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International Conference on Computer and Communications (ICCC)

作者： Rong Yujie Wu Muqing Chen Yiming Beijing University of Posts and Telecommunications School of Information and communication Engineering Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Acknowledgment Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Different from the traditional TCP/IP network, Software Defined network (SDN) uses a layered architecture to decouple the control plane and data plane. The controller is the core device of the control plane, and the control plane is mainly responsible for the decision-making control of the routing strategy. Because the processing power of a single controller is limited, the multi-controller network architecture has become an inevitable choice in the WAN. The layout of the controllers in SDN has an impact on the performance of the entire network. The controller placement problem (CPP) is an NP-hard problem. The challenges of CPP solution are as follows. 1) the number of controllers; 2) the position of the controllers and the position of the switches under their management in the network topology. The clustering by fast search and find of density peaks (CFSFDP) algorithm is a density-based clustering algorithm. It has few initial parameters and does not require iterative solution. However, the important parameter dc of the CFSFDP algorithm is usually selected based on experience, and the number of cluster centers cannot be determined automatically. This paper proposes a new controller placement algorithm by improving the CFSFDP algorithm. We use the idea of information entropy to automatically determine the dc value, and automatically determine the number and location of the controllers based on the jumping point in the decision graph. It can be seen from the experimental results that compared with random placement, k-means and CFSFDP algorithm, our proposed algorithm can effectively reduce network propagation delay and improve network performance.

关键词： Wide area networks Clustering algorithms TCPIP Control systems Software defined networking Information entropy Propagation delay

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A Two-Pathway Convolutional Neural network with Temporal Pyramid network for Action Recognition

A Two-Pathway Convolutional Neural Network with Temporal Pyr...

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International Conference on Computer and Communications (ICCC)

作者： Zhu Jie Wu Muqing Xu Weiyao Beijing University of Posts and Telecommunications School of Information and communication Engineering Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Acknowledgment Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

In order to solve the problem of imperfect capture of visual rhythm in action recognition, this paper proposes a novel model which is a combination of a two-pathway network and temporal pyramid networks (TPNs). Specifically, our work involves two aspects, on the one hand, we integrate TPNs into the fast pathway and the slow pathway of SlowFast network to capture multi-level features, and then merge the prediction results of the two pathways in the final recognition stage, which boosts performance of our network by enhancing the semantics extraction at input layer and feature layer. On the other hand, we apply a ConvLSTM module to improve the capability of temporal modeling in TPN, which can further strengthen the capture of features in the long-term dimensions, and the advanced TPN promotes the fusion of temporal and spatial features. Experiments on the Kinetics-400 dataset demonstrate the superiority of our novel architecture combining two-pathway network and advanced TPN in action recognition.

关键词： Visualization Computational modeling Semantics Neural networks Feature extraction Rhythm Convolutional neural networks

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Dual Path Nets with A New Multi-scale Backbone architecture For Pulmonary Nodule Detection

Dual Path Nets with A New Multi-scale Backbone Architecture ...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Gong Maozhi Wu Muqing Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Acknowledgment Beijing University of Posts and Telecommunications School of Information and communication Engineering Beijing China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

This article mainly completed the detection of lung nodules using deep neural network and built a complete end-to-end detection system. U-net structure has significant advantages in image segmentation. We propose a dual-path network with a U-shaped structure that combines the advantages of residual learning and intensive learning for the detection of lung nodules. And on this basis, the residual module of Resnet was modified. The feature image fed into the residual module is divided into multiple blocks for convolution. Smaller residual blocks are added to each part to broaden the receptive field of each layer. So, it has the ability of extracting multi-scale feature. This network structure is called Res2net. The processed feature images are fused. We also added the SE module to focus on the relationship between feature information channels. It can strengthen the role of useful channels and suppress the output of useless channels. These changes significantly improve the detection effect of lung nodules. We used the position regression of lung nodules to return the predicted position of lung nodules and achieved good results. The detection indicator FROC is significantly better than other detection projects.

关键词： Feature extraction Lung Convolution Three-dimensional displays Computed tomography Lung cancer Residual neural networks

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Queue-sensing-based CSAT: A downlink transmission scheme for LTE-M in unlicensed spectrum

Queue-sensing-based CSAT: A downlink transmission scheme for...

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2019 International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2019

作者： Gong, Zelin Wang, Ying Luo, Tao Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789897583575

A shortage of licensed spectrum is a major hindrance to the capacity of Long Term Evolution for Metro (LTE-M) to carry urban rail transit services at present. Inspired by LTE-Unlicensed (LTE-U) offloading some of data traffic transmitted in licensed spectrum by accessing the unlicensed 5GHz frequency band, we propose a queue-sensing-based Carrier Sensing Adaptive Transmission (CSAT) scheme for LTE-M in unlicensed spectrum based on LTE-U. The scheme calculates the minimum amount of data that should be transmitted during current transmission duration by queue length at the beginning of each LTE-U cycle, according to which, LTE-U adjusts its duty cycle to meet the requirement of LTE-M service. This paper focuses on the downlink transmission scheme for LTE-M in unlicensed spectrum, and verifies its performance by simulation, which tells that Queue-sensing-based CSAT will not only provide a reliable transmission for LTE-M services, but also maximize the fairness of channel occupancy in unlicensed spectrum. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Long Term Evolution (LTE)

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Effective Fusion of 3DCNN and Convolutional GRU for Gesture Recognition

Effective Fusion of 3DCNN and Convolutional GRU for Gesture ...

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International Conference on Computer and Communications (ICCC)

作者： Luo Yangbiao Wu Muqing Xu Weiyao Beijing University of Posts and Telecommunications School of Information and communication Engineering Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Acknowledgment Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Gesture recognition is a challenging topic in the field of computer vision. This paper combines the advantages of 3DCNN and ConvRNN in spatial and temporal feature extraction respectively and proposes a network structure of “Res3D&Dilated+ConvGRU+2DCNN”. After that, a variant of ConvGRU is proposed, which further exerts the advantages of ConvGRU in temporal feature extraction. The experiment compares the effect of combining dilated convolution in the temporal dimension with 3D Resnet, and the effectiveness of the ConvGRU variant in spatiotemporal information fusion. In the 3D dilated convolutional network, we designed a suitable convolution kernel structure and step size so that subsequent modules can be reused directly. The modular structure design can be easily replaced. The comparison on the IsoGD dataset shows our network effectively integrates spatial and long-short terms features in gesture recognition videos. The variant of ConvGRU also enables each module to play its own advantages in the framework.

关键词： Three-dimensional displays Convolution Gesture recognition Feature extraction Spatiotemporal phenomena Kernel Videos

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From point to space: 3D moving human pose estimation using commodity WiFi

arXiv

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arXiv 2020年

作者： Wang, Yiming Guo, Lingchao Lu, Zhaoming Wen, Xiangming Zhou, Shuang Meng, Wanyu School of Information and Communication Engineering Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, we present Wi-Mose, the first 3D moving human pose estimation system using commodity WiFi. Previous WiFi-based works have achieved 2D and 3D pose estimation. These solutions either capture poses from one perspective or construct poses of people who are at a fixed point, preventing their wide adoption in daily scenarios. To reconstruct 3D poses of people who move throughout the space rather than a fixed point, we fuse the amplitude and phase into Channel State Information (CSI) images which can provide both pose and position information. Besides, we design a neural network to extract features that are only associated with poses from CSI images and then convert the features into key-point coordinates. Experimental results show that Wi-Mose can localize key-point with 29.7mm and 37.8mm Procrustes analysis Mean Per Joint Position Error (P-MPJPE) in the Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios, respectively, achieving higher performance than the state-of-the-art method. The results indicate that Wi-Mose can capture high-precision 3D human poses throughout the space. Copyright © 2020, The Authors. All rights reserved.

关键词： Channel state information

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CentiTrack: Towards centimeter-level passive gesture tracking with commodity WiFi

arXiv

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arXiv 2020年

作者： Han, Zijun Lu, Zhaoming Wen, Xiangming Zheng, Wei Zhao, Jingbo Guo, Lingchao School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks China

Gesture awareness plays a crucial role in promoting human-computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability and reliability. In this paper, we propose CentiTrack, the first centimeter-level passive gesture tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the Channel State Information (CSI) measurement error sources in the physical layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal Component Analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of dynamic path reflected off the hand. In addition, we adopt the Multiple Signal Classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range and device cost, compared with the state-of-the-arts. © 2020, CC-BY.

关键词： Channel state information

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Exploiting Visual Semantic Reasoning for Video-Text Retrieval

arXiv

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arXiv 2020年

作者： Feng, Zerun Zeng, Zhimin Guo, Caili Li, Zheng Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China

Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic relations to which existing methods pay less attention. To address this issue, we propose a Visual Semantic Enhanced Reasoning network (ViSERN) to exploit reasoning between frame regions. Specifically, we consider frame regions as vertices and construct a fully-connected semantic correlation graph. Then, we perform reasoning by novel random walk rule-based graph convolutional networks to generate region features involved with semantic relations. With the benefit of reasoning, semantic interactions between regions are considered, while the impact of redundancy is suppressed. Finally, the region features are aggregated to form frame-level features for further encoding to measure video-text similarity. Extensive experiments on two public benchmark datasets validate the effectiveness of our method by achieving state-ofthe-art performance due to the powerful semantic reasoning. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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C-RAN Enabled Seamless Mobility Mechanism in Autonomous Driving

C-RAN Enabled Seamless Mobility Mechanism in Autonomous Driv...

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General Assembly and Scientific Symposium, URSI

作者： Jian Zhang Luhan Wang Wei Zheng Xiangming Wen Beijing Advanced Innovation Center for Future Internet Technology Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9789463968003

ISBN: (纸本)9781728156903

Connected autonomous driving has already been a new paradigm of driving for safety, efficiency and mobility. Connected autonomous driving can overcome the perceptual limitations of single vehicle, which has a high demand for high speed and seamless network connection. With the superiority of C-RAN, autonomous driving network can effectively support a vast number of vehicles communications and flexible multi-RRH cooperative transmission to improve throughput. However, due to the high mobility of vehicle, the problem of long latency or even link break caused by frequent hard handover still exists. In this context, based on cooperative transmission and the idea of multiple connections, we studied seamless mobility mechanism in C-RAN enabled autonomous driving. In this paper, we propose a user-centric dynamic cooperative cluster updating scheme with the purpose of guaranteeing the seamless of the vehicle during handover process due to mobility. Simulation results demonstrate that the proposed scheme can effectively solve the interruption latency or link interruption problem caused by handover. And compared with the non-cooperative case, the proposed scheme is superior in throughput gain.

关键词： Handover Throughput Autonomous vehicles Delays 5G mobile communication Vehicle dynamics Signal to noise ratio

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When healthcare meets off-the-shelf wifi: A non-wearable and low-Costs approach for in-home monitoring

arXiv

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arXiv 2020年

作者： Guo, Lingchao Lu, Zhaoming Zhou, Shuang Wen, Xiangming He, Zhihong Beijing Laboratory of Advanced Information Networks China Beijing Key Laboratory of Network System Architecture and Convergence China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

As elderly population grows, social and health care begin to face validation challenges, in-home monitoring is becoming a focus for professionals in the field. Governments urgently need to improve the quality of healthcare services at lower costs while ensuring the comfort and independence of the elderly. This work presents an in-home monitoring approach based on off-the-shelf WiFi, which is low-costs, non-wearable and makes all-round daily healthcare information available to caregivers. The proposed approach can capture fine-grained human pose figures even through a wall and track detailed respiration status simultaneously by off-the-shelf WiFi devices. Based on them, behavioral data, physiological data and the derived information (e.g., abnormal events and underlying diseases), of the elderly could be seen by caregivers directly. We design a series of signal processing methods and a neural network to capture human pose figures and extract respiration status curves from WiFi Channel State Information (CSI). Extensive experiments are conducted and according to the results, off-the-shelf WiFi devices are capable of capturing fine-grained human pose figures, similar to cameras, even through a wall and track accurate respiration status, thus demonstrating the effectiveness and feasibility of our approach for in-home monitoring. Copyright © 2020, The Authors. All rights reserved.

关键词： Channel state information

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