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Compact Physical and Electrical Patch Antenna Engineered for 5G Mobile Devices and Multiband Systems

Progress in Electromagnetics Research B 2025年 111卷 71-81页

作者： Jemaludin, Nazrin Haziq Al-Gburi, Ahmed Jamal Abdullah Abdulhameed, Muhannad K. Hashim, Sarah R. Mohammed, Dunya Zeki Saeidi, Tale Gupta, Anupma Zakaria, Zahriladha Nurhayati, Nurhayati Elabd, Rania Hamdy Jalan Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Computer Scince Department University of Kerbala Karbala Iraq Air Conditioning Engineering Department Faculty of Engineering Warith Al-Anbiyaa University Iraq Department of Cyber Security Al-Zahraa University for Women Karbala Iraq Department of Electronic and Communications Engineering Gilgamesh University Baghdad Iraq Co. Donegal LetterkennyF92 FC93 Ireland Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamilnadu Chennai602105 India Department of Electrical Engineering Universitas Negeri Surabaya Surabaya60231 Indonesia Department Electronic and Communication Higher Institute of Engineering and Technology in New Damietta Damietta Egypt

This article presents a compact multi-band microstrip patch antenna designed for 5G, Ku, and K-band applications. The antenna operates at 3.5 GHz and 15.6 GHz, supporting 5G communications (3.3–3.6 GHz) and satellite applications (15.6–20 GHz). Fabricated on an FR4 substrate (Εr = 4.3, tan δ = 0.025) with dimensions of 13 × 10 × 1.6 mm3 (0.15λo × 0.12λo × 0.02λo), where λo represents the wavelength at the lower frequency of 3.5 GHz, the antenna achieves return losses of −19 dB and −39 dB at the respective frequencies, with peak gains of −2.8 dBi and 3.7 dBi. The design’s consistency is validated through a comparative analysis with recent studies. The antenna was placed near the ear and mouth area of a human head phantom model to perform a comprehensive SAR analysis. SAR analysis confirms compliance with safety standards, maintaining SAR levels below 2 W/kg. The proposed design demonstrates promising performance for modern communication systems. © (2024), (Electromagnetics Academy). All Rights Reserved.

关键词： Slot antennas

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Multi-residual generative adversarial networks for QR code Deblurring

Multi-residual generative adversarial networks for QR code D...

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2022 International Conference on Electronic Information technology, EIT 2022

作者： Wang, Mingyue Chen, Kecheng Lin, Fanqiang College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510655096

Motion blur and ambient noise are the main reasons that affect quick response (QR) code recognition. In this paper, we propose a novel deep learning approach to deblur the QR codes and realize the effective recognition of deblurring QR codes by using generative adversarial networks (GANs). We estimate the blur kernel and ambient noise of the blur QR code in the dataset using GANs, so as to realize the transformation from the blur QR code image to the sharp image. We also propose an expansion method of QR codes dataset, and achieve better generalization performance of the model. The experimental results show that our approach can effectively estimate the blur kernel and ambient noise that can realize the deblurring of QR code. © 2022 SPIE.

关键词： Generative adversarial networks

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Blockchain-based, Decentralized Evidence Archive System using IPFS

Blockchain-based, Decentralized Evidence Archive System usin...

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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Dave, Maharshi Banoth, Rajkumar Marwadi University Department of Cyber Security Gujarat Rajkot India Marwadi University Department of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9781665478847

In spite of growth in technology, Indian Judiciary system somehow lacks digitalization. In the court trials cases, every argument by the lawyers, evidence presentation, witness/suspect cross examination everything will be noted down by the stenographer and everyday hearings details will be printed at the end of every court sessions. Therefore, the details about particular case will be in physical files as well as in digital format and can be accessed whenever it is needed like in the situation of case reopening. Data integrity is important in the judiciary system;when it comes to court cases, evidence integrity must be protected because even little changes in the evidence can lead to false judgments, and historical data is crucial. Where historical data archiving is necessary, Blockchain technology is suited. In the modern era, Blockchain technology is regarded as more reliable technology than any other. Blockchain technology can be used in the justice system to provide privacy and integrity, as well as efficient auditability and traceability, for storing case records and evidences. This research study has proposed a novel method using InterPlanetary File System distributed data storage to store case details and evidences on top of the Ethereum Blockchain. The case details can be stored using text and image files. The Ethereum smart contract is used for storing hash value of data in the Blockchain. The storage and access of the data in InterPlanetary File System is studied and explained using an experimental setting. © 2022 IEEE.

关键词： Smart contract

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Enhanced Brain Stroke Prediction: An Ensemble of Random Forest, Logistic Regression and XGBoost 2

Enhanced Brain Stroke Prediction: An Ensemble of Random Fore...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Sundaram, Karthic Lanitha, B. Kamaraj, K. Ramamoorthy, Arun Kumar KPR Institute of Engineering and Technology Department of Information Technology Coimbatore India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India University of South Wales Digital Forensics and Cyber Security Treforest United Kingdom

ISBN: (纸本)9798331534950

Stroke, a leading cause of disability and mortality globally, necessitates early prediction and intervention to mitigate its devastating effects. Recognizing the challenges faced by stroke survivors, the focus shifts to the crucial role of rehabilitation and lifestyle changes in optimizing recovery and quality of life. Machine learning (ML) algorithms emerges as a powerful tool for predicting stroke risk and enhancing patient outcomes. ML algorithms, by analysing extensive datasets, can identify patterns and early warning signs, enabling more personalized treatment plans. To enhance prediction accuracy, an ensemble model is proposed, leveraging Random Forest (RF), Logistic Regression (LR) and XGBoost techniques. The ensemble model exhibits an impressive accuracy rate of 97.4%, exceeding other models. © 2024 IEEE.

关键词： Prediction models

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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VQR: vulnerability analysis in quadratic residues-based authentication protocols

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7559-7574页

作者： Ghahramani, Meysam HaddadPajouh, Hamed Javidan, Reza Kumari, Saru Computer Engineering and IT Department Shiraz University of Technology Shiraz Iran Cyber Science Lab School of Computer Science University of Guelph 50 Stone Rd E GuelphON Canada Department of Mathematics Ch. Charan Singh University Uttar Pradesh Meerut India

Ensuring security and respect for users’ privacy, especially in electronic health-care systems, is an important task that is achievable by authentication protocols. The security of many protocols is based on public-key cryptography, the breaking of which depends on solving a hard problem, e.g. Quadratic Residue (QR) which is compatible with constrained devices and is based on integer factoring with the Mean time complexity of exp((c+ o(1)) log 1 / 3(n) log 2 / 3(log (n))) for some c2(n)) to calculate user’s session keys and finding confidential user’s data by passive monitoring of the transmitted data over public networks for 2048-bit public keys in 6.9 μs. To prove the efficiency of the proposed attacks, we address 12 vulnerable QR-based authentication protocols and show that the structure of all vulnerable transmitted messages is limited to 6 cases while messages in (n,n-n) are safe from the proposed method. Also, two recent authentication protocols have been analyzed in detail to show the consequences of the proposed vulnerability. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Public key cryptography

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Detection of DDoS attacks in SDN using Machine Learning

Detection of DDoS attacks in SDN using Machine Learning

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Ashodia, Namita Makadiya, Kishan Marwadi University Department of Cyber Security Gujarat Rajkot India Marwadi University Department of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9781665484251

With the growth in network industry, traditional network is being replaced with Software Defined Network. SDN is growing technology, which provides the control of network communication flexible. SDN decouples into three planes namely control plane, data plane and application plane, making it centralized. Due to centralization, the network is cost effective, dynamic and resource management is efficient. Network administrators believes that SDN has a breakthrough in network industry due to programmability and remote accessing while network providers appreciate SDN as it has reduced capital expenditure (CAPEX) and operational expenditure (OPEX). In spite of vast capabilities, the SDN face security issues as it has complex architecture and centralization feature. The SDN introduces network security challenges among which Distributed Denial of Service has devastating effect on the entire network. In SDN, network becomes vulnerable when large number of packets are requested with random IP address. When the packet is not matched in flow entry rules by the switch, packet gets encapsulated in packet_in messages and it passes to controller for instruction and as number of requests increases it will limit the resources and becomes bottleneck for the entire network leading to generation of DDoS attack. It is important to have quick provisional methods to pre vent the switches and controller from breaking down. To resolve this problem, we proposed a machine learning technique which includes Naïve Bayes, Decision tree, k-nearest neighbors, Logistic Regression, and Random Forest for detection of malicious traffic. The experiment outcomes shows that Random Forest and Decision Tree algorithms provides better accuracy and decision rate compared to other algorithms. © 2022 IEEE.

关键词： Decision trees

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AGIR: Automating cyber Threat Intelligence Reporting with Natural Language Generation

AGIR: Automating Cyber Threat Intelligence Reporting with Na...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Perrina, Filippo Marchiori, Francesco Conti, Mauro Verde, Nino Vincenzo University of Padova Department of Mathematics Padua Italy Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Leonardo Cyber&Security Leonardo S.p.A. Rome Italy

ISBN: (纸本)9798350324457

cyber Threat Intelligence (CTI) reporting is pivotal in contemporary risk management strategies. As the volume of CTI reports continues to surge, the demand for automated tools to streamline report generation becomes increasingly apparent. While Natural Language Processing techniques have shown potential in handling text data, they often struggle to address the complexity of diverse data sources and their intricate interrelationships. Moreover, established paradigms like STIX have emerged as de facto standards within the CTI community, emphasizing the formal categorization of entities and relations to facilitate consistent data sharing. In this paper, we introduce AGIR (Automatic Generation of Intelligence Reports), a transformative Natural Language Generation tool specifically designed to address the pressing challenges in the realm of CTI reporting. AGIR's primary objective is to empower security analysts by automating the labor-intensive task of generating comprehensive intelligence reports from formal representations of entity graphs. AGIR utilizes a two-stage pipeline by combining the advantages of template-based approaches and the capabilities of Large Language Models such as ChatGPT. We evaluate AGIR's report generation capabilities both quantitatively and qualitatively. The generated reports accurately convey information expressed through formal language, achieving a high recall value (0.99) without introducing hallucination. Furthermore, we compare the fluency and utility of the reports with state-of-the-art approaches, showing how AGIR achieves higher scores in terms of Syntactic Log-Odds Ratio (SLOR) and through questionnaires. By using our tool, we estimate that the report writing time is reduced by more than 40%, therefore streamlining the CTI production of any organization and contributing to the automation of several CTI tasks. © 2023 IEEE.

关键词： cyber Threat Intelligence Natural Language Generation STIX Threat Reports

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Blockchain-Based Framework for Stock Market Operations: IPOs, Trading, and Dividend Distribution 15

Blockchain-Based Framework for Stock Market Operations: IPOs...

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15th IEEE International Conference on Cloud Computing technology and Science, CloudCom 2024

作者： Sharshar, Muhammad Elzeky, Kareem Othman, Menna Khaled, SeifAldin Moubarak, Hosam Gomaa, Walid Department of Computer Science United Arab Emirates Mbzuai Department of Natural Language Processing United Arab Emirates Egypt-Japan University of Science and Technology Cyber-Physical Lab Department of Computer Engineering Egypt Egypt-Japan University of Science and Technology Accounting Department Egypt

ISBN: (纸本)9798331507589

This study examines blockchain technology's potential to enhance the stock market by offering a decentralized alternative to the traditional system. The study explores the challenges of the current traditional stock market, such as long settlement times, weak transparency, dependency on centralized clearing houses, and heightened fees. The proposed novel system leverages blockchain technology to create a fully decentralized stock market encompassing all fundamental operations from Initial Public Offerings (IPOs) to stock trading and extending to distributing profits or dividends. It connects all market participants through an Ethereum consortium blockchain fuelled by smart contracts. This approach replaces traditional intermediaries like brokers with automated regulations, ensuring more rapid transaction settlements, reduced operational errors and fees, and strengthened security measures. The proposed blockchain-based system, consisting of four smart contracts for IPO, stock exchange, dividend distribution, and participant management, demonstrates remarkable performance, showing promise in efficiently handling the current load of the largest stock markets like the London Stock Exchange (LSE). Through theoretical and practical testing, the system achieved a remarkable throughput of 120 transactions per second and latency or finality of just 4 seconds, underscoring its capability to significantly enhance the efficiency of stock market operations. The implementation code is available via GitHub upon request. © 2024 IEEE.

关键词： Decentralized finance

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ContraPonzi: Smart Ponzi Scheme Detection for Ethereum via Contrastive Learning 4

ContraPonzi: Smart Ponzi Scheme Detection for Ethereum via C...

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4th Asia Service Sciences and Software engineering Conference, ASSE 2023

作者： Wu, Jiajing Liu, Jieli Chen, Jinze Chen, Ting Li, Jingwei Song, Shuwei He, Jiahao School of Software Engineering Sun Yat-sen University China School of Computer Science and Engineering Sun Yat-sen University China School of Cyber Security University of Electronic Science and Technology of China China

ISBN: (纸本)9798400708534

In recent years, blockchain technology has witnessed rapid development and received considerable attention. However, its decentralized and pseudonymous nature has also attracted many criminal activities. Among them, Ponzi schemes, a classic form of financial fraud, also hide their true face in smart contracts, causing huge losses to blockchain users. Although numerous methods have been proposed to detect Ponzi contracts, these methods still have limitations in terms of generalization and feature learning. To address this issue, we conduct research on Ethereum, the currently largest blockchain platform enabling smart contracts, and propose a novel contrastive learning-based smart Ponzi scheme detection method named ContraPonzi. This method first extracts control flow graph information from bytecodes and models it as attribute graphs that preserve both semantic and structural information. Next, by augmenting the bytecode data of multi-version compilers and maximizing the graph representation similarity of multi-version bytecodes of the same contract, a pre-training graph encoder is obtained and then can be used in Ponzi contract detection. Experimental results on real-world data demonstrate that ContraPonzi is significantly superior to the state-of-the-art in Ethereum Ponzi scheme detection. © 2023 ACM.

关键词： Smart contract

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