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arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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Aggregation capacity of wireless sensor networks: Extended network case

Aggregation capacity of wireless sensor networks: Extended n...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Cheng Wang Changjun Jiang Yunhao Liu Xiang-Yang Li Shaojie Tang Huadong Ma Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China TNLIST School of Software Tsinghua University China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781424499199

A critical function of wireless sensor networks (WSNs) is data gathering. While, one is often only interested in collecting a relevant function of the sensor measurements at a sink node, rather than downloading all the data from all the sensors. This paper studies the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. It focuses on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power *** derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive perfectly compressible functions and type-threshold perfectly compressible functions, the aggregation capacities for random extended WSNs with n nodes are of order Θ ((log n) -β/2-1 ) and Θ (((log n) -β/2 )/(log log n)), respectively, where β >; 2 denotes the power attenuation exponent in the generalized physical model.

关键词： Wireless sensor networks Signal to noise ratio Interference Throughput Lattices Routing PROM

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Petri Net-Based Cycle Time Analysis of Dual-Arm Cluster Tools With Wafer Revisiting and Swapping Strategy

Petri Net-Based Cycle Time Analysis of Dual-Arm Cluster Tool...

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： NaiQi Wu Feng Chu Chengbin Chu MengChu Zhou Department of Industrial Engineering School of Mechatronics EngineeringGuangdong University of Tech Laboratoire d'IBISC FRE CNRS 3190 Université d'Evry Val d'EssonneCE 1455 Courcouronnes 91020 Evry C Laboratoire G(e)nie Industriel Ecole Centrale ParisGrande voie des Vignes92295 CH(A)TENAY-MALABRY The MoE Key Laboratory of Embedded System and Service Computing Tongji UniversityShanghai 200092Ch

There are wafer fabrication processes in cluster tools that require revisiting. It is shown that swapping is efficient in operating a dual-arm cluster tool. For dual-arm cluster tools with wafer revisiting, if a swap strategy is applied, it forms a three wafer periodical process with three wafers completed in each period. Such a period contains three cycles in a revisiting process and another three cycles in non-revisiting process. Hence, analysis and scheduling of dual-arm cluster tools with wafer revisiting become very complicated. In this work, a Petri net model is developed to describe the operations of such tools. Based on it, it is found that if a swap strategy is applied to a dual-arm cluster tool with wafer revisiting, it is always in a transient state. A systematic method is presented to analyze its performance.

关键词： Robots Semiconductor device modeling Firing Color Fires Load modeling Transient analysis

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Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

Random Occlusion Recovery with Noise Channel for Person Re-i...

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16th International Conference on Intelligent computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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CSformer: Bridging Convolution and Transformer for Compressive Sensing

arXiv

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arXiv 2021年

作者： Ye, Dongjie Ni, Zhangkai Wang, Hanli Zhang, Jian Wang, Shiqi Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518052 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Convolution neural networks (CNNs) have succeeded in compressive image sensing. However, due to the inductive bias of locality and weight sharing, the convolution operations demonstrate the intrinsic limitations in modeling the long-range dependency. Transformer, designed initially as a sequence-to-sequence model, excels at capturing global contexts due to the self-attention-based architectures even though it may be equipped with limited localization abilities. This paper proposes CSformer, a hybrid framework that integrates the advantages of leveraging both detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. In the sampling module, images are measured block-by-block by the learned sampling matrix. In the reconstruction stage, the measurement is projected into dual stems. One is the CNN stem for modeling the neighborhood relationships by convolution, and the other is the transformer stem for adopting global self-attention mechanism. The dual branches structure is concurrent, and the local features and global representations are fused under different resolutions to maximize the complementary of features. Furthermore, we explore a progressive strategy and window-based transformer block to reduce the parameter and computational complexity. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing, which achieves superior performance compared to state-of-the-art methods on different datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing

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New approximation operator of rough fuzzy sets

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Tongji Daxue Xuebao/Journal of Tongji University 2008年第6期36卷 827-831页

作者： Cheng, Yi Miao, Duoqian Liu, Yong Feng, Qinrong Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China College of Mathematical Sciences Ocean University of China Qingdao 266071 China Department of Computer Science and Technology Tongji University Shanghai 201804 China College of Information Science and Engineering Ocean University of China Qingdao 266071 China

For Dubois rough fuzzy sets, the membership of the lower or upper approximations is defined as the elements memberships' infimum or supsmum in equivalent class. As a result of not considering the elements whose memberships are between the minimum and maximum, some useful information of these elements may be lost in the information processing. The paper presents a new operator of rough fuzzy sets that every element's membership in equivalent class is taken into account. Based on the new operator, algebra properties are put forward and rough fuzzy membership is defined. Moreover, the paper presents accurate degree, classified quality, dependence degree and attribute reduction algorithm. At last, an example proves that the algorithm is efficient.

关键词： Fuzzy sets

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Joint power allocation and beamforming for non-orthogonal multiple access (NOMA) in 5G millimeter-wave communications

arXiv

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arXiv 2017年

作者： Xiao, Zhenyu Zhu, Lipeng Choi, Jinho Xia, Pengfei Xia, Xiang-Gen School of Electronic and Information Engineering Beihang University Beijing100191 China Beijing Key Laboratory for Network-Based Cooperative Air Traffic Management Beijing Laboratory for General Aviation Technology Beijing100191 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Korea Republic of School of Electronics and Information Engineering Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China College of Information Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

In this paper we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) communications (mmWave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mmWave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mmWave-NOMA to more-user mmWave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieve close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access (TDMA). Copyright © 2017, The Authors. All rights reserved.

关键词： Millimeter waves

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Scaling Laws of Multicast Capacity for Power-Constrained Wireless Networks under Gaussian Channel Model

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IEEE Transactions on Computers 2012年第5期61卷 713-725页

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Shaojie Tang Yuan He Xufei Mao Yunhao Liu Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Engineering Building of Electronics and Information Engineering University of Tongji Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL USA Tsinghua National Laboratory for Information Science and Technology (TNLIST) Tsinghua University Beijing China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Hong Kong University of Science and Technology Kowloon Hong Kong China Tsinghua National Laboratory for Information Science and Technology (TNLIST) School of Software Tsinghua University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

We study the asymptotic networking-theoretic multicast capacity bounds for random extended networks (REN) under Gaussian channel model, in which all wireless nodes are individually power-constrained. During the transmission, the power decays along path with attenuation exponent α >; 2. In REN, n nodes are randomly distributed in the square region of side length √n. There are n s randomly and independently chosen multicast sessions. Each multicast session has n d + 1 randomly chosen terminals, including one source and n d destinations. By effectively combining two types of routing and scheduling strategies, we analyze the asymptotic achievable throughput for all n s = ω(1) and nd. As a special case of our results, we show that for n s = Θ(n), the per-session multicast capacity for REN is of order Θ(1/√n d n) when nd = O(n/(log n) a+1 ) and is of order Θ(1/n d · (log n) -n/2 ) when n d = Ω(n/log n).

关键词： Lattices Throughput Upper bound Road transportation Receivers Interference Routing

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Behavioral authentication for security and safety

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Security and Safety 2024年第3期3卷 50-85页

作者： Cheng Wang Hao Tang Hangyu Zhu Junhan Zheng Changjun Jiang Department of Computer Science and Technology Tongji UniversityShanghai 201804China Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghai 201804China Shanghai Artificial Intelligence Laboratory Shanghai 200232China

The issues of both system security and safety can be dissected integrally from the perspective of behavioral appropriateness. That is, a system that is secure or safe can be judged by whether the behavior of certain agent(s) is appropriate or not. Specifically, a so-called appropriate behavior involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behavioral authentication can be graded into three levels, i.e., the authentication of behavioral Identity, Conformity, and Benignity. In a broad sense, for the security and safety issue, behavioral authentication is not only an innovative and promising method due to its inherent advantages but also a critical and fundamental problem due to the ubiquity of behavior generation and the necessity of behavior regulation in any system. By this classification, this review provides a comprehensive examination of the background and preliminaries of behavioral authentication. It further summarizes existing research based on their respective focus areas and characteristics. The challenges confronted by current behavioral authentication methods are analyzed, and potential research directions are discussed to promote the diversified and integrated development of behavioral authentication.

关键词： behavioral authentication security and safety behavior modeling anomaly detection machine learning artificial intelligence

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Multi-task variational information bottleneck

arXiv

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arXiv 2020年

作者： Qian, Weizhu Chen, Bowei Zhang, Yichao Wen, Guanghui Gechter, Franck Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté France Adam Smith Business School University of Glasgow United Kingdom Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai200092 China Department of Systems Science School of Mathematics Southeast University Nanjing210016 China School of Engineering RMIT University MelbourneVIC3000 Australia Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté and Mosel LORIA UMR CNRS Université de Lorraine 7503 France

Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence. Its applications to computer vision, signal processing, and speech recognition are ubiquitous. Although this subject has attracted considerable attention recently, the performance and robustness of the existing models to different tasks have not been well balanced. This article proposes an MTL model based on the architecture of the variational information bottleneck (VIB), which can provide a more effective latent representation of the input features for the downstream tasks. Extensive observations on three public data sets under adversarial attacks show that the proposed model is competitive to the state-of-the-art algorithms concerning the prediction accuracy. Experimental results suggest that combining the VIB and the task-dependent uncertainties is a very effective way to abstract valid information from the input features for accomplishing multiple tasks. © 2020, CC BY.

关键词： Deep learning

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