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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Park, Junho Music, Austin Delgado, Daniel Berman, Joseph Dodson, Albert Liu, Yunmei Ruiz, Jaime Huang, He Kaber, David Zahabi, Maryam Texas A&m University Wm Michael Barnes '64 Department of Industrial & Systems Engineering College StationTX United States University of Florida Department of Computer & Information Science & Engineering GainesvilleFL United States North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States North Carolina State University University of North Carolina at Chapel Hill Joint Department of Biomedical Engineering RaleighNC United States University of Florida Department of Industrial and Systems Engineering GainesvilleFL United States

ISBN: (纸本)9798350337020

Amputees use prosthetic devices to perform activities of daily living. However, some users reject their devices due to the lack of usability or high cognitive workload. Although virtual reality has been studied in this domain for training purposes, there has not been any investigation on usability and cognitive workload of using virtual reality simulations for training of prosthetic devices. The objective of this study was to compare cognitive workload and usability of using virtual reality-based simulation of electromyography based prosthetic devices and physical devices. The findings suggested that using virtual reality simulations were helpful in reducing cognitive workload and increasing perceived usability of prosthetic devices. © 2023 IEEE.

关键词： cognitive workload prosthesis training usability virtual reality

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Epistemic Planning for Heterogeneous Robotic systems

arXiv

引用

arXiv 2023年

作者： Bramblett, Lauren Bezzo, Nicola The Departments of Systems and Information Engineering and Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles. Copyright © 2023, The Authors. All rights reserved.

关键词： Information dissemination

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A Method for Minimally-Invasive Injection of Wireless Microdevices into Brain Tissue 46

A Method for Minimally-Invasive Injection of Wireless Microd...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Acebal, Cassandra Kainth, Gurleen Villanueva, Harry Hajjar, Ava Fuhrer, Valery Wanczuk, Paul Lim, Alexander S. Srinivasan, Shriya Otto, Kevin J. Khalifa, Adam University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering United States University of Florida Department of Psychology United States University of Florida Department of Biology United States University of Florida Department of Industrial and Systems Engineering United States Harvard University School of Engineering and Applied Sciences United States University of Florida Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350371499

As the field advances with the introduction of a new generation of electrodes, the demand for refined injection methodologies becomes evident. These electrodes, frequently termed microdevices, are intricately microfabricated and require precise insertion into neural tissue. Despite the existence of conventional injection methods, there is a pressing need to formulate strategies aimed at minimizing the damage associated with the deployment of microdevices of varied sizes and configurations. In recognition of this need, the introduced technique can be applied for damage mitigation when injecting microdevices into brain tissue. We introduce a novel injection aid and describe an injection technique that results in reduced tissue damage. The damage of injecting into agarose and the brain, with and without the aid of a tool that creates microvibrations is evaluated. Cryosectioning data is presented to assess the damage caused by the injection, which was found to be 7.4% less when using microvibrations. © 2024 IEEE.

关键词： Implant Injection Miniaturized Minimally-Invasive Wireless

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The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs

The Lynchpin of In-Memory Computing: A Benchmarking Framewor...

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Neuromorphic systems (ICONS), International Conference on

作者： Md Tawsif Rahman Chowdhury Huynh Quang Nguyen Vo Paritosh Ramanan Murat Yildirim Gozde Tutuncuoglu Electrical and Computer Engineering Wayne State University Detroit USA Industrial Engineering and Management Oklahoma State University Stillwater USA Industrial and Systems Engineering Wayne State University Detroit USA

ISBN: (数字)9798350368659

ISBN: (纸本)9798350368666

The Von Neumann bottleneck, a fundamental challenge in conventional computer architecture, arises from the inability to execute fetch and data operations simultaneously due to a shared bus linking processing and memory units. This bottleneck significantly limits system performance, increases energy consumption, and exacerbates computational complexity. Emerging technologies such as Resistive Random Access Memories (RRAMs), leveraging crossbar arrays, offer promising alternatives for addressing the demands of data-intensive computational tasks through in-memory computing of analog vector-matrix multiplication (VMM) operations. However, the propagation of errors due to device and circuit-level imperfections remains a significant challenge. In this study, we introduce MELISO (In-Memory Linear Solver), a comprehensive end-to-end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the error propagation in VMM operations, analyzing the impact of RRAM device metrics on error magnitude and distribution. This paper introduces the MELISO framework and demonstrates its utility in characterizing and mitigating VMM error propagation using state-of-the-art RRAM device metrics.

关键词： Measurement Energy consumption Virtual machine monitors Neuromorphics System performance Random access memory computer architecture Benchmark testing In-memory computing Computational complexity

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Public Healthcare Informatics for COVID-19 from Social Media Data

Public Healthcare Informatics for COVID-19 from Social Media...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vishwa V. Kumar Avimanyu Sahoo Ritika Kumar Nicholas Loyd Industrial & Systems Engineering and Engineering Management Department of Electrical and Computer Engineering The University of Alabama in Huntsville USA North Alabama Medical Center Florence AL USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The COVID-19 pandemic has profoundly affected various facets of society. This research explores the discourse on social media, specifically "X" (Twitter), to understand public sentiment and needs regarding healthcare supply chain challenges, such as the availability of testing kits, oxygen supplies, and hospital beds, during the pandemic. By identifying pertinent opinionated keyphrases and employing advanced natural language processing (NLP) techniques, we sifted through over 3.9 million tweets to collect health informatics. The content of these tweets is analyzed to investigate the word trends and sentiments. The extracted sentiments were then organized into ten distinct categories using the K-means clustering method that pointed out top-ten healthcare supply needs. The findings underscore the value of social media as a tool for gathering timely and actionable insights on healthcare supply chain logistics, offering critical data for effective response strategies during health crises.

关键词： COVID-19 Social networking (online) Pandemics Hospitals Supply chains Training data Medical services Natural language processing Real-time systems Testing

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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3167-3181页

作者： Mesfer Al Duhayyim Hadeel Alsolai Siwar Ben Haj Hassine Jaber SAlzahrani Ahmed SSalama Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent *** remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic *** order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and ***,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on *** this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing *** presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing *** accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature ***,Elman neural network(ENN)model is applied to allot proper class labels into the input ***,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this *** experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct *** comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

关键词： Hyperspectral images remote sensing deep learning hurricane optimization algorithm crop classification parameter tuning

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Achieving Optimal Complexity Guarantees for a Class of Bilevel Convex Optimization Problems

Achieving Optimal Complexity Guarantees for a Class of Bilev...

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American Control Conference (ACC)

作者： Sepideh Samadi Daniel Burbano Farzad Yousefian Department of Industrial and Systems Engineering Rutgers University Piscataway NJ USA Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We design and analyze a novel regularized accelerated proximal gradient method for a class of bilevel optimization problems where from the optimal solution set of a composite convex optimization problem, we seek to find a solution that minimizes a secondary convex objective function. When the optimal solution set of the lower-level problem admits a weak sharpness property, we significantly improve existing iteration complexity to O(E- 0.5 ) for both suboptimality and infeasibility error metrics, where ∊ > 0 denotes an arbitrary scalar. We also obtain guarantees in absence of the weak sharpness property. In addition, contrary to some existing methods that require solving the optimization problem sequentially (initially solving an optimization problem to approximate the solution of the lower-level problem followed by a second scheme), our method concurrently solves the bilevel optimization problem. To the best of our knowledge, the proposed algorithm achieves the best-known iteration complexity, which matches the optimal complexity for single-level convex optimization. Preliminary numerical experiments on a sparse linear regression problem validate the efficacy of our approach.

关键词： Measurement Gradient methods Linear regression Linear programming Approximation algorithms Convex functions Complexity theory

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Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks

arXiv

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arXiv 2024年

作者： Kaushik, Chiraag Romberg, Justin Muthukumar, Vidya School of Electrical and Computer Engineering Georgia Institute of Technology United States School of Industrial & Systems Engineering Georgia Institute of Technology United States

The classical iteratively reweighted least-squares (IRLS) algorithm aims to recover an unknown signal from linear measurements by performing a sequence of weighted least squares problems, where the weights are recursively updated at each step. Varieties of this algorithm have been shown to achieve favorable empirical performance and theoretical guarantees for sparse recovery and p-norm minimization. Recently, some preliminary connections have also been made between IRLS and certain types of non-convex linear neural network architectures that are observed to exploit low-dimensional structure in high-dimensional linear models. In this work, we provide a unified asymptotic analysis for a family of algorithms that encompasses IRLS, the recently proposed lin-RFM algorithm (which was motivated by feature learning in neural networks), and the alternating minimization algorithm on linear diagonal neural networks. Our analysis operates in a "batched" setting with i.i.d. Gaussian covariates and shows that, with appropriately chosen reweighting policy, the algorithm can achieve favorable performance in only a handful of iterations. We also extend our results to the case of group-sparse recovery and show that leveraging this structure in the reweighting scheme provably improves test error compared to coordinate-wise reweighting. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic analysis

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Wheat Field Fire Smoke Detection from UAV Images using CNN-CBAM

Wheat Field Fire Smoke Detection from UAV Images using CNN-C...

引用

Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Ahmed Alsalem Mohamed Zohdy Department of Industrial and Systems Engineering Oakland University Rochester MI USA Department of Electrical and Computer Engineering Oakland University Rochester MI USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Wheat field fires pose a significant threat to wildlife habitats, agricultural resources, and especially farmer incomes. The frequency of these fires has increased noticeably in recent years, likely due to human activity in agricultural areas and the exacerbating effects of climate change. Rapid action is crucial once smoke is detected, as it marks the first step in early fire detection. To address this challenge, we propose an enhanced CNN model specifically designed to identify smoke in wheat field fires. As part of this approach, a dataset of 3900 UAV images was compiled to depict smoke from wheat field fires. The proposed work also integrates the CBAM into the CNN to improve the model’s ability to extract features for smoke patch characteristics of wheat field fires. Furthermore, the CNN architecture includes decoupled heads to extract relevant data from various sources. Testing on our wheat field fire smoke dataset has demonstrated the effectiveness of this proposed method, achieving an accuracy of up to 0.91 and outperforming the previous model.

关键词： Head Wildlife Habitats Probability Feature extraction Autonomous aerial vehicles Sensors Convolutional neural networks Internet of Things Testing

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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