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European Conference on Antennas and Propagation, EuCAP

作者： Hanguang Liao Atif Shamim Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

Quasi-isotropic antennas have gained attention due to the emergence of the Internet of Things (IoT) and Wireless Sensing Networks (WSNs), for their orientation-insensitive communication ability. For those applications, electrically small (ES) antennas are usually preferred, which can save space for the IoT or sensing nodes, while reducing the material cost. Several compact isotropic antennas have been reported recently. However, only very few of them have shown dual-band operation ability. A novel design method to design a dual-band quasi-isotropic ES antenna is presented in this conference proceeding. The utilization of a band stop filter (BSF) enables the conventional single-band quasi-isotropic split ring resonator (SRR) antenna to behave in a dual-band operation, while maintaining the quasi-isotropic radiation for both bands. The proposed antenna is designed, fabricated, and measured, which shows a dual-band operation (both bands in ka<1 region) while maintaining decent performance.

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Evaluation of Chest CT-Scan Anomaly Detection Models Using Principal Component Analysis 25

Evaluation of Chest CT-Scan Anomaly Detection Models Using P...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alyabrodi, Ala Al-Daja, Shrouq Injadat, MohammadNoor Moubayed, Abdallah Elrashidi, Ali Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Data Science & Ai Department Zarqa Jordan King Fahd University of Petroleum & Minerals Research Center for Intelligent Secure Systems Computer Engineering Department and Interdisciplinary Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Machine learning-based improvements in anomaly detection, visualization, and segmentation are made possible by the growing digitization of medical imaging, which reduces the workload for medical specialists. Nevertheless, trustworthy labeled data is necessary for supervised machine learning and is sometimes expensive, time-consuming, or impossible to get. Consequently, techniques that need either little labeling (unsupervised methods) or partial labeling (semi-supervised methods) have been used increasingly been used. One approach that can be used to tackle medical imaging tasks like segmentation and classification is anomaly detection, which makes use of both unsupervised and semi-supervised methods. This study investigates the anomaly detection capabilities of deep learning and machine learning models trained on chest CT scans. To adopt this approach, firstly, by evaluating several feature extraction techniques (Principal Component Analysis (PCA), Autoencoder, and variance threshold), PCA achieves the best results on the selected dataset. After that, different machine learning and deep learning models (Isolation Forest, K-means, feed-forward neural networks, and convolutional neural networks) are assessed to detect anomalies using PCA for dimensionality reduction. The experiments show that the NN-Feedforward model with PCA outperformed the other models, also attains remarkably low false alarm rate, demonstrating its effectiveness in reducing false positives and precisely detecting anomalies. © 2024 IEEE.

关键词： computerized tomography

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025, CC BY.

关键词： Mobile edge computing

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Criteria-based Unsupervised Feature Selection Techniques: A Comparative Study Over Chest CT Scan 25

Criteria-based Unsupervised Feature Selection Techniques: A ...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Daja, Shrouq Alyabrodi, Ala' Moubayed, Abdallah Injadat, MohammadNoor Elrashidi, Ali Zarqa University Data Science & Ai Department Zarqa Jordan Zarqa University Cyber Security Department Zarqa Jordan King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Research on classifying chest CT scans as normal or abnormal using machine learning and deep learning has garnered significant attention. To address this, various feature selection (FS) methods are employed to reduce dimensionality and identify key features. This paper evaluates criteria tailored to unsupervised binary datasets, such as feature subsets, redundancy, and diversity, by comparing Mutual Information-Based Methods (MI) with Clustering-Based Feature Selection (Agglomerative Clustering) ones. Additionally, it examines Graph Structure, Spectral Properties, and Quality criteria by comparing Graph-Based Methods with Spectral Feature Selection-be (SPEC). For feature reduction, Agglomerative Clustering may offer a slight advantage due to better handling of diversity and redundancy, though the difference is minimal. If these aspects are crucial, Agglomerative Clustering might be preferred. Conversely, for Graph-Based vs. SPEC, SPEC Method is more suitable for feature reduction due to its coherent feature selection and focus on capturing significant patterns related to medical interventions. © 2024 IEEE.

关键词： Contrastive Learning

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A Case Study on Modeling Adequacy of a Grid with Subsynchronous Oscillations Involving IBRs

A Case Study on Modeling Adequacy of a Grid with Subsynchron...

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IEEE General Meeting Power& Energy Society

作者： Lilan Karunaratne Nilanjan Ray Chaudhuri Amirthagunaraj Yogarathnam Meng Yue School of Electrical Engineering and Computer Science Pennsylvania State University University Park PA USA Interdisciplinary Science Department Brookhaven National Laboratory Upton NY USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

A case study on modeling adequacy of a grid in presence of renewable resources based on grid-forming converters (GFCs) is the subject matter of this paper. For this purpose, a 4-machine 11-bus IEEE benchmark model is modified by considering GFCs replacing synchronous generators that led to unstable subsynchronous oscillations (SSOs). We aim to: (a) understand if transmission network dynamics should be considered in such cases, (b) revisit the space-phasor-calculus (SPC) in d-q frame under balanced condition that captures such phenomena and lends itself to eigenvalue analysis, and (c) emphasize limitations of such models while underscoring their importance for large-scale power system simulations. Time-domain and frequency-domain results from SPC and quasistationary phasor calculus (QPC) models are compared with electromagnetic transient (EMT)-based simulations. It is shown that models with transmission line dynamics in SPC framework can capture the SSO mode while QPC models that neglect these dynamics fail to do so.

关键词： Renewable energy sources Power transmission lines Frequency-domain analysis Power system dynamics Power system simulation Synchronous generators Eigenvalues and eigenfunctions Time-domain analysis Oscillators Grid forming

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NEW INSIGHTS FOR THE STABILITY-PLASTICITY DILEMMA IN ONLINE CONTINUAL LEARNING 11

NEW INSIGHTS FOR THE STABILITY-PLASTICITY DILEMMA IN ONLINE ...

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11th International Conference on Learning Representations, ICLR 2023

作者： Jung, Dahuin Lee, Dongjin Hong, Sunwon Jang, Hyemi Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of DeepBrain AI Inc. Korea Republic of Department of Cyber Security Ewha Womans University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, wherein data comes strictly in a streaming manner, the plasticity of online continual learning is more vulnerable than offline continual learning because the training signal that can be obtained from a single data point is limited. To overcome the stability-plasticity dilemma in online continual learning, we propose an online continual learning framework named multi-scale feature adaptation network (MuFAN) that utilizes a richer context encoding extracted from different levels of a pre-trained network. Additionally, we introduce a novel structure-wise distillation loss and replace the commonly used batch normalization layer with a newly proposed stability-plasticity normalization module to train MuFAN that simultaneously maintains high plasticity and stability. MuFAN outperforms other state-of-the-art continual learning methods on the SVHN, CIFAR100, miniImageNet, and CORe50 datasets. Extensive experiments and ablation studies validate the significance and scalability of each proposed component: 1) multi-scale feature maps from a pre-trained encoder, 2) the structure-wise distillation loss, and 3) the stability-plasticity normalization module in MuFAN. Code is publicly available at https://***/whitesnowdrop/MuFAN. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Stability

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Need for Enhanced Contingency-Dependent DER Aggregation Scheme for Transient Analysis in Modern Power Grid: A Case Study

Need for Enhanced Contingency-Dependent DER Aggregation Sche...

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IEEE General Meeting Power& Energy Society

作者： Amirthagunaraj Yogarathnam Nilanjan Ray Chaudhuri Meng Yue Interdisciplinary Science Department Brookhaven National Laboratory Upton NY USA School of Electrical Engineering and Computer Science Pennsylvania State University University Park PA USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The growing impact of distributed energy resources (DERs) on transmission operations has underscored the need for comprehensive modeling and simulation approaches in transient analysis. However, owing to their dispersed deployment, the DERs situated at varying locations "see" distinct grid conditions, potentially triggering individual protection and control responses during transmission-level contingencies. This requires modeling the DERs situated near fault locations with high fidelity, sometimes even individually, to accurately capture phenomena like voltage dips, while DERs beyond this boundary can be effectively aggregated. Consequently, there is a lack of investigation of the contingency-dependent DER behaviors in the literature. In this paper, we embark on the initial step in this direction, surpassing the conventional practice that typically aggregates a large number of DERs in distribution feeders using a single DER model at the point of common coupling. To that end, a hybrid EMT-phasor simulation platform for transmission-and-distribution systems is developed where the modified IEEE 39-bus system and the IEEE 33-node feeder with DERs represent the transmission and distribution systems, respectively. Then, the limitation of the commonly used single DER aggregated model for capturing the partial tripping of DERs and the need for an enhanced aggregation scheme is highlighted. Furthermore, the study proposes and demonstrates a preliminary contingency-dependent DER aggregation scheme utilizing hybrid time-domain simulation on the test system models. This pioneering endeavor leads to a promising trajectory for future research, which includes exploration into analytical and/or machine learning-based methodologies aimed at discerning the areas of interest where enhanced DER aggregation is needed.

关键词： Couplings Analytical models Voltage fluctuations Contingency management Fault location Distributed power generation Trajectory Transient analysis Time-domain analysis Protection

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Control of Magnetic Skyrmions in an Exchange Biased van der Waals Ferromagnet

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1710-1711页

作者： McCray, Arthur R C Lebedev, Dmitry Arpaci, Sevdenur Te Velthuis, Suzanne G E Lopez-Dominguez, Victor Amiri, Pedram Khalili Hersam, Mark C. Petford-Long, Amanda K. Phatak, Charudatta Materials Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program Northwestern University Evanston IL United States Department of Materials Science and Engineering Northwestern University Evanston IL United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States

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QoS-Aware High Throughput Scheduling for Wireless Networks 3

QoS-Aware High Throughput Scheduling for Wireless Networks

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kar, Abhijeet Vashisht, Nitish Kumar, Ritesh Sharma, Sonal Indira, G. Kachhoria, Renu Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Karnataka Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

This paper seems on the challenge of providing first-class-of-provider (quality of service) conscious throughput scheduling for wireless networks. It starts by presenting an outline of the present-day scheduling techniques used to help wireless networks, where the point of interest is maximizing spectral efficiency. It then seems at a way to design scheduling algorithms to recall the quality of service constraints. Specifically, it looks at how strategies, price management, adaptive modulation coding, and channel and interference prediction may be utilized to make specific QoS-aware scheduling. Subsequently, how artificial intelligence may be used to aid in designing quality of service-conscious scheduling algorithms and how machines gain knowledge of-based strategies consisting of deep reinforcement getting to know may be used in this context. Sooner or later, the paper presents a QoS-conscious scheduling set of rules that combines present scheduling techniques and considers the restrictions earlier than presenting an evaluation of the efficacy of this technique. © 2023 IEEE.

关键词： Evaluation maximizing modulation reinforcement techniques

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Natural Language Processing to Improve Content Retrieval Performance 3

Natural Language Processing to Improve Content Retrieval Per...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jadhav, Varsha D. Doda, Devendra Kumar Swarup, Lakshya Kumar, Ritesh Pandian, Rajesh Rufina, Princy Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Karnataka Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India

ISBN: (纸本)9798350319125

Latest studies in herbal Language Processing (NLP) have targeted improving the accuracy of statistics retrieval through leveraging machines better to recognize the use and scope of human language. Employing NLP strategies makes it far more feasible to system massive units of files and better identify the applicable content in them. Content related to a user's query can then be taken care of and ranked based on the similarity of the language used in the file. NLP strategies permit a more accurate representation of content material and can help retrieve the maximum relevant statistics. Furthermore, NLP can be prolonged beyond traditional search engines like Google to research textual content-primarily based information, such as emails and social media posts, for areas that include sentiment analysis and customer support. This paper explores the capacity of NLP to enhance content material retrieval overall performance in more than a few situations. It additionally outlines valuable techniques for evaluating the effectiveness of such structures. Dialogue and conclusion refer to approaches by which the implementation of NLP can convey improved accuracy to content material retrieval. © 2023 IEEE.

关键词： Discussion effectiveness implementation representation similarity

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