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

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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

Global Finite-Time Stabilization for a Chain of Integrators in the Presence of Actuator Saturation by Homogeneous Feedback

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Global Finite-Time Stabilization for a Chain of Integrators ...

IEEE Conference on Decision and Control

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

This paper considers the problem of globally stabilizing a chain of integrators with actuator saturation in finite time. The proposed method is adapted from an existing scheduled low gain approach by relaxing the range of the value of the low gain parameter. Due to the special structure of the considered system, the proposed feedback law not only results in finite-time stability but also is homogeneous in the whole state space. The homogeneity of the proposed feedback law allows its implementation through the discretization of a compact set of the state space and thus obviation from numerical difficulties in the existing scheduled low gain design. A numerical example is presented to validate our theoretical results and demonstrate that our approach achieves faster convergence than the existing methods.

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties

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arXiv 2024年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2024, CC BY.

关键词： Ammonia

RDARS Empowered Massive MIMO System: Two-Timescale Transceiver Design with Imperfect CSI

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arXiv 2023年

作者： Ma, Chengzhi Wang, Jintao Yang, Xi Yang, Guanghua Zhang, Wei Ma, Shaodan State Key Laboratory of Internet of Things for Smart City the Department of Electrical and Computer Engineering University of Macau China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China School of Intelligent Systems Science and Engineering and GBA B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China School of Electrical Engineering & Telecommunications University of New South Wales Sydney Australia

In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive multiple-input multiple-output (MIMO) system with imperfect channel state information (CSI) and propose a practical two-timescale (TTS) transceiver design to reduce the communication overhead and computational complexity of the system. In the RDARS-aided system, not only distribution gain but also reflection gain can be obtained by a flexible combination of the distributed antennas and reflecting surface, which differentiates the system from the others and also makes the TTS design challenging. To enable the optimal TTS transceiver design, the achievable rate of the system is first derived in closed-form. The rate expression is general and covers that of the distributed antenna systems (DAS) and reconfigurable intelligent surface (RIS) aided systems as special cases. Then the TTS design aiming at the weighted sum rate maximization is considered. To solve the challenging non-convex optimization problem with high order design variables, i.e., the transmit powers and the phase shifts at the RDARS, a block coordinate descent based method is proposed to find the optimal solutions in semi-closed forms iteratively. Specifically, two efficient algorithms are proposed with provable convergence for the optimal phase shift design, i.e., Riemannian Gradient Ascent based algorithm by exploiting the unit-modulus constraints, and Two-Tier Majorization-Minimization based algorithm with closed-form optimal solutions in each iteration. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of deploying RDARS in massive MIMO systems to provide substantial rate improvement with a significantly reduced total number of active antennas/RF chains and lower transmit power when compared to the DAS and RIS-aided systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

STAR-RIS Enhanced Covert Communication with Delay Constraint

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STAR-RIS Enhanced Covert Communication with Delay Constraint

IEEE International Conference on Communications in China (ICCC)

作者： Beixi Cheng Jinsong Hu Youjia Chen Feng Shu Zhizhang David Chen College of Physics and Information Engineering Fuzhou University Fuzhou Fujian Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Information and Communication Engineering Hainan University Haikou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

This work confirms the performance gain achieved by deploying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in covert communication. To maximize the system covert rate, we jointly optimize the power allocation coefficients at the transmitter and the transmission and reflection coefficients at the STAR-RIS, subject to the delay-constrained and power-constrained. Furthermore, we propose a two-stage alternating optimization algorithm based on the semidefinite relaxation (SDR) method to derive the optimal solutions to the above non-convex optimization problem. The effectiveness and superiority of the proposed algorithm and STAR-RIS-assisted covert scheme are demonstrated by the numerical results.

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Reconfigurable Distributed Antennas and Reflecting Surface: A New Architecture for Wireless Communications

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arXiv 2023年

作者： Ma, Chengzhi Yang, Xi Wang, Jintao Yang, Guanghua Zhang, Wei Ma, Shaodan The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The School of Intelligent Systems Science and Engineering GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China The School of Electrical Engineering & Telecommunications University of New South Wales Sydney Australia

Distributed Antenna Systems (DASs) employ multiple antenna arrays in remote radio units to achieve highly directional transmission and provide great coverage performance for future-generation networks. However, the utilization of fully digital or hybrid active antenna arrays results in a significant increase in hardware costs and power consumption for DAS. To address these issues, integrating DAS with Reconfigurable Intelligent Surfaces (RIS) offers a viable approach to ensure coverage and transmission performance while maintaining low hardware costs and power consumption. To incorporate the merits of RIS into the DAS from practical consideration, a novel architecture of "Reconfigurable Distributed Antennas and Reflecting Surfaces (RDARS)" is proposed in this paper. Specifically, based on the design of the additional direct-through state together with the existing high-quality fronthaul link, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers and perform the connected mode as remote distributed antennas of the BS to receive or transmit signals. Additionally, RDARS also inherits the low-cost and low-energy-consumption benefits of fully passive RISs by default configuring the elements as passive to perform the reflection mode. As a result, RDARS encompasses both DAS and RIS as special cases, offering flexible control over the trade-off between distribution gain and reflection gain to enhance performance. To unveil the potential of such architecture, the ergodic achievable rate under the RDARS architecture is analyzed and closed-form expression with meaningful insights is derived. The theoretical analysis proves that the RDARS can achieve a higher achievable rate than both DAS and fully passive RIS with the passive beamforming gain provided by elements acting reflection mode while combating the "multiplicative fading" suffered by RISs through the connected mode performed at the RDARS. Simulation results also demonstr

关键词： Reconfigurable architectures

Learning Hierarchical Graph Representation for Image Manipulation Detection

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arXiv 2022年

作者： Pan, Wenyan Zhou, Zhili Ling, Miaogen Geng, Xin Wu, Q.M. Jonathan School of Computer and Software & Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China School of Computer Science and Engineering Southeast University Nanjing China Department of Electrical and Computer Engineering University of Windsor WindsorON Canada

The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images to locate the manipulated regions. However, these approaches ignore the feature correlations, i.e., feature inconsistencies, between manipulated regions and non-manipulated regions, leading to inferior detection performance. To address this issue, we propose a hierarchical Graph Convolutional Network (HGCN-Net), which consists of two parallel branches: the backbone network branch and the hierarchical graph representation learning (HGRL) branch for image manipulation detection. Specifically, the feature maps of a given image are extracted by the backbone network branch, and then the feature correlations within the feature maps are modeled as a set of fully-connected graphs for learning the hierarchical graph representation by the HGRL branch. The learned hierarchical graph representation can sufficiently capture the feature correlations across different scales, and thus it provides high discriminability for distinguishing manipulated and non-manipulated regions. Extensive experiments on four public datasets demonstrate that the proposed HGCN-Net not only provides promising detection accuracy, but also achieves strong robustness under a variety of common image attacks in the task of image manipulation detection, compared to the state-of-the-arts. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

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Mixture of Experts for Intelligent Networks: A Large Languag...

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study

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arXiv 2025年

作者： Liu, Tianhao Liu, Jiqiang Zhang, Tao Wang, Jian Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100045 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology 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 Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication. © 2025, CC BY.

关键词： Generative adversarial networks