under Embedded Hardware Constraints 19

Neural Architecture Search for Automotive Grid Fusion Networ...

引用

19th IEEE International conference on Machine Learning and applications, ICMLA 2020

作者： Balazs, Gabor Stechele, Walter Technische Universität München Munich Germany

ISBN: (纸本)9781728184708

The goal of automotive sensor fusion is to generate an environmental model with low-cost, mass-produced sensors - typically camera and radar. The central fusion controller needs sophisticated post-processing algorithms to achieve this goal despite extensive pre-processing on the sensor side. Recent advances show the capabilities of deep convolutional auto-encoders for sensor fusion but are still limited to high-performance hardware. Limited memory and processing capabilities of embedded devices lead to the multi-objective optimization problem addressed in this work. The proposed evolutionary neural architecture search is configured to find novel sensor fusion network architectures optimized for embedded hardware. The discovered encoding and decoding cells improve fusion performance compared to that of corresponding cells from literature. These lightweight network architectures allow the deployment on constrained embedded devices, thus leverage environmental perception of level 2/2+ automated cars. © 2020 IEEE.

关键词： Network architecture

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1st International conference on Frontiers in Smart Systems Technologies, ICFSST 2019

1st International Conference on Frontiers in Smart Systems T...

引用

1st International conference on Frontiers in Smart Systems Technologies, ICFSST 2019

ISBN: (纸本)9789811550287

The proceedings contain 71 papers. The special focus in this conference is on Frontiers in Smart Systems Technologies. The topics include: Development of smart electricity energy management system using iot;iot-based automatic library management robot;an efficient method for moving vehicle detection in real-time video surveillance;fpga-based reconfigurable architectures for dsp computations;classification by learning of wavelet and texture features;analysis of brushless dc motor using deep neural network and bat algorithm;sensible autonomous machine using deep learning and convolutional neural networks;speaking mouth system for dumb people using hand gestures;improvement in performance attributes of multivariable system using rtda controller;a survey on crack detection algorithms for concrete structures;a comparative analysis of alexnet and googlenet with a simple dcnn for face recognition;time-varying analysis of channel estimation in ofdm;autonomous vehicle sunshade using aluminum panels;design and evaluate low-cost wireless sensor network infrastructure to monitor the jetty docking area in rural areas;iot-based energy analytic platform for foundry units;energy efficient multifocus image fusion in dct domain;performance comparison of various machine learning algorithms for ultrasonic fetal image classification problem;automatic speed control system in vehicles using vanet;design and development of a new microstrip patch antenna for ism band applications;smart connected street lighting system;identity management using blockchain network for fail-safe e-governance;predictive education—from idea to implementation;iot-based intelligent healthcare module;a survey on methodologies for handling imbalance problem in multiclass classification;video captioning for proactive video management using deep machine learning;preface.

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Soft body sensor networks: architectures and applications

Soft body sensor networks: Architectures and Applications

引用

2020 IEEE International conference on Networking, Sensing and Control, ICNSC 2020

作者： Dong, Wentao Cheng, Xiao Yao, Daojin School of Electrical and Automation Engineering East China Jiaotong University Nanchang China Rail Transportation Technology Innovation Center East China Jiaotong University Nanchang China

ISBN: (纸本)9781728168531

Body sensor networks (BSNs) are designed to collect, process, and transmit the biological data from different parts of human body from wearable electronics with limited resources. Conventional BSN node is also designed and fabricated by the rigid sensors, printed circuit board (PCB), and integrated chip (IC), which are not compatible with the soft human skin. The concepts of soft body sensor networks (SBSNs) are given in the manuscript based on wireless sensor networks and flexible electronics. SBSN node is composed of soft sensors, flexible data modulation and process unit and flexible wireless communication unit, and all the components of SBSN node are designed to be bended and be compatible with soft skin surface by flexible electronic. The architecture of SBSNs are built with several layers: soft sensors, data collection, wireless communication, body could, data fusion algorithm and monitoring interface for further online healthy monitoring. Multisensory decision fusion algorithm has been presented with biometric and medical monitoring applications which is subsequently analyzed for medical diagnosis. Emotion recognitions are realized to predict healthy state based on SBSNs using the intelligent data fusion algorithms. © 2020 IEEE.

关键词： sensor nodes

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fusion of Data from Radar and Depth sensors Applied for Healthcare-Oriented Characterization of Persons' Gait 23

Fusion of Data from Radar and Depth Sensors Applied for Heal...

引用

23rd IEEE conference on Signal Processing algorithms, architectures, Arrangements, and applications (SPA)

作者： Mazurek, Pawel Wagner, Jakub Morawski, Roman Z. Warsaw Univ Technol Fac Elect & Informat Technol Inst Radioelect & Multimedia Technol Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9788362065363

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： measurement data fusion impulse-radar sensor depth sensor healthcare

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Neural Architecture Search for Automotive Grid fusion Networks Under Embedded Hardware Constraints

Neural Architecture Search for Automotive Grid Fusion Networ...

引用

International conference on Machine Learning and applications (ICMLA)

作者： Gabor Balazs Walter Stechele Technische Universität München Munich Germany

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

The goal of automotive sensor fusion is to generate an environmental model with low-cost, mass-produced sensors-typically camera and radar. The central fusion controller needs sophisticated post-processing algorithms to achieve this goal despite extensive pre-processing on the sensor side. Recent advances show the capabilities of deep convolutional auto-encoders for sensor fusion but are still limited to high-performance hardware. Limited memory and processing capabilities of embedded devices lead to the multi-objective optimization problem addressed in this work. The proposed evolutionary neural architecture search is configured to find novel sensor fusion network architectures optimized for embedded hardware. The discovered encoding and decoding cells improve fusion performance compared to that of corresponding cells from literature. These lightweight network architectures allow the deployment on constrained embedded devices, thus leverage environmental perception of level 2/2+ automated cars.

关键词： Training Computer architecture sensor fusion Hardware Encoding Decoding Automotive engineering

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RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

RGB and LiDAR fusion based 3D Semantic Segmentation for Auto...

引用

IEEE Intelligent Transportation Systems conference (IEEE-ITSC)

作者： El Madawi, Khaled Rashed, Hazem El Sallab, Ahmad Nasr, Omar Kamel, Hanan Yogamani, Senthil Valeo R&D Cairo Cairo Egypt Cairo Univ Giza Egypt Valeo Vis Syst Galway Ireland

ISBN: (纸本)9781538670248

LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. Our main contribution is to convert the RGB image to a polar-grid mapping representation used for LiDAR and design early and mid-level fusion architectures. Additionally, we design a hybrid fusion architecture that combines both fusion algorithms. We evaluate our algorithm on KITTI dataset which provides segmentation annotation for cars, pedestrians and cyclists. We evaluate two state-of-the-art architectures namely SqueezeSeg and PointSeg and improve the mIoU score by 10% in both cases relative to the LiDAR only baseline.

关键词： Semantic Segmentation

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Multi-sensor-fusion System for People Counting applications 1

Multi-Sensor-Fusion System for People Counting Applications

引用

1st International conference on Societal Automation (SA)

作者： Stec, Michal Herrmann, viktor Stabernack, Benno Fraunhofer Heinrich Hertz Inst HHI Embedded Syst Grp Berlin Germany Univ Potsdam Embedded Syst Architectures Signal Proc Potsdam Germany

ISBN: (纸本)9781728133454

For economical as well as environmental reasons, logistical planning and efficient assignment of transport vehicles in public transportation need a precise knowledge of passenger utilization. To provide reliable figures sophisticated counting methods are demanded. Previously developed systems, mostly using a single 2D image sensor or a 3D depth sensor, can not fully achieve the required accuracy. In this paper, we present a robust people counting algorithm, based on multi sensor data fusion. Our solution runs on embedded systems with reasonable requirements with respect to computational power. 3D distance information, obtained from the ToF system, is used to perform the basic detection of objects. Once detected, the objects get additional classification features exploiting the data provided by a RGB camera and an IR thermal sensor. We describe the current state of fusing and processing of the collected data including the detection, classification (vital, non-vital), as well as sequential tracking and counting. We provide current counting results along with insights to future development concepts to improve the stated algorithms especially in terms of vital, nonvital classification and object recognition.

关键词： Multi sensor fusion 3D vision

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Soft body sensor networks: architectures and applications

Soft body sensor networks: Architectures and Applications

引用

IEEE International conference on Networking, Sensing and Control

作者： Wentao Dong Xiao Cheng Daojin Yao School of Electrical and Automation Engineering East China Jiaotong University Nanchang China Rail Transportation Technology Innovation Center East China Jiaotong University Nanchang China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

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RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

RGB and LiDAR fusion based 3D Semantic Segmentation for Auto...

引用

International conference on Intelligent Transportation

作者： Khaled El Madawi Hazem Rashed Ahmad El Sallab Omar Nasr Hanan Kamel Senthil Yogamani Valeo R&D Cairo Egypt Cairo University Valeo Visions Systems Ireland

ISBN: (纸本)9781538670255

关键词： Laser radar Three-dimensional displays Semantics Image segmentation Feature extraction Computer architecture Cameras

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Nodes deployment optimization algorithm based on improved evidence theory 18th

Nodes deployment optimization algorithm based on improved ev...

引用

18th International conference on algorithms and architectures for Parallel Processing - ICA3PP 2018 Collocated Workshops: ICA3PP 2018 Workshop on Intelligent algorithms for Large-scale Complex Optimization Problems, IALCOP 2018, ICA3PP 2018 Workshop on Security and Privacy in Data Processing, SPDP 2018

作者： Song, Xiaoli Gong, Yunzhan Jin, Dahai Li, Qiangyi Jing, Hengchang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Henan University of Science and Technology HenanLuoyang471023 China

ISBN: (纸本)9783030052331

Underwater wireless sensor networks (UWSNs) applications for ocean monitoring, deep sea surveillance, and locating natural resources are gaining popularity. To monitor the underwater environment or any object of interest, these applications are required to deploy underwater connected node sensors for obtaining useful data. For thriving UWSNs, it is essential that an efficient and secure node deployment mechanism is in place. In this article, we are presenting a novel nodes deployment scheme which is based on evidence theory approach and cater-for 3D-UWSNs. This scheme implements sonar probability perception and an enhanced data fusion model to improve prior probability deployment algorithm of D-S evidence theory. The viability of our algorithm is verified by performing multiple simulation experiments. The simulation results reveal that as compared to other schemes, our algorithm deploys fewer nodes with enhanced network judgment criteria and expanded detection capabilities for a relatively large area. © Springer Nature Switzerland AG 2018.

关键词： sensor nodes

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