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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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Error Correction Decoding Algorithms of RS Codes Based on an Earlier Termination Algorithm to Find the Error Locator Polynomial

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IEEE Transactions on Information Theory 2025年第4期71卷 2564-2575页

作者： Jiang, Zhengyi Shi, Hao Huang, Zhongyi Song, Linqi Bai, Bo Zhang, Gong Hou, Hanxu Tsinghua University Department of Mathematics Sciences Beijing100190 China City University of Hong Kong Department of Computer Science Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China Huawei Technologies Company Ltd. Theory Laboratory Central Research Institute 2012 Labs Hong Kong Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523820 China

Reed-Solomon (RS) codes are widely used to correct errors in storage systems. Finding the error locator polynomial is one of the key steps in the error correction procedure of RS codes. Modular Approach (MA) is an effective algorithm for solving the Welch-Berlekamp (WB) key-equation problem to find the error locator polynomial that needs 2t steps, where t is the error correction capability. In this paper, we first present a new MA algorithm that only requires 2e steps and then propose two fast decoding algorithms for RS codes based on our MA algorithm, where e is the number of errors and e ≤ t. We propose the Improved-Frequency Domain Modular Approach (I-FDMA) algorithm that needs 2e steps to solve the error locator polynomial and present our first decoding algorithm based on the I-FDMA algorithm. We show that, compared with the existing methods based on MA algorithms, our I-FDMA algorithm can effectively reduce the decoding complexity of RS codes when e 0 -Shortened I-FDMA ( t0 -SI-FDMA) algorithm ( t0 is a predetermined even number less than 2t − 1 ) based on the new termination mechanism to solve the error number e quickly. We propose our second decoding algorithm based on the SI-FDMA algorithm for RS codes and show that the multiplication complexity of our second decoding algorithm is lower than our first decoding algorithm (the I-FDMA decoding algorithm) when 2e 0 + 1. © 1963-2012 IEEE.

关键词： Frequency domain analysis

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Optical polarized orthogonal matrix

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Photonics Research 2025年第2期13卷 373-381页

作者： SHUJUN ZHENG JIAREN TAN XIANMIAO XU HONGJIE LIU YI YANG XIAO LIN XIAODI TAN Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal University Department of Electrical and Computer Engineering Duke University College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal University

Multiplexing technology serves as an effective approach to increase both information storage and transmission capability. However, when exploring multiplexing methods across various dimensions, the polarization dimension encounters limitations stemming from the finite orthogonal combinations. Given that only two mutually orthogonal polarizations are identifiable on the basic Poincaré sphere, this poses a hindrance to polarization modulation. To overcome this challenge, we propose a construction method for the optical polarized orthogonal matrix(OPOM), which is not constrained by the number of orthogonal combinations. Furthermore, we experimentally validate its application in high-dimensional multiplexing of polarization holography. We explore polarization holography technology, capable of recording amplitude, phase, and polarization, for the purpose of recording and selective reconstruction of polarization multi-channels. Our research reveals that, despite identical polarization states, multiple images can be independently manipulated within distinct polarization channels through orthogonal polarization combinations, owing to the orthogonal selectivity among information.

关键词： Multiplex holography Optical data storage Optical encryption Orbital angular momentum multiplexing Signal transmission Three dimensional displays

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First Characterization of GaN Power Device and IC at Deep Cryogenic Temperatures down to 100 mK

First Characterization of GaN Power Device and IC at Deep Cr...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Xin Yang Matthew Porter Zineng Yang Zichen Xi Liyang Jin Liyan Zhu Linbo Shao Yuhao Zhang Center for Power Electronics Systems (CPES) Virginia Tech USA Bradley Department of Electrical and Computer Engineering Virginia Tech USA Department of Electrical and Electronic Engineering The University of Hong Kong China

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

electrical energy conversion at deep cryogenic temperatures (T < 4.2 K) is desirable for space, quantum, and astronomical applications. However, the functionality of power semiconductors within this temperature range remains uncertain. Particularly, high-voltage or dynamic switching tests have not been reported for GaN devices below 77 K. For the first time, we evaluate the static and dynamic performances of discrete GaN HEMT and GaN power IC at temperatures as low as 100 mK. We employ a dilution refrigerator connected to a custom circuit setup that integrates double-pulse testing and dynamic on-resistance (RON) measurements. Key findings at temperatures below 1 K include: 1) GaN HEMTs maintain normally-off operation and high breakdown voltage; 2) discrete GaN HEMT and GaN power IC are both capable of hard switching under gate control; and 3) the dynamic RON are approximately ~4.5 times lower than at 295 K. These results pave the way for developing power converters operational at temperatures below the current boundaries. 1

关键词： Performance evaluation Semiconductor device measurement Cryogenics Switches HEMTs Logic gates Loss measurement Temperature control Power semiconductor devices Switching circuits

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One-bit Compressed Sensing using Generative Models

arXiv

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arXiv 2025年

作者： Kafle, Swatantra Joseph, Geethu Varshney, Pramod K. ANDRO Computational Solutions NY United States Electrical Engineering and Computer Science Department Syracuse University NY United States Signal Processing Systems group Electrical Engineering Mathematics and Computer Science faculty the Delft University of Technology Netherlands

This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pretrained neural network, learns to map from a low-dimensional latent space to a higher-dimensional set of sparse vectors. This generator is then used to reconstruct sparse vectors from their one-bit measurements by searching over its range. The presented algorithm provides an excellent reconstruction performance because the generative model can learn additional structural information about the signal beyond sparsity. Furthermore, we provide theoretical guarantees on the reconstruction accuracy and sample complexity of the algorithm. Through numerical experiments using three publicly available image datasets, MNIST, Fashion-MNIST, and Omniglot, we demonstrate the superior performance of the algorithm compared to other existing algorithms and show that our algorithm can recover both the amplitude and the direction of the signal from one-bit measurements. © 2025, CC BY.

关键词： Compressed sensing

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