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Self-Awareness for Autonomous Systems

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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Morphological geodesic active contour based automatic aorta segmentation in thoracic CT images

Morphological geodesic active contour based automatic aorta ...

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International Conference on computer Vision and image processing, CVIP 2016

作者： Dasgupta, Avijit Mukhopadhyay, Sudipta Mehre, Shrikant A. Bhattacharyya, Parthasarathi Computer Vision and Image Processing Laboratory Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Institute of Pulmocare & Research KolkataWest Bengal700156 India

ISBN: (纸本)9789811021060

Automatic aorta segmentation and quantification in thoracic computed tomography (CT) images is important for detection and prevention of aortic diseases. This paper proposes an automatic aorta segmentation algorithm in both contrast and non-contrast CT images of thorax. The proposed algorithm first detects the slice containing the carina region. Circular Hough Transform (CHT) is applied on the detected slice to localize ascending and descending aorta (circles with lowest variances) followed by a morphological geodesic active contour to segment the aorta from CT stack. The dice similarity coefficients (DSC) between the ground truth and the segmented output were found to be 0.8845 ± 0.0584 on LIDC-IDRI dataset. © Springer Science+Business Media Singapore 2017.

关键词： image segmentation

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Deep learning enabled fast optical characterization of two-dimensional materials

arXiv

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arXiv 2019年

作者： Han, Bingnan Lin, Yuxuan Yang, Yafang Mao, Nannan Li, Wenyue Wang, Haozhe Fatemi, Valla Zhou, Lin Wang, Joel I-Jan Ma, Qiong Cao, Yuan Rodan-Legrain, Daniel Bie, Ya-Qing Navarro-Moratalla, Efrén Klein, Dahlia MacNeill, David Wu, Sanfeng Leong, Wei Sun Kitadai, Hikari Ling, Xi Jarillo-Herrero, Pablo Palacios, Tomás Yin, Jihao Kong, Jing Image Processing Center School of Astronautics Beihang University Beijing100191 China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemistry Boston University BostonMA02215 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Instituto de Ciencia Molecular Universidad de Valencia c/Catedrático José Beltrán 2 Paterna46980 Spain Division of Materials Science and Engineering Boston University BostonMA02215 United States

Characterization of nanomaterial morphologies with advanced microscopy and/or spectroscopy tools plays an indispensable role in nanoscience and nanotechnology research 1 2, 3, 4, 5, as rich information about the chemical compositions, crystallography, other physical and chemical properties, as well as the growth mechanism can be extracted from morphology analysis. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features are often unused because of difficulties in processing the data and finding the correlations. Such difficulties can be well addressed by deep learning 6 7, 8, 9. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural network based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can be used to predict physical properties of the materials. Finally, a transfer learning technique is applied to adapt the pretrained network to more optical characterization applications such as identifying layer numbers of chemically synthesized graphene domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Data handling

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TRIZ Innovative Method for Eliminating Fissures in 3D Panorama image Stitching

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IOP Conference Series: Earth and Environmental Science 2020年第3期440卷

作者： Jiu Yong Yangping Wang Xiaomei Lei The National Computer Science and Technology Experimental Teaching Demonstration Center Lanzhou Jiaotong Univeristy Lanzhou China The National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Lanzhou China College of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing Lanzhou China Lanzhou Yuxin Information Technology Co. Ltd Lanzhou China Meteorological Information and Technological Supporting Center Gansu Meteorological Service Lanzhou China

roaming splices several photographs taken by the camera into a panoramic image, and then uses computer graphics and image technology to construct panoramic space for the panoramic image. image stitching will directly affect the user's roaming experience, but some panoramic images will have obvious color seams after stitching. TRIZ is a knowledge-based, human-oriented inventive problem solving systematic method. Based on TRIZ innovation method, this paper describes, analyses, resolves and evaluates the final problem plan. By using TRIZ innovation method, we can break through the thinking barrier, break the thinking stereotype, and analyze the problem with new vision, and carry out logical and non-logical systematic thinking. The problem of stitching cracks in the process of 3D panorama stitching is solved thoroughly and effectively. Finally, according to the law of technological evolution, the future development trend is predicted, and competitive and innovative products are developed.

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Detection of Fusarium wilt on Phalaenopsis stem base region using band selection techniques 38

Detection of Fusarium wilt on Phalaenopsis stem base region ...

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38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

作者： Lee, Meng-Chueh Ma, Kenneth-Yeonkong Ouyang, Yen-Chieh Ou-Yang, Mang Guo, Horng-Yuh Liu, Tsang-Sen Chen, Hsian-Min Wu, Chao-Cheng Chang, Chgein-I Department of Communications Engineering National Chung Hsing University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan National Chiao-Tung University Department of Electrical and Computer Engineering Hsinchu City Taiwan Taiwan Agriculture Research Institute Nan-Tou Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

ISBN: (纸本)9781538671504

Phalaenopsis is a significant agriculture product with high economic value in Taiwan. However, the fusarium wilt causes Phalaenopsis leaves turning yellow, thinning, water loss, and finally died. This paper presents an emerging method to detect fusarium wilt on Phalaenopsis stem base. In order to build the detection models, the hyperspectral databases are generated form two statues of Phalaenopsis samples, which are health and disease sample. We applied band selection (BS) processing base on band prioritization (BP) and band de-correlation (BD) to extract the significant bands and eliminate the redundant bands. Then, three algorithms were used, orthogonal subspace projection (OSP), constrain energy minimization (CEM), and support vector machine (SVM) to detect the fusarium wilt. © 2018 IEEE

关键词： Diseases Hyperspectral imaging Support vector machines Agriculture Databases Entropy

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Dense Trajectory Action Recognition Algorithm Based on Improved SURF

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IOP Conference Series: Earth and Environmental Science 2019年第3期252卷

作者： Hu Zhao Jianwu Dang Song Wang Yangping Wang Decheng Gao School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing Lanzhou China Gansu Provincial Key Lab of System Dynamics and Reliability of Rail Transport Equipment Lanzhou China Gansu Institute of Metrology Lanzhou China

In order to improve the time-consuming and large error problem of camera motion estimation in dense trajectory feature extraction of video, a dense trajectory action recognition algorithm based on Improved Speeded-Up Robust Features (SURF) is proposed. The algorithm mainly performs dense sampling of video images, and then executes camera motion estimation. In the feature point detection stage, the Gaussian pyramid layer was constructed dynamically to improve the real-time and accuracy of feature point extraction. Based on the SURF algorithm, the brightness center algorithm is used to obtain direction of feature. Binary Robust Independent Elementary Feature (BRIEF) is used to generate feature descriptors to determine matching points and optimized images, then to conducts feature tracking and feature extraction on the images to classify features. The experimental results show that the algorithm performs better in terms of speed when removing camera motion, and improves the real-time performance of feature extraction and the accuracy of action recognition.

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Improved Moving Target Detection Based on Multi-Model Mean Model

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IOP Conference Series. Earth and Environmental Science 2019年第5期252卷 052134-052134页

作者： Wang, Weiwei Gao, Deyong Wang, Yangping Gao, Decheng School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing Lanzhou China Gansu Provincial Key Lab of System Dynamics and Reliability of Rail Transport Equipment Lanzhou China Gansu Institute of Metrology Lanzhou China

Aiming at the problem of low detection accuracy of multi-mode mean model in complex scenarios, an improved detection method of moving target based on multi-mode mean model is ***, the background model is constructed using the multi-mode mean value model. According to different scene information, different thresholds are set and adjusted adaptively. The foreground image obtained by background difference method is detected by frame difference method, and the experiment is compared and analyzed. The detection rate and the error rate are reduced, and the detection accuracy is improved. Finally, the simulation results of three-segment video verify the effectiveness of the proposed method.

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Shape classification according to LBP persistence of critical points 19th

Shape classification according to LBP persistence of critica...

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19th IAPR International Conference on Discrete Geometry for computer imagery, DGCI 2016

作者： Janusch, Ines Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms TU Wien Vienna Austria

ISBN: (纸本)9783319323596

This paper introduces a shape descriptor based on a combination of topological image analysis and texture information. Critical points of a shape’s skeleton are determined first. The shape is described according to persistence of the local topology at these critical points over a range of scales. The local topology over scale-space is derived using the local binary pattern texture operator with varying radii. To visualise the descriptor, a new type of persistence graph is defined which captures the evolution, respectively persistence, of the local topology. The presented shape descriptor may be used in shape classification or the grouping of shapes into equivalence classes. Classification experiments were conducted for a binary image dataset and the promising results are presented. Because of the use of persistence, the influence of noise or irregular shape boundaries (e.g. due to segmentation artefacts) on the result of such a classification or grouping is bounded. © Springer International Publishing Switzerland 2016.

关键词： Local binary pattern

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Correction to: Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods

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Journal of translational medicine 2021年第1期19卷 203页

作者： Yuchen Du Qiuying Chen Ying Fan Jianfeng Zhu Jiangnan He Haidong Zou Dazhen Sun Bowen Xin David Feng Michael Fulham Xiuying Wang Lisheng Wang Xun Xu Department of Automation The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University (SJTU) 800 Dongchuan RD. Minhang District Shanghai 200240 People's Republic of China. Department of Preventative Ophthalmology Shanghai Eye Diseases Prevention and Treatment Center Shanghai Eye Hospital No. 380 Kangding Road Shanghai 200040 China. Department of Ophthalmology Shanghai Key Laboratory of Ocular Fundus Diseases Shanghai Engineering Center for Visual Science and Photo Medicine Shanghai General Hospital SJTU School of Medicine Shanghai China. National Clinical Research Center for Eye Diseases Shanghai 20080 China. Biomedical and Multimedia Information Technology Research Group School of Computer Science The University of Sydney Sydney NSW 2006 Australia. Department of Molecular Imaging Royal Prince Alfred Hospital and the University of Sydney Sydney Australia. Department of Automation The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University (SJTU) 800 Dongchuan RD. Minhang District Shanghai 200240 People's Republic of China. lswang@***. Department of Preventative Ophthalmology Shanghai Eye Diseases Prevention and Treatment Center Shanghai Eye Hospital No. 380 Kangding Road Shanghai 200040 China. drxuxun@***. Department of Ophthalmology Shanghai Key Laboratory of Ocular Fundus Diseases Shanghai Engineering Center for Visual Science and Photo Medicine Shanghai General Hospital SJTU School of Medicine Shanghai China. drxuxun@***. National Clinical Research Center for Eye Diseases Shanghai 20080 China. drxuxun@***.

An amendment to this paper has been published and can be accessed via the original article.

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A novel color image encryption algorithm based on genetic recombination and the four-dimensional memristive hyperchaotic system

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Chinese Physics B 2016年第10期25卷 76-88页

作者：柴秀丽甘志华路杨张苗辉陈怡然 School of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University Department of Electrical and Computer Engineering University of Pittsburgh School of Software Henan University Research Department Henan University

Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.

关键词： genetic recombination memristive hyperchaotic system color image encryption confusion diffusion

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