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arXiv 2024年

作者： Sun, Zexin Chen, Mingyu Baillieul, John Division of Systems Engineering Boston University United States Department of Electrical and Computer Engineering Boston University United States Departments of Mechanical Engineering Electrical and Computer Engineering The Division of Systems Engineering Boston University BostonMA02215 United States

Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In this paper, we present a novel data-driven linear estimator based on Koopman operator theory to extract meaningful finite-dimensional representations of complex nonlinear systems. The Koopman model is used together with deep reinforcement networks that learn the optimal stepwise actions to predict future states of nonlinear systems. Our estimator is also adaptive to a diffeomorphic transformation of the estimated nonlinear system, which enables it to compute optimal state estimates without re-learning. © 2024, CC BY-NC-SA.

关键词： Deep reinforcement learning

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Enhancing Post-Stroke Upper Limb Rehabilitation Through Haptic Feedback in Virtual Reality-Based Gaming

Enhancing Post-Stroke Upper Limb Rehabilitation Through Hapt...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Pillai, Branesh M. Siripala, Napas Sai-Aroon, Kongtup Sivaraman, Dileep Vinjamuri, Ramana Chumnanvej, Sorayouth Suthakorn, Jackrit Mahidol University Faculty of Engineering Department of Biomedical Engineering Thailand University of Maryland Baltimore County Vinjamuri lab in the Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States Mahidol University Neurosurgery Division Faculty of Medicine Ramathibodi Hospital Department of Surgery Bangkok Thailand

ISBN: (纸本)9781665481090

This preliminary study explores developing and implementing upper-limb rehabilitation tools utilizing haptic feedback and 3D spatial recognition analysis in a virtual reality (VR) environment. The effectiveness of upper limb rehabilitation in post-stroke patients was investigated by analyzing the joint and gimbal range of motion (ROM), ROM ratios, and force distribution through various drawing tasks. This study evaluated the impact of different drawing patterns on joint and gimbal ROM, and the force applied during rehabilitation exercises. This study revealed significant variability in ROM across different tasks by combining ROM measurements with force data and employing heat maps for visual representation. These findings emphasize the need for task-specific adjustments to improve rehabilitation outcomes. This study highlights the potential of incorporating advanced technologies, such as VR-based tools with haptic feedback and 3D spatial recognition, to enhance patient engagement and rehabilitation effectiveness. © 2024 IEEE.

关键词： Neuromuscular rehabilitation

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Decoding the future: exploring and comparing ABE standards for cloud, IoT, blockchain security applications

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Multimedia Tools and Applications 2025年第13期84卷 12299-12327页

作者： Singamaneni, Kranthi Kumar Yadav, Kusum Aledaily, Arwa N. Viriyasitavat, Wattana Dhiman, Gaurav Kaur, Amandeep Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology T.S Gandipet Hyderabad500075 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 173212 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India MEU Research Unit Middle East University Amman Jordan

Data security poses a significant challenge in cloud computing, the Internet of Things (IoT), and blockchain applications. Attribute-Based Encryption (ABE) has emerged as a promising cryptographic solution for secure data sharing and access management in these contexts. This research article focuses on enhancing security in cloud, IoT, and blockchain applications through a comprehensive examination and comparison of various ABE standards. The study begins with an in-depth literature review on ABE requirements, offering insights into the evolving landscape, challenges, and trends. This review serves as the foundation for the research, which aims to compare ABE standards based on cryptographic techniques, encryption/decryption processes, and access control rules, considering criteria like flexibility, efficiency, scalability, and security. Statistical analyses complement the comparative study, generating empirical data on ABE standard performance. Key metrics, including key size, storage overhead, encryption/decryption time, and computational complexity, are considered to identify trends and draw meaningful conclusions. The research also explores use cases for different ABE models within blockchain, IoT, and cloud computing applications, pinpointing scenarios where specific ABE models excel. The study contributes to advancing access control and secure data-sharing technologies in specified domains, aiding researchers, practitioners, and developers in selecting the most appropriate encryption method. By systematically outlining advantages and disadvantages of various Cipher Text ABE standards, the research facilitates informed decision-making. Additionally, practical use cases ensure alignment between ABE models and the distinctive traits and requirements of each domain, expanding the toolset for enhancing data security in blockchain, IoT, and cloud computing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springe

关键词： Internet of things

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High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity

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npj Computational Materials 2023年第1期9卷 2231-2242页

作者： Meiirbek Islamov Hasan Babaei Ryther Anderson Kutay B.Sezginel Jeffrey R.Long Alan J.H.McGaughey Diego A.Gomez-Gualdron Christopher E.Wilmer Department of Chemical&Petroleum Engineering University of PittsburghPittsburghPA 15261USA Department of Chemistry University of CaliforniaBerkeleyBerkeleyCA 94720USA Department of Chemical and Biological Engineering Colorado School of MinesGoldenCO 80401USA Department of Chemical and Biomolecular Engineering University of CaliforniaBerkeleyBerkeleyCA 94720USA Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA Department of Mechanical Engineering Carnegie Mellon UniversityPittsburghPA 15213USA Department of Electrical&Computer Engineering University of PittsburghPittsburghPA 15261USA

Thermal energy management in metal-organic frameworks(MOFs)is an important,yet often neglected,challenge for many adsorption-based applications such as gas storage and *** its importance,there is insufficient understanding of the structure-property relationships governing thermal transport in *** provide a data-driven perspective into these relationships,here we perform large-scale computational screening of thermal conductivity k in MOFs,leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 *** found that high thermal conductivity in MOFs is favored by high densities(>1.0 g cm^(−3)),small pores(<10Å),and four-connected metal *** also found that 36 MOFs exhibit ultra-low thermal conductivity(<0.02 W m^(−1) K^(−1)),which is primarily due to having extremely large pores(~65Å).Furthermore,we discovered six hypothetical MOFs with very high thermal conductivity(>10 W m^(−1) K^(−1)),the structures of which we describe in additional detail.

关键词： conductivity thermal pores

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BiLSTM-Based VAE-GAN for Predicting Future Road States in Autonomous Driving

BiLSTM-Based VAE-GAN for Predicting Future Road States in Au...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Kim, Donghyun Kwon, Jaerock Nam, Haewoon Hanyang University Division of Electrical Engineering Ansan Korea Republic of University of Michigan-Dearborn Electrical and Computer Engineering Dearborn United States

ISBN: (纸本)9798331507022

The ability to accurately predict future road conditions is essential for the advancement of autonomous driving systems. This study introduces a BiLSTM-based VAE-GAN framework that leverages both temporal and spatial information to generate high-quality future road images. The proposed architecture combines the reconstruction capabilities of Variational Auto-Encoders (VAEs) with the adversarial training of Generative Adversarial Networks (GANs), while incorporating Bidirectional Long Short-Term Memory (BiLSTM) to effectively capture temporal dependencies in sequential driving data. To train the model, diverse datasets were collected from the CARLA simulation environment, encompassing various road conditions and vehicle states. The training process minimizes reconstruction loss, KL divergence, and adversarial loss, enabling the generation of visually consistent and semantically accurate future road images. Quantitative evaluations using PSNR and MSE metrics demonstrate the model's ability to outperform conventional VAE-based approaches, achieving high structural similarity and low reconstruction errors. The results highlight the potential of the proposed framework to enhance decision-making and lane-keeping performance in autonomous vehicles. By predicting future road states with high fidelity, the BiLSTM-based VAE-GAN framework lays the groundwork for integrating generative models into real-world autonomous driving applications, contributing to safer and more reliable driving systems. © 2025 IEEE.

关键词： Generative adversarial networks

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aXonica: A support package for MRI based Neuroimaging

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Biotechnology Notes 2024年第1期5卷 120-136页

作者： Wajid, Bilal Jamil, Momina Awan, Fahim Gohar Anwar, Faria Anwar, Ali Dhanani School of Science and Engineering Habib University Karachi Pakistan Muhammad Ibn Musa Al-Khwarizmi Research & Development Division Sabz-Qalam Lahore Pakistan Department of Electrical Engineering University of Engineering & Technology Lahore Pakistan Out Patient Department Mayo Hospital Lahore Pakistan Department of Computer Science University of Minnesota Minneapolis United States

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian, BIDS Apps, Flywheel, QMENTA, Boutiques, Brainlife and Neurodesk. Overall, all these solutions complement each other. NeuroDebian covers neuroscience and has a wider scope, providing only 51 tools for MRI. Whereas, BIDS Apps is committed to the BIDS format, covering only 45 software related to MRI. Boutiques is more flexible, facilitating its pipelines to be easily installed as separate containers, validated, published, and executed. Whereas, both Flywheel and Qmenta are propriety, leaving four for users looking for ‘free for use’ tools, i.e., NeuroDebian, Brainlife, Neurodesk, and BIDS Apps. This paper presents an extensive survey of 317 tools published in MRI-based neuroimaging in the last ten years, along with ‘aXonica,’ an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than that of NeuroDebian (51), BIDS App (45), Flywheel (70), and Neurodesk (85). Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an

关键词： Bioimaging Magnetic resonance imaging (MRI) Neuroimaging Ubuntu

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Investigation on Structures of Axial Gap Type Magnetic Multiple Spur Gear for In-Wheel Motor System of Electric Vehicle

Investigation on Structures of Axial Gap Type Magnetic Multi...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Kamijo, Taiga Aiso, Kohei Akatsu, Kan Aoyama, Yasuaki Shibaura Institute of Technology Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan Yokohama National University Division of Intelligent Systems Engineering Department of Faculty of Engineering Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

ISBN: (纸本)9798350314274

A novel drive system using Magnetic Multiple Spur Gear (MMSG) and multiple high-speed motors is characterized by small size, lightweight, and high efficiency even at high-speed region, it is expected to apply to in-wheel motor systems. However, there is a risk of contact between the rotors when applied to in-wheel motor system. In this study, a novel axial-gap MMSG is proposed to solve this problem, stabilize the air gap between the high-speed and low-speed rotors, and effectively utilize the magnets, and its performance is evaluated by finite element analysis. © 2023 IEEE.

关键词： Electric vehicles

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Neuromimetic Dynamic Networks with Hebbian Learning

Neuromimetic Dynamic Networks with Hebbian Learning

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American Control Conference (ACC)

作者： Zexin Sun John Baillieul Division of Systems Engineering Boston University Boston MA Department of Mechanical Engineering Electrical and Computer Engineering Division of Systems Engineering at Boston University Boston MA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Continuing work on what we have called neuromimetic control system designs is reported. The focus here is on control system models in which the dynamics of networks of neuron-like states are governed by hybrid con-tinuous/discrete, linear/nonlinear models. The models studied support Hebbian-like learning of network structure, and formal analysis grounded in graph theory and classical control allows us to prove that the biological model exhibits boundedness, stability, and structural controllability. The results make contact with previous results involving sym-cactus graphs. Simulations using a 14-node generalized sym-cactus network with two input types validate the model's effectiveness in capturing key neural dynamics.

关键词： Analytical models Biological system modeling Controllability Stability analysis Graph theory Hebbian theory

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Optimization of Microwave Resonant Cavity Flowmeter Design for High Temperature Fluid Sensing Applications

Optimization of Microwave Resonant Cavity Flowmeter Design f...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Fang, Tianyang Saniie, Jafar Bakhtiari, Sasan Heifetz, Alexander Illinois Institute of Technology Ecasp Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States Nuclear Science and Engineering Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9781665493765

This paper investigates optimization of parameters to enhance performance of a microwave resonant cavity transducer for high temperature fluid flow sensing in advanced reactors. The cylindrical microwave cavity flowmeter is a novel sensor that measures fluid flow velocity through deflection of the cavity wall due to dynamic fluid pressure. This sensor is designed to operate in harsh environments of a nuclear reactor because sensor material is resilient to high temperatures, ionizing radiation, and corrosion. Parameters that determine performance characteristics of the transducer include dimensions of the microwave cavity, cavity electromagnetic excitation method, and cavity material. We investigate transducer design through computer simulations, with the objective of maximizing cavity Q-factor. © 2023 IEEE.

关键词： Microwave resonators

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