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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Stavros Tsimpoukis Dimitrios Tyrovolas Sotiris Ioannidis Ian F. Akyildiz George K. Karagiannidis Christos Liaskos Institute of Computer Science Foundation for Research and Technology Hellas Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Crete Chania Greece Truva Inc Alpharetta GA USA Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon Computer Science Engineering Department University of Ioannina Ioannina Greece

ISBN: (数字)9798350365955

ISBN: (纸本)9798350365962

Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect geometric and material features of an object through RF waves. On the other hand, the PWE focuses on the the conversion of the wireless RF propagation in a controllable, by software, entity through the utilization of Reconfigurable Intelligent Surfaces (RISs), which can have a controllable interaction with impinging RF waves. In that sense, this dynamic synergy leverages the potential of RF Imaging to detect the structure of an object through RF wavefronts and the PWE’s ability to selectively replicate those RF wavefronts from one spatial location to wherever an XR-RF mobile user is presently located. Then the captured wavefront, through appropriate hardware, is mapped to the visual representation of the object through machine learning models. As a key aspect of the XR-RF’s system workflow is the wavefront copying mechanism, this work introduces a new PWE configuration algorithm for XR-RF. Moreover, it is shown that the waveform replication process inevitably yields imprecision in the replication process. After statistical analysis, based on simulation results, it is shown that this imprecision can be effectively modeled by the gamma distribution.

关键词： Radio frequency Wireless communication Wireless sensor networks Analytical models Extended reality Simulation Receiving antennas

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Generative Adversarial Networks for Dynamic Malware Behavior: A Comprehensive Review, Categorization, and Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Gebrehans, Ghebrebrhan Ilyas, Naveed Eledlebi, Khouloud Lunardi, Willian T. Andreoni, Martin Yeun, Chan Yeob Damiani, Ernesto Lab Khalifa University of Science and Technology Abu Dhabi United Arab Emirates University of California Riverside United States Khalifa University of Science and Technology Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Center for Secure Cyber-physical Systems Abu Dhabi United Arab Emirates University of Milan Dept. of Computer Science Italy

This paper highlights the critical role of Machine Learning (ML) in combating the dynamic nature of cybersecurity threats. Unlike previous studies focusing mainly on static analysis, this work surveys the literature on dynamic analysis-based malware generation and detection. The study addresses the complexities of applying GANs to tabular data with heavy-tailed and multimodal distributions. It also examines the challenges of generating sequential malware behavior data and categorizes GAN-based models and their primary use cases. Furthermore, the paper evaluates adversarial losses and their limitations in generating dynamic malware behavior. Finally, it identifies existing metrics to assess GAN generalization in malware research and suggests future research directions based on identified limitations. © 2020 IEEE.

关键词： Generative adversarial networks

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Direct data-driven LPV control of nonlinear systems: An experimental result

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IFAC-PapersOnLine 2023年第2期56卷 2263-2268页

作者： Chris Verhoek Hossam S. Abbas Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology 5600MB Eindhoven The Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck 23558 Lübeck Germany Systems and Control Lab Institute for Computer Science and Control 1111 Budapest Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points.

关键词： Data-Driven Control Linear Parameter-Varying systems

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Multiband highly sensitive metamaterial-based THz absorber for edible oil adulteration sensing

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Optik 2024年 307卷

作者： Anowarul Haque, S.M. Sohayeb Hossain, Md Alqahtani, Abdulrahman Islam, Mohammad Tariqul Moniruzzaman, Md. Samsuzzaman, Md. Dept. of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Dept. of Electrical and Electronic Engineering Mymensingh Engineering College Bangladesh Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam Bin Ab-dulaziz University Al-Kharj11942 Saudi Arabia Department of Medical Equipment Technology College of Applied Medical Science Majmaah University Majmaah City11952 Saudi Arabia Dept. of Electrical and Electronic Engineering College of Engineering and Technology International University of Business Agriculture and Technology Dhaka Uttara1230 Bangladesh Dept. of Computer and Communication Engineering Faculty of Computer Science and Engineering Patuakhali Science and Technology University Bangladesh Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Dhaka Savar1216 Bangladesh

A terahertz (THz) wave technology is very appealing for applications in detection in biomedicine, biological spectroscopy, material characterization, detection, imaging, sensing, and other convenient applications. Metamaterials (MMs) are artificial structures that have structures in a regular periodic arrangement and don't exist in nature. By utilizing a proposed terahertz metamaterial absorber, a broad spectrum of adulterated lubricants can be detected. It is an efficient detection process that enables the development of a THz detection approach by making use of the metamaterial as a sensor. The proposed terahertz absorber is analyzed and optimized using the CST Microwave Studio 2019 software. The article proposed a multiband THz metamaterial absorber with a unit size of 70 µm. According to the demonstrated results, we can achieve five narrow absorption peaks with perfect absorption within the frequency range between (0.2–4.2) Terahertz. Also, the result shows that, the average absorbance of 99.07% at the resonance frequencies of 0.56, 1.42, 2.676, 3.14, and 3.89 THz. The E (electric) and H (magnetic)-field distributions on the resonator at different resonance frequencies were also analyzed as well as. The recommended metamaterial absorber has a good sensing capability at different resonant frequencies, with refractive index sensitivities of 80 GHz/RIU, 240 GHz/RIU, 480 GHz/RIU, 480 GHz/RIU, and 520 GHz/RIU obtained by modifying the refractive index of the sample holder or the analytic layer, respectively. As a result, the proposed detector has a high refractive index sensitivity, high Q-factor, and high FOM, making it suitable for detecting oil adulteration. © 2024 Elsevier GmbH

关键词： Metamaterials

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Efficient Blind Channel Estimation for IRS-Assisted Joint Communication and Sensing in 6G Networks

Efficient Blind Channel Estimation for IRS-Assisted Joint Co...

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Communications and Networking (MECOM), IEEE Middle East Conference on

作者： Ali A. Siddig Arafat Al-Dweik Youssef Iraqi Anshul Pandey Jean-Pierre Giacalone Ridha Hamila Ashraf Al-Rimawi Dept. of Computer and Information Eng. Khalifa University of Science and Technology Abu Dhabi UAE College of Computing Mohammed VI Polytechnic Benguerir Morocco Secure Systems Research Center Technology Innovation Institute (TII) Abu Dhabi UAE Dept. of Electrical Eng. Qatar University Doha Qatar Dept. of Electrical and Computer Eng. Birzeit University Birzeit Palestine

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

To achieve the ultimate performance of intelligent reflecting surface (IRS), an accurate channel estimation is required, which in turns induces significant signaling overhead. This work presents a novel blind channel estimation scheme for IRS-assisted heterogeneous communications and sensing networks. The proposed scheme consists of two transmission stages, one for sensing and the other for communications. By noting that the sensing information have generally less stringent quality of service (QoS) requirements, the sensing stage is used for joint blind sensing data detection and blind channel estimation for the subsequent communications stage. Channel estimation is achieved using a specific frame structure where the modulated sensing symbols cooperate to estimate the channel coefficients for all IRS reflecting elements blindly and jointly. The simulation results obtained show that the proposed blind channel estimation accuracy is comparable to pilot-based schemes.

关键词： Blind equalizers Phase shift keying Accuracy Simulation Amplitude shift keying Symbols Channel estimation Estimation Quality of service Sensors

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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International electrical engineering Congress (iEECON)

作者： Pruek Vanna-Iampikul Visutr Boonnateephisit Somphop Limsoontharakul Jednipat Moonrinta Matthew Dailey Mongkol Ekpanyapong Department of Electrical Engineering Burapha University Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. Computational Science Prince of Songkla University Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. of Industrial Systems Engineering School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

LiDAR technology has rapidly advanced, offering highly accurate solutions for tree geometry estimation, which is particularly valuable in agriculture and forestry. Its ability to capture precise 2D and 3D data has revolutionized tree detection and measurement. In Eucalyptus plantations, continuous monitoring is critical to maximizing growth, enhancing paper production, and mitigating environmental damage. However, existing solutions face challenges in real-time monitoring due to factors such as parasitic interference, low-light conditions, and uneven terrain. In this work, we present a novel, end-to-end framework that enhances Eucalyptus tree measurement and monitoring. Our framework significantly improves data collection efficiency by allowing measurements at walking speed, reducing measurement distance by 50% compared to traditional manual approaches. This system integrates 2D-LiDAR with additional environmental sensors to extract key tree parameters, including circumference, position, and tree count. The collected data is stored in real-time and uploaded to a centralized server for further analysis. Experimental evaluations across 160 trees, under five distinct farm conditions, demonstrate that our system achieves 89% accuracy in tree diameter estimation and 98.5% accuracy in tree counting, significantly outperforming traditional methods. These results establish our framework as a highly effective solution for improving the accuracy and efficiency of tree monitoring in challenging environments.

关键词： Geometry Accuracy Laser radar Plantations Estimation Vegetation Production Real-time systems Time measurement Monitoring

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Channel Phase-Error Initialization via Inverse Designed On-Chip Silicon Metalens

Channel Phase-Error Initialization via Inverse Designed On-C...

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IEEE International Conference on Group IV Photonics

作者： Jinhyeong Yoon Jae-Yong Kim Junhyeong Kim Jihwan Park Hyo-Hoon Park Hamza Kurt School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Dept. C4ISR Systems Center Defense Agency for Technology and Quality Republic of Korea

We propose a method for phase initialization and error correction using silicon on-chip metalens. Leveraging inverse design techniques, we engineered on-chip metalens with high-performance characteristics of 61.3% foc... 详细信息

ISBN: (数字)9798350394047

ISBN: (纸本)9798350394054

关键词： Performance evaluation Focusing Bandwidth Apertures Silicon photonics Numerical simulation Nanoscale devices

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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13th International electrical engineering Congress, iEECON 2025

作者： Vanna-Iampikul, Pruek Boonnateephisit, Visutr Limsoontharakul, Somphop Moonrinta, Jednipat Dailey, Matthew Ekpanyapong, Mongkol Burapha University Department of Electrical Engineering Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Prince of Songkla University Dept. Computational Science Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand School of Engineering and Technology Asian Institute of Technology Dept. of Industrial Systems Engineering Pathumthani Thailand

ISBN: (纸本)9798331543952

关键词： Deforestation

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Low-Cost and Effective Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks 21

Low-Cost and Effective Fault-Tolerance Enhancement Technique...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Thai-Hoang Nguyen Muhammad Imran Jaehyuk Choi Joon-Sung Yang Dept. of Electrical and Computer Engineering and Dept. of Electrical Engineering National University of Sciences and Technology Islamabad Pakistan Dept. of Semiconductor Systems Engineering Sungkyunkwan University Suwon South Korea School of Electrical and Electronic Engineering and Dept. of Semiconductor Systems Engineering Yonsei University Seoul South Korea

ISBN: (纸本)9781665432740

Deep Neural Networks (DNNs) have been found to outperform conventional programming approaches in several applications such as computer vision and natural language processing. Efficient hardware architectures for deploying DNNs on edge devices have been actively studied. Emerging memory technologies with their better scalability, non-volatility, and good read performance are ideal candidates for DNNs which are trained once and deployed over many devices. Emerging memories have also been used in DNNs accelerators for efficient computations of dot-product. However, due to immature manufacturing and limited cell endurance, emerging resistive memories often result in reliability issues like stuck-at faults, which reduce the chip yield and pose a challenge to the accuracy of DNNs. Depending on the state, stuck-at faults may or may not cause error. Fault-tolerance of DNNs can be enhanced by reducing the impact of errors resulting from the stuck-at faults. In this work, we introduce simple and light-weight Intra-block Address remapping and weight encoding techniques to improve the fault-tolerance for DNNs. The proposed schemes effectively work at the network deployment time while preserving the network organization and the original values of the parameters. Experimental results on state-of-the-art DNN models indicate that, with a small storage overhead of just 0.98%, the proposed techniques achieve up to 300× stuck-at faults tolerance capability on Cifar10 dataset and 125× on Imagenet datatset, compared to the baseline DNNs without any fault-tolerance method. By integrating with the existing schemes, the proposed schemes can further enhance the fault resilience of DNNs.

关键词： deep learning hardware

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