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IMPULSE NOISE REMOVAL BY L1 WEIGHTED NUCLEAR NORM MINIMIZATION

Journal of Computational Mathematics 2023年第6期41卷 1171-1191页

作者： Jian Lu Yuting Ye Yiqiu Dong Xiaoxia Liu Yuru Zou Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and StatisticsShenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Pazhou LabGuangzhou 510335China Department of Applied Mathematics and Computer Science Technical University of Denmark2800 Kgs.LyngbyDenmark

In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research *** assigning different weights to singular values,the weighted nuclear norm minimization(WNNM)has been utilized in many ***,most of the work on WNNM is combined with the l 2-data-fidelity term,which is under additive Gaussian noise *** this paper,we introduce the L1-WNNM model,which incorporates the l 1-data-fidelity term and the regularization from *** apply the alternating direction method of multipliers(ADMM)to solve the non-convex minimization problem in this *** exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse *** results show that our method can effectively remove impulse noise.

关键词： Image denoising Weighted nuclear norm minimization l 1-data-fidelity term Low rank analysis Impulse noise

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CPAL: Cross-prompting Adapter with LoRAs for RGB+X Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5858-5871页

作者： Liu, Ye Wu, Pengfei Wang, Miaohui Liu, Jun Nanjing University of Posts and Telecommunications School of Automation and Artificial Intelligence Nanjing China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Lancaster University School of Computing and Communications Lancaster United Kingdom

As sensor technology evolves, RGB+X systems combine traditional RGB cameras with another type of auxiliary sensor, which enhances perception capabilities and provides richer information for important tasks such as semantic segmentation. However, acquiring massive RGB+X data is difficult due to the need for specific acquisition equipment. Therefore, traditional RGB+X segmentation methods often perform pretraining on relatively abundant RGB data. However, these methods lack corresponding mechanisms to fully exploit the pretrained model, and the scope of the pretraining RGB dataset remains limited. Recent works have employed prompt learning to tap into the potential of pretrained foundation models, but these methods adopt a unidirectional prompting approach i.e., using X or RGB+X modality to prompt pretrained foundation models in RGB modality, neglecting the potential in non-RGB modalities. In this paper, we are dedicated to developing the potential of pretrained foundation models in both RGB and non-RGB modalities simultaneously, which is non-trivial due to the semantic gap between modalities. Specifically, we present the CPAL (Cross-prompting Adapter with LoRAs), a framework that features a novel bi-directional adapter to simultaneously fully exploit the complementarity and bridging the semantic gap between modalities. Additionally, CPAL introduces low-rank adaption (LoRA) to fine-tune the foundation model of each modal. With the support of these elements, we have successfully unleashed the potential of RGB foundation models in both RGB and non-RGB modalities simultaneously. Our method achieves state-of-the-art (SOTA) performance on five multi-modal benchmarks, including RGB+Depth, RGB+Thermal, RGB+Event, and a multi-modal video object segmentation benchmark, as well as four multi-modal salient object detection benchmarks. The code and results are available at: https://***/abelny56/CPAL. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年第5期9卷 1-19页

作者： Li, Yueyuan Yuan, Wei Zhang, Songan Yan, Weihao Shen, Qiyuan Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Innovation Center of Intelligent Connected Vehicle Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving has grown substantially. However, there is a growing concern about the validity of algorithms developed and evaluated in simulators, indicating a need for a thorough analysis of the development status of the simulators. To address existing gaps in research, this paper undertakes a comprehensive review of the history of simulators, proposes a utility-based taxonomy, and investigates the critical issues within open-source simulators. Analysis of the past thirty years' development trajectory reveals a trend characterized by an increase in open-source simulators and an expansion of their functionality scope. The categorization of simulators based on feature functionalities delineates five primary classes: traffic flow, sensory data, driving policy, vehicle dynamics, and comprehensive simulators. Furthermore, the paper identifies critical unresolved issues in open-source simulators, including concerns regarding the fidelity of sensory data, representation of traffic scenarios, and accuracy in vehicle dynamics simulation, all of which have the potential to undermine experimental confidence. Additionally, challenges in data format inconsistency, labor-intensive map construction processes, sluggish step updating, and insufficient support for Hardware-In-the-Loop testing are discussed as hindrances to experimental efficiency. In light of these findings, the survey furnishes task-oriented recommendations to aid in the selection of simulators, taking into account factors such as accessibility, maintenance status, and quality, while highlighting the inherent limitations of existing open-source simulators in validating algorithms and facilitating real-world experimentation. IEEE

关键词： Autonomous vehicles

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The Internet of Things under Federated Learning:A Review of the Latest Advances and Applications

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Computers, Materials & Continua 2025年第1期82卷 1-39页

作者： Jinlong Wang Zhenyu Liu Xingtao Yang Min Li Zhihan Lyu School of Information and Control Engineering Qingdao University of TechnologyQingdao260043China Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving Anhui Jianzhu UniversityHefei230000China Department of Game Design Uppsala UniversityUppsala75310Sweden

With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application *** the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious *** learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data *** review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy *** this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy ***,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning *** also list various application scenarios of FL on the ***,we propose several open research challenges and possible solutions.

关键词： Federated learning Internet of Things sensors machine learning privacy security

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ANALYSIS OF TRIPARTITE GAME IN POWER SYSTEMS BASED ON UNCERTAIN NASH EQUILIBRIUM THEORY

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 117-128页

作者： Wu, Liang Yan, Xiaolin Yao, Zhangsong Zhao, Teng School of Management and Ningxia Key Laboratory of intelligent information and Big Data Processing North Minzu University Ningxia Yinchuan750021 China School of Mathematical Sciences Tiangong University Tianjin300387 China School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China School of Mathematics and Information Science North Minzu University Ningxia Yinchuan750021 China

Power systems serve as the fundamental infrastructure for the socioeconomic development of modern societies. Researching power systems can stimulate the growth of the power industry and contribute to the sustainable development of society. This paper aims to address uncertainties prevalent in power systems, including extreme weather events, disruptions in renewable energy supply, and adjustments in economic policies. To achieve this objective, an uncertain Nash equilibrium model is established. Subsequently, a game theoretic analysis is conducted to maximize the interests of users, grid companies, and proxy purchasers. Under the condition of not necessarily continuous differentiability, the Riemann-Stieltjes discrete approximation method has been proposed. The difficulty of calculating the expectation of the optimal revenue function has been resolved. Weak first-order equilibrium condition based on Clarke’s generalized gradient, the convergence and convergence rate of uncertain Nash equilibrium solutions are proved. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： power system tripartite game uncertain Nash equilibrium uncertainty theory

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Efficient Homomorphic Approximation of Max Pooling for Privacy-Preserving Deep Learning 6th

Efficient Homomorphic Approximation of Max Pooling for Pri...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Zhang, Peng Qiu, Dongyan Duan, Ao Liu, Hongwei The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen518118 China

ISBN: (纸本)9789819645657

Privacy-Preserving Deep Learning (PPDL) using Fully Homomorphic Encryption (FHE) addresses potential data privacy exposure risks associated with deploying deep learning models in untrusted cloud environments. FHE-based PPDL enables users to encrypt their data locally, allowing cloud service providers to perform computations directly on the encrypted data without ever accessing it. However, FHE faces challenges in efficiently handling nonlinear computations, which are essential to deep learning frameworks, particularly in implementing activation functions and max pooling layers. To address this challenge, we improve the homomorphic max pooling approximation scheme HMaxPool, and propose an efficient homomorphic max pooling algorithm, denoted as HMaxPool+. Specifically, we first redefine the approximation of the maximum function, and then design sub-algorithm to address the potential output expansion issue. The experimental results validate that both the homomorphic max pooling algorithm itself and the corresponding homomorphic SqueezeNet neural network demonstrate enhancements in computational efficiency and accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Dim-light image processing strategy rooted in improved dual-branch generative adversarial network

Dim-light image processing strategy rooted in improved dual-...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Li, keyi Wei, Zongshou Key Laboratory of Opt-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510681514

Images taken in dim lighting conditions usually suffer from drawbacks such as weak contrast along with unwanted interference. As a solution to this issue, this paper suggests a dim-light image processing strategy rooted in an improved dual-branch adversarial generative model. This method utilizes a generator network that combines a dim-light processing module and an image segmentation module, along with a patch discriminator network, trained using an adversarial loss function. The experiments demonstrate that the suggested approach for improving low-light image quality produces visually more captivating results when applied to real-world datasets. Moreover, it surpasses existing methods in terms of both preserving image structure and reducing noise. © 2024 SPIE.

关键词： Generative adversarial networks

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A Novel Second-Order Neurodynamic System to Fixed-Time Nash Equilibrium Seeking 14

A Novel Second-Order Neurodynamic System to Fixed-Time Nash ...

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14th International Conference on information Science and Technology, ICIST 2024

作者： Song, Yao Ju, Xingxing Yao, Chaoli College of Electronics and Information Engineering Sichuan University Chengdu China Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Shanghai China Hainan University Key Laboratory of Engineering Modeling and Statistical Computation of Hainan Province Haikou China

ISBN: (纸本)9798350353334

In this paper, we investigate fixed-time (FT) Nash equilibrium (NE) seeking problem for non-cooperative games. A novel second-order NE seeking neurodynamic system is proposed to accelerate the convergence. The proposed neurodynamic system's trajectory converges to NE within fixed-time under potential function and strongly monotone potential function respectively. It is shown that the upper bounds of the settling time are independent of the initial state of the system. The robustness of the proposed NE seeking neurodynamic system under bounded perturbations is further studied. The practicability of the proposed NE seeking system is illustrated via a simulation example and an analog circuit implementation on Multisim 14.3. © 2024 IEEE.

关键词： fixed-time Nash Equilibrium neurodynamic system Non-cooperative games

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GOAL: Generalized Jointly Sparse Linear Discriminant Regression for Feature Extraction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4959-4971页

作者： Lu, Haoquan Lai, Zhihui Zhang, Junhong Yu, Zhuozhen Wen, Jiajun Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China

Ridge regression (RR)-based methods aim to obtain a low-dimensional subspace for feature extraction. However, the subspace's dimensionality does not exceed the number of data categories, hence compromising its capability of feature representation. Moreover, these methods with L2-norm metric and regularization cannot extract highly robust features from data with corruption. To address these problems, in this article, we propose generalized jointly sparse linear discriminant regression (GOAL), a novel regression method based on joint L2,1-norm and capped-L2-norm, which can integrate sparsity, locality, and discriminability into one model to learn a full-rank robust feature extractor. The sparsely selected discriminative features are robust enough to characterize the decision boundary between classes. Locality is related to manifold structure and Laplacian smoothing, which can enhance the robustness of the model. By using the multinorm metric and regularization regression framework, the proposed method obtains the projection with joint sparsity and guarantees that the rank of the projection matrix will not be limited by the number of classes. An iterative algorithm is proposed to compute the optimal solution. Complexity analysis and proofs of convergence are also given in the article. Experiments on well-known datasets demonstrate our model's superiority and generalization ability. © 2020 IEEE.

关键词： Data mining

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Performance analysis of UAV-based mixed underwater PLC-RF systems

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Digital Communications and Networks 2024年第5期10卷 1332-1340页

作者： Shenghai Chen Liang Yang Xianfu Lei College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua418000China School of Information Science and Technology Southwest Jiaotong UniversityChengdu610031China

In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)*** this network,a buoy located at the sea is used as a relay to transmit signals from the underwater signal source to the UAV through the PLC *** assume that the UPLC channel obeys a log-normal distribution and that the RF link follows the Rician *** this model,we obtained the closed-form expressions for the Outage Probability(OP),Average Bit-error-rate(ABER),and Average Channel Capacity(ACC).In addition,the asymptotic analysis of the OP and ABER was performed,and an upper bound for the average capacity was ***,the analytical results were verified by Monte Carlo simulation thereby demonstrating the effect of impulse noise and the altitude of the UAV on network performance.

关键词： ABER Average bit-error-rate ACC Average channel capacity OP Outage probability UAV Unmanned aerial vehicle UPLC Underwater power line communication

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