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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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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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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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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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Enhanced User Interest and Expertise Modeling for Expert Recommendation

Enhanced User Interest and Expertise Modeling for Expert Rec...

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International Conference on Pattern Recognition

作者： Tongze He Caili Guo Yunfei Chu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Bejing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing

The rapid development of Community Question Answering (CQA) satisfies users' request for professional and personal knowledge. In CQA, one key issue is to recommend users with high expertise and willingness to answer the given questions, namely expert recommendation. However, most of existing methods for expert recommendation ignore some key information, such as time information and historical feedback information, degrading the performance. On the one hand, users' interest are changing over time. It is biased if we don't consider the dynamics. On the other hand, feedback information is critical to estimate users' expertise. To solve these problems, we propose a unified framework for expert recommendation to exploit user interest and expertise more precisely. Considering the inconsistency between them, we propose to learn their embeddings separately. We leverage Long Short-Term Memory (LSTM) to model user's short-term interest and combine it with long-term interest. The user expertise is learned by the designed user expertise network, which explicitly models feedback on users' historical behavior. The extensive experiments on a large-scale dataset from a realworld CQA site demonstrate the superior performance of our method than state-of-the-art solutions to the problem.

关键词： Knowledge discovery Pattern recognition

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Sequence Elimination Algorithm for Multi-Server Multi-User Mobile Edge Computing Offloading

Sequence Elimination Algorithm for Multi-Server Multi-User M...

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

作者： Yi Chu Ruixiang Li Fang Wang Junlong Ren Yuanyuan Qiao Jie Yang Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing China The Technology Research Institute Aisino Corporation Beijing China

ISBN: (纸本)9781665405836

Edge computing is a new paradigm of distributed computing, in which computing offloading attracts wide attention from scholars. Existing work mainly focuses on single-server and single-user, and generally only optimizes energy consumption or execution time respectively. However, optimizing one may lead to the decline in another. In order to remedy this defect, we propose sequence elimination algorithm based on genetic algorithm. The algorithm can optimize energy consumption or execution time respectively in the scenario of multi-user and multi-server. The simulation results show that the sequence elimination algorithm can effectively reduce the energy consumption and execution time in the edge computing offloading problem.

关键词： Energy consumption Multi-access edge computing Simulation Conferences Distributed computing Genetic algorithms

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Bandwidth and Power Allocation for Task-Oriented Semantic Communication

arXiv

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arXiv 2022年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Jiujiu Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

Deep learning enabled semantic communication has been studied to improve communication efficiency while guaranteeing intelligent task performance. Different from conventional communications systems, the resource allocation in semantic communications no longer just pursues the bit transmission rate, but focuses on how to better compress and transmit semantic to complete subsequent intelligent tasks. This paper aims to appropriately allocate the bandwidth and power for artificial intelligence (AI) task-oriented semantic communication and proposes a joint compressiom ratio and resource allocation (CRRA) algorithm. We first analyze the relationship between the AI task’s performance and the semantic information. Then, to optimize the AI task’s perfomance under resource constraints, a bandwidth and power allocation problem is formulated. The problem is first separated into two subproblems due to the non-convexity. The first subproblem is a compression ratio optimization problem with a given resource allocation scheme, which is solved by a enumeration algorithm. The second subproblem is to find the optimal resource allocation scheme, which is transformed into a convex problem by successive convex approximation method, and solved by a convex optimization method. The optimal semantic compression ratio and resource allocation scheme are obtained by iteratively solving these two subproblems. Simulation results show that the proposed algorithm can efficiently improve the AI task’s performance by up to 30% comprared with baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Bandwidth

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Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications

arXiv

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arXiv 2022年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Jiang, Nan 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 Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. In this paper, a deep learning-based task-oriented communication architecture is proposed where the user extracts, compresses and transmits semantics in an end-to-end (E2E) manner. Furthermore, an approach is proposed to compress the semantics according to their importance relevant to the task, namely, adaptable semantic compression (ASC). Assuming a delay-intolerant system, supporting multiple users indicates a problem that executing with the higher compression ratio requires fewer channel resources but leads to the distortion of semantics, while executing with the lower compression ratio requires more channel resources and thus may lead to a transmission failure due to delay constraint. To solve the problem, both compression ratio and resource allocation are optimized for the task-oriented communication system to maximize the success probability of tasks. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm by separating the problem into two subproblems and solving iteratively to obtain the convergent solution. Furthermore, considering the scenarios where users have various service levels, a compression ratio, resource allocation, and user selection (CRRAUS) algorithm is proposed to deal with the problem. In CRRAUS, users are adaptively selected to complete the corresponding intelligent tasks based on branch and bound method at the expense of higher algorithm complexity compared with CRRA. Simulation results show that the proposed ASC approach can reduce the size of transmitted data by up to 80% without reducing task success, and the proposed CRRA and CRRAUS algorithms can obtain at least 15% and 10% success gains over baseline algorithms, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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OFDM-Based Digital Semantic Communication with Importance Awareness

arXiv

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arXiv 2024年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Ni, Wanli Quek, Tony Q.S. 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 Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Tsinghua University Beijing100084 China The Dept. of Information Systems Technology and Design Singapore University of Technology and Design 487372 Singapore

Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance. However, existing works mainly focus on analog SemCom with simple channel models, which may limit its practical application. To reduce this gap, we propose an orthogonal frequency division multiplexing (OFDM)based SemCom system that is compatible with existing digital communication infrastructures. In the considered system, the extracted semantics is quantized by scalar quantizers, transformed into OFDM signal, and then transmitted over the frequency-selective channel. Moreover, we propose a semantic importance measurement method to build the relationship between target task and semantic features. Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance. However, the optimization objective function cannot be accurately characterized using a mathematical expression due to the neural network-based semantic codec. Given the complex nature of the problem, we first propose a low-complexity sub-carrier allocation method that assigns sub-carriers with better channel conditions to more critical semantics. Then, we propose a deep reinforcement learning-based bit allocation algorithm with dynamic action space. Simulation results demonstrate that the proposed system achieves 9.7% and 28.7% performance gains compared to analog SemCom and conventional bit-based communication systems, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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