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Optimized Metamaterials for Design of Enhanced-Performance High Order Mode Dipole-Driven Yagi-Uda Antenna for Millimeter Wave Applications

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IEEE Access 2025年 13卷 79385-79396页

作者： Esmail, Bashar A. F. Isleifson, Dustin Koziel, Slawomir Pietrenko-Dabrowska, Anna University of Manitoba Department of Electrical and Computer Engineering WinnipegMBR3T 5V6 Canada Reykjavik University Department of Engineering Reykjavík102 Iceland Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Gdańsk80-233 Poland

This paper presents the development of a Yagi antenna optimized for third-order mode operation within the millimeter-wave (mmWave) spectrum. A third-order mode driven dipole, along with a reflector, is introduced to enhance the antenna’s gain. The rigorous numerical optimization procedure was employed to precisely adjust the dimensions and positions of the driven dipole, director, and reflectors. The optimized Yagi antenna operates within the fifth generation (5G) band at 28 GHz, with a bandwidth of 2.6 GHz. Radiation pattern analysis indicates that the gain of the antenna in this higher resonant mode exceeds that of a conventional Yagi-Uda antenna, achieving a gain of 9.35 dBi at 28 GHz. To further increase the gain and address the path loss challenges in the mmWave spectrum, a near-zero index metamaterial (NZIM) array was integrated. A 5 x 5 unit cell array was embedded into the same antenna substrate, positioned in front of the reflector. The metamaterial array was optimized using the trust-region (TR) algorithm, resulting in a significant gain enhancement, reaching 13.8 dBi at 28 GHz while maintaining the operational bandwidth in terms of impedance matching. The antenna was subsequently fabricated and tested, with experimental results demonstrating a strong correlation with the simulated outcomes across all key parameters. © 2013 IEEE.

关键词： Antenna reflectors

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Forgery-Aware Adaptive Learning With Vision Transformer for Generalized Face Forgery Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4116-4129页

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Kot, Alex C. Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China Laboratory School of Electrical and Electronic Engineering Jurong West 639798 Singapore Sun Yat-sen University Guangdong Key Laboratory of Information Security School of Computer Science and Engineering Guangzhou510006 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen518116 China China-Singapore International Joint Research Institute Guangzhou China

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. To this end, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm for generalized face forgery detection, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. © 1991-2012 IEEE.

关键词： Forgery Faces Adaptation models Transformers Feature extraction Adaptive learning computer vision Face recognition Deepfakes Visualization

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Flexible Hybrid Electronics for Printing of Nano and Chip Scale Structures 1

Flexible Hybrid Electronics for Printing of Nano and Chip Sc...

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1st International Conference on Advances in computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, Neelima Gupta, Radha Singh, Rakesh Kumar Shruthi, T.S. Gupta, Parbhat Singhal, Abhinav Uttaranchal University Department of Computer Science and Engineering Dehradun India Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India IIMT College of Engineering U.P. Greater Noida India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad Modinagar India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Adaptable sensors, circuits, and substrates are joined with unbending electronic parts to make adaptable flexible hybrid electronics (FHE). The printing of nanomaterials has benefits over the costly, multi-step, and blunder inclined cleanroom-based nano-microfabrication process, like expense adequacy, high-throughput, steadfastness, and adaptability. FHE usually consists of devices that range in size from nano to chip, and it might be difficult to integrate them using a standard procedure. The mix of ultrathin chips and nanowires (NWs) based electronic layers on a solitary substrate is shown here utilizing printed gadgets. The performance of the transistors on UTCs (ultrathin chips) is thoroughly studied in order to optimise the printing process. The adequacy of the printing system is affirmed by electrical information accumulated from 14 semiconductors on 3 unmistakable chips, which uncover next to no adjustment of execution following exchange printing. Building UV photodetectors utilizing DIW printing further exhibits the unrivaled grade nature of ZnO-NWs-put together electronic layers delivered with respect to a similar substrate. © 2025 IEEE.

关键词： Substrates

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Empowering Dysarthric Communication: Hybrid Transformer-CTC-Based Speech Recognition System

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IEEE Access 2025年 13卷 82479-82491页

作者： Vinotha, R. Hepsiba, D. Anand, L. D. Vijay Bruntha, P. Malin Dinh, Linh Dang, Hien Karunya Institute of Technology and Sciences Department of Robotics Engineering Tamil Nadu Coimbatore641114 India Karunya Institute of Technology and Sciences Department of Biomedical Engineering Tamil Nadu Coimbatore641114 India Karunya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641114 India Suffolk University Department of Information Systems BostonMA02108 United States Thuyloi University Faculty of Computer Science and Engineering Hanoi116705 Viet Nam Molloy University Department of Mathematics and Computer Science Rockville CentreNY11570 United States

Dysarthria, a motor speech disorder, impairs the muscles involved in speech production, leading to challenges in articulation, pronunciation, and overall communication. This results in slow, slurred speech that is difficult to understand. Augmentative and Alternative Communication (AAC) aids integrated with speech recognition technology offer a promising solution for individuals with dysarthria. However, Automatic Speech Recognition (ASR) systems trained on typical speech data often struggle to recognize dysarthric speech due to its unique speech patterns and limited training data. To address these challenges, a hybrid Transformer-CTC model has been proposed for improving ASR performance on dysarthric speech. The Transformer architecture employs a self-attention mechanism that models complex dependencies between speech features, enabling it to identify and emphasize important patterns even when training data is limited. This ability is particularly crucial for dysarthric speech, where speech signals often exhibit high variability. On the other hand, Connectionist Temporal Classification (CTC) acts as an effective transcription layer. It aligns speech features with character sequences without requiring precise input-output alignment, making it well-suited for handling the inconsistencies and distortions present in dysarthric speech. The integration of these components creates a powerful architecture capable of learning nuanced speech patterns and delivering accurate transcriptions for dysarthric speech. The model was trained using the UA speech corpus, containing 13 hours of speech from 15 speakers with varying dysarthria levels. The proposed hybrid system achieves an impressive Word Recognition Accuracy (WRA) of 89%, demonstrating its effectiveness in accurately transcribing dysarthric speech. This innovative approach significantly advances the development of ASR technologies tailored to diverse and variable speech patterns, ultimately enhancing communication for in

关键词： Speech analysis

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Unified Disease Prediction System Using Machine Learning Techniques 3

Unified Disease Prediction System Using Machine Learning Tec...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Verma, Shalini Rallan, Reema Singh, Teekam Kumar, Naresh Lovely Professional University Department of Computer Application Punjab India Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University Dehradun India Maya Devi University Computer Science Department Dehradun India

ISBN: (纸本)9798331542375

This innovative research uses datasets from Kaggle to leverage Machine Learning (ML) techniques to develop a complete unified disease prediction system. With its comprehensive coverage of diabetes, heart disease, and Parkinson's disease, the dataset offers a strong basis for predictive modelling. Support Vector Machines (SVM) are used in the study for Parkinson's and diabetes predictions, while logistic regression is used for heart disease predictions. The system's reliability and usability are improved by this varied computational approach, which guarantees nuanced forecasts for any medical condition. The popular area of interest is the nexus between new technology and healthcare, focusing on the critical role in data-driven decision-making, which plays an important role in preventative medicine. The research highlights the value of early diagnosis and preventative healthcare actions in addition to advancing healthcare technologies. By merging intuitive machine learning algorithms. For heart disease prediction using logistic regression model exhibited 81.96% accuracy, while SVM system for diabetes and Parkinson's disease exhibited 77.27 and 87.17%, respectively. The open-source Streamlit platform integration provides an intuitive user interface for the integrated module. This project's technology integration is an innovative way to build a more robust and adaptable healthcare system that promotes early detection and preventative treatment of prevalent health issues. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Age of Information in Gossip Networks: A Friendly Introduction and Literature Survey Invited Paper

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IEEE Transactions on Communications 2025年

作者： Kaswan, Priyanka Mitra, Purbesh Srivastava, Arunabh Ulukus, Sennur The Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Gossiping is a communication mechanism, used for fast information dissemination in a network, where each node of the network randomly shares its information with the neighboring nodes. To characterize the notion of fastness in the context of gossip networks, age of information (AoI) is used as a timeliness metric. In this article, we summarize the recent works related to timely gossiping in a network. We start with the introduction of randomized gossip algorithms as an epidemic algorithm for database maintenance, and how the gossiping literature was later developed in the context of rumor spreading, message passing and distributed mean estimation. Then, we motivate the need for timely gossiping in applications such as source tracking and decentralized learning. We evaluate timeliness scaling of gossiping in various network topologies, such as, fully connected, ring, grid, generalized ring, hierarchical, and sparse asymmetric networks. We discuss age-aware gossiping and the higher order moments of the age process. We also consider different variations of gossiping in networks, such as, file slicing and network coding, reliable and unreliable sources, information mutation, different adversarial actions in gossiping, and energy harvesting sensors. Finally, we conclude this article with a few open problems and future directions in timely gossiping. © 1972-2012 IEEE.

关键词： Information dissemination

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MARIE:One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

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computer Modeling in engineering & sciences 2025年第1期142卷 279-298页

作者： Diana Abi-Nader Hassan Harb Ali Jaber Ali Mansour Christophe Osswald Nour Mostafa Chamseddine Zaki Computer Science Department Faculty of SciencesLebanese UniversityBeirut146404Lebanon College of Engineering and Technology American University of the Middle EastEgaila54200Kuwait Lab-STICC CNRS UMR 6285ENSTA-BretagneBrest29200France

Security and safety remain paramount concerns for both governments and individuals *** today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to ***,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent *** advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying *** paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection *** SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction *** experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy *** these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.

关键词： Firearm and gun detection single shot multi-box detector deep learning one-stage detector MobileNet inception convolutional neural network

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A New Heterogeneous Hybrid Massive MIMO Receiver With an Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 17-29页

作者： Shu, Feng Shi, Baihua Chen, Yiwen Bai, Jiatong Li, Yifan Liu, Tingting Han, Zhu You, Xiaohu Hainan University School of Information and Communication Engineering Haikou570228 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Hainan University Collaborative Innovation Center of Information Technology Haikou570228 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210003 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low cost in future green wireless communication, the conventional hybrid analog and digital MIMO receive structure emerges a natural choice. But it exists an issue of the phase ambiguity in direction of arrival (DOA) estimation and requires at least two time-slots to complete one-time DOA measurement with the first time-slot generating the set of candidate solutions and the second one to find a true direction by received beamforming over this set, which will lead to a low time-efficiency. To address this problem,a new heterogeneous sub-connected hybrid analog and digital ( H2 AD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity, and then a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC;2) find the class of the true solutions and compute the class mean. To infer the set of true solutions, we propose two new clustering methods: weight global minimum distance (WGMD) and weight local minimum distance (WLMD). Next, we also enhance two classic clustering methods: accelerating local weighted k-means (ALW-K-means) and improved density. Additionally, the corresponding closed-form expression of Cramer-Rao lower bound (CRLB) is derived. Simulation results show that the proposed frameworks using the above four clustering can approach the CRLB in almost all signal to noise ratio (SNR) regions except for extremely low SNR (SNR © 2024 The Authors.

关键词： Signal to noise ratio

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Unveiling anomalies: transformative insights from transformer-based autoencoder models

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International Journal of computers and Applications 2025年第1期47卷 29-44页

作者： Dehkordi, Sajad Balali Nasri, Saeed Dami, Sina Department of Electrical Engineering Najaf Abad Branch Islamic Azad University Najaf Abad Iran Digital Processing and Machine Vision Research Center Najaf Abad Branch Islamic Azad University Najaf Abad Iran Department of Computer Engineering West Tehran Branch Islamic Azad University Tehran Iran

Nowadays, Intrusion Detection Systems (IDS) play a critical role in safeguarding networks by identifying and mitigating unauthorized access and malicious activities, yet face challenges in accurately detecting sophisticated threats while minimizing false positives. In this paper, we present a comprehensive analysis of a Transformer-based Autoencoder model for network intrusion detection using the NSL-KDD dataset. We evaluate the model's anomaly detection performance based on key metrics such as Mean Squared Error (MSE), Precision-Recall Area Under Curve (AUC), and Root Mean Squared Error (RMSE) on the test set. Our findings reveal promising results, with the model achieving low MSE, high AUC, and relatively low RMSE values, indicating its effectiveness in detecting anomalies. Additionally, we provide an in-depth examination of the model architecture, highlighting the role of encoder and decoder layers, dropout regularization, and optimization techniques such as Adam optimizer. Our analysis sheds light on the efficacy of Transformer-based Autoencoder models for network intrusion detection tasks, offering insights into their architectural design and performance evaluation. The novelty of this study lies in the implementation of a Transformer-based Autoencoder architecture for network intrusion detection, showcasing its superior performance in anomaly detection compared to traditional methods. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Network intrusion

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Automated Crack Detection in Pipelines Using Advanced Image Processing 2

Automated Crack Detection in Pipelines Using Advanced Image ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Subramani, Nandhagopal Jithesh, M.D. Harish, R. Rithish Hari, G. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798331505745

The automated detection of pipeline cracks is vital for maintaining the safety and operational lifespan of this crucial infrastructure. Early crack detection prevents costly repairs and minimizes the risk of catastrophic failures and environmental damage. Existing methods primarily rely on manual inspections and traditional image processing techniques, which often lead to inaccuracies and inefficiencies. These methods suffer from high false-negative rates and poor adaptability to different environmental conditions. This study proposes a deep learning framework utilizing YOLOv10 to automate the detection of pipeline cracks with enhanced accuracy. Studies using deep learning report detection accuracies between 80% and 92%, although performance varies across different studies. The range reflects the influence of factors such as dataset and model specifics. © 2025 IEEE.

关键词： Deep learning

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