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A High Isolation Ku-Band Dual-Polarized Phased Array With C-Shaped and H-Shaped Slots Aperture-Coupled Feeding

A High Isolation Ku-Band Dual-Polarized Phased Array With C-...

IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Song Zhou Pengfei Yu Wenhao Feng Yang You Yunlong Lu JiFu Huang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Faculty of Electrical Engineering and Computer Science East China Research Institute of Electronic Engineering Hefei China

ISBN: (数字)9798331520717

ISBN: (纸本)9798331520724

A dual-polarized phased array for Ku-band satellite communications is proposed. Multilayer radiation patches is excited by aperture-coupled feeding of C-shaped and H-shaped slots for horizontal and vertical polarization. The C-shaped and H-shaped slots T-orthogonal configurations are ingeniously designed with distances, apart to achieve high isolation between ports and full ku-band bandwidth coverage. The proposed dual-polarized antenna element has 36.5% simulated -10 dB impedance bandwidth (BW) in the range of 10.42 GHz to 15.06 GHz for port 1, and 35.3% simulated -10 dB impedance BW in the range of 10.45 GHz to 14.94 GHz for port 2, with port isolation less than -35 dB. For the 1×8 array, the horizontal cross-polarization is less than -21 dB with less than 6.8 dB gain reduction within the ± 60° beam scanning range, and the vertical cross-polarization is less than -26 dB with less than 5.3 dB gain reduction.

关键词： Phased arrays Bandwidth Ku-band Aperture antennas Microwave antenna arrays Broadband communication Broadband antennas Impedance Satellite communications Gain

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Stationary distribution of node2vec random walks on household models

arXiv

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arXiv 2025年

作者： Schroeder, Lars Stegehuis, Clara University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

The node2vec random walk has proven to be a key tool in network embedding algorithms. These random walks are tuneable, and their transition probabilities depend on the previous visited node and on the triangles containing the current and the previously visited node. Even though these walks are widely used in practice, most mathematical properties of node2vec walks are largely unexplored, including their stationary distribution. We study the node2vec random walk on community-structured household model graphs. We prove an explicit description of the stationary distribution of node2vec walks in terms of the walk parameters. We then show that by tuning the walk parameters, the stationary distribution can interpolate between uniform, size-biased, or the simple random walk stationary distributions, demonstrating the wide range of possible walks. We further explore these effects on some specific graph settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Network embeddings

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Diversity-Preserving Robust Watermarking for Diffusion Model Generated Images

Diversity-Preserving Robust Watermarking for Diffusion Model...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Linghong Wan Li Dong Diqun Yan Rangding Wang Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang China

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

This paper introduces a robust watermarking technique for diffusion model-generated images, which effectively balances watermark robustness, image fidelity, and diversity preservation. Unlike traditional post-hoc approaches, the proposed method embeds watermark information directly into the latent noise of the diffusion model, ensuring seamless integration into the image generation process. This approach minimizes perceptual impact while maintaining high visual quality and diversity of the generated images. Experimental results demonstrate the method’s resilience to various image distortions, including noise, compression, blurring etc., significantly outperforming existing watermarking techniques. The proposed method supports both watermark detection and bit-level extraction, providing a practical solution for secure content protection and traceability in generative models without compromising the integrity of the image generation process.

关键词： Visualization Image synthesis Spatial diversity Noise Watermarking Diffusion models Distortion Robustness Protection Resilience

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Membership Inference Attacks Against Cross-Domain Recommender Systems

Membership Inference Attacks Against Cross-Domain Recommende...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Jingru Yang Baisong Liu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

Cross-domain recommender systems can effectively address the data sparsity and cold-start problems in traditional recommender systems. However, such systems may inadvertently leak user membership privacy due to their reliance on shared latent representations and behavioral patterns across domains. In this paper, we propose a membership inference attack (MIA) framework tailored for cross-domain recommender systems, termed CDRMIA, to quantify privacy risks. Unlike prior MIA methods designed for single-domain settings, our attack infers a user's membership status in both the source and target domains using only target-domain recommendations and partial behavioral data. We design cross-domain features based on embedding alignment, behavioral consistency, and prediction confidence, and employ a multi-task learning model to jointly infer membership states. Extensive experiments on two real-world datasets (Amazon and Douban) and three representative cross-domain recommendation models (EMCDR, CMF, CLFM) demonstrate the effectiveness of CDRMIA. Our findings reveal significant privacy leakage risks in cross-domain recommender systems, highlighting the urgent need for robust privacy-preserving mechanisms.

关键词： Seminars Privacy Predictive models Multitasking Information technology Artificial intelligence Recommender systems

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A BPMN-Based Multi-tenant Customizable SaaS Application: A FARUL3S Case Study 23th

A BPMN-Based Multi-tenant Customizable SaaS Application: A F...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Ksiksi, Amira Hamdani, Tarek M. Ltifi, Hela Alimi, Adel M. Sfax Tunisia University of Monastir Mahdia Tunisia Computer Science and Mathematics Department Faculty of Sciences and Techniques of Sidi Bouzid University of Kairouan Sidi Bouzid Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9783031648465

The Ultra-Large Scale Software (ULSS) Systems are characterized by their massive size and complexity in various dimensions. Regulating a ULSS system through constraining the design by using the dominance relations helps to ensure a semantic interoperability between the different domains that are provided by an inter-organizational collaboration. However, a business process model and notation (BPMN)-based multi-tenant customizable SaaS application challenges the existing interoperability approaches due to the business logics introduced by the created BPMN workflows which are not defined during the design stage. In this paper, we propose the System Design Rules Hierarchy version 2 (SDRHv2), a refined version of the SDRH introduced in our previous work. The SDRHv2 aims to cover the tenant-specific BPMN-based customizations through introducing a new level at the bottom of the SDRH hierarchy called Tenant-Specific Design Rules (TSDR). Once the TSDR extends the System Design Rules (SDR) and the Platform Design Rules (PDR) levels of the SDRH in its definition, the tenant-specific data that corresponds to those variables can be easily accessed by the different cooperative organizations working on the same ULSS system. As a case study, we have implemented an administration application, which is a BPMN-based multi-tenant Customizable SaaS application, from scratch based on the Framework for Agile Regulated Ultra-Large-Scale Software systems (FARUL3S) in order to prove the capability of the SDRHv2 to provide semantic interoperability across all levels of the ULSS system hierarchy, and specifically the tenant-specific level. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Systems analysis

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Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

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computers, Materials & Continua 2024年第8期80卷 2005-2030页

作者： Tareq Hamadneh Khalid Kaabneh Omar Alssayed Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Zoubida Benmamoun Zeinab Monrazeri Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Faculty of Engineering Liwa CollegeAbu Dhabi41009United Arab Emirates Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and *** address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making *** this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in *** theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy *** tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) *** function served as input for the SOA ***,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and *** extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the *** study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain *** simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

关键词： Optimization supply chain management sustainable lot size optimization bio-inspired metaheuristic stork

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An Improved Transfer Learning-Based Model with Data Augmentation for Brain Tumor Detection 6

An Improved Transfer Learning-Based Model with Data Augmenta...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Prayogo, Rizal Dwi Hamid, Nur Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350344349

Current advances in deep learning have brought various breakthroughs in processing medical data. However, dealing with a limited number of medical datasets remains a challenge in deep learning and often leads to overfitting. To solve this research gap, here we show a new approach to improve the performance of a transfer learning-based model for brain tumor detection from 253 brain magnetic resonance imaging (MRI) sample images. The concept of transfer learning has been applied using a pretrained Inception V3combined with data augmentation. Modified layers using dropout and regularization have been additionally utilized to deal with overfitting. The proposed method shows an increase in accuracy of 6.430%, a precision of 5.531 %, a recall of 10.545%, and an F1-score of 8.040% compared to the baseline method. We show that our proposed method has been able to effectively enhance performance and reduce overfitting, even with a small number of datasets. Moreover, our proposed method outperforms state-of-The-Art brain tumor detection. © 2024 IEEE.

关键词： Tumors

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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SeedTree: A Dynamically Optimal and Local Self-Adjusting Tree 42

SeedTree: A Dynamically Optimal and Local Self-Adjusting Tre...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Pourdamghani, Arash Avin, Chen Sama, Robert Schmid, Stefan Tu Berlin Germany Ben Gurion University of the Negev School of Electrical and Computer Engineering Israel University of Vienna Faculty of Computer Science Austria Fraunhofer Sit Germany

ISBN: (纸本)9798350334142

We consider the fundamental problem of designing a self-adjusting tree, which efficiently and locally adapts itself towards the demand it serves (namely accesses to the items stored by the tree nodes), striking a balance between the benefits of such adjustments (enabling faster access) and their costs (reconfigurations). This problem finds applications, among others, in the context of emerging demand-aware and reconfigurable datacenter networks and features connections to self-adjusting data structures. Our main contribution is SeedTree, a dynamically optimal self-adjusting tree which supports local (i.e., greedy) routing, which is particularly attractive under highly dynamic demands. SeedTree relies on an innovative approach which defines a set of unique paths based on randomized item addresses, and uses a small constant number of items per node. We complement our analytical results by showing the benefits of SeedTree empirically, evaluating it on various synthetic and real-world communication traces. © 2023 IEEE.

关键词： Trees (mathematics)

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Smart Data Augmentation for Multiclass Brain Tumor Detection with Imbalanced Data 8

Smart Data Augmentation for Multiclass Brain Tumor Detection...

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8th International Conference on Information Technology, Information Systems and electrical engineering, ICITISEE 2024

作者： Prayogo, Rizal Dwi Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350368970

Dealing with class imbalance is a significant issue in classification tasks that often leads to lower prediction performance. Many data augmentation methods have been suggested to tackle this problem, but their effectiveness can vary depending on the dataset. Utilizing augmentation methods can improve the learning and generalization abilities of the proposed convolutional neural network (CNN) model. However, it is challenging to find a single data augmentation method that can effectively address class imbalance problems across all scenarios. In this paper, we present a technique known as smart data augmentation (SDA) that is aimed at effectively improving classification performance by enhancing imbalanced data. SDA involves an equation that can be used to create an augmentation technique, combining different sampling methods for addressing multiclass imbalance and offering easy customization options. We perform the data balancing strategy including undersampling, oversampling, and SDA-based sampling. We evaluate our proposed framework using a CNN model to improve the detection of brain tumors in imbalanced data. Our experiments demonstrate that SDA-based sampling improves brain tumor detection performance by 3.546% in accuracy, 3.310% in precision, 3.432% in recall, and 3.371 % in Fl-score compared to the imbalanced data scenario. The findings suggest that utilizing SDA together with CNN can improve the classification performance of brain tumor detection with imbalanced data. © 2024 IEEE.

关键词： Brain tumor detection class imbalance problem convolutional neural network smart data augmentation

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