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The performance of circularly polarized phased sub-array antennas for 5G laptop devices investigating the radiation effects

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Progress in Electromagnetics Research C 2021年 110卷 267-283页

作者： Mahmoud, Korany R. Baz, Abdullah Alhakami, Wajdi Alhakami, Hosam Montaser, Ahmed M. Department of Electronics and Communications Faculty of Engineering Helwan University Cairo Egypt National Telecommunications Regulatory Authority Ministry of Communication and Information Technology Giza Egypt Computer Engineering Department College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science College of Computer and Information Technology Taif University P. O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science College of Computer and Information Techonogy Umm Al-Qura University Makkah21955 Saudi Arabia Electrical Engineering Department Sohag University Sohag Egypt

In this paper, the performance of circularly polarized (CP) adaptive sub-arrays integrated into 5G laptop device is investigated in the presence of a whole-body human phantom model. In addition, the radiation effect of the steered beam patterns has been analyzed by calculating the specific absorption rate distribution and temperature rise. In this target, a single-feed CP antenna element has been firstly designed to resonate at 28GHz with high realized gain and radiation efficiency. Then, 4 subarrays have been constructed in a rectangular configuration with four-elements for each sub-array. To let the study more realistic, a complete human model is considered to investigate the radiation effects. The measured reflection coefficient and realized gain results of the designed antenna element are found to be -30 dB and 7.82 dB, respectively, in the assigned frequency band. Likewise, the antennas sub-arrays have approximately kept the same impedance matching attitude with high insertion loss of -22 dB and a realized gain and radiation efficiency of 16.85 dB and 86%, respectively, on average. Furthermore, the sub-arrays scan patterns and coverage efficiency have been studied considering the existence of the human body in different scenarios. Regarding the RF exposure, the results show that the resultant maximum values of specific absorption rate and power density do not exceed 1.52 W/kg and 3.5W/m2, respectively, whereas the maximum exposure temperature in such a case is less than 2.8°C after 30 minutes and decreases to 0.5°C after a penetration depth of 3mm which reflects the possibility of safe use. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Radiation efficiency

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Robust Online Multiband Drift Estimation in Electrophysiology Data

Robust Online Multiband Drift Estimation in Electrophysiolog...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Charlie Windolf Angelique C. Paulk Yoav Kfir Eric Trautmann Domokos Meszéna William Muñoz Irene Caprara Mohsen Jamali Julien Boussard Ziv M. Williams Sydney S. Cash Liam Paninski Erdem Varol Departments of Statistics Zuckerman Institute Columbia University Departments of Neurology Center for Neurotechnology and Neurorecovery Massachusetts General Hospital Harvard Medical School Departments of Neurosurgery Departments of Computer Science & Engineering New York University

High-density electrophysiology probes have opened new possibilities for systems neuroscience in human and non-human animals, but probe motion poses a challenge for downstream analyses, particularly in human recordings. We improve on the state of the art for tracking this motion with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) in addition to spikes. Second, we show that the LFP-based approach enables registration at sub-second temporal resolution. Third, we introduce an efficient online motion tracking algorithm, enabling the method to scale up to longer and higher-resolution recordings, and possibly facilitating real-time applications. Finally, we improve the robustness of the approach by introducing a structure-aware objective and simple methods for adaptive parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from human and mouse.

关键词： Tracking Signal processing algorithms Estimation Electrophysiology Robustness Real-time systems Mice

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Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

arXiv

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arXiv 2025年

作者： Roh, Daeyoung Han, Donghee Kim, Daehee Han, Keejun Yi, Mun Graduate School of Data Science KAIST Korea Republic of School of Computer Engineering Hansung University Korea Republic of Department of Industrial and Systems Engineering KAIST Korea Republic of

Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraph-based contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the high-order correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce high-quality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://***/Noverse0/***. © 2025, CC BY.

关键词： Contrastive Learning

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A Study on Kerberos and Graphical Password-Based Web Authentication Scheme 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Murugan, Thangavel Sikdar, Sagar Semwal, Mihir Indira, K. Department of Information Systems & Security College of Information Technology United Arab Emirates University Abu Dhabi Al Ain15551 United Arab Emirates University of Erlangen–Nuremberg Erlangen Germany Bank of America Haryana Gurugram India Department of Computer Science and Engineering Thiagarajar College of Engineering Tamil Nadu Madurai India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Web authentication methods are subject to several attacks due to the rapid expansion of information technology. It is apparent that the evolution of authentication-bypassing strategies, from brute force to dictionary attacks, will advance. Simple word lists, for instance, are employed in traditional dictionary attacks to crack commonly used usernames and passwords. Robust hashing methods can be used to safeguard credentials sent via an insecure communication channel. However, hackers have created a novel hybrid method called a ‘rainbow attack’ that is like dictionary attacks but focuses on password hashes. In today’s digital era, traditional security methods like one-factor authentication are ineffective at combating the previously mentioned cyber-attacks. Even two-factor authentication may be bypassed using advanced cyber-methods. This work focuses on advanced cyber-techniques that can defeat even two-factor authentication. Server-side forging is one technique used to get around the restrictions imposed by two-factor authentication. Therefore, it is obvious that strong authentication entities are needed if the danger of credential bypassing needs to be lowered, especially in the Web 3.0 platform. In this proposed study, the objective is to discuss the authentication strategies and improved strategies based on Kerberos and graphical password. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年 1-33页

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore Software Maintenance

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computers, Materials & Continua 2022年第11期73卷 3827-3845页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Nadeem Iqbal Nazri Kama Azri Azmi Ahmad Salman Khan Yasir Mahmood Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyaKuwait

Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of *** the work is done offshore,these agreements or images must be shared through the Internet or stored over the *** breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of *** article proposes a novel algorithm for securing the NDAs in the form of *** an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the *** the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of *** confusion effects have been realized through an XOR operation between the confused image,and chaotic ***,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it *** machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

关键词： Non-disclosure agreement encryption decryption secret key chaoticmap confusion diffusion

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Pediatric Bone Age Prediction Using Deep Learning

Pediatric Bone Age Prediction Using Deep Learning

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International Conference on computer and Information Technology (ICCIT)

作者： Al Zadid Sultan Bin Habib Md. Ekramul Islam Md Asif Bin Syed Md Younus Ahamed Tanpia Tasnim Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown USA Department of Computer Science & Engineering Stamford University Bangladesh Dhaka Bangladesh Department of Industrial and Management Systems Engineering West Virginia University Morgantown USA Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child’s growth and development. However, conventional bone age prediction methods are often labor-intensive and require specialized radiological expertise. This paper presents a Deep Learning (DL)-based approach to pediatric bone age prediction using EfficientNet with Additive Attention, a state-of-the-art neural network architecture for image classification and regression tasks. The method utilizes over 12,000 X-ray images from the RSNA bone age dataset. It involves image preprocessing, transforming them into three-channel images, and training a Convolutional Neural Network (CNN) to automatically learn the features of hand bone images. This approach provides a more effective and accurate solution for predicting bone age, which is critical in diagnosing pediatric endocrine diseases. This work uses two variations of the EfficientNet model (B0 and B4), where EfficientNetB4 is also finetuned with the Additive Attention mechanism. These three models predict the age for the original age, and their comparison is shown in curves. The predicted ages depict that in most cases, EfficientNetB4 and EfficientNetB4 with Additive Attention (EN-AA) successfully predicted the bone ages more accurately regarding the original age, and their performance was better than the EfficientNetB0. Specific performance metrics are provided to underscore this improvement. Learning curves for training and validation loss confirm effective learning without overfitting or underfitting, further validating our approach’s efficacy in pediatric endocrine disease diagnosis.

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Proceedings of the 2024 IEEE Open Conference of Electrical, Electronic and Information sciences (eStream’ 2024)

Proceedings of the 2024 IEEE Open Conference of Electrical, ...

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Electrical, Electronic and Information sciences (eStream), Open Conference of

作者： Dalius Navakauskas Ćarūnas Paulikas Tomyslav Sledevič Dainius Udris Department of Electronic Systems Vilnius Lithuania Department of Computer Science and Communications Technologies Vilnius Lithuania Department of Electrical Engineering Vilnius Lithuania

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

We are very glad to welcome our colleagues - young scientists, researchers and practitioners to the 11-th IEEE Open Conference of Electrical, Electronic and Information sciences (eStream‘ 2024), held in Vilnius Gediminas Technical University (VILNIUS TECH), Vilnius, Lithuania, on 25 April 2024. The eStream conferences aim to disseminate the research achievements between worldwide groups of scientists and engineers working in different areas of science to reach more tight relationships and generate new ideas for joint projects or other means of collaboration.

关键词： Collaboration

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A Novel Approach to Menstrual Cycle Prediction Using GAN-Generated Data and Transformer Model for Tabular Data

A Novel Approach to Menstrual Cycle Prediction Using GAN-Gen...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Ahamed, Md Younus Syed, Md Asif Bin Tani, Mafuza Nasrin Tasnim, Tanpia Habib, Al Zadid Sultan Bin West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States Army Medical College Rangpur Rangpur5400 Bangladesh Green University of Bangladesh Narayanganj Dhaka1461 Bangladesh

ISBN: (纸本)9798331535476

Accurate menstrual cycle prediction is crucial for women's health and fertility management. While prior studies have utilized machine learning models to predict cycle length and classify regularity, they often struggled with the low Observationto-Variable Ratio (OVR) inherent in menstrual cycle data. This study tackles this issue by employing Generative Adversarial Networks (GANs) to generate synthetic data and Transformerbased models for both classification a nd r egression t asks. We evaluated multiple machine learning models for menstrual cycle phase classification a nd l ength p rediction. F or t he classification of regular versus irregular cycles, TabNet significantly outperformed baseline models, including Logistic Regression, Random Forest, and LSTM, achieving 99.67% accuracy, an AUC of 0.96, an F1 score of 0.99, 92.37% specificity, a nd 99.66% sensitivity, with CTGAN-generated synthetic data enhancing its performance. In the regression task of predicting cycle length, TabNet demonstrated strong predictive power, with an MSE of 1.29, RMSE of 1.13, and an R2 score of 0.87. Although Random Forest performed slightly better with an MSE of 0.958 and an R2 score of 0.90, TabNet remained competitive. These results highlight the potential of GAN-generated data and advanced models like TabNet in overcoming low OVR challenges and improving menstrual cycle prediction. Future research may incorporate personalized health data to further enhance model accuracy and generalizability. © 2024 IEEE.

关键词： Prediction models

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An Intrusion Detection System for IoT Using KNN and Decision-Tree Based Classification

An Intrusion Detection System for IoT Using KNN and Decision...

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2021 International Conference of Modern Trends in Information and Communication Technology Industry, MTICTI 2021

作者： Abdaljabar, Zainab Hussam Ucan, Osman Nuri Ali Alheeti, Khattab M. University of Altinbaş Institute of Graduate Studies Department of Electrical and Computer Engineering Turkey University of Anbar College of Computer Science and Information Technology Department of Computer Networking Systems Iraq

ISBN: (纸本)9781665414449

The Internet of Things (IoT) has grown rapidly in recent years, intending to affect everything from everyday life to large industrial systems. Regrettably, this has attracted the attention of hackers, who have turned the Internet of Things into a target for malicious activity, exposing end nodes to attack. IoT devices' sheer volume and diversity make protecting the IoT infrastructure with a traditional intrusion detection system difficult. So to protect IoT devices, the data flow was investigated in an IoT context to protect these devices from hackers. We used two machine learning classifiers in this work: KNN (K-Nearest Neighbors) and DT (Decision Tree). We calculated the Error Rate, Accuracy, Precision, Recall, and F1 score for each method. When we combined these two classifiers, we obtained outstanding results (100 %). We have a high rate of detection of attacks. The findings are summarized. © 2021 IEEE.

关键词： Decision trees

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