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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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Microfluidic droplet detection using synthetic data generated by a Generative Adversarial Network

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Karimzadeh, Marziyeh Meybodi, Mohsen Alambardar Shams, Mohammadreza Goli-Malekabadi, Zahra Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan81746-73441 Iran Department of Computer Engineering Shahreza Campus University of Isfahan Isfahan Iran Department of Medical Technology and Tissue Engineering School of Life science Engineering College of Interdisciplinary Science and Technology University of Tehran Tehran Iran

Cellular heterogeneity, even among genetically identical cells, results in variations in their properties and behaviors, making single-cell analysis crucial for obtaining detailed insights. However, isolating single cells from a cell population poses major challenges, as conventional laboratory techniques often risk cell damage and involve complex procedures. Droplet microfluidics has emerged as a promising approach for encapsulating cells, particularly single cells, into individual droplets without causing harm. Despite this, factors like cell sedimentation and aggregation can reduce encapsulation efficiency and lead to deviations from the expected Poisson distribution. To address these challenges, leveraging artificial intelligence and deep learning to monitor, detect, and regulate encapsulation conditions in real-time is critical for enhancing system performance. However, deep learning models require substantial training data, and issues like microfluidic channel clogging and the scarcity of certain cell types often limit data availability. To overcome this limitation, researchers are turning to synthetic data generation to supplement training datasets and address data scarcity challenges effectively. This study emphasizes the potential of integrating synthetic data with cutting-edge deep learning techniques to enhance the accuracy and efficiency of single-cell analysis within droplet microfluidic systems. A diverse dataset integrating synthetic and real images was used to train the YOLOv8s model for automated detection and classification of microfluidic droplets, enhancing accuracy and system performance. The model trained on a combination of real and synthetic data outperformed the one trained using conventional data augmentation methods, achieving an mAP0.5 of 98% due to the increased diversity of training images. It also demonstrated faster and more stable training. Additionally, the YOLOv8 network, with a detection rate of approximately 2338 droplets per sec

关键词： Deep learning

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Low Chirp Dual-Racetrack Modulator for PAM-4 Modulation with Low Driving Voltage

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Journal of Lightwave Technology 2025年第10期43卷 4840-4847页

作者： Yu, Fuhao Zeng, Zhaobang Tang, Kaifei Yang, Shichong Ji, Xiang Xin, Yu Gu, Tingyi Jiang, Wei Nanjing University Key Laboratory of Intelligent Optical Sensing and Manipulation Engineering Research Center for Precision Photonics Integration & System Applications Ministry of Education Nanjing210023 China Nanjing University National Laboratory of Solid-State Microstructures College of Engineering and Applied Sciences Institute of Optical Communication Engineering Nanjing210023 China Nanjing University of Information Science & Technology School of Physics and Optoelectronic Engineering Nanjing210044 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

Micro-ring modulators with a small footprint and energy consumption have been explored for densely integrated high throughput optical interconnects. However, their strong dispersive response results in frequency chirp and pulse broadening, which limits their signal transmission distance. Here we explore the low chirp operation of a dual-racetrack modulator symmetrically coupled to a bus waveguide. With opposite index changes in the two resonators, the coherent couplings of two resonators' optical fields with the bus waveguide and their interference can cancel out the output phase variation due to a single resonator. This allows the push-pull operation to achieve a lower chirp compared to the single-drive operation. The advantage of lower chirp operation is experimentally verified through open eye diagram with 100 Gbps PAM-4 transmitted over 2 km single-mode fiber (SMF), with a peak-to-peak driving voltage of 1.6 V. © 1983-2012 IEEE.

关键词： Single mode fibers

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Synthetic Data Generation Via the Permutation Paradigm With Optional k-Anonymity

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3155-3165页

作者： Domingo-Ferrer, Josep Muralidhar, Krishnamurty Martinez, Sergio Universitat Rovira i Virgili CYBERCAT-Center for Cybersecurity Research of Catalonia Department of Computer Engineering and Mathematics Av. Països Catalans 26 CataloniaE-43007 Spain University of Oklahoma Price College of Business 307 West Brooks Adams Hall Room 10 NormanOK73019-4007 United States

Most methods in the literature on synthetic microdata (individual records) generation are parametric, that is, they require knowing or estimating the joint or the conditional distribution of the original microdata. This may be a significant hurdle unless the original microdata are multivariate normal. We propose a rank-based approach to generating synthetic microdata based on the permutation paradigm. We present three different methods and we analyze the utility and the confidentiality they afford. The third method is actually an extension of the second method that adds k-anonymity protection against reidentification to the confidentiality against attribute disclosure offered by the first two methods. Our algorithms only require the identification of the marginal distributions of attributes and yield synthetic attributes that replicate the relationships between the original attributes exclusively based on ranks. This proposal is especially attractive for non-normal or multi-type microdata. © 2004-2012 IEEE.

关键词： Anonymity

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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

Invisible Servoing: A Visual Servoing Approach with Return-C...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Bishoy Gerges Barbara Bazzana Nicolò Botteghi Youssef Aboudorra Antonio Franchi Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands Department of Applied Mathematics University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE.

关键词： Location awareness Visualization Pipelines Noise reduction Autonomous aerial vehicles Visual servoing Trajectory Planning Standards Robots

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Privacy Vulnerabilities in Marginals-based Synthetic Data

Privacy Vulnerabilities in Marginals-based Synthetic Data

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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Steven Golob Sikha Pentyala Anuar Maratkhan Martine De Cock School of Engineering and Technology University of Washington Tacoma USA Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

When acting as a privacy-enhancing technology, synthetic data generation (SDG) aims to maintain a resemblance to the real data while excluding personally-identifiable information. Many SDG algorithms provide robust differential privacy (DP) guarantees to this end. However, we show that the strongest class of SDG algorithms-those that preserve marginal probabilities, or similar statistics, from the underlying data-leak information about individuals that can be recovered more efficiently than previously understood. We demonstrate this by presenting a novel membership inference attack, MAMA-MIA, and evaluate it against three seminal DP SDG algorithms: MST, PrivBayes, and Private-GSD. MAMA-MIA leverages knowledge of which SDG algorithm was used, allowing it to learn information about the hidden data more accurately, and orders-of-magnitude faster, than other leading attacks. We use MAMA-MIA to lend insight into existing SDG vulnerabilities. Our approach went on to win the first SNAKE (SaNitization Algorithm under attacK … ε) competition.

关键词： Differential privacy Machine learning algorithms Technology Machine learning Probability Inference algorithms Synthetic data Identification of persons

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Comparative Analysis of Large Language Models for OCR Post-Processing in Persian: From ParsBERT to GPT

Comparative Analysis of Large Language Models for OCR Post-P...

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International CSI computer Conference, CSICC

作者： Fatemeh Valizadeh Fahimeh Ghasemian Elham Shabaninia Department of Computer Engineering Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Graduate University of Advanced Technology Kerman Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Optical Character Recognition (OCR) refers to the automatic identification of text in images and its conversion into searchable and editable formats. Due to its extensive applications, OCR is considered a crucial and challenging topic in the field of computer vision. In Persian, the unique characteristics of the script often result in OCR outputs with significant errors, which can compromise readability and comprehension of the content, emphasizing the need for error correction, particularly in terms of spelling. This study investigates the performance of four large language models-ParsBERT, LLaMA, Mistral, and GPT-for enhancing OCR outputs. Additionally, an innovative approach that integrates ParsBERT with other models is introduced. These models were evaluated on three corpora of varying sizes and complexities using diverse evaluation metrics, including precision, accuracy, recall, and others. Our comparative analysis highlights the strengths and weaknesses of each model across different scenarios, providing deeper insights into their performance. Furthermore, the results demonstrate that the proposed hybrid approaches outperform standalone models across all corpora, significantly reducing error rates and improving key metrics such as precision, accuracy, and recall.

关键词： Measurement Analytical models Accuracy Error analysis Large language models Computational modeling Optical character recognition Pipelines Natural language processing Error correction

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Assessing the Impact of Physical Configuration and Lead Time on WRF Forecasting of an Extreme Wind Event

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Journal of Meteorological research 2025年第1期39卷 154-171页

作者： Rocío OTERO Matías SUAREZ Edgardo PIEROBON Leandro MATURANO Ignacio MONTAMAT Juan Ezequiel SANCHEZ Lucia SANDALIO Andrés RODRIGUEZ Denis POFFO Hydrometeorology Laboratory Department of ExactPhysical and Natural SciencesNational University of CordobaCórdobaX5016GCAArgentina Institute for Advanced Studies in Engineering and Technology National Scientific and Technical Research CouncilCordobaX5016GCAArgentina Radar and Remote Sensing Laboratory Department of MathematicsAstronomyPhysics and Computer ScienceNational University of CordobaCordobaX5016HUAArgentina

The central region of Argentina is known to be a source of some most extreme weather events in the world,which are partially associated with the passage of cold fronts accompanied often by extreme wind *** may cause severe property damage and even loss of human ***,there is a lack of studies that evaluate the performance of the numerical weather prediction(NWP)models such as weather research and forecasting(WRF)model in anticipating this type of weather in the *** study compares the performance of the operational WRF in Argentina using four combinations of various planetary boundary layer(PBL)and microphysics parameterization schemes under varied lead times in predicting an extreme wind event(gusts>30 m s^(-1))in Central *** results demonstrate that the WRF model is capable of providing an acceptable prediction of wind speed during an extreme *** is found that no single combination outperforms the others,although there is a slight tendency for Combination A,which utilizes the Mellor-Yamada-Janjic(MYJ)parameterization for the PBL and the Eta similarity parameterization for the surface layer,to more accurately capture the extreme wind *** with wind gust observations at five weather stations,the wind gust parameterization predicted the intensity and occurrence time of the peak wind,with an acceptable bias(time of peak<±1 h).Analysis of grid configurations(resolutions of 4 vs 9/3/1 km)revealed that higher resolution does not imply an improvement in the wind gust forecast for this particular *** regard to lead time,a shorter lead time does not necessarily result in more accurate ***,it is beneficial to conduct multiple sensitivity runs in order to obtain and understand the dispersion of forecasted wind speeds.

关键词： extreme wind wind event weather research and forecasting(WRF)model wind gust forecast cold front lead time

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Fast Debiasing of the LASSO Estimator

arXiv

引用

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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CPM-Based Tunable Phase-Attached Radar–Communications (PARC)

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2025年 3卷 520-538页

作者： Sahin, Cenk McCormick, Patrick M. Metcalf, Justin G. Jakabosky, John Blunt, Shannon D. Perrins, Erik S. Owen, Jonathan Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States The University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States The University of Oklahoma Advanced Radar Research Center NormanOK73019 United States Naval Research Laboratory Radar Division WashingtonDC20375 United States

Motivated by the increasing need for efficient use of the electromagnetic spectrum (EMS) in congested and contested environments, a codesigned dual-function radar-communication (DFRC) waveform framework was introduced, which combines the desirable features of a pulsed radar transmission (i.e., constant amplitude and continuous phase) with the ability to embed information in the phase of the waveform via continuous-phase modulation (CPM). This CPM-based phase-attached radar-communication (PARC) waveform operates in a pulse-agile mode, which introduces a coupling of the fast- and slow-time dimensions through what is known as range-sidelobe modulation (RSM). The flexibility of CPM-based PARC via its multiple tunable parameters provides the ability to control this radar performance degradation at the expense of bit error rate (BER) and/or data throughput. Furthermore, the severity of RSM can likewise be mitigated via mismatched filter pulse compression on receive to reduce the variance of the pulse compression responses. Here, we evaluate the radar and communication performance trade space as a function of the CPM-based PARC parameters when assuming both matched and mismatched filter pulse compression at the radar receiver. The efficacy of the CPM-based PARC framework for both radar and communications is experimentally validated in an open-air environment using a radar in a quasi-monostatic configuration and a communication receiver in the field of view of the radar. © 2023 IEEE.

关键词： Matched filters

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