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arXiv 2025年

作者： Carnevale, Guido Bastianello, Nicola Department of Electrical Electronic and Information Engineering Alma Mater Studiorum - Università di Bologna Bologna Italy School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden

In this paper, we design a novel distributed learning algorithm using stochastic compressed communications. In detail, we pursue a modular approach, merging ADMM and a gradient-based approach, benefiting from the robustness of the former and the computational efficiency of the latter. Additionally, we integrate a stochastic integral action (error feedback) enabling almost sure rejection of the compression error. We analyze the resulting method in nonconvex scenarios and guarantee almost sure asymptotic convergence to the set of stationary points of the problem. This result is obtained using system-theoretic tools based on stochastic timescale separation. We corroborate our findings with numerical simulations in nonconvex classification. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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A predictive surrogate model based on linear and nonlinear solution manifold reduction in cardiovascular FSI: A comparative study

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computers in Biology and Medicine 2025年 189卷 109959-109959页

作者： Barzegar Gerdroodbary, M. Salavatidezfouli, Sajad Department of Electromechanical Engineering C-MAST-Center for Mechanical and Aerospace Science and Technology Universidade da Beira Interior Covilha Portugal Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

This study investigates the fluid-structure interaction (FSI) simulation of the abdominal aorta, with a particular focus on the hemodynamic alterations induced by aneurysmal deformations. The hemodynamic behavior within the aorta is highly dependent on the geometric characteristics of the aneurysm, necessitating the use of patient-specific models to ensure accurate predictions. The primary objective of this research is to enhance the predictive capability of flow and structural indices in a complex FSI biomechanical setting under varying physiological conditions, namely rest and exercise states. This paper presents a comparative analysis between two distinct yet promising surrogate models: Proper Orthogonal Decomposition coupled with Long Short-Term Memory (POD + LSTM) and Convolutional Neural Network combined with Long Short-Term Memory (CNN + LSTM). The methodology, model selection, and comparative performance analysis are discussed in detail, providing insights into the efficacy and limitations of each approach in the context of personalized cardiovascular simulations. © 2025 The Authors

关键词： Long short-term memory

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Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks

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Nano 2025年第3期20卷

作者： Asif Raja, Muhammad Junaid Ali Masood, Zaheer Hussain, Ijaz Zameer, Aneela Mehmood, Ammara Raja, Muhammad Asif Zahoor Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Electrical Engineering Capital University of Science and Technology Islamabad Pakistan Department of Computer and Information Sciences Pakistan Institute of Engineering & Applied Sciences Islamabad Pakistan School of Electrical and Data Engineering University of Technology Sydney Australia Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

In the annals of contemporary innovation, the study of miniature marvels nanoscale hardware implants emerged as a pivotal instrument, scarcely perceptible to the naked eye, embodies a prowess of cutting-edge technologies and clandestine intrigue. The objective of this research is to introduce a time delay nonlinear system for nanoscale hardware implants vulnerabilities that portray the exploitation of a system by utilizing the stupendous knacks of advanced deep bidirectional long short-term memory (LSTM) networks for time series predictions. Firmware-level bugs present the potential for escalating privileges and executing of code remotely beneath the operating system, allowing for infiltration or complete intervention within a computer system. The designed deep bidirectional LSTM is configured to precisely predict and forecast the dynamic states of time delay differential system, offering a robust framework for mimicking delays that commonly manifest in the real cyber-physical systems. To orchestrate the system compromises in real scenarios through the activation of bugged hardware, time delay factors τ1 and τ2 are introduced to account the time delays necessary for exploiting the bugged and patched nodes, respectively. Synthetic data is generated to train the LSTM network for all scenarios of the model for the dynamics of bugged, compromised, patched nodes and these acquired information is used for training, testing and validation purposes regarding exploitation of the bugged hardware. Comparative analysis on exhaustive simulations revealed a minimal difference between the LSTM's predictions and those from the numerical outcomes with MSE in the range of 10-7, underscoring the network's effectiveness, robustness and stability in modeling complex system dynamics of hardware vulnerabilities. © 2025 World Scientific Publishing Company.

关键词： Firmware

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Cognitive Radio Receiver Design Employing Pipeline Successive Approximation ADC

Cognitive Radio Receiver Design Employing Pipeline Successiv...

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Learning and technology Conference (L&T)

作者： Muhammad Samee Baig Aziza I. Hussein Electrical and Computer Engineering Department Effat University Saudi Arabia College of Engineering Energy and Technology Research Center Effat University Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Smart cities require better cellular communication, incorporating a high data rate that satisfies the Internet, cloud computing, and the Internet of Things (IoT) requirements. A high data rate demands higher bandwidth with low latency. However, the already saturated or underutilized frequency spectrum hinders this realization. 5G and cognitive radio technology can enhance spectrum utilization to meet the high data demands of future cellular communication. Cognitive radio uses spectrum sensing to identify underutilized frequencies without interfering with licensed users, addressing the scarcity of the electromagnetic spectrum. In this paper, a cognitive radio receiver design with a SAR analog-to-digital converter, which is recognized for having a modest resolution for high bandwidth signals, is implemented. Moreover, the pipeline technique in the SAR design is proposed to increase the resolution to be suitable for 5G applications. The high-level model of the system is simulated using MATLAB Simulink software. The quantization error observed in the regular SAR ADC design is compared to that in the proposed pipeline SAR ADC, highlighting the enhanced accuracy and efficiency of the pipeline SAR ADC, which makes it suitable for spectrum sensing in next-generation cognitive radio systems.

关键词： Quantization (signal) 5G mobile communication Software packages Smart cities Receivers Bandwidth Sensors Cognitive radio Signal resolution Pipeline processing

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A 41.32Gb/s 1.8 pJ/bit Collaborative Baud-Rate CDR With Background Eye-Climbing Algorithm and Low-Power Global Clock Distribution

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IEEE Journal of Solid-State Circuits 2025年

作者： Kim, Jihee Park, Jia Shin, Jiwon Kim, Hanseok Kim, Kahyun Shin, Haengbeom Park, Ha-Jung Choi, Woo-Seok Georgia Institute of Technology Department of Electrical and Computer Engineering AtlantaGA30332 United States Inter University Semiconductor Research Center Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Samsung Electronics Hwaseong18448 Korea Republic of

This article presents design techniques for an energy-efficient multi-lane receiver (RX) with baud-rate clock and data recovery (CDR), which is essential for high-throughput low-latency communication in high-performance computing systems. The proposed low-power global clock distribution not only significantly reduces power consumption across multi-lane RXs but is capable of compensating for the frequency offset without any phase interpolators (PIs). To this end, a fractional divider (FDIV) controlled by CDR is placed close to the global phase locked loop. Moreover, in order to address the suboptimal lock point of conventional baud-rate phase detectors, the proposed CDR employs a background eye-climbing algorithm (ECA), which optimizes the sampling phase and maximizes the vertical eye margin (VEM). Fabricated in a 28 nm CMOS process, the proposed 43.32Gb/s RX shows a low integrated fractional spur of -40.4 dBc at a 2500 ppm frequency offset. Furthermore, it improves bit-error-rate (BER) performance by increasing the VEM by 26 mV. The entire RX achieves the energy efficiency of 1.8 pJ/bit with the aggregate data rate of 128 Gb/s. © 1966-2012 IEEE.

关键词： Phase locked loops

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Dual Frequency Improvements to the Time-Series Ratio Algorithm for Soil Moisture Retrievals in the SMEX02 Campaign

Dual Frequency Improvements to the Time-Series Ratio Algorit...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Horton, Dustin Johnson, Joel T. Al-Khaldi, Mohammad Park, Jeonghwan Bindlish, Rajat Department of Electrical and Computer Engineering ElectroScience Laboratory The Ohio State University ColumbusOH43212 United States NASA Goddard Space Flight Center GreenbeltMD United States Global Science & Technology Inc. GreenbeltMD20770 United States

ISBN: (纸本)9781946815200

Surface soil moisture is a key variable in Earth's hydrological cycle that describes the exchange of moisture between land, air, and sea. Soil moisture plays an integral role in balancing the exchange of energy between the atmosphere and land surface (evapotranspiration), ecology, infiltration, and runoff. Soil moisture information also influences applications such as agriculture and food security by supporting of crop yield prediction, drought forecasting, and irrigation, water resource management and flood dynamics, and weather forecasting. © 2025 USNC-URSI.

关键词： Soil moisture

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TTSlow: Slow Down Text-to-Speech With Efficiency Robustness Evaluations

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 693-704页

作者： Xiaoxue Gao Yiming Chen Xianghu Yue Yu Tsao Nancy F. Chen Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan City Taiwan

Text-to-speech (TTS) has been extensively studied for generating high-quality speech with textual inputs, playing a crucial role in various real-time applications. For real-world deployment, ensuring stable and timely generation in TTS models against minor input perturbations is of paramount importance. Therefore, evaluating the robustness of TTS models against such perturbations, commonly known as adversarial attacks, is highly desirable. In this paper, we propose TTSlow, a novel adversarial approach specifically tailored to slow down the speech generation process in TTS systems. To induce long TTS waiting time, we design novel efficiency-oriented adversarial loss to encourage endless generation process. TTSlow encompasses two attack strategies targeting both text inputs and speaker embedding. Specifically, we propose TTSlow-text, which utilizes a combination of homoglyphs-based and swap-based perturbations, along with TTSlow-spk, which employs a gradient optimization attack approach for speaker embedding. TTSlow serves as the first attack approach targeting a wide range of TTS models, including autoregressive and non-autoregressive TTS ones, thereby advancing exploration in audio security. Extensive experiments are conducted to evaluate the inference efficiency of TTS models, and in-depth analysis of generated speech intelligibility is performed using Gemini. The results demonstrate that TTSlow can effectively slow down two TTS models across three publicly available datasets.

关键词： Perturbation methods Speech processing Robustness Computational modeling Security Optimization Text to speech Speech recognition Predictive models Real-time systems

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation 22

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln United States

ISBN: (纸本)9798331508050

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a ∼ 10-4 second inference latency on a laptop CPU. © 2025 IEEE.

关键词： computer viruses

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Technical and economic feasibility assessment for hybrid energy system electricity and hydrogen generation: A case study

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Global Energy Interconnection 2025年第1期8卷 62-81页

作者： Paul C.Okonkwo Samuel Chukwujindu Nwokolo El Manaa Barhoumi Ibrahim B.Mansir Usman Habu Taura Barun Kumar Das Ahmed Bahgat Radwan Wilfred Emori Ephraim Bonah Agyekum Khalid Al Kaaf Mechanical&Mechatronics Engineering Department Dhofar UniversitySalalah 211the Sultanate of Oman Department of Physics Faculty of Physical SciencesUniversity of CalabarCalabar PMB 1115Nigeria Electrical&Computer Engineering Department Dhofar UniversitySalalah 211the Sultanate of Oman Department of Mechanical Engineering Prince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Centre for Energy Research and Training Ahmadu Bello UniversityPMB 1045ZariaNigeria Oil&Gas Research Center Sultan Qaboos UniversityMuscat 123the Sultanate of Oman Department of Mechanical Engineering Rajshahi University of Engineering&TechnologyRajshahi 6204Bangladesh Center for Advanced Materials Qatar UniversityDoha 2713Qatar School of Materials Science and Engineering Sichuan University of Science and EngineeringZigong 643000PR China Department of Nuclear and Renewable Energy Ural Federal University Named after the First President of Russia BorisEkaterinburg 620002Russia Western Caspian University Baku AZ1001Azerbaijan Department of Civil and Environmental Engineering Dhofar UniversitySalalah 211the Sultanate of Oman

Hydrogen is emerging as a promising alternative to fossil fuels in the transportation *** study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al Buraymi,and Salalah,using Hybrid Optimization of Multiple Electric Renewables(HOMER)*** hybrid energy systems,photovoltaic-wind turbine-battery,photovoltaic-battery,and wind turbine-battery were analyzed for each *** indicated that Duqm offers the lowest net present cost(NPC),levelized cost of energy,and levelized cost of hydrogen,making it the most cost-effective ***,Sensitivity analysis showed that as the life of electrolyzer increases during operation,the initial capital expenditure is distributed over a longer operational period,leading to a reduction in the *** so,renewable energy systems produced no emissions which supports Oman’s mission *** comprehensive analysis confirms the feasibility of establishing a hydrogen refueling station in Duqm,Oman,and highlights advanced optimization techniques’superior capability in designing cost-effective,sustainable energy systems.

关键词： Cities Economic indicator Hydrogen production Optimization Solar

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Detection of Autism Spectrum Disorder Using Quantum Support Vector Machines Algorithm

Detection of Autism Spectrum Disorder Using Quantum Support ...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Arshia Eftekhari Zadeh Ali Mikaeili Barzili Mohammad R. Nemati Mohammad Khoshnevisan Hamid Azadegan Behzad Moshiri School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Engineering Antalya Bilim University Antalya Turkey College of Science Northeastern University Boston MA USA Digital Solution Expert MCI R&D Center Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Ontario ON Canada

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Early detection of Autism Spectrum Disorder (ASD) is crucial for initiating interventions that can significantly improve developmental outcomes. Traditional diagnostic methods are often subjective and time-consuming, leading to delays in diagnosis. While classical machine learning models have been applied to ASD detection with promising results, they face challenges in processing complex, high-dimensional medical data. Quantum computing utilizes superposition, and entanglement offers a novel approach to handle such data more efficiently. This study investigates the application of Quantum Support Vector Machines (QSVM) for ASD detection using the Autism Screening Data for Toddlers (ASDTests) dataset. After preprocessing and feature selection, we trained a QSVM model. We compared its performance with classical machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We evaluated the models based on accuracy, precision, recall, and F1-score across multiple random subsets to assess robustness. The QSVM model achieved the highest mean accuracy (96.7 %), precision (97.2 %), and F1-score (95.9 %) with low variance, indicating strong and consistent performance. Statistical analyses using the Paired t-test and Friedman Test showed marginal improvements over the SVM model (p = 0.0495), with no significant difference compared to RF and NB models. These results suggest that QSVM can enhance the accuracy and robustness of ASD detection compared to classical models. Future work could explore hybrid models combining quantum and classical machine learning approaches to improve performance. Applying these methods to different ASD datasets may provide broader validation and insights into their generalizability.

关键词： Support vector machines Radio frequency Autism Analytical models Quantum computing Accuracy Computational modeling Static VAr compensators Machine learning Nearest neighbor methods

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