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IEEE International Symposium on Information Theory

作者： Mahdi Soleymani Hessam Mahdavifar Tara Javidi Halıcıoğlu Data Science Institute University of California San Diego Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Department of Electrical and Computer Engineering Halıcıoğlu Data Science Institute University of California San Diego

In this paper, we study the quantitative group testing problem, also known as the heavy hitter detection problem and the coin weighing problem, in a non-adaptive setting. In this problem, the aim is to recover k defective items from a group of n items with the smallest possible number of quantitative/additive tests, where each such test returns the number of defective items participating in the test. In the non-adaptive setting, that we study in this paper, all tests are designed at once and can be, in principle, run in parallel. We establish a novel construction method for designing non-adaptive test matrices with a nested structure inspired by the Plotkin concatenation in the coding theory literature. Our proposed algorithm identifies k defective items among the collection of n items with high probability using $k(1 + o(1))\log \left( {\frac{n}{k}} \right)$ non-adaptive tests in the sub-linear regime of k = o(n). Furthermore, our analysis demonstrates that the probability of decoding failure approaches zero exponentially in k as k, n → ∞. Our approach outperforms existing state-of-the-art methods for designing non-adaptive test schemes with efficient decoders for the quantitative group testing problem in terms of the required number of measurements.

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A Resilience Perspective on C-V2X Communication Networks under Imperfect CSI

arXiv

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arXiv 2024年

作者： Shui, Tingyu Saad, Walid Chen, Mingzhe Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

Cellular vehicle-to-everything (C-V2X) networks provide a promising solution to improve road safety and traffic efficiency. One key challenge in such systems lies in meeting different quality-of-service (QoS) requirements of coexisting vehicular communication links, particularly under imperfect channel state information (CSI) conditions caused by the highly dynamic environment. In this paper, a novel analytical framework for examining the resilience of C-V2X networks in face of imperfect CSI is proposed. In this framework, the adaptation phase of the C-V2X network is studied, in which an adaptation power scheme is employed and the probability distribution function (PDF) of the imperfect CSI is estimated. Then, the resilience of C-V2X networks is studied through two principal dimensions: remediation capability and adaptation performance, both of which are defined, quantified, and analyzed for the first time. Particularly, an upper bound on the estimation’s mean square error (MSE) is explicitly derived to capture the C-V2X’s remediation capability, and a novel metric named hazard rate (HR) is exploited to evaluate the C-V2X’s adaptation performance. Afterwards, the impact of the adaptation power scheme on the C-V2X’s resilience is examined, revealing a tradeoff between the C-V2X’s remediation capability and adaptation performance. Simulation results validate the framework’s superiority in capturing the interplay between adaptation and remediation, as well as the effectiveness of the two proposed metrics in guiding the design of the adaptation power scheme to enhance the system’s resilience. Copyright © 2024, The Authors. All rights reserved.

关键词： Distribution functions

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Joint Optimization of Model Splitting and Device Task Assignment for Private Multi-hop Split Learning

Joint Optimization of Model Splitting and Device Task Assign...

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Annual Conference on Information sciences and Systems (CISS)

作者： Dongyu Wei Qianqian Zhang Yuchen Liu Mingzhe Chen Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Department of Electrical and Computer Engineering Rowan University Glassboro NJ USA Department of Computer Science NC State University Raleigh NC USA Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm.

关键词： Training Performance evaluation Privacy Network topology Simulation Heuristic algorithms data models Delays Optimization Convergence

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L-SeqSleepNet: Whole-cycle Long Sequence Modeling for Automatic Sleep Staging

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IEEE Journal of Biomedical and Health Informatics 2023年第10期27卷 4748-4757页

作者： Phan, Huy Lorenzen, Kristian P. Heremans, Elisabeth Chen, Oliver Y. Tran, Minh C. Koch, Philipp Mertins, Alfred Baumert, Mathias Mikkelsen, Kaare B. De Vos, Maarten Amazon Alexa CambridgeMA02142 United States Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Alan Turing Institute LondonNW1 2DB United Kingdom Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark KU Leuven Department of Electrical Engineering Leuven3001 Belgium Lausanne1015 Switzerland Lausanne1015 Switzerland University of Oxford Nuffield Department of Clinical Neurosciences OxfordOX3 9DU United Kingdom University of Lübeck Institute for Signal Processing Lübeck23562 Germany Lübeck23562 Germany The University of Adelaide School of Electrical and Mechanical Engineering AdelaideSA5005 Australia KU Leuven Department of Electrical Engineering and the Department of Development and Regeneration Leuven3001 Belgium KU Leuven Leuven.AI - KU Leuven Institute Leuven3001 Belgium

Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We thus introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, which takes into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single EEG channel input. Our analyses also show that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages. Moreover the network becomes much more robust, meaning that for all subjects where the baseline method had exceptionally poor performance, their performance are improved significantly. Finally, the computation time only grows at a sub-linear rate when the sequence length increases. © 2013 IEEE.

关键词： Deep neural networks

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Exploring the Potentials of polymer-based CMUTs for 3D Ultrasound Computed Tomography

Exploring the Potentials of polymer-based CMUTs for 3D Ultra...

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IEEE Symposium (IUS) Ultrasonics

作者： Martin Angerer Jonas Welsch Carlos D. Gerardo Nicole V. Ruiter Edmond Cretu Robert Rohling Department of Electrical and Computer Engineering University of British Columbia (UBC) Vancouver Canada Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

This work presents a quantitative evaluation of polymer-based Capacitive Micromachined Ultrasonic Transducers (polyCMUTs) for 3D Ultrasound Computed Tomography (3D USCT). The study was motivated by limitations of the currently used PZT fiber technology in terms of bandwidth and transmit sensitivity. We developed finite element models of polyCMUT elements consisting of 127 cells to predict the acoustic performance. We fabricated prototype transducers using a novel method for microstructuring polymer layers. The produced samples reach a fractional bandwidth of 116%, an opening angle of 44° and increase the transmit sensitivity by 54%, compared to the PZT fiber transducers. The developed models allow for accurate predictions of the acoustic field over a large range of angles and frequencies. More work is required to improve the reliability and reduce sample-to-sample variations. Based on the measured performance and the general properties of the technology, polyCMUTs are very promising for 3D USCT.

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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computer Systems science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 1453-1468页

作者： C.S.S.Anupama Rafina Zakieva Afanasiy Sergin E.Laxmi Lydia Seifedine Kadry Chomyong Kim Yunyoung Nam Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Candidate of Pedagogical Sciences Department of Industrial Electronics and Lighting EngineeringKazan State Power Engineering UniversityKazan420066Russia Department of Theories and Principles of Physical Education and Life Safety North-Eastern Federal University Named After M.K.AmmosovYakutsk677000Russia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Gait is a biological typical that defines the method by that people *** is the most significant performance which keeps our day-to-day life and physical *** electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any *** classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery *** approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)*** study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG *** EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is *** addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait *** last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR *** results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.

关键词： Feature fusion human gait recognition deep learning electromyography signals artificial algae algorithm

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Exploiting Deep Learning Architectures for Effective Real-Time data Analysis in Machine Learning 3

Exploiting Deep Learning Architectures for Effective Real-Ti...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shreenidhi, H.S. Rajarajeswari, S. Bhagwat, Suvarna R. Sharma, Khushboo Seth, Kanika Yadav, Rakesh Kumar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper seeks to discover how deep state-of-the-art architectures can be leveraged for robust actual-time information evaluation in device studying packages. The paper begins by supplying a comprehensive assessment of contemporary the modern-day in deep brand new-based totally facts evaluation technology. It then examines case research state-of-the-art real-time records analysis programs to illustrate the viability and blessings of modern-day existing deep latest architectures. Programs encompass self-driving vehicle generation, laptop vision, natural language processing, and time-collection evaluation. This targeted evaluation is supplemented by assessing ultra-modern capability shortcomings and demanding situations associated with using these architectures in actual-time scenarios. Subsequently, the paper provides a set of modern-day pointers and first-rate practices to help practitioners efficiently leverage deep trendy architectures for actual-time information evaluation. With the aid of combining a comprehensive review of current deep-gaining knowledge state modern-based records evaluation technology, a detailed dialogue present day its utility guiding ideas, and a summary of the latest potential shortcomings and demanding situations, this paper offers a practical manual to the effective of deep learning architectures for real-time data analysis. © 2023 IEEE.

关键词： architectures challenges combining recommendations shortcomings

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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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Investigating the Benefits of Adopting Secure Shell (SSH) in Wireless Network Security

Investigating the Benefits of Adopting Secure Shell (SSH) in...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Gavathri, C. Patil, Vivek D. Singh, Dhiraj Kumar Kumar, Ritesh Narmadha, T. Kalra, Hitesh Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

cozy Shell is an encrypted network protocol that has become increasingly popular in network security. Using SSH, organizations can defend their wireless networks from community penetration and malicious interest. SSH affords encryption for statistics transfers, thus stopping records robbery and interception of better-level protocols, which includes statistics confidentiality, integrity, and authentication. Similarly, complex SSH configurations can offer defense in opposition to assaults such as phishing, malware dissemination, and man-in-the-middle attacks. This paper will examine the feasibility and effectiveness of SSH for use in Wi-FiWi-Fi network safety. It will discuss the benefits of SSH for defensive a Wi-FiWi-Fi network and the demanding situations related to its implementation. Additionally, the paper will become aware of the metrics used to degree the overall performance of an SSH-blanketed community and suggest a secure framework for SSH implementation. In the end, this paper will explore the potential for further research within the region of wireless network protection using SSH. © 2023 IEEE.

关键词： Authentication

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