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Biomedical engineering International Conference (BMEiCON)

作者： Navid Shaghaghi Yash Kamdar Ron Huang Andres Calle Jaidev Mirchandani Michael Castillo Departments of Bioengineering Information Systems & Analytics (ISA) Mathematics & Computer Science (MCS) and Computer Science & Engineering (CSE) Ethical Pragmatic & Intelligent Computing (EPIC) and BioInnovation & Design (BID) Laboratories Santa Clara University Santa Clara California USA

ISBN: (纸本)9781665489041

Prediction of the spread of infectious diseases such as the seasonal Influenza is of utmost importance in the preparation for and mitigation of the severity of their impact. eVision (short for Epidemic Vision) is a machine learning time series forecaster under research and development by Santa Clara University’s EPIC (Ethical, Pragmatic, and Intelligent Computing) and BioInnovation & design laboratories. Since eVision’s Long Short-Term Memory (LSTM) neural network makes use of Influenza related keywords in Google Trends as prediction features, it stands to reason that further feature selection from trending keywords relating to the flu in social media posts could enhance its prediction. After close examination, the only social media platforms that prove capable of supplying relevant data for time series analysis are the Twitter micro-blogging and Reddit social news aggregation and discussion forum platforms; as other social media platforms are either meant for sharing images and videos, or private multi-cast communication rather than public broadcasting and discourse. However, due to the burstiness of flu related Reddit posts, no useful feature for time series forecasting can be extracted from that platform; and Twitter, which has been examined for Influenza forecasting by numerous other researchers with successful results, poses a number of obstacles such as changes in policy as well as placing features behind expensive paywalls through the disabling of existing free APIs. Regardless however, the results obtained by the addition of Twitter data as another feature in eVision’s LSTM resulted in an almost negligible predictive improvement as delineated in this paper.

关键词： Correlation Social networking (online) Blogs Time series analysis Influenza Market research Feature extraction

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Effects of control volume outlet variation on axial air cooling of lithium-ion batteries

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International Journal of Thermofluids 2023年 19卷

作者： Mahek, Mehwish Khan Alkhedher, Mohammad Ghazal, Mohammed Abdelkareem, Mohammad Ali Ramadan, Mohamad Olabi, Abdul-Ghani Mechanical and Industrial Engineering Department College of Engineering Abu Dhabi University United Arab Emirates Electrical Computer and Biomedical Engineering Department College of Engineering Abu Dhabi University United Arab Emirates Sustainable Energy & Power Systems Research Centre RISE University of Sharjah Sharjah P.O. Box 27272 United Arab Emirates Chemical Engineering Department Minia University Elminia Egypt School of Engineering International University of Beirut BIU Beirut Lebanon School of Engineering Lebanese International University LIU Beirut Lebanon Mechanical Engineering and Design Aston University School of Engineering and Applied Science Aston Triangle BirminghamB4 7ET United Kingdom

This paper addresses cylindrical lithium-ion cells' heating and non-uniform cooling by simulating a simple battery pack design with 24 cells and axial airflow. Cooling a battery pack using forced air convection in a rectangular container causes non-homogeneous temperature distribution. The middle cells and the second half of the battery pack (near the exit) get less cooling compared to the cells near the inlet. To solve this issue of non-uniform cooling, in this paper, we propose an optimized Battery Thermal Management System (BTMS) which allows higher amounts of turbulent kinetic energy to be generated near the cells closer to the exit and farther away from the inlet. We attempted to create a narrow region in the middle of the pack which leads to an increase in mixing and turbulence in the airflow. This directs the flow precisely, hence getting rid of dead air flow zones behind the cells. The convergence functions like a nozzle, propelling the air flow into the second half of the battery pack. Using COMSOL Multiphysics, the mixing and turbulence of the airflow, as well as the temperature increase and homogeneity inside the battery pack, are investigated. Three separate investigations are conducted in order to determine the optimal shape for the BTMS control volume as well as to determine the effect of system's inlet pressure and outlet velocity on the cooling performance. Results indicated that the system's temperature decreased significantly by lowering the outlet dimensions and introducing convergence to the BTMS's central area. Increasing outlet velocity increased cooling in a case with a single fan significantly, whereas increasing pressure in a case with two fans increased cooling slightly. © 2023 The Authors

关键词： Lithium-ion batteries

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

arXiv

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arXiv 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin School of Automation Guangdong University of Technology Guangzhou510006 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 2024, CC BY.

关键词： Latent semantic analysis

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Improving neural network robustness via persistency of excitation

arXiv

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arXiv 2021年

作者： Sridhar, Kaustubh Sokolsky, Oleg Lee, Insup Weimer, James The PRECISE Center The Departments of Electrical & Systems Engineering and Computer & Information Science University of Pennsylvania PhiladelphiaPA United States

Improving adversarial robustness of neural networks remains a major challenge. Fundamentally, training a neural network via gradient descent is a parameter estimation problem. In adaptive control, maintaining persistency of excitation (PoE) is integral to ensuring convergence of parameter estimates in dynamical systems to their true values. We show that parameter estimation with gradient descent can be modeled as a sampling of an adaptive linear time-varying continuous system. Leveraging this model, and with inspiration from Model-Reference Adaptive Control (MRAC), we prove a sufficient condition to constrain gradient descent updates to reference persistently excited trajectories converging to the true parameters. The sufficient condition is achieved when the learning rate is less than the inverse of the Lipschitz constant of the gradient of loss function. We provide an efficient technique for estimating the corresponding Lipschitz constant in practice using extreme value theory. Our experimental results in both standard and adversarial training illustrate that networks trained with the PoE-motivated learning rate schedule have similar clean accuracy but are significantly more robust to adversarial attacks than models trained using current state-of-the-art heuristics. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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Deep Unfolding Neural Networks for Fluid Antenna-Enhanced Vehicular Communication

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IEEE Transactions on Vehicular Technology 2025年

作者： Feng, Biqian Feng, Chenyuan Wong, Kai-Kit Quek, Tony Q. S. University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China EURECOM Sophia Antipolis 06410 France University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore

Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation FA-enabled communication systems. © 2025 IEEE.

关键词： Deep neural networks

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Models to Identify Small Brain White Matter Hyperintensities Lesions

SSRN

引用

SSRN 2024年

作者： Castillo, Darwin Rodríguez-Álvarez, María José Samaniego, R. Lakshminarayanan, Vasudevan Departamento de Química Universidad Técnica Particular de Loja LojaAp. 11-01-608 Ecuador Valencia Spain Departmento de Radiología Hospital UTPL Loja Ecuador Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo ON Canada Department of Physics Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke, Alzheimer's Disease, and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain lesions constitute a critical area of research in medical imaging processing, significantly impacting both clinical practice and scientific advancement. This project aims to develop and implement deep learning models for detecting and classifying small brain White Matter hyperintensities (WMH) lesions in magnetic resonance images (MRI), specifically lesions concerning ischemic and demyelination diseases. The methods applied to segmentation were the UNet, Segmenting Anything model (SAM), YOLOV8, and Detectron2 (based on MaskRCNN). Experimental results show a Dice coefficient (DSC) of 0.94, 0.50,0.241,0.88 for segmentation of WMH lesion using the UNet, SAM, YOLOv8, and Detectron2, respectively. The Detectron2 model demonstrated an accuracy of 0.98 in detecting and 0.93 in classifying lesions, including small lesions where other models often fail. In conclusion, the methods developed give an outline to detect, segment, and classify small brain lesions, and that could significantly aid clinical diagnostics, providing reliable support for physicians and improving patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Double-IRS aided MIMO communication under LoS channels: Capacity maximization and scaling

arXiv

引用

arXiv 2021年

作者： Han, Yitao Zhang, Shuowen Duan, Lingjie Zhang, Rui Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore Singapore

Intelligent reflecting surface (IRS) is a promising technology to extend the wireless signal coverage and support the high performance communication. By intelligently adjusting the reflection coefficients of a large number of passive reflecting elements, the IRS can modify the wireless propagation environment in favour of signal transmission. Different from most of the prior works which did not consider any cooperation between IRSs, in this work we propose and study a cooperative double-IRS aided multiple-input multiple-output (MIMO) communication system under the line-of-sight (LoS) propagation channels. We investigate the capacity maximization problem by jointly optimizing the transmit covariance matrix and the passive beamforming matrices of the two cooperative IRSs. Although the above problem is non-convex and difficult to solve, we transform and simplify the original problem by exploiting a tractable characterization of the LoS channels. Then we develop a novel low-complexity algorithm whose complexity is independent of the number of IRS elements. Moreover, we analyze the capacity scaling orders of the double-IRS aided MIMO system with respect to an asymptotically large number of IRS elements or transmit power, which significantly outperform those of the conventional single-IRS aided MIMO system, thanks to the cooperative passive beamforming gain brought by the double-reflection link and the spatial multiplexing gain harvested from the two single-reflection links. Extensive numerical results are provided to show that by exploiting the LoS channel properties, our proposed algorithm can achieve a desirable performance with low computational time. Also, our capacity scaling analysis is validated, and the double-IRS system is shown to achieve a much higher rate than its single-IRS counterpart as long as the number of IRS elements or the transmit power is not small. Copyright © 2021, The Authors. All rights reserved.

关键词： MIMO systems

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

引用

MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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Don’t Forget to Buy Milk: Contextually Aware Grocery Reminder Household Robot

arXiv

引用

arXiv 2022年

作者： Ayub, Ali Nehaniv, Chrystopher L. Dautenhahn, Kerstin Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada Department of Systems Design Engineering University of Waterloo WaterlooONN2L3G1 Canada

Assistive robots operating in household environments would require items to be available in the house to perform assistive tasks. However, when these items run out, the assistive robot must remind its user to buy the missing items. In this paper, we present a computational architecture that can allow a robot to learn personalized contextual knowledge of a household through interactions with its user. The architecture can then use the learned knowledge to make predictions about missing items from the household over a long period of time. The architecture integrates state-of-the-art perceptual learning algorithms, cognitive models of memory encoding and learning, a reasoning module for predicting missing items from the household, and a graphical user interface (GUI) to interact with the user. The architecture is integrated with the Fetch mobile manipulator robot and validated in a large indoor environment with multiple contexts and objects. Our experimental results show that the robot can adapt to an environment by learning contextual knowledge through interactions with its user. The robot can also use the learned knowledge to correctly predict missing items over multiple weeks and it is robust against sensory and perceptual errors. © 2022, CC BY.

关键词： Graphical user interfaces

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Analysis of Some Parameters of Synthesized Anodized Aluminum Oxide Matrices 17

Analysis of Some Parameters of Synthesized Anodized Aluminum...

引用

17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Bukliv, Roxoliana Adamiv, Volodymyr Bordun, Ihor Andrushchak, Nazariy Teslyuk, Ihor Andrushchak, Anatoliy Institute of Chemistry and Chemical Technologies Lviv Polytechnic National University Department of Chemistry and Technology of Inorganic Substances Lviv Ukraine O. G. Vlokh Institute of Physical Opics Ivan Franko National University of Lviv Lviv Ukraine Czestochowa University of Technology Department of Electrical Power Engineering Czestochowa Poland Lviv Polytechnic National University Department of Ecology and Sustainable Environmental Management Lviv Ukraine Lviv Politechnic National University Computer-Aided Design Systems Lviv Ukraine Lviv Polytechnic National University Department of Applied Physics and Nanomaterials Science Lviv Ukraine

ISBN: (纸本)9798331520564

Studies have been conducted on anodized aluminum oxide (AAO) nanomatrices manufactured by SmartMembranes (SM) (Germany) and InRedox (USA). An X-ray fluorescence analyzer determined the quantitative and qualitative composition of AAO matrices. It is shown that the AAO matrices have a large transparency window (250 - 2750 nm) and the dependence of the second harmonic generation (SHG) signal on the pore size. The parameters of the porous structure of the samples were estimated by isothermal processes of nitrogen adsorption/desorption at its boiling point (T = 77 K). © 2024 IEEE.

关键词： Harmonic generation

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