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arXiv 2019年

作者： Feng, Daquan She, Changyang Ying, Kai Lai, Lifeng Hou, Zhanwei Quek, Tony Q.S. Li, Yonghui Vucetic, Branka Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China School of Electrical and Information Engineering University of Sydney Sydney Australia Sharp Laboratories of America CamasWA United States ISTD Pillar Singapore University of Technology and Design Singapore

Ultra-reliable low-latency communications (URLLC) has been considered as one of the three new application scenarios in the 5th Generation (5G) New Radio (NR), where the physical layer design aspects have been specified. With the 5G NR, we can guarantee the reliability and latency in radio access networks. However, for communication scenarios where the transmission involves both radio access and wide area core networks, the delay in radio access networks only contributes to part of the end-to-end (E2E) delay. In this paper, we outline the delay components and packet loss probabilities in typical communication scenarios of URLLC, and formulate the constraints on E2E delay and overall packet loss probability. Then, we summarize possible solutions in the physical layer, the link layer, the network layer, and the cross-layer design, respectively. Finally, we discuss the open issues in prediction and communication co-design for URLLC in wide area large scale networks. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Industrial Air Pollution Prediction Using Deep Neural Network 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Pengfei, Yu Juanjuan, He Xiaoming, Liu Kai, Zhang School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

In this paper, a deep neural network model is proposed to predict industrial air pollution, such as PM2.5 and PM10. The deep neural network model contains 9 hidden layers, each layer contains 45 neurons. The output of the hidden layer neurons is calculated using the ReLU activation function, which can effectively reduce the gradient elimination effect of the deep neural network. Twelve air pollutant indicators from industrial factories are collected as the input data, such as CO, NO2, O3, and SO2. About 180,000 real industrial air pollution data from Wuhan City are used to train and test the DNN model. Furthermore, the performance of our approach is compared with the SVM and Artificial neural network methods, and the comparison result shows that our algorithm is accurate and competitive with higher prediction accuracy and generalization ability. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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UWB SAR image segmentation algorithm based on polynomial analysis of statistical distribution

UWB SAR image segmentation algorithm based on polynomial ana...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Wang Yuming Jin Tian Luo Chaopeng College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computhing and Language Information Processing Science and Technology on Near-Surface Detection Laboratory Hunan Normal University Changsha R.P.China College of Electronic Science and Technology Science and Technology on Near-Surface Detection Laboratory National University of Defense Technology Changsha R.P.China Science and Technology on Near-Surface Detection Laboratory Wuxi China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

Aimed at the segmentation problem of ultrawideband synthetic aperture radar (UWB SAR) image, a novel algorithm based on polynomial analysis of statistical distribution is proposed in this letter. Firstly, we estimate the probability density function (PDF) for each pixel via sparse decomposition of which the dictionary consists of orthogonal polynomials (OP). Secondly, the weighting coefficients of corresponding OP are used to segment image using their maximum value. The experimental results validate the efficiency of this algorithm.

关键词： Image segmentation Probability density function Synthetic aperture radar Radar polarimetry Signal processing algorithms Scattering Statistical distributions

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Pose Synchronization of Multiple Rigid Bodies Under Average Dwell Time Condition

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Journal of systems Science & Complexity 2018年第1期31卷 215-233页

作者： DENG Juan WANG Lin LIU Zhixin HU Xiaoming LSC Academy of Mathematics and Systems Science Chinese Academy of Science Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Department of Automation Shanghai Jiaotong University and the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai 200240 China Optimization and Systems Theory KTH Royal Institute of Technology 10044 Stockholm Sweden

This paper considers the pose synchronization problem of a group of moving rigid bodies under switching topologies where the dwell time of each topology may has no nonzero lower bound. The authors introduce an average dwell time condition to characterize the length of time intervals in which the graphs are connected. By designing distributed control laws of angular velocity and linear velocity,the closed-loop dynamics of multiple rigid bodies with switching topologies can be converted into a hybrid dynamical system. The authors employ the Lyapunov stability theorem, and show that the pose synchronization can be reached under the average dwell time condition. Moreover, the authors investigate the pose synchronization problem of the leader-following model under a similar average dwell time condition. Simulation examples are given to illustrate the results.

关键词： Average dwell time hybrid dynamical systems pose synchronization rigid body SE（3）

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Slow Feature Analysis for Human Action Recognition

arXiv

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arXiv 2019年

作者： Zhang, Zhang Tao, Dacheng National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.R China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney Australia

Slow Feature Analysis (SFA) extracts slowly varying features from a quickly varying input signal [1]. It has been successfully applied to modeling the visual receptive fields of the cortical neurons. Sufficient experimental results in neuroscience suggest that the temporal slowness principle is a general learning principle in visual perception. In this paper, we introduce the SFA framework to the problem of human action recognition by incorporating the discriminative information with SFA learning and considering the spatial relationship of body parts. In particular, we consider four kinds of SFA learning strategies, including the original unsupervised SFA (U-SFA), the supervised SFA (S-SFA), the discriminative SFA (D-SFA), and the spatial discriminative SFA (SD-SFA), to extract slow feature functions from a large amount of training cuboids which are obtained by random sampling in motion boundaries. Afterward, to represent action sequences, the squared first order temporal derivatives are accumulated over all transformed cuboids into one feature vector, which is termed the Accumulated Squared Derivative (ASD) feature. The ASD feature encodes the statistical distribution of slow features in an action sequence. Finally, a linear support vector machine (SVM) is trained to classify actions represented by ASD features. We conduct extensive experiments, including two sets of control experiments, two sets of large scale experiments on the KTH and Weizmann databases, and two sets of experiments on the CASIA and UT-interaction databases, to demonstrate the effectiveness of SFA for human action recognition. Experimental results suggest that the SFA-based approach 1) is able to extract useful motion patterns and improves the recognition performance, 2) requires less intermediate processing steps but achieves comparable or even better performance, and 3) has good potential to recognize complex multiperson activities. Copyright © 2019, The Authors. All rights reserved.

关键词： Support vector machines

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Fabry-perot lasers as enablers for parallel reservoir computing

arXiv

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arXiv 2020年

作者： Bogris, Adonis Mesaritakis, Charis Deligiannidis, Stavros Li, Pu Department of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo Athens12243 Greece Department of Information & Communication Systems Engineering University of the Aegean 2 Palama & Gorgyras St. Karlovassi Samos83200 Greece Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China

We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities. With the use of optical injection between a master FP laser and a slave FP laser under feedback we demonstrate the potential for scaling up the processing power at longitudinal mode granularity and perform real-time processing for signal equalization in 25 Gbaud intensity modulation direct detection optical communication systems. We demonstrate the improvement of classification performance as the number of nodes increases and the capability of simultaneous processing of arbitrary data streams. Extensive numerical simulations show that up to 8 longitudinal modes in typical Fabry-Perot lasers can be leveraged so as to enhance classification performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization

Improving Pedestrian Attribute Recognition With Weakly-Super...

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International Conference on Computer Vision (ICCV)

作者： Chufeng Tang Lu Sheng Zhao-Xiang Zhang Xiaolin Hu State Key Laboratory of Intelligent Technology and Systems Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology Tsinghua University College of Software Beihang University Institute of Automation Beijing China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute. However, in this task, the region annotations are not available. How to carve out these attribute-related regions remains challenging. Existing methods applied attribute-agnostic visual attention or heuristic body-part localization mechanisms to enhance the local feature representations, while neglecting to employ attributes to define local feature areas. We propose a flexible Attribute Localization Module (ALM) to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels. Moreover, a feature pyramid architecture is also introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance. The proposed framework does not require additional region annotations and can be trained end-to-end with multi-level deep supervision. Extensive experiments show that the proposed method achieves state-of-the-art results on three pedestrian attribute datasets, including PETA, RAP, and PA-100K.

关键词： Feature extraction Semantics Computer architecture Image recognition Visualization Video surveillance Task analysis

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Sequential 3D Human Pose and Shape Estimation From Point Clouds

Sequential 3D Human Pose and Shape Estimation From Point Clo...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kangkan Wang Jin Xie Guofeng Zhang Lei Liu Jian Yang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China ZJU-SenseTime Joint Lab of 3D Vision

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

This work addresses the problem of 3D human pose and shape estimation from a sequence of point clouds. Existing sequential 3D human shape estimation methods mainly focus on the template model fitting from a sequence of depth images or the parametric model regression from a sequence of RGB images. In this paper, we propose a novel sequential 3D human pose and shape estimation framework from a sequence of point clouds. Specifically, the proposed framework can regress 3D coordinates of mesh vertices at different resolutions from the latent features of point clouds. Based on the estimated 3D coordinates and features at the low resolution, we develop a spatial-temporal mesh attention convolution (MAC) to predict the 3D coordinates of mesh vertices at the high resolution. By assigning specific attentional weights to different neighboring points in the spatial and temporal domains, our spatial-temporal MAC can capture structured spatial and temporal features of point clouds. We further generalize our framework to the real data of human bodies with a weakly supervised fine-tuning method. The experimental results on SURREAL, Human3.6M, DFAUST and the real detailed data demonstrate that the proposed approach can accurately recover the 3D body model sequence from a sequence of point clouds.

关键词： Three-dimensional displays Solid modeling Shape Biological system modeling Estimation Spatial resolution Feature extraction

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Temporal Knowledge Propagation for Image-to-Video Person Re-Identification

Temporal Knowledge Propagation for Image-to-Video Person Re-...

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International Conference on Computer Vision (ICCV)

作者： Xinqian Gu Bingpeng Ma Hong Chang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image and video features increases the difficulty in matching images and videos. To solve this problem, we propose a novel Temporal Knowledge Propagation (TKP) method which propagates the temporal knowledge learned by the video representation network to the image representation network. Specifically, given the input videos, we enforce the image representation network to fit the outputs of video representation network in a shared feature space. With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated. With additional classification and integrated triplet losses, our model can learn expressive and discriminative image and video features for image-to-video re-identification. Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin.

关键词： Videos Image representation Feature extraction Knowledge engineering Visualization Robustness Training

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A STEPWISE METHOD FOR CHANGE DETECTION IN LARGE-SCALE POLARIMETRIC SAR IMAGES

A STEPWISE METHOD FOR CHANGE DETECTION IN LARGE-SCALE POLARI...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Fang Liu Xu Tang Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China School of Artificial Intelligence Xidian University Xi’an China

In this paper, a stepwise method which consists of two main steps is proposed to tackle change detection in large-scale polarimetric Synthetic Aperture Radar (SAR) images. First, down-sample two registered polarimetric SAR images and calculate the corresponding Difference Image (DI), then spatial localization is conducted to orient sub-region which contains changed area in high probability. Second, polarimetric SAR sub-images are selected and they are trained together with down-sampled whole images by Convolutional Neural Network (CNN), where changed areas in sub-region are revealed and the next sub-region is generated accordingly. Repeat these two steps until all interested regions are detected. In general, it collects the whole but coarse information at first to locate important domains with changed areas and then analyzes them for accurate detection result and generate the next sub-region for further detection. Experiment results show that the proposed method performs well in detecting changed areas in large-scale polarimetric SAR images.

关键词： Radar polarimetry Task analysis Convolutional neural networks Detectors Polarimetric synthetic aperture radar

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