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Dual-band and high-isolation shared-aperture antenna for broadband airborne applications

Journal of electronic Science and Technology 2024年第4期22卷 9-18页

作者： Yuan Zhang Dong-Jun Wang Chuan Wu Qiang-Ming Cai Run-Ren Zhang Rong-Qiang Li Peng Gao Yan-Wen Zhao School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China AVIC Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu610091China Electronic Engineering College Chengdu University of Information TechnologyChengdu610225China School of Information Engineering Southwest University of Science and TechnologyMianyang621010China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA

A dual-band and high-isolation shared-aperture antenna for unmanned aerial vehicle(UAV)platforms has been *** shared-aperture antenna consists of a rectangular monopole antenna and a 4-element multiple input multiple output(MIMO)*** order to increase the isolation,several double split ring metamaterial(MTM)structures are introduced between antenna *** antenna radiator and the MTM structure are located on the front and back of the same dielectric substrate,respectively,and are perpendicular to a circular metal *** overall size of the antenna substrate is 124 mm×38 mm×1.016 ***,the antenna prototype is constructed and measured,and the simulated and measured results are in good *** measured results show that the-10 dB bandwidth of the monopole antenna is 1.92 GHz to 2.75 GHz,and the common-6.0 dB bandwidth of the MIMO antenna element is 4.75 GHz to 4.91 GHz,covering 2.2 GHz to 2.4 GHz in the S-band and 4.8 GHz to 4.9 GHz in the 5G band,*** the 5G band,the isolation between any element of the MIMO antenna and the S-band monopole antenna is not less than 21 dB,and the isolation between the MIMO antenna elements is better than 23 dB,indicating t-hat the isolation between the antenna elements is *** proposed antenna is suitable for the application on UAV airborne platforms.

关键词： Dual-band antenna High-isolation Monopole antenna Multiple input multiple output(MIMO) Shared-aperture Unmanned aerial vehicle(UAV)

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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DFNet: Dense fusion convolution neural network for plant leaf disease classification

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Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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Distribution Matching for Multi-Task Learning of Classification Tasks: A Large-Scale Study on Faces & Beyond 38

Distribution Matching for Multi-Task Learning of Classificat...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Kollias, Dimitrios Sharmanska, Viktoriia Zafeiriou, Stefanos School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Engineering and Informatics University of Sussex United Kingdom Department of Computing Imperial College London United Kingdom

ISBN: (纸本)1577358872

Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space, or parameter transfer. To provide sufficient learning support, modern MTL uses annotated data with full, or sufficiently large overlap across tasks, i.e., each input sample is annotated for all, or most of the tasks. However, collecting such annotations is prohibitive in many real applications, and cannot benefit from datasets available for individual tasks. In this work, we challenge this setup and show that MTL can be successful with classification tasks with little, or non-overlapping annotations, or when there is big discrepancy in the size of labeled data per task. We explore task-relatedness for co-annotation and co-training, and propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching. To demonstrate the general applicability of our method, we conducted diverse case studies in the domains of affective computing, face recognition, species recognition, and shopping item classification using nine datasets. Our large-scale study of affective tasks for basic expression recognition and facial action unit detection illustrates that our approach is network agnostic and brings large performance improvements compared to the state-of-the-art in both tasks and across all studied databases. In all case studies, we show that co-training via task-relatedness is advantageous and prevents negative transfer (which occurs when MT model's performance is worse than that of at least one single-task model). Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Classification (of information)

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GNN-Based Routing for Link Reliability Optimization in SD-LEO Satellite Networks

GNN-Based Routing for Link Reliability Optimization in SD-LE...

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2024 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2024

作者： Jamal, Syed Saqib Song, Wang-Cheol Department of Electronic Engineering Jeju National University Korea Republic of Department of Computer Engineering Jeju National University Korea Republic of

ISBN: (纸本)9798350380538

This paper presents a novel routing solution for Low Earth Orbit (LEO) satellite networks, addressing the critical challenges of link reliability and seamless Inter-Satellite Link (ISL) handovers. By integrating Graph Neural Networks (GNNs) with Software-Defined Networking (SDN) principles, our approach offers a dynamic and adaptive framework for managing the complex topology of satellite constellations. The proposed architecture encompasses SDN-based network management, precise Line of Sight (LOS) calculations, link cost computation, and GNN-based aggregation of ISL performance metrics. This solution utilizes real-time data on latency, bandwidth, packet loss, and elevation angles to assess link quality and initiate handovers to the most reliable links. Simulation results demonstrate significant improvements in network resilience and performance, showing reduced latency and packet loss during handovers while maintaining consistent bandwidth, thus ensuring more efficient and reliable global connectivity in LEO satellite networks. ©2024 IEEE.

关键词： Satellite links

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Chaos-Based Novel Watermarked Satellite Image Encryption Scheme

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computer Modeling in engineering & Sciences 2025年第4期143卷 1049-1070页

作者： Mohamed Medani Yahia Said Nashwan Adnan Othman Farrukh Yuldashev Mohamed Kchaou Faisal Khaled Aldawood Bacha Rehman Applied College of Muhayil Aseer King Khalid UniversityAbha62529Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Department of Computer Engineering Al-Kitab UniversityAltun Kupri36001Iraq Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100070Uzbekistan Department of Industrial Engineering College of EngineeringUniversity of BishaBishaP.O.Box 001Saudi Arabia Department of Computer Science Solent UniversitySouthamptonSO140YNUK

Satellite images are widely used for remote sensing and defence applications,however,they are subject to a variety of *** ensure the security and privacy of these images,theymust be watermarked and encrypted before ***,this paper proposes a novel watermarked satellite image encryption scheme based on chaos,Deoxyribonucleic Acid(DNA)sequence,and hash *** watermark image,DNA sequence,and plaintext image are passed through the Secure Hash Algorithm(SHA-512)to compute the initial condition(keys)for the Tangent-Delay Ellipse Reflecting Cavity Map(TD-ERCS),Henon,and Duffing chaotic maps,*** bitwise XOR and substitution,the TD-ERCS map encrypts the watermark *** ciphered watermark image is embedded in the plaintext *** embedded plaintext image is permuted row-wise and column-wise using the Henon chaotic *** permuted image is then bitwise XORed with the values obtained from the Duffing *** additional security,the XORed image is substituted through a dynamic *** evaluate the efficiency and performance of the proposed algorithm,several tests are performed which prove its resistance to various types of attacks such as brute-force and statistical attacks.

关键词： DNA sequence TD-ERCS chaoticmap henon chaoticmap duffing chaotic system SHA-512 encryption technique watermark embedding

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Elevated-temperature dry sliding wear behaviors of dual-cathode HiPIMS TiN and TiZrN films

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Emerging Materials Research 2025年第1期14卷 98-106页

作者： Lin, Yu-Tse Liu, Wen-Chung Kuo, Chin-Chiuan Department of Power Mechanical Engineering National Formosa University Huwei Yunlin County Taiwan Department of Mechanical and Computer Aided Engineering National Formosa University Huwei Yunlin County Taiwan Graduate Institute of Aeronautical and Electronic Engineering National Formosa University Huwei Yunlin County Taiwan

Titanium–zirconium nitride films with three Zr/Ti ratios (16/35, 22/29, and 25/26) were deposited using Ti and Zr dual-cathode reactive high-power impulse closed-field magnetron sputtering by adjusting the average power ratio of two cathode materials. The sliding wear resistances of the obtained TiZrN films at different temperatures were compared. The results revealed that the ternary TiZrN film exhibited a higher hardness (approximately 35 GPa) and lower elastic modulus (approximately 360–380 GPa) than those of the binary TiN film. This contributed to the lower friction coefficient and wear resistance at room temperature. However, the addition of Zr became disadvantageous with an increase in wear temperature. Above 4008C, the surface hardness of the TiZrN film decreased dramatically with an increase in the Zr fraction. This is attributed to the increased vulnerability of the film to oxidation. Structural discontinuity induced by the inhomogeneous oxidation (a multilayer structure with voids, embedded oxides, and oxynitrides formed in the surface layer) roughened the film surface and reduced the surface strength, which accelerated the wear of TiZrN films at elevated temperature in an ambient atmosphere. Such ternary nitride coatings should not be applied in oxidative high temperature environments without obstructing the oxidation through designed composition and structure. © 2025 Emerald Publishing Limited: All rights reserved.

关键词： Vacuum deposited coatings

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Text Analysis and Recognition of Emotional Content Using Deep Learning Methods and BERT 23

Text Analysis and Recognition of Emotional Content Using Dee...

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23rd IEEE/ACIS International Conference on computer and Information Science, ICIS 2023

作者： Andrikakis, Efthymios Perikos, Isidoros Paraskevas, Michael Hatzilygeroudis, Ioannis University of Patras Department of Computer Engineering & Informatics Patras Greece University of Patras Computer Technology Institute and Press Diophantus Department of Computer Engineering & Informatics Patras Greece Department of Electrical and Computer Engineering University of Peloponnese Computer Technology Institute and Press Diophantus Patras Greece

ISBN: (纸本)9798350345841

Recognizing the emotional content of Natural Language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. In this paper, deep learning, deep neural networks as well as Transformers were examined. Specifically, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. The results of the study show that BERT achieved the best overall performance, getting an F1-score of 0.86 on the Twitter Data dataset. Also, the Bi-LSTM with Attention Mechanism and Bi-LSTM using Word2vec performed quite well, achieving F1-scores of 0.83 and 0.82, respectively. © 2023 IEEE.

关键词： Deep neural networks

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Scalable Hybrid Switching-Inspired Sofware Defined Networking Challenges: From the Perspective of Machine Learning Approach 8

Scalable Hybrid Switching-Inspired Sofware Defined Networkin...

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Blose, Max Akinyemi, L.A. University of Cape Town Department of Electrical Engineering South Africa Lagos State University Department of Electronic and Computer Engineering Nigeria

ISBN: (纸本)9798350314892

The Software-Defined Networking technology promises to enhance network performance and reduce costs for service providers by providing scalability, flexibility, and programmability through the separation of the control plane from the data plane. However, the separation between the control plane and data plane in the implementation of SDN presents scalability issues, as the controller has limited computational resources. In this document, we propose an SDN hybrid model switch as a scalable framework for hybrid routing with machine learning. The proposed hybrid switching solution is benchmarked against an OpenFlow switch based on network performance metrics, such as throughput, packet exchange rate, delay, and CPU load. The simulation results are statistically examined and compared by plotting the performance results on a graph. The proposed hybrid switching solution can be implemented into a Data Centre network, to guarantee scalable switching and enhanced network latency. © 2024 IEEE.

关键词： Scalability

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Open-Set Recognition in Unknown DDoS Attacks Detection With Reciprocal Points Learning

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IEEE Access 2024年 12卷 56461-56476页

作者： Shieh, Chin-Shiuh Ho, Fu-An Horng, Mong-Fong Nguyen, Thanh-Tuan Chakrabarti, Prasun National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung82444 Taiwan Nha Trang University Department of Electronics and Automation Engineering Nha Trang650000 Viet Nam Sir Padampat Singhania University Department of Computer Science and Engineering Rajasthan Udaipur313601 India

The internet, a cornerstone of modern life, has profound implications across personal, business, and society. However, its widespread use has posed challenges, especially concerning privacy and cybersecurity. Besides, the threats on the internet are increasing in terms of danger, intensity, and complexity. Distributed denial-of-service (DDoS) attacks have emerged as a common and dangerous cybersecurity threat capable of disabling the network systems of targeted organizations and services. Therefore, various security strategies, such as firewalls and intrusion detection systems (IDS), are employed to protect against DDoS attacks. Enhancing the defensive capabilities of IDS systems through machine learning (ML) and deep learning (DL) technologies is a significant trend nowadays. However, despite notable successes, detecting DDoS attacks using ML and DL technologies still faces challenges, especially with Unknown DDoS Attacks. In this research, the primary goal is to address the unknown DDoS detection problem through efficient and advanced techniques. Our proposed method, CNN-RPL, integrates Convolutional Neural Network (CNN) with Reciprocal Points Learning (RPL), a novel Open-Set Recognition (OSR) technology. This model can effectively handle both known and unknown attacks. The CNN-RPL model demonstrates excellent results, achieving an accuracy exceeding 99.93% against known attacks in the CICIDS2017 dataset. Simultaneously, the model achieves a commendable average accuracy of up to 98.51% against unknown attacks in the CICDDoS2019 dataset. In particular, the CNN-RPL model simplifies the architecture of the deep neural network by significantly reducing the number of training parameters without compromising defense capabilities. Therefore, our proposed method is genuinely efficient, particularly flexible, and lightweight compared to traditional methods. This can equip organizations and businesses with a highly applicable yet powerful security approach against the evolv

关键词： Denial-of-service attack

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