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Regularization with multilevel non-stationary tight framelets for image restoration

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arXiv 2021年

作者： Li, Yan-Ran Chan, Raymond H.F. Shen, Lixin Zhuang, Xiaosheng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong Department of Mathematics Syracuse University SyracuseNY13244 United States

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

Classification decision based on a hybrid method of weighted knn and hyper-sphere SVM

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International Journal of Performability engineering 2018年第5期14卷 985-994页

作者： Chen, Peng Shi, Guoyou Liu, Shuang Zhang, Yuanqiang Špelič, Denis Navigation College Dalian Maritime University Dalian116026 China Department of Software Engineering Dalian Neusoft University of Information Dalian116030 China School of Computer Science and Engineering Dalian Minzu University Dalian116605 China Faculty of Electrical Engineering and Computer Science University of Maribor MariborSI-2000 Slovenia

Hyper-sphere Support Vector Machine (SVM) is very effective for solving multi-class classification problems. Considering data distribution is very important for convergence of solving support vectors, a weight factor is imported into the original hyper-sphere SVM. After computing data for each training class, this weight factor is decided by its center-distance ratio. In the training process, data with bigger weight is put into the data processing thread first and is then followed by smaller ones. To save computation cost, a parallel genetic algorithm based SMO multi-threading is adopted. For a test sample, its class decision is based on its position with each classification of hyper-sphere. If all class-specific hyper-spheres are independent of each other, a new test sample can be classified correctly. But, if some hyper-spheres have common spaces, that is, one hyper-sphere intersects with one or more hyper-spheres, it is hard to decide the class of the test sample. Based on detailed analysis of three decision rules for the intersection data classification, one decision rule that combines the kNN method is put forward in this paper. For other simple inclusion cases, the simple decision rule is defined. Through two real experimental results of navigation tracking and ship meeting situations classification, our new proposed algorithm has a higher classification accuracy and boasts a lower computation cost than other algorithms. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Support vector machines

Ant Colony Algorithm for Container-based Microservice Scheduling in Hybrid Cloud

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Journal of Physics: Conference Series 2021年第1期1994卷

作者： Miao Lin Jianqing Xi Weihua Bai Jiayin Wu School of Software Engineering South China University of Technology Guangzhou 510006 China School of Computer Science Zhaoqing University Zhaoqing 526061 China School of Computer Guangdong Vocational College of Post and Telecom Guangzhou 510630 China

The microservice model divides the application into a group of loosely coupled and collaborative fine-grained services, which results in the workflow scheduling problem, especially in the complex environment of resource diversity such as hybrid cloud. To solve this problem, this paper establishes a workflow scheduling model of microservice based on multiple container instances and proposes an ant colony optimization (ACO) algorithm to optimize finish time and resource cost. The algorithm proposed optimizes update strategies of multi-objective heuristic information to improve the selection probability of the dominant path. The comparative experiments show that the proposed optimization algorithm achieves better results in scheduling objectives in the hybrid cloud.

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Forecasting in non-stationary environments with fuzzy time series

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arXiv 2020年

作者： de Lima e Silva, Petrônio Cândido Junior, Carlos Alberto Severiano Alves, Marcos Antonio Silva, Rodrigo Cohen, Miri Weiss Guimarães, Frederico Gadelha Laboratory Federal University of Minas Gerais Belo Horizonte Brazil Federal Institute of Education Science and Technology of Northern Minas Gerais Januária Campus Brazil Federal Institute of Education Science and Technology of Minas Gerais Sabará Brazil Department of Computer Science Federal University of Ouro Preto Ouro Preto Brazil Department of Software Engineering Braude College of Engineering Karmiel Israel Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil

In this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS) method with time varying parameters adapted from the distribution of the data. In this approach, we employ Non-Stationary Fuzzy Sets, in which perturbation functions are used to adapt the membership function parameters in the knowledge base in response to statistical changes in the time series. The proposed method is capable of dynamically adapting its fuzzy sets to reflect the changes in the stochastic process based on the residual errors, without the need to retraining the model. This method can handle non-stationary and heteroskedastic data as well as scenarios with concept-drift. The proposed approach allows the model to be trained only once and remain useful long after while keeping reasonable accuracy. The flexibility of the method by means of computational experiments was tested with eight synthetic non-stationary time series data with several kinds of concept drifts, four real market indices (Dow Jones, NASDAQ, SP500 and TAIEX), three real FOREX pairs (EUR-USD, EUR-GBP, GBP-USD), and two real cryptocoins exchange rates (Bitcoin-USD and Ethereum-USD). As competitor models the Time Variant fuzzy time series and the Incremental Ensemble were used, these are two of the major approaches for handling non-stationary data sets. Non-parametric tests are employed to check the significance of the results. The proposed method shows resilience to concept drift, by adapting parameters of the model, while preserving the symbolic structure of the knowledge base. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

Blockchain-based approach for securing spectrum trading in multibeam satellite systems

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arXiv 2020年

作者： Li, Feng Lam, Kwok-Yan Jia, Min Zhao, Jun Li, Xiuhua Wang, Li School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China College of Marine Electrical Engineering Dalian Maritime University Dalian116026 China

This paper presents a blockchain-based approach for securing spectrum sharing in multi-beam satellite systems. Satellite spectrum is a scarce resource that requires highly efficient management schemes for optimized sharing by network users. However, spectrum sharing is vulnerable to attacks by malicious protocol participants. In order to ensure efficient spectrum management in the face of dishonest satellite users or cyber attackers, it is important for spectrum sharing mechanism to provide transparency and traceability of the trading process so as to enable the system to detect, and hence eliminate, unauthorized access by malicious users. We address these requirements by proposing the use of blockchain which, apart from its ability to provide transparency and traceability, ensures an immutable means for keeping track of user trading reputation. Besides, in order to address the practical constraints of heterogeneous user nodes, we also propose the use of edge computing to support users with limited computing power. In this paper, we propose a blockchain-based spectrum trading framework and, based on which, a multibeam satellite spectrum sharing algorithm for interference pricing and heterogeneous spectrum demands is devised to improve the efficiency of satellite spectrum. By leveraging on the system characteristics of blockchain, a dynamic spectrum sharing mechanism with traceability, openness and transparency for whole trading process is presented. Numerical results are also provided to evaluate the system benefits and spectrum pricing of the proposed mechanism. © 2020, CC BY.

关键词： Blockchain

Dynamic Weight Allocation–Based Network Security and Anomaly Detection Model for Intelligent VANETs

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Transactions on Emerging Telecommunications Technologies 2025年第6期36卷

作者： Aadam Quraishi Rakeshnag Dasari Sushilkumar Dangiya Sateesh Kumar Nallamala Krishna Kanth Kondapaka Swaroop Reddy Gayam Isa Bayhan Uguloy Berdieva Rubal Jeet M.D. Research Cognitive and AI laboratory Interventional treatment institute Houston Texas USA Department of CSE Acharya Nagarjuna University Nagarjuna Nagar Guntur India Department of Electronics and Communication Engineering GTU – School of Engineering and Technology Ahmedabad India Independent Researcher USA Independent Researcher California USA Independent Researcher and Senior Software Engineer at TJMax USA Assistant Professor of Tourist Guiding Dept Bolu Abant Izzet Baysal University Golkoy Campus Bolu Turkey The Department of Tax and Taxation Tashkent State University of Economics Tashkent Uzbekistan Chandigarh Group of Colleges Jhanjeri Chandigarh Engineering College Department of Computer Science and Engineering Punjab India

Determining the weights of evaluation metrics is one of the key factors influencing the cybersecurity and anomaly detection of intelligent vehicular ad hoc networks (VANETs). To address the limitations of traditional weighting methods, which often overlook the impact of changes in metric attribute states on evaluation weights, this paper proposes a dynamic weight allocation–based network security and anomaly detection model. The model begins by decomposing and analyzing the security and anomaly detection objectives of VANETs, constructing a comprehensive evaluation metric system. The network security assessment model for VANETs presented in this research overcomes the drawbacks of conventional static models by utilizing a dynamic weight allocation technique. Based on current network conditions, a state variable weight method was created that dynamically computes security values by combining incentive and penalty mechanisms. A ranking-based weighting algorithm is employed to analyze the correlation between security and anomaly detection metrics. Subsequently, the proposed dynamic weight allocation algorithm calculates the dynamic weights of individual metrics within the system, enabling a robust assessment of network security and anomaly detection for intelligent VANETs. The evaluation results provide security level classifications and identify anomalies effectively. Experimental results demonstrate that the model significantly enhances the rationality and accuracy of intelligent VANET evaluations, contributing to improved cybersecurity and anomaly detection.

关键词： anomaly detection dynamic weight allocation intelligent vehicle network security VANETs

Topology Sequences-based Fuzzy Track Initialization Method For Radars and ADS-B*

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Topology Sequences-based Fuzzy Track Initialization Method F...

Asian Control Conference

作者： Xiaobin Li Hongfeng Ma En Fan College of Software Engineering Lanzhou Institute of Technology Lanzhou China College of Electronic and Information Engineering Lanzhou Institute of Technology Lanzhou China Department of Computer Science and Engineering Shaoxing University Shaoxing China

ISBN: (纸本)9781728102634

Radars and automatic dependent surveillance -broadcast (ADS-B) are two important surveillance technologies in aerial surveillance networks. For this situations with different systematic errors of 2D radars and their time-difference-arrival (TOA) measurements in the fusion center, a fuzzy track initialization method based on topology sequences (TS-FTI) for heterogeneous sensors is proposed. In this proposed method, topology sequences of various measurements from a radar and an ADS-B receiver are respectively constructed. Then the topology sequence method is utilized to classify these measurements. The measurement sets of candidate targets can be constructed by accumulating measurements in several successive sampling intervals. Finally, a fuzzy Hough transform (FHT) method is used to detect tracks. The performance of the TS-FTI method is evaluated by simulation experiments, and it is found to be better than that of the track initialization method based on standard Hough transform (HT-TI) in calculated complexity and correct initialization rate.

关键词： Radar tracking Topology Receivers Radar measurements Target tracking Surveillance

Application of quantisation-based deep-learning model compression in JPEG image steganalysis

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Application of quantisation-based deep-learning model compre...

作者： Wu, Xiancheng Shao, Zilong Ou, Pei Tan, Shunquan College of Information Engineering Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Steganography can hide secret information in an innocent cover medium. Its opponent is steganalysis, which is used to discriminate whether a suspicious carrier contains a hidden message or not. With the rapid development of deep-learning frameworks, deep-learning-based steganalytic models have hold the dominant position in the field of steganalysis. In recent years, some scholars have successfully utilised model compression methods in the field of image classification. However, as far as the authors know, no prior works are devoted to the application of model compression methods in the field of deep-learning-based steganalysis. In this study, the authors explore the effect of two quantisation schemes, namely 8-bit calculation and floating-point calculation, on the performance of XuNet, a state-of-the-art deep-learning steganalytic model. The experimental results show that the two deep-learning model quantisation schemes are applicable to steganalysis. It is even possible to compress the network size while retaining satisfactory performance. © 2018 Institution of engineering and Technology. All rights reserved.

关键词： Steganography

Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems

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arXiv 2020年

作者： Zhang, Haijun Zhang, Haisen Liu, Wei Long, Keping Dong, Jiangbo Leung, Victor C.M. Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China China Mobile Group Design Institute Co. Ltd Beijing100080 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBC V6T 1Z4 Canada

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multi-antenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering, hybrid precoding and power optimization. Based on channel correlation characteristics, a fast convergence scheme for user clustering in THz-NOMA-MIMO system using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. Considering the fronthaul link capacity constraint, we design a distributed alternating direction method of multipliers (ADMM) algorithm for power allocation to maximize the EE of THz-NOMA cache-enabled system with imperfect successive interference cancellation (SIC). The simulation results show that the proposed user clustering scheme can achieve faster convergence and higher EE, the design of the hybrid precoding of the sub-connection structure can achieve lower power consumption and power optimization can achieve a higher EE for the THz cache-enabled network. Copyright © 2020, The Authors. All rights reserved.

关键词： MIMO systems