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arXiv 2018年

作者： Ran, Maosong Hu, Jinrong Chen, Yang Chen, Hu Sun, Huaiqiang Zhou, Jiliu Zhang, Yi College of Computer Science Sichuan University Chengdu610065 China Department of Computer Science Chengdu University of Information Technology Chengdu610225 China Lab of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing210096 China School of Cyber Science and Engineering Southeast University Nanjing210096 China Ministry of Education Nanjing210096 China Department of Radiology West China Hospital of Sichuan University Chengdu610041 China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis. Over the past few years, many algorithms with impressive performances have been proposed. In this paper, inspired by the idea of deep learning, we introduce an MRI denoising method based on the residual encoder-decoder Wasserstein generative adversarial network (RED-WGAN). Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders combined with deconvolution operations are introduced into the generator network. Furthermore, to alleviate the oversmoothing shortcoming of the traditional mean squared error (MSE) loss function, the perceptual similarity, which is implemented by calculating the distances in the feature space extracted by a pretrained VGG-19 network, is incorporated with the MSE and adversarial losses to form the new loss function. Extensive experiments are implemented to assess the performance of the proposed method. The experimental results show that the proposed RED-WGAN achieves performance superior to several state-of-the-art methods in both simulated and real clinical data. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A Low-Area Overhead Secure Scan Architecture Resisting Scan-Based Attacks for Crypto Chips

A Low-Area Overhead Secure Scan Architecture Resisting Scan-...

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2017 International MultiConference of Engineers and computer Scientists, IMECS 2017

作者： Weizheng, Wang Jincheng, Wang Shuo, Cai Peng, Liu Tieqiao, Liu College of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha Hunan410114 China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Information Science and Engineering Department Hunan University Changsha410082 China Management School Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9789881404732

Scan design is a widely used design-for-testability (DFT) technique that improves the controllability and observability of integrated circuits (ICs) resulting in the facilitation of the testing. However, it can also be used to access secret information of crypto chips, and thus threaten dramatically the security of the cipher keys. In this paper, we propose a secure scan DFT architecture to thwart scan-based side-channel attacks. This architecture provides the scan chain reset mechanism, and thus can prevent these attacks based on mode switching. Meanwhile, the secret key is isolated from scan chains of an advanced encryption standard (AES) design in the test mode. Therefore, it can also halt the test-mode-only scan attacks. The proposed secure scan DFT technique ensures the security without compromising the testability of original chip. Most important of all, the secure scan test is implemented with extremely low hardware overhead.

关键词： Design for testability

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Enhanced Geometric Map:a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation

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机器人 2016年第3期38卷 311-321页

作者： LI Haifeng HU Zunhe LIU Jingtai College of Computer Science and Technology Civil Aviation University of China Tianjin 300300 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou 350121 China Institute of Robotics and Information Automatic System Nankai University Tianjin 300071 China Tianjin Key Laboratory of Intelligent Robotics Tianjin 300071 China

To facilitate scene understanding and robot navigation in large scale urban environment, a two-layer enhanced geometric map(EGMap) is designed using videos from a monocular onboard camera. The 2D layer of EGMap consists of a 2D building boundary map from top-down view and a 2D road map, which can support localization and advanced map-matching when compared with standard polyline-based maps. The 3D layer includes features such as 3D road model,and building facades with coplanar 3D vertical and horizontal line segments, which can provide the 3D metric features to localize the vehicles and flying-robots in 3D space. Starting from the 2D building boundary and road map, EGMap is initially constructed using feature fusion with geometric constraints under a line feature-based simultaneous localization and mapping(SLAM) framework iteratively and progressively. Then, a local bundle adjustment algorithm is proposed to jointly refine the camera localizations and EGMap features. Furthermore, the issues of uncertainty, memory use, time efficiency and obstacle effect in EGMap construction are discussed and analyzed. Physical experiments show that EGMap can be successfully constructed in large scale urban environment and the construction method is demonstrated to be very accurate and robust.

关键词：被提高的几何学的地图都市模型机械手航运同时发生的定位而且映射局部的包裹调整

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Energy-efficient resource allocation in noma heterogeneous networks

arXiv

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arXiv 2018年

作者： Zhang, Haijun Fang, Fang Cheng, Julian Long, Keping Wang, Wei Leung, Victor C.M. Engineering and Technology Research Center for Convergence Networks University of Science and Technology Beijing Beijing100083 China School of Engineering University of British Columbia KelownaBCV1X 1V7 Canada College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Non-orthogonal multiple access (NOMA) has attracted much recent attention owing to its capability for improving the system spectral efficiency in wireless communications. Deploying NOMA in heterogeneous network can satisfy users' explosive data traffic requirements, and NOMA will likely play an important role in the fifth-generation (5G) mobile communication networks. However, NOMA brings new technical challenges on resource allocation due to the mutual cross-tier interference in heterogeneous networks. In this article, to study the tradeoff between data rate performance and energy consumption in NOMA, we examine the problem of energy-efficient user scheduling and power optimization in 5G NOMA heterogeneous networks. The energy-efficient user scheduling and power allocation schemes are introduced for the downlink 5G NOMA heterogeneous network for perfect and imperfect channel state information (CSI) respectively. Simulation results show that the resource allocation schemes can significantly increase the energy efficiency of 5G NOMA heterogeneous network for both cases of perfect CSI and imperfect CSI. Copyright © 2018, The Authors. All rights reserved.

关键词： Heterogeneous networks

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Robust Monocular Visual Odometry using Optical Flows for Mobile Robots 35

Robust Monocular Visual Odometry using Optical Flows for Mob...

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第35届中国控制会议

作者： LI Haifeng HU Zunhe CHEN Xinwei College of Computer Science and Technology Civil Aviation University of China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Department of Computer Science Minjiang University

ISBN: (纸本)9781509009107

A robust optical flow-based visual odometry method using a single onboard camera is proposed in this *** improve the quality of the noisy optical flows,a correction method across multiple frames is ***,the optical flows in the plane at infinity are detected and removed as these optical flows have very low signal to noise ratio for robot translation ***,a RANSAC approach for robot ego-motion estimation is *** experiments are carried out and the results show that the proposed method is able to estimate the camera trajectory robustly with reasonable accuracy.

关键词： visual odometry optical flows mobile robot

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Variational neural machine translation

Variational neural machine translation

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2016 Conference on Empirical Methods in Natural Language processing, EMNLP 2016

作者： Zhang, Biao Xiong, Deyi Su, Jinsong Duan, Hong Zhang, Min Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Xiamen University Xiamen361005 China

ISBN: (纸本)9781945626258

Models of neural machine translation are often from a discriminative family of encoder-decoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to learn this conditional distribution for neural machine translation: a variational encoder-decoder model that can be trained end-to-end. Different from the vanilla encoder-decoder model that generates target translations from hidden representations of source sentences alone, the variational model introduces a continuous latent variable to explicitly model underlying semantics of source sentences and to guide the generation of target translations. In order to perform efficient posterior inference and large-scale training, we build a neural posterior approximator conditioned on both the source and the target sides, and equip it with a reparameterization technique to estimate the variational lower bound. Experiments on both Chinese-English and English-German translation tasks show that the proposed variational neural machine translation achieves significant improvements over the vanilla neural machine translation baselines. © 2016 Association for Computational Linguistics

关键词： Neural machine translation

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Enabling fast recovery from unanticipated data access failure in named data networking 16

Enabling fast recovery from unanticipated data access failur...

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11th International Conference on Future Internet Technologies, CFI 2016

作者： Hu, Xiaoyan Gong, Jian School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China Jiangsu Provincial Key Laboratory of Computer Network Technology China

ISBN: (纸本)9781450341813

We show that an aggregated Interest in Named Data Networking (NDN) may fail to retrieve desired data as the Interest sent upstream in advance is recognized as a duplicate one due to its multipath forwarding and dropped by an upstream node. Furthermore, we introduce NDRUDAF, a mechanism based on negative acknowledgement for the router that aggregates the Interests to detect such unanticipated data access failure and assist the requester whose Interest is aggregated fast recovering from the failure. We qualitatively analyze the performance of the NDN with our proposal NDRUDAF and compare its performance with that of the present NDN. NDRUDAF improves the system performance in case of the unanticipated data access failure in terms of data access delay and network resource utilization efficiency at routers. © Copyright 2016 ACM.

关键词： Routers

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Survey of key technologies on millimeter-wave CMOS integrated circuits

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IOP Conference Series: Materials Science and Engineering 2018年第1期351卷

作者： Fei Yu Lei Gao Lixiang Li Shuo Cai Wei Wang Chunhua Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation & School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China College of Information Science and Engineering Hunan University Changsha 410082 China

In order to provide guidance for the development of high performance millimeter-wave complementary metal oxide semiconductor (MMW-CMOS) integrated circuits (IC), this paper provides a survey of key technologies on MMW-CMOS IC. Technical background of MMW wireless communications is described. Then the recent development of the critical technologies of the MMW-CMOS IC are introduced in detail and compared. A summarization is given, and the development prospects on MMW-CMOS IC are also discussed.

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Nuclear norm regularized coding with local position-patch and nonlocal similarity for face hallucination

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Digital Signal processing 2017年 64卷 107-120页

作者： Guangwei Gao Jian Yang Zhihui Lai Pu Huang Jielin Jiang Hao Gao Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 PR China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 PR China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 PR China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 PR China

Learning-based face hallucination methods have received much attention and progress in past few decades. Specially, position-patch based approaches have been proposed to replace the probabilistic graph-based or manifold learning-based ones. As opposed to the existing patch based methods, where the input image patch matrix is converted into vectors before combination coefficients calculation, in this paper, we propose to directly use the image matrix based regression model for combination coefficients computation to preserve the essential structural information of the input patch matrix. For each input low-resolution (LR) patch matrix, its combination coefficients over the training image patch matrices at the same position can be computed. Then the corresponding high-resolution (HR) patch matrix can be obtained with the LR training patches replaced by the corresponding HR ones. The nonlocal self-similarities are finally utilized to further improve the hallucination performance. Various experimental results on standard face databases indicate that our proposed method outperforms some state-of-the-art algorithms in terms of both visual quantity and objective metrics.

关键词： Face hallucination Position-patch Matrix based regression Nuclear norm Nonlocal similarity

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Variational neural discourse relation recognizer

Variational neural discourse relation recognizer

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2016 Conference on Empirical Methods in Natural Language processing, EMNLP 2016

作者： Zhang, Biao Xiong, Deyi Su, Jinsong Liu, Qun Ji, Rongrong Duan, Hong Zhang, Min Xiamen University Xiamen361005 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China ADAPT Centre School of Computing Dublin City University Ireland Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781945626258

Implicit discourse relation recognition is a crucial component for automatic discourse-level analysis and nature language understanding. Previous studies exploit discriminative models that are built on either powerful manual features or deep discourse representations. In this paper, instead, we explore generative models and propose a variational neural discourse relation recognizer. We refer to this model as VarNDRR. VarNDRR establishes a directed probabilistic model with a latent continuous variable that generates both a discourse and the relation between the two arguments of the discourse. In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound. This allows VarNDRR to be trained with standard stochastic gradient methods. Experiments on the benchmark data set show that VarNDRR can achieve comparable results against state-of-the-art baselines without using any manual features. © 2016 Association for Computational Linguistics

关键词： Gradient methods

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