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International Conference on Microelectronics, ICM

作者： Egídio Ieno Júnior Luis Manuel Garcés Socarrás Tales Cleber Pimenta Department of Mechatronics Centro Federal de Educação Tecnológica Varginha Brasil Department of Automation and Computing Technological University of Havana Havana Cuba Institute of Systems Engineering and Information Technology Universidade Federal de Itajubá Itajubá Brasil

ISBN: (纸本)9781538681688;9781538681671

Methods of moving object detection for General Purpose Processors (GPPs) present an approach that does not allow a successful implementation in the Field Programmable Gate Array (FPGA). This is because correct pixel classification is directly related to the need for many resources in terms of memory or complex mathematical operations to model the reference image. Therefore, these implementations do not allow real-time processing. In addition, these methods implement only conventional techniques of digital imaging processing. Thus, we propose an architecture for FPGA that combines filter operations with fuzzy integral to improve the detection of moving vehicles. This proposed fuzzy decision system for detecting moving vehicles seeks to not compromise the results in terms of pixel classification, even validating the system through a reference image obtained by a simple modeling mechanism. Results are verified in terms of the resources occupied, maximum frequency of operation and similarity measure in the FPGA Spartan-6 LX100. The implementation of the proposed low-cost FPGA system processes high-definition images in real time and improves pixel classification.

关键词： Iron Field programmable gate arrays Microelectronics Real-time systems Decision making Proposals Object detection

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Siamese-ResNet: Implementing Loop Closure Detection based on Siamese Network

Siamese-ResNet: Implementing Loop Closure Detection based on...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Qiu, Kai Ai, Yunfeng Tian, Bin Wang, Bin Cao, Dongpu School of Aritificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China School of Software Engineering University of Science and Technology of China HeFei230026 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781538644522

Deep learning has made significant breakthroughs in the tasks of image classification, detection, segmentation, etc. However, the application of deep learning in robotics is still scarce. SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM. This paper attempts to use supervised learning methods to solve the loop closure detection problem in vision SLAM. We proposed Siamese-ResNet network, which combines Siamese network with ResNet to detect loop closure. To show the effectiveness of Siamese-ResNet, we evaluate Siamese-ResNet and FabMap2.0 on several open published datasets, like TUM SLAM dataset and FabMap SLAM dataset. Compared with FabMap2.0, Siamese-ResNet shows higher accuracy, better robustness and shorter time-consuming. © 2018 IEEE.

关键词： Robotics

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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A survey of industrial control system testbeds

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IOP Conference Series: Materials Science and engineering 2019年第4期569卷

作者： Yangyang Geng Yi Wang Wenwen Liu Qiang Wei Ke Liu Haolan Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001 China. Department of Research and Development Center Beijing sifang automation co.ltd Beijing 100085 China

Industrial Control System(ICS) testbed is the basis of ICS safety research. In order to build an ICS testbed, it is necessary to have a deep understanding of the current research status. This paper introduced the structure and composition of the typical ICS system. Then it simply analyzed the technical characteristics, system framework and advantages and disadvantages of four types of typical ICS testbeds. Finally, it summarized application scenarios of ICS testbed and pointed out some problems in testbed construction and the development direction of ICS testbed with the fusion of cyber and physical.

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Boosted convolutional neural networks for motor imagery EEG decoding with multiwavelet-based time-frequency conditional granger causality analysis

arXiv

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arXiv 2018年

作者： Li, Yang Lei, Mengying Zhang, Xianrui Cui, Weigang Guo, Yuzhu Huang, Ting-Wen Wei, Hua-Liang Department of Automation Sciences and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Advanced Innovation Center for Big Date-based Precision Medicine Beihang University Beijing China Department of Automation Sciences and Electrical Engineering Beihang University Beijing China Department of Mathematics Texas A&M University at Qatar Doha23874 Qatar Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3DJ United Kingdom

Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to non-stationarity of perceptual decision processes. This paper presents a novel boosted convolutional neural networks (ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by the multiwavelet-based time-frequency (TF) causality analysis. Specifically, multiwavelet basis functions are first combined with Geweke spectral measure to obtain high-resolution TF-conditional Granger causality (CGC) representations, where a regularized orthogonal forward regression (ROFR) algorithm is adopted to detect a parsimonious model with good generalization performance. The causality images for network input preserving time, frequency and location information of connectivity are then designed based on the TF-CGC distributions of alpha band multichannel EEG signals. Further constructed boosted ConvNets by using spatio-temporal convolutions as well as advances in deep learning including cropping and boosting methods, to extract discriminative causality features and classify MI tasks. Our proposed approach outperforms the competition winner algorithm with 12.15% increase in average accuracy and 74.02% decrease in associated inter subject standard deviation for the same binary classification on BCI competition-IV dataset-IIa. Experiment results indicate that the boosted ConvNets with causality images works well in decoding MI-EEG signals and provides a promising framework for developing MI-BCI systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical tests

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Porcellio scaber algorithm (PSA) for solving constrained optimization problems 3

Porcellio scaber algorithm (PSA) for solving constrained opt...

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3rd International Conference on Mechanical, Electronic and Information Technology engineering, ICMITE 2017

作者： Zhang, Yinyan Li, Shuai Guo, Hongliang Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a better performance than many existing methods or algorithms. The results indicate that the PSA is a promising algorithm for constrained optimization. © The Authors, published by EDP Sciences, 2017.

关键词： Constrained optimization

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Automatic Segmentation and 3D Reconstruction of Spine Based on FCN and Marching Cubes in CT Volumes

Automatic Segmentation and 3D Reconstruction of Spine Based ...

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International Conference on Modelling, Identification and Control (ICMIC)

作者： Lingyu Fang Jiwei Liu Jianfei Liu Ronghu Mao The intelligent science and technology department Graduate student of University of Science and Technology Beijina University of Science and Technology Artificial psychology emotional computing Specialized Committee director Beijing Electrical Engineering and Automation Anhui University Department of Radiation Oncology Henan Cancer Hospital Henan China

The spine is of great significance in the course of radiotherapy. The accurate location of the spine can provide reference for the determination of the tumor target area and the endanger organ in the radiotherapy plan. However, for some low-resolution areas of CT images, traditional methods cannot achieve a good segmentation effect. Due to the lack of data marked by doctors, there are few studies on the use of deep learning methods for segmentation of the spine. We use threshold segmentation and manual labeling methods to make our own data sets. This article combines the Fully Convolutional Neural Network (FCN) and the Marching Cubes (MC) algorithms to automatically segment and reconstruct the spine in the CT images. And we improved the network structure of FCN because FCN finally lost many details in one step down sampling. In the study, we used data from 40 patients, of which 30 were for training and 10 for testing. The final segmentation accuracy of the improved network is over 93%. The experimental results show that this method has a good segmentation effect and can better restore the shape of the spine and ribs. This preliminary result showed that our spine segmentation method had a great potential to reduce human efforts in labeling CT images in radiation therapy.

关键词： Image segmentation Computed tomography Isosurfaces Training Image reconstruction Ribs Three-dimensional displays

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Fault-Tolerant Cooperative Tracking Control of Nonlinear Affine Systems Using Integral Sliding Mode Control

Fault-Tolerant Cooperative Tracking Control of Nonlinear Aff...

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IEEE International Symposium on Industrial Electronics

作者： Qichao Ma Jiahu Qin Yu Kang Wei Xing Zheng Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beiiinz China School of Computing Engineering and Mathematics Western Sydney University Sydney Australia

The cooperative tracking problem for a class of nonlinear affine systems are addressed. Actuator faults and external disturbance/model uncertainty are allowed such that we aim to design distributed controllers that can tolerate them while realize cooperative tracking. A general setup is considered, that is, actuator faults can take such forms as actuator degradation and total failure. Then, to guarantee the feasibility of fault-tolerant control, we assume that the actuator has sufficiently many healthy components which enables us to apply integral sliding mode control (ISMC) to develop the distributed controllers that can fulfill the control target and the external disturbance can simultaneously be successfully rejected. We also provide the simulation results to verify the validity of our theoretical findings.

关键词： Cooperative tracking Fault-tolerant control Nonlinear affine systems Integral sliding mode control

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DoS Attack Power Allocation Against Remote State Estimation via a Block Fading Channel

DoS Attack Power Allocation Against Remote State Estimation ...

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Chinese Control Conference (CCC)

作者： Heng Zhang Yanzheng Zhu Zhaojian Li Lidong He Wei Xing Zheng School of Science Huaihai Institute of Technology Lianyungang China School of Computing Western Sydney University Sydney NSW Australia Department of Mechanical Engineering Michigan State University Michigan USA School of Automation Nanjing University of Science and Technology Nanjing China

This paper studies the Denial-of-Service (DoS) attack power allocation issue, which aims to aggravate the quality of remote state estimation and save the expenditure of attack power concurrently. Different from the previous literature based on the assumption of time-invariant channel states, we consider the scenario that the observations are transmitted through a standard block fading communication channel. From viewpoint of the DoS attacker, we construct an optimization problem considering jointly the average estimation error and the attack power consumption. Then the framework of Markov decision process is employed to manifest the existence of optimal attack allocation strategy. Since the closed-form of the optimal attack strategy cannot be expressed directly, we restrain it to a specific attack policy to present the unclear part in the solution, and then provide a suboptimal attack power strategy. Finally, a simulation example is presented to show the effectiveness of our proposed attack strategy.

关键词： Resource management State estimation Fading channels Estimation error Denial-of-service attack Standards Communication channels

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Towards a deep insight into landmark-based visual place recognition: Methodology and practice

arXiv

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arXiv 2018年

作者： Yang, Bo Li, Jun Xu, Xiaosu Zhang, Hong Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education School of Instrument Science and Engineering Southeast University Nanjing China Department of Computing Science University of Alberta EdmontonAB Canada School of Automation Southeast University Nanjing China

In this paper, we address the problem of landmark-based visual place recognition. In the state-of-the-art method, accurate object proposal algorithms are first leveraged for generating a set of local regions containing particular landmarks with high confidence. Then, these candidate regions are represented by deep features and pairwise matching is performed in an exhaustive manner for the similarity measure. Despite its success, conventional object proposal methods usually produce massive landmark-dependent image patches exhibiting significant distribution variance in scale and overlap. As a result, the inconsistency in landmark distributions tends to produce biased similarity between pairwise images yielding the suboptimal performance. In order to gain an insight into the landmark-based place recognition scheme, we conduct a comprehensive study in which the influence of landmark scales and the proportion of overlap on the recognition performance is explored. More specifically, we thoroughly study the exhaustive search based landmark matching mechanism, and thus derive threefold important observations in terms of the beneficial effect of specific landmark generation strategies. Inspired by the above observations, a simple yet effective dense sampling based scheme is presented for accurate place recognition in this paper. Different from the conventional object proposal strategy, we generate local landmarks of multiple scales with uniform distribution from entire image by dense sampling, and subsequently perform multi-scale fusion on the densely sampled landmarks for similarity measure. The experimental results on three challenging datasets demonstrate that the recognition performance can be significantly improved by our efficient method in which the landmarks are appropriately produced for accurate pairwise matching. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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