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Covariance estimation from compressive data partitions using a projected gradient-based algorithm

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arXiv 2021年

作者： Monsalve, Jonathan Ramirez, Juan Esnaola, Iñaki Arguello, Henry Department of Electrical Engineering Universidad Industrial de Santander Bucaramanga680002 Colombia Department of Computer Science Universidad Rey Juan Carlos Madrid28933 Spain Department of Automatic Control and Systems Engineering The University of Sheffield Western Bank Sheffield United Kingdom Department Systems Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia

Covariance matrix estimation techniques require high acquisition costs that challenge the sampling systems’ storing and transmission capabilities. For this reason, various acquisition approaches have been developed to simultaneously sense and compress the relevant information of the signal using random projections. However, estimating the covariance matrix from the random projections is an ill-posed problem that requires further information about the data, such as sparsity, low rank, or stationary behavior. Furthermore, this approach fails using high compression ratios. Therefore, this paper proposes an algorithm based on the projected gradient method to recover a low-rank or Toeplitz approximation of the covariance matrix. The proposed algorithm divides the data into subsets projected onto different subspaces, assuming that each subset contains an approximation of the signal statistics, improving the inverse problem’s condition. The error induced by this assumption is analytically derived along with the convergence guarantees of the proposed method. Extensive simulations show that the proposed algorithm can effectively recover the covariance matrix of hyperspectral images with high compression ratios (8 − 15% approx) in noisy scenarios. Additionally, simulations and theoretical results show that filtering the gradient reduces the estimator’s error recovering up to twice the number of eigenvectors. © 2021, CC BY-NC-ND.

关键词： Covariance matrix

AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication

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AImotion Challenge Results: a Framework for AirSim Autonomou...

Computers and Automation (CompAuto), International Conference on

作者： Bruno J. Souza Lucas C. de Assis Dominik Rößle Roberto Z. Freire Daniel Cremers Torsten Schön Munir Georges Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Control and Automation Eng. Department Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Technische Hochschule Ingolstadt AImotion Bavaria Institute Ingolstadt Germany Department of Informatics Technical University of Munich Munich Germany

ISBN: (纸本)9781665481953

The use of simulation environments is becoming more significant in the development of autonomous cars, as it allows for the simulation of high-risk situations while also being less expensive. In this paper, we presented a framework that allows the creation of an autonomous vehicle in the AirSim simulation environment and then transporting the simulated movements to the TurtleBot. To maintain the car in the correct direction, computer vision techniques such as object detection and lane detection were assumed. The vehicle's speed and steering are both determined by Proportional-Integral-Derivative (PID) controllers. A virtual personal assistant was developed employing natural language processing to allow the user to interact with the environment, providing movement instructions related to the vehicle's direction. Additionally, the conversion of the simulator movements for a robot was implemented to test the proposed system in a practical experiment. A comparison between the real position of the robot and the position of the vehicle in the simulated environment was considered in this study to evaluate the performance of the algorithms.

关键词： Computer vision Lane detection Robot kinematics Atmospheric modeling Computational modeling Virtual environments Natural language processing

Task Partition-Based Intelligent Offloading for Cache-Assisted Cloud-Edge Cooperation Networks

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Task Partition-Based Intelligent Offloading for Cache-Assist...

IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Fang Haizhen Luo Xiaoping Yang Haofei Xie Fangqing Tan Shu-Ming Tseng Mianxiong Dong Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin China Key Laboratory of Industrial Internet of Things & Networked Control Chongqing University of Posts and Telecommunications Chongqing China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks, it is urgent to efficiently allocate limited network resources to improve content distribution in cloud-edge environments. In this paper, we proposes a task partition-based intelligent offloading scheme to optimize resource allocation in cache-assisted cloud-edge cooperation environments. Specifically, we formulate the task partition-based optimal computation offloading problem as a latency minimization model in the cache-aided cloud-edge collaboration system. A new deep reinforcement learning (DRL) algorithm is designed to make optimal subtask offloading and resource allocation decisions based on current network state information, improving resource utilization and network delay. Simulation results demonstrate that the proposed model achieves lower-latency content delivery than the existing popular models in cache-enabled cloud-edge cooperation networks, and fast converges.

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Transfer of Knowledge of Product Configurator in the Educational Process 18

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Transfer of Knowledge of Product Configurator in the Educati...

18th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2020

作者： Behunova, A. Knapcikova, L. Behun, M. Lumnitzer, E. Technical University of Košice Faculty of Manufacturing Technologies with A Seat in Prešov Department of Industrial Engineering and Informatics Prešov Slovakia Technical University of Košice Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Slovakia Technical University of Košice Faculty of Mechanical Engineering Department of Process and Environmental Engineering Košice Slovakia

ISBN: (纸本)9780738123660

Education, the educational process is characterized as a purposeful activation of conditions that allow the optimal development of each individual about individual predispositions. Education is a comprehensive process of personality development, which includes cognition, practice and teaching. The aim of the paper is the transfer of new acquired experience, skills and knowledge and especially the change of work behaviour through learning and application of new methodologies. For the student himself, subsequently, the graduate, as well as functioning on the labour market, achieving competition ability, prestige, or obtaining various awards is an influential motivating factor. In the case of the introduction of a new product, it is crucial to cultivate the maintenance and increase of the company's market share and thus satisfy customer demand. Not every product has to satisfy every customer, and therefore the definition of the target market is a critical part of the product at the beginning of the life cycle. Quantitative market research should be carried out at all stages of the design process. Product development is a creative discipline that requires a systematic approach to managing the operations needed to bring a new product to market. The result of innovative concepts and the use of product configurators is applied in almost every single sphere, not excluding the education market itself and the implementation of new methods. © 2020 IEEE.

关键词： Competition

Learning hierarchical behavior and motion planning for autonomous driving

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arXiv 2020年

作者： Wang, Jingke Wang, Yue Zhang, Dongkun Yang, Yezhou Xiong, Rong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou China School of Computing Informatics and Decision Systems Engineering Arizona State University

Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learningbased solution. Due to the coupled action space of behavior and motion, it is challenging to solve HBMP problem using reinforcement learning (RL) for long-horizon driving tasks. We transform HBMP problem by integrating a classical samplingbased motion planner, of which the optimal cost is regarded as the rewards for high-level behavior learning. As a result, this formulation reduces action space and diversifies the rewards without losing the optimality of HBMP. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment, so that models trained in a fast event-based simulator, SUMO, can be used to initialize and accelerate the RL training in a dynamics based simulator, CARLA. Experimental results demonstrate the effectiveness of the method. Besides, the model is successfully transferred to the real-world, validating the generalization capability. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

EAI/Springer Innovations in Communication and Computing

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Preface

EAI/Springer Innovations in Communication and Computing 2023年 vii页

作者： Behúnová, Annamaria Knapcikova, Lucia Behún, Marcel Technical University of Košice Faculty of Mining Ecology Process Control and Geotechnology Institut of earth sources Košice Slovakia Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Industrial Engineering and Informatics Prešov Slovakia

Graph attention layer evolves semantic segmentation for road pothole detection: A benchmark and algorithms

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arXiv 2021年

作者： Fan, Rui Wang, Hengli Wang, Yuan Liu, Ming Pitas, Ioannis Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR Hong Kong Industrial R&d Center SmartMore Shenzhen518000 China School of Informatics University of Thessaloniki Thessaloniki541 24 Greece

Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/ understanding or 3-D point cloud modeling and segmentation algorithms to detect (i.e., recognize and localize) road potholes from vision sensor data, e.g., RGB images and/or depth/disparity images. The latter approaches generally address road pothole detection using convolutional neural networks (CNNs) in an end-to-end manner. However, road potholes are not necessarily ubiquitous and it is challenging to prepare a large wellannotated dataset for CNN training. In this regard, while computer vision-based methods were the mainstream research trend in the past decade, machine learning-based methods were merely discussed. Recently, we published the first stereo visionbased road pothole detection dataset and a novel disparity transformation algorithm, whereby the damaged and undamaged road areas can be highly distinguished. However, there are no benchmarks currently available for state-of-the-art (SoTA) CNNs trained using either disparity images or transformed disparity images. Therefore, in this paper, we first discuss the SoTA CNNs designed for semantic segmentation and evaluate their performance for road pothole detection with extensive experiments. Additionally, inspired by graph neural network (GNN), we propose a novel CNN layer, referred to as graph attention layer (GAL), which can be easily deployed in any existing CNN to optimize image feature representations for semantic segmentation. Our experiments compare GAL-DeepLabv3+, our best-performing implementation, with nine SoTA CNNs on three modalities of training data: RGB images, disparity images, and transformed disparity images. The experimental results suggest that our proposed GAL-DeepLabv3+ achieves the best overall pothole detection accuracy on all training data modalities. The source code, dataset, and benchmark are publicly available at m

关键词： Landforms

Social and Ethical Issues in Various Medical Procedures

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Social and Ethical Issues in Various Medical Procedures

Progress in Applied Electrical engineering (PAEE)

作者： Piotr Sterniuk Waldemar Karwowski Henryk Racheniuk Adrian Luckiewicz Anna Wieczorek Mateusz Paw၂owski Witold Libionka Anna W၂odarczyk Katarzyna Bojkowska-Otrebska Adam Sudo၂ Bartosz Pańczyszak Aleksandra Kawala-Sterniuk Technology Transfer Center Opole University of Technology Opole Poland Department of Industrial Engineering and Management Systems University of Central Florida Orlando FL USA Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Vital Medic Hospital Kluczbork Poland Faculty of Health Sciences Wroclaw Medical University Wroclaw Poland Department of Neurosurgery Medical University of Gdansk Gdansk Poland Faculty of Stobrawskie Centrum Medyczne Clinical Department of Geriatrics Kup Poland Institute of Medical Sciences University of Opole Opole Poland Faculty of Natural Sciences and Technology University of Opole Opole Poland Faculty of Health Sciences Jan Dlugosz University in Czestochowa Czestochowa Poland

Medical procedures, in particular those invasive, generate significant ethical, legal, and social concerns, including the issues of humanity and person-hood, stigma, autonomy, privacy and security, research ethics, safety, moral and legal responsibility, fair access, and justice. It was pointed out that future research should focus on developing practical solutions to the ethical challenges of medical experimental procedures with human participants, facilitated by the collection of empirical data on the perspectives shared by the general public, researchers and of course study participants.

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