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Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

China Communications 2024年第3期21卷 118-136页

作者： Dai Lin Fang Yi Guan Yongliang Mohsen Guizani School of Information Engineering Guangdong University of TechnologyGuangzhou 51006China The State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710126China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)Abu DhabiUnited Arab Emirates

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC ***, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

关键词： bit-interleaved coded modulation generalized spatial modulation multiple-input multipleoutput protograph LDPC codes visible light communication

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Efficient Multi-Query Oriented Continuous Subgraph Matching 40

Efficient Multi-Query Oriented Continuous Subgraph Matching

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Ma, Ziyi Yang, Jianye Zhou, Xu Xiao, Guoqing Wang, Jianhua Yang, Liang Li, Kenli Lin, Xuemin School of Artificial Intelligence Hebei University of Technology China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control China Cyberspace Institute of Advanced Technology Guangzhou University China PengCheng Laboratory Department of New Networks China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (纸本)9798350317152

Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many efficient CSM algorithms have been recently proposed, they are mainly designed to process a single query. However, in some application scenarios, multi-query oriented continuous subgraph matching (MQCSM) may be of more interest. To our knowledge, the two existing solutions to MQCSM are outdated due to unsatisfactory performance. In this paper, we propose MQ-Match, an efficient approach to MQCSM. First, we design a compact yet effective index structure CCG, which maintains the local matching result of vertices in the data graph using a directed graph. The directed edges in CCG can be utilized as an effective pruning rule for the subsequent incremental matching algorithm when expanding a partial match. Then, we develop a computation sharing incremental matching algorithm. In specific, a set of matching trees is constructed based on the depth-first search trees of the query graphs. By utilizing CCG, we conduct subgraph matching for the matching tree to collect the incremental matches for the query graphs, where the common structures of query graphs are matched only once. Extensive experiments show that MQ-Match can achieve 3.1x-7071.4x speedup over the competitors, and consumes much less memory under the majority of the experiment settings. © 2024 IEEE.

关键词： Directed graphs

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Stackelberg Game for Wireless Powered and Backscattering Enabled sensor networks

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China Communications 2024年第3期21卷 189-204页

作者： Lyu Bin Cao Yi Wang Shuai Guo Haiyan Hao Chengyao Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou 215006China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

关键词： backscatter communication energy interaction stackelberg game wireless powered sensor network

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Attention mechanism-based monocular depth estimation and visual odometry

Attention mechanism-based monocular depth estimation and vis...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Qieshi Lin, Dian Ren, Ziliang Kang, Yuhang Wu, Fuxiang Cheng, Jun Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen China Chinese University of Hong Kong Hong Kong Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9781665436786

In recent years, visual-inertial simultaneous localization and mapping (SLAM) have been widely researched and used. The monocular vision-based depth estimation and visual odometry (VO) plays a significant role in the SLAM system because of its low cost and high efficiency, which can be used to analyze the indoor environment for intelligent move robot application. Existing methods generally aim at the difference in pixel contrast between the previous and later frames to obtain the environment structure and camera pose. But the indifferent attention to the overall image makes the network spend extra computing power and hard to obtain the ideal results. For overcoming these disadvantages, an attention mechanism is proposed on basis of the original network and modified some convolution methods to make the network pay more attention to the key areas in the image and improve the accuracy of the estimation. The proposed approach is evaluated on the KITTI dataset, compared with the state-of-the-art methods achieves better results. © 2021 IEEE.

关键词： Vision

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Self-Attention Prediction Correction with Channel Suppression for Weakly-Supervised Semantic Segmentation

Self-Attention Prediction Correction with Channel Suppressio...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Guoying Sun Meng Yang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Integrated Services Networks Xidian University Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China

Single-stage weakly-supervised semantic segmentation (WSSS) with image-level labels has become a new research hotspot in the community for its lower cost and higher training efficiency. However, the pseudo label of WSSS generally suffers from somewhat noise, which limits the segmentation performance. In this paper, to explore the integral foreground activation, we propose the Channel Suppression (CS) module for preventing only activating the most discriminative regions, thereby improving the initial pseudo labels. To rectify the in-correct prediction, we explore the Self-Attention Prediction Correction (SAPC) module, which adaptively generates the category-wise prediction rectification weights. After extensive experiments, the proposed efficient single-stage framework achieves excellent performance with 67.6% mIoU and 39.9% mIoU on PASCAL VOC 2012 and MS COCO 2014 datasets, significantly exceeding several recent single-stage methods.

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Fine-Grained Depth Knowledge Distillation for Cloth-Changing Person Re-identification

Fine-Grained Depth Knowledge Distillation for Cloth-Changing...

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International Joint Conference on Neural networks (IJCNN)

作者： Yuhan Yao Jiawei Feng Ancong Wu Jiangqun Ni Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China School of Cyber Science and Technology Sun Yat-sen University Shenzhen China Department of New Networks Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Key Laboratory of Information Security Technology China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The mission of cloth-changing person re-identification (CC-ReID) is to discover cloth-invariant and identity-related cues, while traditional person ReID methods rely on appearance features that are biased to cloth-related cues. To tackle this cloth-biased problem, many CC-ReID methods introduced auxiliary body shape information to extract cloth-invariant features, such as 2D sketch images or the 3D Skinned Multi-Person Linear (SMPL) model. However, 2D auxiliary information lacks 3D spatial features, while the 3D SMPL model encounters challenges in capturing features at a finer granularity due to manually defined parameters. To extract fine-grained 3D shape features, we estimate depth maps that contain richer shape information and propose a Fine-grained Depth feature Mining and Distillation (FDMD) framework. We introduce a depth branch and design a fine-grained local feature interaction module to mine fine-grained 3D body shape knowledge from estimated depth maps by exploring the context of semantic-aware local body-part features. To integrate cloth-invariant depth knowledge into the appearance features, the fine-grained 3D shape features are transferred to an appearance branch by feature-space-aligned distillation. Extensive experiments demonstrate that FDMD can achieve state-of-the-art performance on three widely used CC-ReID benchmarks PRCC, Celeb-reID and LaST.

关键词： Solid modeling Three-dimensional displays Shape Neural networks Benchmark testing Feature extraction Data mining

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Efficient Multi-Query Oriented Continuous Subgraph Matching

Efficient Multi-Query Oriented Continuous Subgraph Matching

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International Conference on Data Engineering

作者： Ziyi Ma Jianye Yang Xu Zhou Guoqing Xiao Jianhua Wang Liang Yang Kenli Li Xuemin Lin School of Artificial Intelligence Hebei University of Technology China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University China Cyberspace Institute of Advanced Technology Guangzhou University China Department of New Networks PengCheng Laboratory China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

关键词： Heuristic algorithms Memory management Directed graphs Data engineering Fraud Computational efficiency Indexes

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An Efficient Multi-band Infrared Small Objects Detection Approach for Low-Altitude Artificial intelligence of Thing

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IEEE Internet of Things Journal 2025年

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Hangzhou310024 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria

As a cutting-edge technology of low-altitude Artificial intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multi-band infrared small object detection approach for low-altitude intelligent surveillance services. Firstly, we propose the Multi-band infrared Image Fusion algorithm based on Cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based Multi-scale Dense Small Object Detection (MsDSOD) algorithm is proposed. The algorithm consists of the Global-Local Object Detection (G-LOD) network, the Object Dense Area Extraction (O-DAE) module, and the Weighted Boxes Fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean Average Precision (mAP) of MsDSOD is improved by 0.80% and the Average Precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

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Toward Ambient intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration

arXiv

引用

arXiv 2022年

作者： Liu, Peixi Zhu, Guangxu Wang, Shuai Jiang, Wei Luo, Wu Poor, H. Vincent Cui, Shuguang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Shenzhen Research Institute of Big Data Shenzhen China The Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen518060 China Department of Electrical and Computer Engineering Princeton University Princeton NJ08544 United States Shenzhen Research Institute of Big Data Shenzhen China Peng Cheng Laboratory China

With the breakthroughs in deep learning and contactless sensors, the recent years have witnessed a rise of ambient intelligence applications and services, spanning from healthcare delivery to intelligent home. Federated edge learning (FEEL), as a privacy-enhancing paradigm of collaborative learning at the network edge, is expected to be the core engine to achieve ambient intelligence. Sensing, computation, and communication (SC2) are highly coupled processes in FEEL and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at the edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC2 resource jointly affect the FEEL performance. In this paper, we address the problem of joint SC2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing in ambient intelligence. First, by analyzing the wireless sensing process in human motion recognition, we find that there exists a thresholding value for the sensing transmit power, exceeding which yields sensing data samples with approximately the same satisfactory quality. Then, the joint SC2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time, energy supply, and sensing quality of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determine the joint sensing and communication resource allocation that maximizes the total number of samples that can be sensed during the entire training process;the second one concerns the partition of the attained total number of sensed samples over all the communication rounds to determine the batch size at each round for convergence speed maximization. The first subproblem on joint sensing and communication resource allocation is converted to a single-variable optimization problem by exploiting

关键词： Motion estimation

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Clustering effect of (linearized) adversarial robust models 21

Clustering effect of (linearized) adversarial robust models

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Yang Bai Xin Yan Yong Jiang Shu-Tao Xia Yisen Wang Tsinghua Berkeley Shenzhen Institute Tsinghua University Tsinghua Shenzhen International Graduate School Tsinghua University Tsinghua Berkeley Shenzhen Institute Tsinghua University and Tsinghua Shenzhen International Graduate School Tsinghua University and PCL Research Center of Networks and Communications Peng Cheng Laboratory China Tsinghua Shenzhen International Graduate School Tsinghua University and PCL Research Center of Networks and Communications Peng Cheng Laboratory China Key Lab. of Machine Perception School of Artificial Intelligence Peking University and Institute for Artificial Intelligence Peking University

ISBN: (纸本)9781713845393

Adversarial robustness has received increasing attention along with the study of adversarial examples. So far, existing works show that robust models not only obtain robustness against various adversarial attacks but also boost the performance in some downstream tasks. However, the underlying mechanism of adversarial robustness is still not clear. In this paper, we interpret adversarial robustness from the perspective of linear components, and find that there exist some statistical properties for comprehensively robust models. Specifically, robust models show obvious hierarchical clustering effect on their linearized sub-networks, when removing or replacing all non-linear components (e.g., batch normalization, maximum pooling, or activation layers). Based on these observations, we propose a novel understanding of adversarial robustness and apply it on more tasks including domain adaption and robustness boosting. Experimental evaluations demonstrate the rationality and superiority of our proposed clustering strategy.

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