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arXiv 2025年

作者： Khennoufa, Faical Abdellatif, Khelil Yanikomeroglu, Halim Ozturk, Metin Elganimi, Taissir Kara, Ferdi Rabie, Khaled LGEERE Laboratory Department of Electrical Engineering Echahid Hamma Lakhdar University El-Oued Algeria Laboratory Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey The Department of Electrical and Electronic Engineering University of Tripoli Libya The Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Division of Communications Systems KTH Royal Institute of Technology Sweden The Department of Computer Engineering King Fahd University of Petroleum and Minerals Saudi Arabia

In order to bolster the next generation of wireless networks, there has been a great deal of interest in non-terrestrial networks (NTN), including satellites, high altitude platform stations (HAPS), and uncrewed aerial vehicles (UAV). To unlock their full potential, these platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and next-generation multiple access (NGMA). However, in practical applications, transceivers often suffer from radio frequency (RF) impairments, which limit system performance. In this regard, this paper explores the potential of multi-layer NTN architecture to mitigate path propagation loss and improve network performance under hardware impairment limitations. First, we present current research activities in the NTN framework, including RIS, multiple access technologies, and hardware impairments. Next, we introduce a multi-layer NTN architecture with hardware limitations. This architecture includes HAPS super-macro base stations (HAPS-SMBS), UAVs–equipped with passive or active transmissive RIS–, and NGMA techniques, like non-orthogonal multiple access (NOMA), as the multiple access techniques to serve terrestrial devices. Additionally, we present and discuss potential use cases of the proposed multi-layer architecture considering hardware impairments. The multi-layer NTN architecture combined with advanced technologies, such as RIS and NGMA, demonstrates promising results;however, the performance degradation is attributed to RF impairments. Finally, we identify future research directions, including RF impairment mitigation, UAV power management, and antenna designs. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Digital Self-Interference Cancellation in Wideband Star Radio Using an Auxiliary Receiver

Digital Self-Interference Cancellation in Wideband Star Radi...

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IEEE Annual Conference on Wireless and Microwave Technology (WAMICON)

作者： Kefayet Ullah Satheesh Bojja Venkatakrishnan John L. Volakis Department of Electrical and Computer Engineering Florida International University Radio-Frequency Communications & Terahertz Laboratory (RFCOM) Miami USA

ISBN: (数字)9798331509606

ISBN: (纸本)9798331509613

This paper presents a novel digital self-interference cancellation (DSIC) technique in full-duplex radios supporting simultaneous transmission and reception (STAR). Key to the design approach is the development of a behavioral model of the full-duplex radio front-end at the digital baseband. An auxiliary receiver (RX) chain is deployed to capture the transmitter (TX) chain and the leakage channel response. The leaked signal is digitally reconstructed and analog cancellation is performed at the low-noise amplifier (LNA) input to prevent desensitization. The cancellation algorithm is developed on a $16-\text{nm}$ AMDXilinx radio frequency system-on-chip (RFSoC) incorporating gigasample speed data converters and field programmable gate array (FPGA) on the same chip. The architecture is directly implemented on the programmable logic (PL) and the model coefficients are calculated offline in the ARM processor of the SoC. The hardware demonstration employs a reduced number of off-the-shelf analog components that provide an impressive digital cancellation of 20 dB for a maximum modulation bandwidth of 400 MHz.

关键词： Wireless communication Interference cancellation Auxiliary transmitters Radio transmitters Stars Modulation Receivers Full-duplex system Wideband Field programmable gate arrays

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Sustainable resource management in construction: computer vision for recognition of electro-construction waste (ECW)

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Resources, Conservation and Recycling 2025年 221卷

作者： Senanayake, Aseni Gautam, Birat Harandi, Mehrtash Arashpour, Mehrdad Department of Civil Engineering Monash University Melbourne Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia

A significant amount of Electro-construction waste (ECW) often ends up in landfills, leading to adverse impacts on the environment and human health. Although waste sorting utilises automated technologies like computer vision (CV), implementation in the construction industry remains limited. This study addresses the gap by evaluating the effectiveness of CV in ECW recognition to enhance resource recovery. A novel dataset was curated by sourcing images from web and applying background subtraction techniques to simulate realistic construction site conditions. This method significantly enhanced model accuracy by up to 16 %, demonstrating its potential for scalable and automated dataset generation. The study classified ECW into four critical categories: cables, switches, lights, and AC ducts. Performance evaluation across two model architectures Convolutional Neural Networks (CNNs) and Transformers showed competitive results, achieving classification accuracies of 91.52 %, 93 %, 89.81 %, and 93.62 % for ResNet50, ConvNeXt, Vision Transformer, and Swin Transformer, respectively, with low inference times suitable for real-time applications. The findings highlight the transformative potential of CV-driven solutions in sustainable waste management practices. By enabling accurate and real-time ECW recognition, this research contributes to enhanced recycling efficiency, reduced environmental impact, and resource conservation within the construction industry. © 2025

关键词： Construction industry

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Identification of Medicinal Plants and Related Fungal Diseases Based on Deep Learning Model

Identification of Medicinal Plants and Related Fungal Diseas...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Mathina Kani M Mahalakshmi R Shunmugavel V Abdul Kareem M Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Computer Science and Business Systems Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In today's technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. These readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multi-platform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms.

关键词： Medicinal plants Deep learning Measurement Visualization Accuracy Medical services Manuals Rapid prototyping Mobile computing Diseases

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Fast Debiasing of the LASSO Estimator

arXiv

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arXiv 2025年

作者： Banerjee, Shuvayan Saunderson, James Srivastava, Radhendushka Rajwade, Ajit Department of Mathematics IIT Bombay IIT Bombay Monash Research Academy India Department of Electrical and Computer Systems Engineering Monash University Australia Department of Mathematics IIT Bombay India Department of Computer Science and Engineering IIT Bombay India

In high-dimensional sparse regression, the Lasso estimator offers excellent theoretical guarantees but is well-known to produce biased estimates. To address this, [5] introduced a method to "debias" the Lasso estimates for a random sub-Gaussian sensing matrix A. Their approach relies on computing an "approximate inverse" M of the matrix AＴA/n by solving a convex optimization problem. This matrix M plays a critical role in mitigating bias and allowing for construction of confidence intervals using the debiased Lasso estimates. However the computation of M is expensive in practice as it requires iterative optimization. In the presented work, we re-parameterize the optimization problem to compute a "debiasing matrix" W := AMＴ directly, rather than the approximate inverse M. This reformulation retains the theoretical guarantees of the debiased Lasso estimates, as they depend on the product AMＴ rather than on M alone. Notably, we provide a simple, computationally efficient, closed-form solution for W under similar conditions for the sensing matrix A used in the original debiasing formulation, with an additional condition that the elements of every row of A have uncorrelated entries. Also, the optimization problem based on W guarantees a unique optimal solution, unlike the original formulation based on M. We verify our main result with numerical simulations. © 2025, CC BY.

关键词： Iterative methods

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Design, Modeling and Simulation of Cybercriminal Personality-Based Cyberattack Campaigns 24

Design, Modeling and Simulation of Cybercriminal Personality...

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Proceedings of the Winter Simulation Conference

作者： Jeongkeun Shin Geoffrey B. Dobson L. Richard Carley Kathleen M. Carley Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA and Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9798331534202

Cybersecurity challenges are inherently complex, characterized by both advanced technical elements and complex aspects of human cognition. Although extensive research has explored how victims' human factors affect their susceptibility to cyberattacks, the influence of cybercriminals' personalities on the pattern of cyberattack campaigns and the resulting damage to organizations has not received equivalent attention. To bridge this research gap through computer simulation, we introduce a cyberattack campaign designer that enables modelers to construct cyberattack campaigns. This tool allows modelers to define how the process or pattern of the cyberattack campaign can change based on the cybercriminal's personality and simulates how these personality differences influence the magnitude of cyberattack damage on the target organization. In this paper, through computer simulation, we demonstrate how cautious and reckless personalities result in variations in the cyberattack pattern and, consequently, affect the magnitude of cyberattack damage, despite identical cyberattack objectives and techniques used at each step.

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MLFusion: Multilevel Data Fusion using CNNS for atrial fibrillation detection

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computers in Biology and Medicine 2025年 189卷 109901-109901页

作者： John, Arlene Parhi, Keshab K. Cardiff, Barry John, Deepu Biomedical Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 NB Netherlands Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States School of Electrical and Electronic Engineering University College Dublin DublinD04 FX62 Ireland

Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor multimodal data using convolutional neural networks (CNNs), which introduces a data-driven approach to automatically select the optimal level of information abstraction for fusion. Unlike traditional methods, the proposed model allows for a self-learning fusion process, eliminating the need for manual selection of the fusion level by the designer. The model uniquely integrates feature extraction and fusion into a unified framework, enhancing efficiency and performance. Additionally, the fusion network incorporates signal quality indicators (SQIs) as input streams, enabling the model to account for the quality of each input signal during the fusion process. The methodology is evaluated on the task of atrial fibrillation detection through the fusion of two multimodal signal inputs taken from a portion of the MIMIC III database. The fusion network, using electrocardiogram (ECG) and photoplethsymogram (PPG) signals as inputs, and trained with an average loss function, achieved an accuracy of 99.33% and a sensitivity of 99.74%. The model's robustness in the presence of noisy inputs is also analyzed, demonstrating the effectiveness of the SQI-based multi-level fusion approach. This novel methodology represents a significant advancement in data fusion by offering a fully automated, quality-aware approach to multi-sensor multimodal signal fusion. © 2025

关键词： Convolutional neural networks

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Vessel segmentation for x-separation

arXiv

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arXiv 2025年

作者： Kim, Taechang Ji, Sooyeon Min, Kyeongseon Kim, Minjun Youn, Jonghyo Oh, Chungseok Kim, Jiye Lee, Jongho Laboratory for Imaging Science and Technology Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Division of Computer Engineering Hankuk University of Foreign Studies Yongin Korea Republic of

Purpose: x-separation is an advanced quantitative susceptibility mapping (QSM) method that is designed to generate paramagnetic (xpara) and diamagnetic (|xdia|) susceptibility maps, reflecting the distribution of iron and myelin in the brain. However, vessels have shown artifacts, interfering with the accurate quantification of iron and myelin in applications. To address this challenge, a new vessel segmentation method for x-separation is developed. Methods: The method comprises three steps: 1) Seed generation from R2∗ and the product of xpara and |xdia| maps;2) Region growing, guided by vessel geometry, creating a vessel mask;3) Refinement of the vessel mask by excluding non-vessel structures. The performance of the method was compared to conventional vessel segmentation methods both qualitatively and quantitatively. To demonstrate the utility of the method, it was tested in two applications: quantitative evaluation of a neural network-based x-separation reconstruction method (x-sepnet-R2∗) and population-averaged region of interest (ROI) analysis. Results: The proposed method demonstrates superior performance to the conventional vessel segmentation methods, effectively excluding the non-vessel structures, achieving the highest Dice score coefficient. For the applications, applying vessel masks report notable improvements for the quantitative evaluation of x-sepnet-R2∗ and statistically significant differences in population-averaged ROI analysis. These applications suggest excluding vessels when analyzing the x-separation maps provide more accurate evaluations. Conclusion: The proposed method has the potential to facilitate various applications, offering reliable analysis through the generation of a high-quality vessel mask. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Comparison of Event-Based Processing Algorithms for Experimental 2D2C Velocimetry

Comparison of Event-Based Processing Algorithms for Experime...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Gunasekaran, Sidaard Mongin, Michael Alsattam, Osama A. Hirakawa, Keigo Mechanical and Aerospace Engineering Department University of Dayton DaytonOH45469 United States U.S. Air Force Research Laboratory AFRL AIAA Wright–Patterson Air Force Base OH45433 United States Department of Electrical and Computer Engineering University of Dayton DaytonOH45469 United States

ISBN: (纸本)9781624107238

Event-based cameras, which operate without traditional frames and instead capturea synchronous changes in intensity, offer a transformative approach to flow diagnostics. This study investigates the application of event-based methods for velocimetry around an SD7003wing and compares their performance to traditional Particle Image Velocimetry (PIV). Four event-based algorithms—Weighted Tracking (Linear and Non-Linear fits), Kalman Filter, and Motion Compensation—are evaluated using a combination of free stream, near-surface, and wake flow scenarios. Results demonstrate that event-based methods successfully replicate global flow trends observed in PIV, including mean and median velocity estimates, while also capturing subtle flow features such as near-surface pressure gradients and wake asymmetry with higher resolution. Unlike PIV, which is constrained by frame rate and windowed processing, event-based methods track individual tracer particles, resolving flow features even in regions with steep gradients. The weighted tracking methods exhibited the closest agreement with PIV, while Kalman Filter and Motion Compensation methods showed slight biases in Uvelocity, hypothesized to result from input parameter tuning. The findings highlight the potential of event-based methods as a viable candidate for flow diagnostics. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Wakes

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Benchmarking GAN-Based vs Classical Data Augmentation on Biomedical Images 27th

Benchmarking GAN-Based vs Classical Data Augmentation on Bi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mantegna, Massimiliano Tronchin, Lorenzo Tortora, Matteo Soda, Paolo Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Rome Italy Department of Naval Electrical Electronics and Telecommunications Engineering University of Genova Via all’Opera Pia 11a Genoa16145 Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Messina Italy

ISBN: (纸本)9783031876592

The medical field faces significant data shortages due to the high image acquisition and maintenance costs. Data Augmentation aims to mitigate this by increasing data availability and enhancing image generalization. However, traditional DA methods often produce data with limited quality and diversity. Generative Adversarial Networks present a promising alternative, offering potential solutions to data scarcity issues. This paper evaluates the impact of GAN-based data augmentation in medical imaging and provides a benchmark for the efficacy of synthetic data in downstream classification tasks. To this aim, we performed a wide set of tests using three different GAN architectures on six 2D datasets from the standardized MedMNIST biomedical image collection, conducting a total of 696 experiments. Our results reveal that while GAN-based DA methods show promise with low-dimensional datasets, traditional DA methods still outperform them. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mammography

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