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arXiv 2024年

作者： Chen, Hao Ding, Cunsheng Cheng, Zhiqiang Xie, Conghui The College of Information Science and Technology/Cyber Security Jinan University Guangdong Province Guangzhou510632 China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Sum-rank codes have known applications in the multishot network coding, the distributed storage and the construction of space-time codes. U. Martínez-Peñas introduced the cyclic-skew-cyclic sum-rank codes and proposed the BCH bound on the cyclic-skew-cyclic sum-rank codes in his paper published in IEEE Trans. Inf. Theory, vol. 67, no. 8, 2021. Afterwards, many sum-rank BCH codes with lower bounds on their dimensions and minimum sum-rank distances were constructed. Sum-rank Hartmann-Tzeng bound and sum-rank Roos bound on cyclic-skew-cyclic codes were proposed and proved by G. N. Alfarano, F. J. Lobillo, A. Neri, and A. Wachter-Zeh in 2022. In this paper, cyclic, negacyclic and constacyclic sum-rank codes are introduced and a direct construction of cyclic, negacyclic and constacyclic sum-rank codes of the matrix size m × m from cyclic, negacyclic and constacyclic codes over Fqm in the Hamming metric is proposed. The cyclic-skew-cylic sum-rank codes are special cyclic sum-rank codes. In addition, BCH and Hartmann-Tzeng bounds for a type of cyclic sum-rank codes are developed. Specific constructions of cyclic, negacyclic and constacyclic sum-rank codes with known dimensions and controllable minimum sum-rank distances are proposed. Moreover, many distance-optimal binary sum-rank codes and an infinite family of distance-optimal binary cyclic sum-rank codes with minimum sum-rank distance four are constructed. This is the first infinite family of distance-optimal sum-rank codes with minimum sum-rank distance four in the literature. © 2024, CC BY-NC-ND.

关键词： Network coding

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A Secure Text Steganography using Randomized Mathematical Functions and LSB

A Secure Text Steganography using Randomized Mathematical Fu...

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International Conference on Electronics, Materials engineering and Nano-technology (IEMENTech)

作者： Sakyojit Banerjee Samanawaya Datta Dipanjan Ghosh Susovan Jana Department of Computer Science & Engineering (Internet of Things and Cyber Security including Blockchain Technology) Institute of Engineering & Management Kolkata India Department of Computer Science & Engineering (IoT) Institute of Engineering & Management Kolkata India

In the realm of information security, hiding data within ordinary images, known as text steganography, has become a crucial method for secret communication. The Least Significant Bit (LSB) technique is a popular way to embed text within images. It works by tweaking the least significant bits of image pixels to represent hidden text in binary form. This alteration is subtle, ensuring the image looks normal and enabling efficient sharing through various platforms. This research paper introduces a creative expansion of LSB steganography. It utilizes LSB steganography for replacing the Least Significant Bits of the carrier image with the binary of the encoded text after the text has been passed through various mathematical functions like polynomials, integrals, and differentials. These functions are called randomly to encode text. Thus, enabling randomicity in the encoding of the text allows a secure and efficient way of encrypting and decrypting the text needed.

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Decentralized Autonomous Farm Management

Decentralized Autonomous Farm Management

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National Conference on Communications (NCC)

作者： Nirban Roy Shuvam Chakraborty Katha Khan Biswadip Basu Mallik Department of Computer Science & Engineering (Internet of Things and Cyber Security including Blockchain Technology) Institute of Engineering & Management Kolkata India Department of Computer Science & Engineering Indian Institute of Technology Delhi New Delhi India Department of Electrical Engineering Institute of Engineering & Management Kolkata India Department of Basic Science & Humanities Institute of Engineering & Management Kolkata India

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

This paper presents an autonomous farm management system leveraging IoT-enabled sensor nodes and a GPS-free autonomous rover for precision agriculture. The proposed system addresses key challenges in farming, such as anomaly detection, resource optimization, and efficient navigation, by employing a robust communication architecture utilizing ESP-NOW and MQTT protocols. Sensor nodes equipped with solar-powered sensors monitor soil parameters like moisture, pH, temperature, and nutrient levels, while forming a resilient mesh network for real-time data synchronization. The rover, guided by a decentralized localization approach, employs advanced path optimization strategies-Minimum Spanning Tree (MST) for static anomalies and a Gradient Boosting Regressor (GBR)-based dynamic routing mechanism for real-time adaptability. Results demonstrate significant reductions in resource consumption and high accuracy in anomaly detection. This architecture is highly adaptable, enabling its application in other non-GPS environments, such as remote exploration and disaster management.

关键词： Temperature sensors Precision agriculture Location awareness Accuracy Navigation Boosting Real-time systems Optimization Anomaly detection Farming

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The Polar CenterNet for Radar Signal Identification 4

The Polar CenterNet for Radar Signal Identification

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4th International Conference on Intelligent Information Processing, IIP 2022

作者： Wang, Lin Mi, Siya Tian, Ye Wang, Fengsheng He, Shen Southeast University School of Cyber Science and Engineering Nanjing China Institute of China Mobile Communications Corporation Department of Security Technology Research Beijing China

ISBN: (纸本)9781665492225

The radar signal sorting is important in the reconnaissance phase of electronic battlefield, and its performance affects the subsequent decision for the situational awareness seriously. With the wide adoption of various types of radars, the electromagnetic environment is more complex than ever. In this paper, a dynamic multi-parameter processing technique based on CenterNet is proposed. The proposed method can map the pulse description words (PDW) as dots in PDW polar diagram, then the radar pulse can be sorted by identifying the PDW dots using the improved CenterNet. The detected pulse signals are identified to prove the proposed method, and 97.74% accuracy can be achieved. Moreover, the proposed method provides a PDW visualization approach for the complex application scenarios. © 2022 IEEE.

关键词： Image classification

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Enhanced resource allocation in distributed cloud using fuzzy meta-heuristics optimization

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computer Communications 2023年第1期209卷 14-25页

作者： Sangaiah, Arun Kumar Javadpour, Amir Pinto, Pedro Rezaei, Samira Zhang, Weizhe International Graduate Institute of AI National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Computer Science and Technology Cyberspace Security Harbin Institute of Technology Shenzhen China ADiT-Lab Electrical and Telecommunications Department Instituto Politécnico de Viana do Castelo Portugal Leiden University P.O. Box 9512 RA Leiden2300 Netherlands

Cloud computing is a modern technology that has become popular today. A large number of requests has made it essential to propose a resources allocation framework for arriving requests. The network can be made more efficient and less costly this way. The cloud–edge paradigm has been considered a growing research area in the computing industry in recent years. The increase in the number of customers and requests for cloud data centers (CDCs) has created the need for robust servers and low power consumption mechanisms. Ways to reduce energy in the CDC having appropriate algorithms for resource allocation. The purpose of this study was to develop an intelligent method for dynamic resource allocation using Takagi–Sugeno–Kang (TSK) neural-fuzzy systems and ant colony optimization (ACO) techniques to reduce energy consumption by optimizing resource allocation in cloud networks. It predicts future loads using a drop-down window to track CPU usage. By optimizing virtual machine migration, ACO can reduce energy consumption. Simulations are provided by examining the implementation and a variety of parameters such as the number of requests made wasted resources, and requests rejected. In this paper, we propose the use of virtual machine migration to accomplish two main goals: evacuating additional and non-optimal virtual machines (scaling and shutting down additional active physical machines) and solving the resource granulation problem. We evaluated and compared our results with literature for rejection rates of virtual and physical machine applications. The performances of our algorithms are compared to different criteria such as performance in request rejection, dynamic CPU resource allocation with reinforcement learning, multi-objective resource allocation, NSGAIII, Whale optimization and Forecast Particle Swarm allocation. A comparison of some evaluation criteria showed that the proposed method is more efficient than other methods. © 2023 Elsevier B.V.

关键词： Ant colony optimization

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Contrast as a Method of Image Processing in Increasing Diagnostic Efficiency When Studying Liver Fatty Tissue Levels 2

Contrast as a Method of Image Processing in Increasing Diagn...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abu-Jassar, Amer Al-Sharo, Yasser Boboyorov, Sardor Lyashenko, Vyacheslav Ajloun National University Faculty of Information Technology Department of Computer Science Ajloun Jordan Ajloun National University Faculty of Information Technology Department of Cyber Security Ajloun Jordan Tashkent Medical Academy Termiz Branch Termiz Uzbekistan Kharkiv National University of Radio Electronics Department of Media Systems and Technology Kharkiv Ukraine

ISBN: (纸本)9798350373363

The paper addresses several challenges associated with analyzing digital medical images, focusing on identifying and assessing fatty liver tissue levels. The methodology enhances the perception of transition zones delineating fatty lesions by employing a contrast technique on the input image. Classic histogram equalization is utilized for contrast, ensuring an even distribution of brightness levels. The evaluation of processed image quality relies on indicators such as entropy, NIQE, and BRISQUE. The paper explores diverse strategies for enhancing the original image based on individual or combined values of quality indicators, which effectively improves the identification of transition zones in fatty lesions. The findings are illustrated using real digital images of fatty liver lesions. © 2023 IEEE.

关键词： Image enhancement

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Optimized Machine Learning Models for Early Alzheimer's Disease Detection: A Feature-Reduction Approach

Optimized Machine Learning Models for Early Alzheimer's Dise...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Anfal Alqudah Yazan Masalha Abdelmajid Bader Heba Abdel-Nabi Department of Computer Engineering Networks and Information Security Princess Sumaya University for Technology Amman Jordan Computer Engineering Department Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Alzheimer's Disease (AD) is a neurodegenerative disease that affects an individual's memory, cognitive abilities, and daily function-skills, progressive in nature. Early diagnosis of AD has implications for early intervention and management. In this context, the Alzheimer's Disease Dataset was utilized in the current study, comprising 2,149 instances and 35 features to construct predictive models using different Machine Learning (ML) techniques. In order to increase the relevance of features for better performance of the models, the dataset was preprocessed rigorously and retained only the top 12 most correlated attributes. The different machine learning models like Decision Tree, Random Forest, CatBoost, and a Voting Classifier were trained and evaluated by various metrics such as accuracy, precision, recall, and F1-score. Among the tested models, CatBoost provided the highest accuracy of 96%, outperforming other algorithms. This demonstrates the potential of feature reduction in maintaining the performance of models. The result emphasizes the ability of ML to enhance early detection of Alzheimer's disease and provides the ground for a future improvement of AD diagnostic methodologies.

关键词： Neuroimaging Accuracy Machine learning Predictive models Feature extraction Encoding Reliability Alzheimer's disease Random forests Overfitting

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HY-PC:Enabling Consistent Positioning and Communication Using Visible Light

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China Communications 2023年第4期20卷 180-194页

作者： Xiangyu Liu Yaoheng Chen Lei Guo Song Song School of Information Science and Engineering Shenyang Ligong UniversityShenyang 110158China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Hangzhou Institute of Advanced Technology Hangzhou 310056China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China

Visible light(VL)plays an important role in achieving high-precision positioning and low bit error radio(BER)data ***,most VL-based systems can not achieve positioning and communication,*** are two problems:1)the hybrid systems are difficult to extract distinguishable positioning beacon features without affecting communication performance,2)in the hybrid systems,the lost data bits in the inter-frame gap(IFG)are hard to recover,which affects positioning and communication ***,in this article,we propose a novel VL-based hybrid positioning and communication system,named HY-PC system,to solve the above ***,we propose the robust T-W mapping for recognizing specific Light Emitting Diodes(LEDs),which can provide stable LED recognition accuracy without adding extra beacon data and does not decrease the communication ***,we also propose the novel linear block coding and bit interleaving mechanism,which can recover the lost data bits in the IFG and improve data communication ***,we use commercial off-the-shelf devices to implement our HY-PC system,extensive experimental results show that our HY-PC system can achieve consistent high-precision positioning and low-BER data communication,simultaneously.

关键词： visible light positioning visible light communication T-W Mapping linear block coding bit interleaving

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Joint alignment and steering to manage interference

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Digital Communications and Networks 2024年第2期10卷 429-438页

作者： Zhao Li Xiujuan Liang Yinghou Liu Jia Liu Zheng Yan School of Cyber Engineering Xidian UniversityXi'an710126China Technol.on Commun.Net.Lab. The 54th Research Inst.of China Elec.Technol.Group Corp.Shijiazhuang050081China School of Computer Science and Technology Xidian UniversityXi'an710126China The Department of Communications and Networking(Comnet) Aalto University02150EspooFinland

In wireless communication networks,mobile users in overlapping areas may experience severe interference,therefore,designing effective Interference Management(IM)methods is crucial to improving network ***,when managing multiple disturbances from the same source,it may not be feasible to use existing IM methods such as Interference Alignment(IA)and Interference Steering(IS)*** is because with IA,the aligned interference becomes indistinguishable at its desired Receiver(Rx)under the cost constraint of Degrees-of-Freedom(DoF),while with IS,more transmit power will be consumed in the direct and repeated application of IS to each *** remedy these deficiencies,Interference Alignment Steering(IAS)is proposed by incorporating IA and IS and exploiting their advantages in *** IAS,the interfering Transmitter(Tx)first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx,and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned ***,two improved versions of IAS,i.e.,IAS with Full Adjustment at the Interfering Tx(IAS-FAIT)and Interference Steering and Alignment(ISA),are *** former considers the influence of IA on the interfering user-pair's *** orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components,thus ensuring the Spectral Efficiency(SE)of the interfering communication *** ISA,the power cost for IS at the interfered Tx is minimized,hence improving SE performance of the interfered *** the proposed methods are realized at the interfering and interfered Txs cooperatively,the expenses of IM are shared by both *** in-depth simulation results show that joint use of IA and

关键词： Interference Interference management Interference alignment Interference steering Spectral efficiency

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