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IEEE Communications Society Conference on sensor and Ad Hoc Communications and Networks (SECON)

作者： C. Singhal Y. Wu F. Malandrino S. Ladron de Guevara Contreras M. Levorato C. F. Chiasserini INRIA France UC Irvine USA CNR-IEIIT Italy CNIT Italy Politecnico di Torino Italy Chalmers University of Technology Sweden

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network. To minimize the cost of the AI inference task subject to requirements on latency, quality, and - crucially - reliability of the inference process, it is vital to optimize (i) the set of sensors/data sources and (ii) the DNN architecture, (iii) the network nodes executing sections of the DNN, and (iv) the resources to use. To this end, we leverage dynamic gated neural networks with branches, and propose a novel algorithmic strategy called Quantile-constrained Inference (QIC), based upon quantile-Constrained policy optimization. QIC makes joint, high-quality, swift decisions on all the above aspects of the system, with the aim to minimize inference energy cost. We remark that this is the first contribution connecting gated dynamic DNNs with infrastructure-level decision making. We evaluate QIC using a dynamic gated DNN with stems and branches for optimal sensor fusion and inference, trained on the RADIATE dataset offering Radar, LiDAR, and Camera data, and real-world wireless measurements. Our results confirm that QIC matches the optimum and outperforms its alternatives by over 80%.

关键词： Wireless communication Wireless sensor networks Costs Soft sensors Heuristic algorithms Neural networks Logic gates sensor fusion Dynamic scheduling Time measurement

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Multi-Modal Tracking Using LiDAR and visual Signals

Multi-Modal Tracking Using LiDAR and Visual Signals

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International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Sanjay Kumar Mohamed Hassan Marcos Escudero-viñolo Abdul Hannan Anam Manzoor António Godinho Ivan Miguel Pires Paulo Jorge Coelho Autonomous University of Madrid Madrid Spain University of Bordeaux Bordeaux France University of Management and Technology Punjab Pakistan Polytechnic Institute of Coimbra Technology and Management School of Oliveira do Hospital Oliveira do Hospital Portugal Research Center for Arts and Communication Santarém Polytechnic University Santarém Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestião de Agueda Universidade de Aveiro Agueda Portugal School of Technology and Management Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Multi-object tracking (MOT) is a crucial technique for detecting and tracking multiple objects over time in a scene. It involves locating objects in consecutive frames of a video or sequential observations and establishing correspondences to maintain their identity. MOT is essential in various applications, including autonomous vehicles, robotics, and video surveillance. In autonomous vehicles, it is typically performed using 3D models (LiDAR/stereo) or 2D models (one or more RGB cameras). The LiDAR 3D point-cloud provides a 360-degree view of its surroundings but has a low resolution compared to the camera's visual signals. This paper presents a sensor fusion approach for multi-object detection and tracking, combining object detection information from sensors, LiDAR, and cameras and applying data association algorithms like Hungarian algorithms for tracking to increase the overall system. The EagerMOT approach for 3D Multi-Object Tracking via sensor fusion achieves state-of-the-art results across several MOT tasks on the KITTI dataset, achieving an accuracy of 87.95 %.

关键词： visualization Solid modeling Three-dimensional displays Laser radar Robot vision systems sensor fusion Cameras video surveillance Autonomous vehicles Signal resolution

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Advancements in Object Detection for Unmanned Aerial Vehicles: applications, Challenges, and Future Perspectives

Advancements in Object Detection for Unmanned Aerial Vehicle...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Anıl Sezgin Aytuğ Boyacı Siemens Corporate Technology Istanbul Turkey Department of Computer Engineering National Defence University Air Force Academy Istanbul Turkey

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Unmanned Aerial Vehicles (UAVs) are used in different fields ranging from recreational vehicles to agriculture, environmental monitoring, infrastructure inspection, disaster management, security, surveillance and logistics. This paper provides an overview of UAV applications and highlights the importance of object detection in improving drone autonomy. Object detection facilitates tasks such as precision agriculture, disaster response, environmental protection, infrastructure inspection and autonomous navigation. The paper reviews recent advances in UAV-specific object detection algorithms and methodologies and highlights challenges such as varying altitudes, motion blur, real-time processing constraints and limited computational resources. Solutions and adaptations are discussed to overcome these challenges, including lightweight neural network architectures, transfer learning, data augmentation, edge computing, multi-sensor fusion, attention mechanisms and adaptive algorithms. The study highlights the importance of integrating object detection systems into UAVs to improve their use in various sectors and contribute to improvements in surveillance missions.

关键词： Reviews Surveillance Transfer learning Object detection Inspection Autonomous aerial vehicles Real-time systems

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Multi-sensor aided deep pose tracking 2nd

Multi-sensor aided deep pose tracking

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2nd Modeling, Estimation and Control Conference (MECC)

作者： Lee, Hojun Toner, Tyler Tilbury, Dawn Barton, Kira Univ Michigan Dept Robot Ann Arbor MI 48109 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

Accurate pose estimation of nearby objects is critical for robots to dynamically interact with their surroundings. The complexity of this task has led researchers to explore deep learning methods. Nowadays, many works have solely focused on developing complicated neural network architectures to estimate pose from a simple monocular camera. However, most of these methods struggle with inherent limitations of a single sensor system, like occlusion, which are commonly encountered in mobile robotics applications. Online, occlusion-robust pose estimation is extremely important in such cases, as mobility of a robot introduces major uncertainty that complicates manipulation. Hence, we present Multi-sensor aided Deep Pose Tracking (MS-DPT), a framework for online object pose estimation to enable robust mobile manipulation. A Convolutional Neural Network (CNN) identifies key objects in an RGB-D image, from which object pose estimates are generated using a variant of the Iterative Closest Point (ICP) algorithm. An extended Kalman filter is used to fuse this pose estimate with onboard motion sensors to compensate for occlusion and robot motion during manipulation. This three stage method focuses on cohering different modalities and algorithms to improve the pose tracking stability and continuity in cases where the target object becomes heavily occluded by an obstacle or a mobile robot itself. The proposed approach accurately tracks textureless objects with high symmetries while operating at 10 FPS during experiments. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Object pose estimation Neural-network model Robot vision Data fusion Tracking

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Research on the System Design and Application of Mobile Manipulator Hybrid Robot for Transporting in Intelligent Factory

Research on the System Design and Application of Mobile Mani...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Xiaohong Qin Lianhao Zhang Bin Zhao Shenyang Institute of Science and Technology Shenyang China Northeastern University Shenyang China SIASUN Robot & Automation Co. Shenyang China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Based on the development and application status of mobile manipulator hybrid robot, this paper takes the handling process requirements of intelligent factories in China as the research objective to design and develop a composite robot platform system. The hardware system mainly includes mobile platforms, manipulators, and sensors. The software system mainly consists of control systems, software architectures, algorithm libraries and corresponding communication protocols. This platform system has the key technologies of scene information perception, mobile path planning and self-collision detection of the manipulator during the grasping process, which are necessary in engineering project applications. The corresponding innovative algorithms are embedded in it, and scene tests and effect analysis are carried out in a memory module production factory. Test results show that, compared with mainstream products and application technologies in the field, this mobile manipulator hybrid robot can use multiple sensors to obtain scene information and conduct high-precision data fusion, simultaneously meet multiple measurement indicators of mobile path planning, and quickly achieve pre-collision warning without the application of sensors, verifying the effectiveness and advancement of the independently developed platform system.

关键词： Software architecture Service robots Software algorithms Memory modules sensor fusion Manipulators Robot sensing systems Production facilities Path planning Intelligent sensors

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Dynamic Buffer Sizing for Out-of-order Event Compensation for Time-sensitive applications

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ACM TRANSACTIONS ON sensor NETWORKS 2020年第1期17卷 1–23页

作者： Weiss, Wolfgang Jimenez, victor J. Exposito Zeiner, Herwig JOANNEUM RES Forsch Gesell MbH DIGITAL Inst Informat & Commun Technol Steyrergasse 17 A-8010 Graz Austria VIRTUAL VEHICLE Res GmbH Inffeldgasse 21a A-8010 Graz Austria

Today's sensor network implementations often comprise various types of nodes connected with different types of networks. These and various other aspects influence the delay of transmitting data and therefore of out-of-order data occurrences. This turns into a crucial problem in time-sensitive applications where data must be processed promptly and decisions must be reliable. In this article, we research dynamic buffer sizing algorithms for multiple, distributed, and independent sources, which reorder event streams, thus enabling subsequent time-sensitive applications to work correctly. To be able to evaluate such algorithms, we had to record datasets first. Five novel dynamic buffer sizing algorithms were implemented and compared to state-of-the-art approaches in this domain. The evaluation has shown that the use of a dynamic time-out buffering method is preferable over a static buffer. The higher the variation of the network or other influences in the environment, the more necessary it becomes to use an algorithm that dynamically adapts its buffer size. These algorithms are universally applicable, easy to integrate in existing architectures, and particularly interesting for time-sensitive applications. Dynamic time-out buffering is still a trade-off between reaction time and out-of-order event compensation.

关键词： Out-of-order event compensation time-sensitive applications distributed systems event processing multi-source event data fusion

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Whose Data are Reliable: sensor Declared Data Reliability

Whose Data are Reliable: Sensor Declared Data Reliability

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Sakib Shahriar Shafin Gour Karmakar Iven Mareels Venki Balasubramanian Ramachandra Rao Kolluri Centre for Smart Analytics (CSA) Institute of Innovation Science and Sustainability Federation University Australia Ballarat VIC Australia IBM Consulting IBM Australia Pty Ltd

sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor’s historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.

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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...

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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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Multi-sensor aided deep pose tracking

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IFAC-PapersOnLine 2022年第37期55卷 326-332页

作者： Hojun Lee Tyler Toner Dawn Tilbury Kira Barton Department of Robotics University of Michigan Ann Arbor MI 48109 USA Department of Mechanical Engineering University of Michigan Ann Arbor MI 48109 USA

关键词： Object pose estimation Neural-network model Robot vision Data fusion Tracking

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Soft body sensor networks: architectures and applications

Soft body sensor networks: Architectures and Applications

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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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