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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Wang, Ruinan 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)9781728186351

ISBN: (纸本)9781728186351

Lung cancer affects human health severely and pulmonary nodule is an important manifestation of lung cancer. The diagnosis of lung cancer requires detection of each suspicious nodule. Early detection of lung cancer can greatly improve the survival rate of patients. It is very important to accurately find the location of pulmonary nodules in CT images and label them. Currently, CT is the most commonly used method to detect pulmonary nodules. In this paper, inspired by convolutional neural networks and the mechanisms of human attention, we propose a novel architectural block, which we term the Dual Path-Channel Attention (DPCA) block. This block can effectively prevent vanishing gradient phenomenon, enhance the transmission of context information and can weight related features according to the importance of different channels. We further propose a simple, highly efficient 3D pulmonary nodule detection model named DPCA-Net, which is constructed by DPCA block as a key component. Compared with other excellent pulmonary nodule detection models, our model achieves effective improvement on the public large-scale LUNA16 dataset. © 2020 IEEE.

关键词： Convolutional neural networks

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Task-Oriented Image Semantic Communication Based on Rate-Distortion Theory

arXiv

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arXiv 2022年

作者： Liu, Fangfang Tong, Wanjie Yang, Yang Sun, Zhengfen Guo, Caili The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Beijing Laboratory of Advanced Information Net Works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

Task-oriented image semantic communication is a new communication paradigm, which aims to transmit semantics for artificial intelligent (AI) tasks while ignoring the reconstruction quality of the images. However, in some applications, such as autonomous driving, both image reconstruction quality and the performance of the followed AI tasks must be simultaneously considered. To tackle this challenge, this paper proposes a task-oriented semantic communication scheme with semantic reconstruction (TOSC-SR). Its main goal is to simultaneously minimize pixel-level and task-relevant semantic-level distortion during communications under a certain rate, which formulates a new rate-distortion optimization problem. To successfully measure the loss at the semantic level, a new form of semantic distortion measured by the mutual information between the semantic-reconstructed images and the task labels is proposed. Then, we derive an analytical solution for the formulated problem, where the self-consistent equations of the problem are obtained to determine the optimal mapping of the source and the semantic-reconstructed images. To implement TOSC-SR, we further obtain an extended form of rate-distortion form based on the variational approximation of mutual information, which is applicable to multiple AI tasks. Experimental results show that the proposed approach outperforms the traditional JPEG, JPEG2000, BPG, VVC-based image communication systems and deep learning based benchmarks in terms of image reconstruction quality, AI task performance, and multi-task generalization ability. © 2022, CC BY.

关键词： Semantics

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An Effective Controller Placement Algorithm Based on Clustering in SDN 6

An Effective Controller Placement Algorithm Based on Cluster...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Yujie, Rong Muqing, Wu Yiming, Chen 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: (纸本)9781728186351

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. © 2020 IEEE.

关键词： Controllers

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A Two-Pathway Convolutional Neural network with Temporal Pyramid network for Action Recognition 6

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

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Jie, Zhu Muqing, Wu Weiyao, Xu 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: (纸本)9781728186351

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. © 2020 IEEE.

关键词： Convolutional neural networks

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Effective Fusion of 3DCNN and Convolutional GRU for Gesture Recognition 6

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

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Yangbiao, Luo Muqing, Wu Weiyao, Xu 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: (纸本)9781728186351

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 'Res3DDilated+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. © 2020 IEEE.

关键词： Convolution

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Study on Pedestrian Detection Based on an Improved YOLOv4 Algorithm 6

Study on Pedestrian Detection Based on an Improved YOLOv4 Al...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Wen, Boyuan Wu, Muqing 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)9781728186351

Pedestrian detection, which is widely used in automatic driving and pedestrian analysis, has always been a hot research topic in the fields of artificial intelligence and computer vision. With the development of deep learning, pedestrian detectors are becoming more accurate and faster. However, most of them can't strike a balance between accuracy and speed well. Therefore, in this study, we propose a pedestrian detection model based on an improved YOLOv4 algorithm which concerns both detection accuracy and efficiency. The detection model combines a new type of SPP (Spatial Pyramid Pooling) network and K-means clustering algorithm with YOLOv4 model for easier feature extraction. Furthermore, Mish activation function is applied in the neck of the detection model, replacing Leaky ReLU activation function to improve the detection performance. Our pedestrian detector achieves excellent results on the Caltech pedestrian dataset: 84.7% AP at a real-time speed of 36.4 FPS on Titan XP. © 2020 IEEE.

关键词： K-means clustering

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Mobility-Aware Proactive Video Segment Caching Based on Deep Reinforcement Learning

Mobility-Aware Proactive Video Segment Caching Based on Deep...

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IEEE International Conference on network Infrastructure and Digital Content (IC-NIDC)

作者： Xuefei Li Jiawei Wang Zhilong Zhang Danpu Liu Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665405836

Maintaining efficient and successive video streaming services in cellular networks is challenging due to user mobility and ever-increasing volume of data traffic. A promising solution is to cache popular contents at the edge of wireless networks. Although caching schemes have been widely discussed, few of them jointly considered the characteristics of streamed video data and user mobility. In this paper, we construct a dynamic caching decision framework based on Long Short-Term Memory (LSTM) and Deep Q-network (DQN). Based on this framework, a mobility-aware segment-level caching strategy is proposed to maximize the cache hit rate. Simulation results show that our proposed method can achieve 20% performance improvement by comparing with baseline caching algorithms.

关键词： Cellular networks Simulation Wireless networks Heuristic algorithms Conferences Reinforcement learning Bandwidth

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Exploiting visual semantic reasoning for video-text retrieval 29

Exploiting visual semantic reasoning for video-text retrieva...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

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

ISBN: (纸本)9780999241165

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. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

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Performance analysis and enhancement of random access process in cellular-IoT

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The Journal of China Universities of Posts and Telecommunications 2019年第6期26卷 1-10页

作者： Xing Shuchen Wen Xiangming Lu Zhaoming Pan Qi Jing Wenpeng Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information NetworksBeijing University of Posts and TelecommunicationsBeijing 100876China

Narrowband Internet of things(NB-IoT) and enhanced machine-type communications(eMTC) are two new IoT-oriented solutions introduced by the 3 rd generation partnership project(3 GPP) in Rel-13. In order to meet the new requirements(such as long battery life, low device cost, low deployment cost, extended coverage and support for a massive number of devices) of machine-to-machine(M2 M) communication, these two technologies had some improvements on the random access(RA) mechanism compared to traditional long term evolution(LTE). For example, repetition of preamble transmission and coverage enhancement(CE) levels have been proposed to offer communication services in a wider area. In addition, NB-IoT has adopted a new spectrum allocation method and proposed a new type of preamble structure to meet the requirement of big amount of connections. We summarize details and differences of the RA process in LTE, eMTC and NB-IoT. Afterwards, as an improvement, we propose an enhanced access protocol for NB-IoT. Finally, performance analysis and comparison are presented in terms of access success probability, average access delay, access spectrum efficiency and average number of RA attempts.

关键词： NB-IoT eMTC LTE RA access protocol

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A Fast Global Adaptive Solution to Low-light Images Enhancement in Visual SLAM 6

A Fast Global Adaptive Solution to Low-light Images Enhancem...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Cheng, Weishao Zhang, Yanting Qi, Yonggang Liu, Jun Liu, Fang Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Donghua University Shanghai201620 China

ISBN: (纸本)9781728186351

Simultaneous Localization and Mapping (SLAM) is considered as an essential basis for autonomous robots and vehicles. Visual SLAM applies visual sensors to collect information and estimate the motion of the sensor in an unknown environment. However, it relies heavily on feature matching within images, so that the quality of the images has a great effect on the efficiency. In a low light intensity environment, the performance of motion estimation declines seriously. This paper presents a global adaptive solution to compensate the light condition and applies this solution to motion estimation in ORB-SLAM as an optimization module. key of the solution is to obtain global adaptive brightness gain for image intensifiers. The performance of the image intensifier is also evaluated in this paper as well as the proof of higher estimation accuracy. The result of the strengthened ORB-SLAM on the TUM benchmark shows that there is a significant performance boost especially in low luminance environment. © 2020 IEEE.

关键词： Luminance

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