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Quantum quasi-cyclic low-density parity-check error-correcting codes

Chinese Physics B 2009年第10期18卷 4154-4160页

作者：李渊曾贵华 Moon Ho Leeb Laboratory of Coding and Communication Security Department of Electronic Engineering Shanghai Jiaotong University Institute of Information & Communication Department of Information & Communication EngineeringChonbuk National University

In this paper, we propose the approach of employing circulant permutation matrices to construct quantum quasicyclic （QC） low-density parity-check （LDPC） codes. Using the proposed approach one may construct some new quantum codes with various lengths and rates of no cycles-length 4 in their Tanner graphs. In addition, these constructed codes have the advantages of simple implementation and low-complexity encoding. Finally, the decoding approach for the proposed quantum QC LDPC is investigated.

关键词： quantum LDPC code quasi-cycliC circulant permutation matrix CSS code

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Explainable and Transferable Adversarial Attack for ML-Based network Intrusion Detectors

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Hangsheng Han, Dongqi Zhuang, Shangyuan Wang, Zhiliang Sun, Jiyan Liu, Yinlong Liu, Jiqiang Dong, Jinsong Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China Faculty of Beijing University of Posts and Telecommunications Beijing China Tsinghua University Department of Network Sciences and Cyberspace Beijing China Faculty of Beijing Jiaotong University Beijing China Department of Computing Singapore

Despite being widely used in network intrusion detection systems (NIDSs), machine learning (ML) has proven to be vulnerable to adversarial attacks. White-box and black-box adversarial ML attacks of NIDS have been explored in several studies. However, white-box attacks unrealistically assume that the attackers have full knowledge of the target NIDSs. Meanwhile, existing black-box attacks can not achieve high attack success rate due to the weak adversarial transferability between models (e.g., neural networks and tree models). Additionally, neither of them explains why adversarial examples exist and why they can transfer across models. To address these challenges, this paper introduces ETA, an Explainable Transfer-based Black-Box Adversarial Attack framework. ETA aims to achieve two primary objectives: 1) create transferable adversarial examples applicable to various ML detectors and 2) provide insights into the existence of adversarial examples and their transferability within NIDSs. Specifically, we first provide a general transfer-based adversarial attack method applicable across the entire ML space. Following that, we exploit a unique insight based on cooperative game theory and perturbation interpretations to explain adversarial examples and adversarial transferability. On this basis, we propose an Important-Sensitive Feature Selection (ISFS) method to guide the search for adversarial examples, achieving stronger transferability and ensuring traffic-space constraints. Finally, the experimental results on three NIDSs datasets show that our method performs significantly effectively against several classical and state-of-the-art ML classifiers, outperforming the latest baselines. We conduct three interpretation experiments and two cases to verify our interpretation method's correctness. Meanwhile, we uncover two major misconceptions about applying machine learning to NIDSs systems. © 2004-2012 IEEE.

关键词： Adversarial machine learning

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Storage and repair bandwidth tradeoff for heterogeneous cluster distributed storage systems

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Science China(information Sciences) 2020年第2期63卷 131-145页

作者： Jingzhao WANG Yuan LUO Kenneth W.SHUM Department of Computer Science and Engineering Shanghai Jiao Tong University Institute of Network Coding The Chinese University of Hong Kong

The storage and repair bandwidth tradeoff is an important issue in distributed storage systems(DSSs) where large scale data are stored in multiple nodes with erasure coding to ensure reliability. There are lots of studies on the DSS model with multiple clusters where the repair bandwidths from intra-cluster and cross-cluster nodes are differentiated to improve repair efficiency based on the realistic network topological structures. At the same time, separate nodes are also prevalent due to the variety of practical networks, but the work on the cluster DSS model with multiple separate nodes is insufficient, which is a main motivation of this paper. We formulate the tradeoff bound between storage repair bandwidth for a heterogeneous DSS model consisting of clusters and separate nodes by analyzing the min-cuts of heterogeneous information flow graphs corresponding to the orders of failed nodes. Additionally, the tradeoff bounds are investigated in multiple aspects when the repair bandwidth constraints and the amount of separate nodes vary, ***, a class of regenerating codes are illustrated to achieve the tradeoff in the heterogeneous cases.

关键词： distributed storage system cluster separate node repair bandwidth information flow graph

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A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases

arXiv

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arXiv 2025年

作者： Saleem, Sajjad Hussain, Adil Majeed, Nabila Akhtar, Zahid Siddique, Kamran Department of Information and Technology Washington University of Science and Technology United States Department of Physics Kansas State University ManhattanKS United States Department of Network and Computer Security State University of New York Polytechnic Institute Utica United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020 in conjunction with an ensemble of machine vision classifiers, including voting and stacking. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. A deep learning ensemble model Xception showed the highest accuracy. © 2025, CC BY.

关键词： Image segmentation

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IoT-Driven Predictive Analytics for Precision Harvest Timing with LightGBM Models

IoT-Driven Predictive Analytics for Precision Harvest Timing...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S. Lourdu Jame Chitra Sabapathy Ranganathan S.K. Saravanan Bharat Tidke E. Punarselvam M. Rajmohan Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Mphasis Corporation Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Pune India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In agriculture, precise harvest time is essential to maximize production and quality. A predictive analytics system powered by the Internet of Things (IoT) and using LightGBM models for accurate harvest time determination is presented in this study. The framework incorporates IoT sensors to gather environmental data, such as soil moisture, light intensity, temperature, and humidity, which are important markers of crop maturity. The data is analyzed using machine learning models, namely LightGBM, to estimate when to harvest accurately. The proposed framework seeks to improve agricultural efficiency by using real-time data analytics. This would allow farmers to make better crop output, waste, and resource utilization choices. Farmers may dynamically adjust their harvesting plans in response to changing crop conditions due to the IoT-driven strategy that enables constant monitoring of environmental variables. By reducing input waste and environmental effects, this capacity enhances agricultural operations' total productivity while supporting sustainable farming practices. The proposed model can transform harvest management using advanced predictive analytics and IoT technologies, as shown by case studies and simulations confirming its efficacy in several agricultural settings.

关键词： Temperature sensors Analytical models Accuracy Humidity Predictive models Agriculture Real-time systems Data models Sensors Predictive analytics

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An Overview on Economic Analysis of Internet of Everything

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IEEE Communications Surveys and Tutorials 2025年

作者： Ding, Ningning Ouyang, Xiaomin Gao, Lin Huang, Jianwei Xing, Guoliang Guangzhou511453 China Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong Harbin Institute of Technology School of Electronics and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage Guangdong Shenzhen518172 China The Chinese University of Hong Kong Department of Information Engineering 999077 Hong Kong

The Internet of Everything (IoE) integrates people, processes, data, and things into a unified network, enabling highly interconnected systems with enhanced performance in terms of delay, reliability, and connection density. With the advent of next-generation networks, IoE has garnered significant attention for its transformative potential across various domains. However, while most existing studies focus on developing enabling technologies and deployment frameworks, the economic dimensions of IoE systems remain critically underexplored, despite being essential for ensuring their sustainability and scalability. This paper addresses this gap by providing a comprehensive review of the economic analysis of IoE systems. Specifically, we categorize existing research into four key aspects: information collection, information transmission, information fusion, and service application. We identify major economic challenges and propose modeling approaches, including game-theoretic and learning-based methods. Furthermore, we explore diverse IoE application scenarios, such as metaverse solutions and large-scale industrial systems, and summarize prevailing business patterns in the industry. By highlighting the economic dimension as a core component of IoE systems, this survey offers valuable insights into future research directions and outlines critical open challenges. © 1998-2012 IEEE.

关键词： Internet of Everything

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network coding theory:An introduction

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Frontiers of Electrical and Electronic engineering in China 2010年第3期5卷 363-390页

作者： Raymond W.YEUNG Institute of Network Coding and Department of Information Engineering The Chinese University of Hong KongHong KongChina

For a long time,store-and-forward had been the transport mode in network *** other words,information had been regarded as a commodity that only needs to be routed through the network,possibly with replication at the intermediate *** the late 1990’s,a new concept called network coding fundamentally changed the way a network can be *** the paradigm of network coding,information can be processed within the network for the purpose of *** was demonstrated that compared with store-and-forward,the network throughput can generally be increased by employing network *** then,network coding has made significant impact on different branches of information *** impact of network coding has gone as far as mathematics,physics,and *** expository work aims to be an introduction to this fast-growing subject with a detailed discussion of the basic theoretical results.

关键词： network coding network communications wireless communications cryptography

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Structured Tensor Decomposition Based Channel Estimation and Double Refinements for Active RIS Empowered Broadband Systems

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Yirun Wang, Yongqing Shen, Yuyao Wang, Gongpu Tellambura, Chintha Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer Science and Technology Beijing100044 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Channel parameter recovery is critical for the next-generation reconfigurable intelligent surface (RIS)-empowered communications and sensing. Tensor-based mechanisms are particularly effective, inherently capturing the multi-dimensional nature of wireless channels. However, existing studies assume either a line-of-sight (LOS) scenario or a blocked TX-RX channel. This paper solves a novel problem: tensor-based channel parameter estimation for active RIS-aided multiple-antenna broadband connections in fully multipath environments with the TX-RX link. System settings are customized to construct a fifth-order canonical polyadic (CP) signal tensor that matches the five-dimensional channel. Four tensor factors contain redundant columns, rendering the classical Kruskal's condition for decomposition uniqueness unsatisfied. The fifth-order Vandermonde structured CP decomposition (VSCPD) is developed to address this challenge, making the tensor factorization problem solvable using only linear algebra and offering a relaxed general uniqueness condition. With VSCPD as a perfect decoupling scheme, a sequential triple-stage channel estimation algorithm is proposed based on one-dimensional parameter estimation. The first stage enables multipath identification and algebraic coarse estimation. The following two stages offer optional successive refinements at the cost of increased complexity. The closed-form Cramér-Rao lower bound (CRLB) is derived to assess the estimation performance. Herein, the noise covariance matrix depends on multipath parameters in our active-RIS scenario. Numerical results are provided to verify the effectiveness of proposed algorithms under various evaluation metrics. Our results also show that active RIS can significantly improve channel estimation performance compared to passive RIS. © 2025 IEEE.

关键词： Tensors

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Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

arXiv

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arXiv 2025年

作者： Naeem, Muhammad Ahtsam Saleem, Muhammad Asim Sharif, Muhammad Imran Akber, Shahzad Saleem, Sajjad Akhtar, Zahid Siddique, Kamran School of Information and Software Engineering University of Electronic Science and Technology of China China Faculty of Computing Riphah International University Faisalabad Campus Pakistan Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Information and Technology Washington University of Science and Technology AlexandriaVA22314 United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image;(2) feature extraction is performed using a Deep CNN model, then these extracted features are concatenated;(3) feature selection is performed using wrapper-based feature selection;(4) classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features. © 2025, CC BY.

关键词： Feature Selection

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Digital Twin for Intelligent Parcel Sorting: Advancing Real-Time Management and Optimization in Logistics networks

Digital Twin for Intelligent Parcel Sorting: Advancing Real-...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Wei Zhang Renming Liu Yuanming Tian Le Liao Ran Li Chuan Huang Provincial Key Laboratory of Future Networks of Intelligence Shenzhen Future Network of Intelligence Institute School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shunfeng Technology Tsinghua Shenzhen International Graduate School Shenzhen China Guangdong Provincial Key Laboratory of Future Networks of Intelligence Shenzhen Future Network of Intelligence Institute School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shunfeng Technology Shenzhen China Department of Information Engineering Chinese University of Hong Kong Hong Kong SAR Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering Shenzhen Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Parcel distribution centers (PDCs), as the pivotal nodes in logistic networks, facilitate parcel sorting, consolidation, and distribution. In the past decades, the rapid growth of e-commerce and steadily increasing parcel volumes have imposed big challenges on the efficiency and reliability of PDCs. Though recent studies have focused on using simulation-based approaches for decision-making at PDCs, the dynamic and complex nature of logistics networks is yet to be thoroughly captured due to the commonly used simplified simulation models with idealized assumptions. Our work aims to establish a parcel-sorting digital twin system (PSDTS) under practical settings, with real-time responses, sufficient fidelity, and high real-world applicability. This paper presents the detailed design and implementation of the PSDTS in PDCs, which is the first industrial digital twin system deployed in logistics. Specifically, we first establish a standardized Industrial Internet of Things (IIoT) framework that ensures seamless integration of heterogeneous sorting devices, enabling standardized data transmission and processing. Then, based on the standardized IIoT framework, the PSDTS is capable of real-time interaction, precise validation, and intelligent management, achieving a new level of operational transparency and system optimization. Validation across various PDCs with real-world sorting data demonstrates the exceptional accuracy of the PSDTS, providing a robust foundation for intelligent operations, optimization, and real-time decision-making. This work represents a significant step forward in intelligent logistics management and operational optimization, setting a cornerstone for the continued evolution of logistics networks driven by Digital Twin technology.

关键词： Accuracy Decision making Throughput Real-time systems Digital twins Data communication Optimization Sorting Logistics Industrial Internet of Things

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