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arXiv 2022年

作者： Cheng, Ziyang Wu, Linlong Wang, Bowen Shankar, Bhavani Liao, Bin Ottersten, Björn School of Information & Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Luxembourg Luxembourg The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China

As a promising technology in beyond-5G (B5G) and 6G, dual-function radar-communication (DFRC) aims to ensure both radar sensing and communication on a single integrated platform with unified signaling schemes. To achieve accurate sensing and reliable communication, large-scale arrays are anticipated to be implemented in such systems, which brings out the prominent issues on hardware cost and power consumption. To address these issues, hybrid beamforming (HBF), beyond its successful deployment in communication-only systems, could be a promising approach in the emerging DFRC ones. In this article, we investigate the development of the HBF techniques on the DFRC system in a self-contained manner. Specifically, we first introduce the basics of the HBF based DFRC system, where the system model and different receive modes are discussed with focus. Then we illustrate the corresponding design principles, which span from the performance metrics and optimization formulations to the design approaches and our preliminary results. Finally, potential extension and key research opportunities, such as the combination with the reconfigurable intelligent surface, are discussed concisely. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Defect image generation through feature disentanglement using StyleGAN2-ADA

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Neurocomputing 2025年 647卷

作者： He, Zhiquan Wu, Kangxing Wen, Yang Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China

The limited availability of real defect samples and the high cost associated with manual defect generation present significant challenges in automated defect detection systems. In this paper, we propose a novel few-shot defect generation approach based on StyleGAN2-ADA, designed to produce high-quality defect samples for improving defect and anomaly detection performance. The proposed methodology comprises two stages: first, two independent few-shot generation models learn the distributions of defect-rich and defect-free datasets, respectively. Next, an indirect decoupling strategy is employed to incorporate prior knowledge of defect distributions, while still facilitating the unconditional generation of defect samples. To further enhance the generation process, we introduce a defect mask attention mechanism that specifically targets defect regions and the objects of interest, effectively preventing the generation of defect-free images and reducing background degradation. This method not only enhances the precision of defect generation but also allows for the flexible and realistic simulation of random defect variations. Extensive experiments on the MVTec AD and VisA dataset demonstrate the superiority of our approach, outperforming existing GAN-based defect generation methods in few-shot scenarios. Specifically, the proposed method excels across 35 different defects and 80 evaluation metrics, with 55 metrics surpassing all previous methods and 28 ranking second. Our approach achieves an average improvement of 23% on the KID metric. © 2025 Elsevier B.V.

关键词： Anomaly detection

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Computer Vision and Image Understanding

SSRN

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SSRN 2022年

作者： Tang, Wenming Gong, Yuanhao Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science The University of Nottingham United Kingdom

Graph neural networks (GNNs) are ideally suited for mesh denoising. However, existing solutions such as those based on graph convolutional networks (GCNs) are built for a fixed graph thus making them not naturally generalisable to unseen meshes. Furthermore, their graph Laplacian based global node embedding algorithms can cause excessive smoothing while achieving feature preserving mesh denoising requires a GNN to posses local processing capability. This paper presents a mesh denoising method via a new Dense lOcal Graph neural NETwork (DOGNET). DOGNET implements a local node embedding algorithm that generates node embeddings through aggregating information from a node’s connected local neighbours which automatically make DOGNET inductive as well as effective for feature preserving mesh denoising. We present extensive experimental results to demonstrate quantitatively and qualitatively that DOGNET is superior to SOTA meshing denoising techniques. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Achieving High Efficiency in Schnorr-Based Multi-Signature Applications in Blockchain

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Electronics (Switzerland) 2025年第9期14卷 1883-1883页

作者： Zhang, Peng Ge, Fa Tang, Zujie Xie, Weixin Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China

Multi-signature applications allow multiple signers to collaboratively generate a single signature on the same message, which is widely applied in blockchain to reduce the percentage of signatures in blocks and improve the throughput of transactions. The k-sum attacks are one of the major challenges in designing secure multi-signature schemes. In this work, we address k-sum attacks from a novel angle by defining a Public Third Party (PTP), which is an automatic process that can be verifiable by the public and restricts the signing phase from continuing until receiving commitments from all signers. Further, a two-round multi-signature scheme HEMS with PTP is proposed, which is secure based on the discrete logarithm assumption in the random oracle model. As each signer communicates directly with the PTP instead of other co-signers, the total amount of communication is significantly reduced. In addition, as PTP participates in the computation of the aggregation and signing algorithms, the computation cost left for each signer and verifier remains the same as the basis Schnorr signature. To the best of our knowledge, this is the high efficiency that a Schnorr-based multi-signature scheme can achieve. Further, HEMS is applied in a blockchain platform, e.g., Fabric, to improve transaction efficiency. © 2025 by the authors.

关键词： multi-signature Schnorr signature k-sum attacks Public Third Party blockchain

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Joint Subcarrier-Pair and Power Allocation Based on SWIPT in Multi-Relay OFDM System

Joint Subcarrier-Pair and Power Allocation Based on SWIPT in...

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2020 International Conference on Computing, Networking and Communications, ICNC 2020

作者： Lu, Weidnag Liu, Guangzhe Si, Peuyuan Huang, Guoxing Li, Bo Gong, Yi Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Harbin Institute of Technology School of Information and Electrical Engineering Weihai264209 China Southern University of Science and Technology Shenzhen Engineering Laboratory of Intelligent Information Processing for IoT Shenzhen518055 China

ISBN: (数字)9781728149059

ISBN: (纸本)9781728149059

In this paper, we address the transmission rate optimization problem for a multi-relay orthogonal frequency division multiplexing (OFDM) system with simultaneous wireless information and power transfer (SWIPT), where each amplify-and-forward (AF) relay does not consume its own energy. By introducing a subcarrier-pair factor, we provide the joint subcarrier-pair and power allocation scheme based on the Hungarian algorithm and Lagrange dual method to maximize the transmission rate. Simulations show that the proposed algorithm can perform better on information transmission rate than other algorithms. © 2020 IEEE.

关键词： Orthogonal frequency division multiplexing

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Large Depth Range Binary-Focusing Projection 3d Shape Reconstruction Via Unpaired Data Learning

SSRN

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SSRN 2023年

作者： Tan, Ji Wang, Xu He, Zhaoshui Su, Wenqing Huang, Tao Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

The binary defocusing technique (BDT) shows prospects in high-speed 3D reconstruction. However, the defocusing projection mechanism results in low accuracy at inappropriate defocus levels due to the high-order harmonics, thereby constraining the achievable depth range. To overcome the problems, this paper proposes a large depth range binary-focusing projection technique, aimed at fundamentally solving the compromise of the reconstructed depth range. Based on the focused-projection strategy, we develop a cycle generative adversarial framework for the binary-to-sinusoidal domain translation to obtain high-quality phase information in both the focused and improperly defocused regions. Additionally, a dedicated unpaired dataset is built to perform self-supervised learning through the utilization of a cycle consistency strategy. Compared with the traditional BDT, the proposed method is not limited by the defocusing degree but inherits the advantages of high-speed projection, thus showing substantial potential to achieve super-fast and large depth range dynamic reconstruction and measurement. Experiments verify the performance of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Transparent Four-Port MIMO DRAs Decoupled by Conformal Strips

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strip...

引用

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Chao He Nan Yang Kwok Wa Leung School of Electronics and Information Technology and Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao China Department of Electrical Engineering State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Kowloon China Information and Communication Technology Centre CityU Shenzhen Research Institute Shenzhen China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

A four-port multi-input multi-output (MIMO) dielectric resonator (DR) antenna (DRA) is presented in this paper. Combing four rectangular DRs together can form a single DR element. With shape modification and conformal strips, the four ports can be decoupled. To verify the design idea, a transparent prototype can be designed and fabricated for 2.4-GHz band applications. The measured results show an impedance bandwidth of 5.8% and an isolation level of 24 dB.

关键词： Antenna measurements Impedance measurement Shape Prototypes Glass Bandwidth Gain measurement Impedance Microstrip Gain

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Dual Auction Mechanism for Transaction Relay and Validation in Complex Wireless Blockchain Network

arXiv

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arXiv 2023年

作者： Wang, Weiyi Jiao, Yutao Chen, Jin Dai, Wenting Kang, Jiawen Xu, Yuhua The College of Communications Engineering Army Engineering University of PLA Nanjing210007 China The School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China

In traditional public blockchain networks, transaction fees are only allocated to full nodes (i.e., miners). However, the lack of relay rewards reduces the willingness of light nodes to relay transactions, especially in the energy-constrained complex wireless network. This paper proposes a novel dual auction mechanism to allocate transaction fees for relay and validation behaviors in the wireless blockchain network. The dual auction mechanism consists of two sub-auction stages: the relay sub-auction and the validation sub-auction. In the relay sub-auction, relay nodes choose transactions based rewards to forward. Besides, relay nodes adjust the relaying probability through a no-regret algorithm to improve efficiency. In the validation sub-auction, full nodes select transactions using Vickrey-Clarke-Grove (VCG) mechanism to construct the block. We prove that the designed dual auction mechanism is Incentive Compatibility (IC), Individual Rationality (IR), and Computational Efficiency (CE). We also derive the upper bound of the social welfare difference between the social optimal auction and our proposed one. Extensive simulation results demonstrate that the proposed dual auction mechanism decreases energy and bandwidth resource consumption and effectively improves social welfare without sacrificing the throughput and the security of the wireless blockchain network. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks

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SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification

arXiv

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arXiv 2024年

作者： Li, Zuoyong Lin, Qinghua Fan, Haoyi Zhao, Tiesong Zhang, David The Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China The School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou45000 China The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Shenzhen518172 China

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76% and 89.13% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in https://***/AlchemyEmperor/SIAVC. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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