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arXiv 2025年

作者： Han, Shaohang Vahs, Matti Tumova, Jana Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures United Kingdom

Ensuring safety for autonomous robots operating in dynamic environments can be challenging due to factors such as unmodeled dynamics, noisy sensor measurements, and partial observability. To account for these limitations, it is common to maintain a belief distribution over the true state. This belief could be a non-parametric, sample-based representation to capture uncertainty more flexibly. In this paper, we propose a novel form of Belief Control Barrier Functions (BCBFs) specifically designed to ensure safety in dynamic environments under stochastic dynamics and a sample-based belief about the environment state. Our approach incorporates provable concentration bounds on tail risk measures into BCBFs, effectively addressing possible multimodal and skewed belief distributions represented by samples. Moreover, the proposed method demonstrates robustness against distributional shifts up to a predefined bound. We validate the effectiveness and real-time performance (approximately 1 kHz) of the proposed method through two simulated underwater robotic applications: object tracking and dynamic collision avoidance. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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Remote Hands-On Experience for Students in Undergraduate computer and electrical engineering 27

Remote Hands-On Experience for Students in Undergraduate Com...

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27th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2023

作者： Hall, Janne Tanner, April Eldek, Abdelnasser Division of Information Technology Jackson State University JacksonMS39217 United States Department of Elect. & Comp. Engineering and Computer Science Jackson State University JacksonMS39217 United States Department of Electrical Engineering Lamar University BeaumontTX77710 United States

ISBN: (纸本)9781713880325

The focus of this research was to analyze current methods for online education for electrical and computer engineering undergraduate programs. From this research, an online model for virtual labs in undergraduate engineering is introduced. Experiential learning was exhibited by students through the hands-on experience with lab equipment combined with user feedback. Software used in this research to simulate traditional lab equipment affords institutions the opportunity to provide instructive methods to remote students who are unable to complete on-campus curricula. The devices used to remotely complete the virtual labs facilitated high-value lab practices and skills comparable to those presented in an on-campus lab. As examined during this research, the opportunity to operate virtual devices with the simulation software delivered an extensive laboratory experience with the capability for creating a teaching method for engineering concepts. Data from the students' feedback on the simulated, interactive learning is presented in this paper. © 2023 Proceedings of World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI. All rights reserved.

关键词： Students

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Advancement of Gate Oxides from SiO2to High-k Dielectrics in Microprocessor and Memory 3

Advancement of Gate Oxides from SiO2to High-k Dielectrics in...

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3rd International Conference on Recent Advances in Fundamental and Applied sciences, RAFAS 2021

作者： Sharma, U. Kumar, G. Mishra, S. Thomas, R. School of Electronics and Electrical Engineering Lovely Professional University Punjab 144411 India Division of Research and Development Lovely Professional University Punjab 144411 India School of Computer Science and Engineering Lovely Professional University Punjab 144411 India School of Chemical Engineering and Physical Sciences Lovely Professional University Punjab 144411 India

Silicon and its native oxides (SiO2) have led the continuous development in the integrated circuits for decades. The excellent insulating properties of SiO2 and silicon/SiO2 interface quality were used as capacitor dielectrics in random-access memories (RAM's) and transistor gate dielectrics in complementary metal-oxide semiconductor (CMOS's), respectively, for the memory and logic devices. These are the core components and act as the heart of the semiconductor industry. However, reducing the thickness of SiO2 to increase the capacitance at a reduced lateral size resulted into high leakage current and large power consumption in the aforementioned high-density semiconductor devices. To overcome this concern, high-permittivity materials were considered in replacing the SiO2 to achieve high capacitance at a higher thickness. Numerous materials ranging from Al2O3 (k∼9) to perovskites (k∼102-104) are being actively investigated but Hafnium based compound high-k dielectric/metal gate (k∼10-15) captivate great attention and used for logic and memory devices. However, finding a real high-k material (k∼25-40) with silicon is a major challenge by itself as it requires an ideal interface with minimum defects. Therefore, with this article, we review the efforts in replacing SiO2 with higher-k dielectrics over the years to match the performance of processor from generation to generation. © Published under licence by IOP Publishing Ltd.

关键词： Silica

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Vision-Based Fuzzy Control System for Smart Walkers: Enhancing Usability for Stroke Survivors with Unilateral Upper Limb Impairments

arXiv

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arXiv 2025年

作者： Chalaki, Mahdi Zakerimanesh, Amir Soleymani, Abed Mushahwar, Vivian Tavakoli, Mahdi Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada Department of Medicine Division of Physical Medicine and Rehabilitation University of Alberta Canada Department of Biomedical Engineering University of Alberta Canada University of Alberta Canada

Mobility impairments, particularly those caused by stroke-induced hemiparesis, significantly impact independence and quality of life. Current smart walker controllers operate by using input forces from the user to control linear motion and input torques to dictate rotational movement;however, because they predominantly rely on user-applied torque exerted on the device handle as an indicator of user intent to turn, they fail to adequately accommodate users with unilateral upper limb impairments. This leads to increased physical strain and cognitive load. This paper introduces a novel smart walker equipped with a fuzzy control algorithm that leverages shoulder abduction angles to intuitively interpret user intentions using just one functional hand. By integrating a force sensor and stereo camera, the system enhances walker responsiveness and usability. Experimental evaluations with five participants showed that the fuzzy controller outperformed the traditional admittance controller, reducing wrist torque while using the right hand to operate the walker by 12.65% for left turns, 80.36% for straight paths, and 81.16% for right turns. Additionally, average user comfort ratings on a Likert scale increased from 1 to 4. Results confirmed a strong correlation between shoulder abduction angles and directional intent, with users reporting decreased effort and enhanced ease of use. This study contributes to assistive robotics by providing an adaptable control mechanism for smart walkers, suggesting a pathway towards enhancing mobility and independence for individuals with mobility impairments. Copyright © 2025, The Authors. All rights reserved.

关键词： Fuzzy control

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Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction to ICF Applications

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年

作者： Xu, Xiaojian Klasky, Marc McCann, Michael T. Hu, Jason Fessler, Jeffrey A. University of Michigan Department of Electrical Engineering & Computer Science MI48109 United States Los Alamos National Laboratory Theoretical Division Los AlamosNM87545 United States

Reconstructing 3D cone beam computed tomography (CBCT) images from a limited set of projections is an important inverse problem in many imaging applications from medicine to Inertial Confinement Fusion (ICF). The performance of traditional methods such as filtered back projection (FBP) and model-based regularization is sub-optimal when the number of available projections is limited. In the past decade, deep learning (DL) has gained great popularity for solving CT inverse problems. A typical DL-based method for CBCT image reconstruction is to learn an end-to-end mapping by training a 2D or 3D network. However, 2D networks fail to fully use global information. While 3D networks are desirable, they become impractical as image sizes increase because of the high memory cost. This paper proposes Swap-Net, a memory-efficient 2.5D network for sparse-view 3D CBCT image reconstruction. Swap-Net uses a sequence of novel axes-swapping operations to reconstruct 3D volumes in an end-to-end fashion without using full 3D convolutions. Simulation results on ICF show that Swap-Net consistently outperforms baseline methods both quantitatively and qualitatively in terms of reducing artifacts and preserving details of complex hydrodynamic simulations of relevance to the ICF community. © 2015 IEEE.

关键词： computerized tomography

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Multimodal feature learning framework for disease biomarker discovery

Multimodal feature learning framework for disease biomarker ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ghandikota, Sudhir Yella, Jaswanth K. Jegga, Anil G. University of Cincinnati Department of Electrical Engineering and Computer Science OH United States Cincinnati Children's Hospital Medical Center Division of Biomedical Informatics OH United States

ISBN: (纸本)9781665468190

Most in silico biomarker discovery methods are based on a single disease data modality. However, they often fail to identify and understand the relationships between different biomarkers emerging from various data types. Heterogeneous multimodal analysis methods facilitate the integration of multiple disease-related data resources, allowing us to gain a more accurate understanding of the mechanisms involved in a disease. Multimodal biomarkers resulting from such integrated analyses are complementary and robust and enable a holistic understanding of the disease. In this study, we proposed a novel neural network model that integrates and analyzes data from tissue transcriptomics, protein-protein interactions, and gene functional annotation resources. We employed a network-based approach by constructing a heterogeneous knowledge graph consisting of different biomedical entities and their inherent relationships. In addition, we utilized contextualized gene features mined from the biomedical literature. Our multimodal framework accurately predicts the differential activity of genes in idiopathic pulmonary fibrosis (IPF). Evaluation experiments were performed to determine the benefits of implementing the multimodal approaches. We also employed transcription factor enrichment analysis (TFEA) to validate the learned features. Finally, the gene clusters identified using multimodal gene features in IPF were found to be biologically relevant and meaningful. © 2022 IEEE.

关键词： Biomarkers

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Photon-pair generation using inverse-designed thin-film lithium niobate mode converters

Photon-pair generation using inverse-designed thin-film lith...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Kwon, Kiwon Heo, HyungJun Lee, Dongjin Kim, Hyeongpin Jang, Hyeong-Soon Shin, Woncheol Lim, Hyang-Tag Kim, Yong-Su Han, Sang-Wook Kim, Sangin Shin, Heedeuk Kwon, Hyounghan Jung, Hojoong Seoul02792 Korea Republic of Pohang37673 Korea Republic of Seoul02792 Korea Republic of Department of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Division of Nano and Information Technology KIST School Korea University of Science and Technology Seoul02792 Korea Republic of

Spontaneous parametric down-conversion is a fundamental technique for producing entangled photon pairs. Here, we employ modal phase matching with straight waveguide instead of the PPLN method and show SPDC efficiency ...

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Classical and quantum experiments using hybrid Si3N4-LiNbO3 quantum photonic chip

Classical and quantum experiments using hybrid Si3N4-LiNbO3 ...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Kwon, Kiwon Heo, HyungJun Lee, Donghwa Jang, Hyeong-Soon Kim, Sangin Shin, Heedeuk Kim, Yong-Su Han, Sang-Wook Jung, Hojoong Seoul02792 Korea Republic of Seoul02792 Korea Republic of Pohang37673 Korea Republic of Department of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Division of Nano and Information Technology KIST School Korea University of Science and Technology Seoul02792 Korea Republic of

We fabricated various optical devices on a hybrid Si3N4-LiNbO3 platform and performed classical and quantum experiments. In this experiment, we observed 5:5 power splitting ratio and Hong-Ou-Mandel quantum interferenc... 详细信息

关键词： Quantum interference devices

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electrically Tunable Terahertz Resonance in Antiferromagnetic NiO/Pt Heterostructures

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physical Review Applied 2023年第1期20卷 014023-014023页

作者： Dongsheng Yang Wen Wen Chang Xu Kyusup Lee Ting Yu Hyunsoo Yang Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore

Antiferromagnets that facilitate terahertz (THz) spin resonances have the potential to revolutionize high-speed electronics at the nanoscale. The electrical control of THz spin resonances is the key to such THz devices; however, experimental demonstration has remained elusive. In this work, we demonstrate electrically tunable THz spin resonance in an antiferromagnetic NiO/Pt heterostructure by employing both low-wavenumber Raman and continuous-wave THz spectroscopy techniques. A redshift of over 100 GHz in the NiO spin resonance frequency of around 1 THz is observed by applying charge currents along the adjacent Pt layer. A control experiment with NiO/Cu and temperature-dependent measurement confirm that the dominant tuning mechanism is Joule heating. Finally, a prototype device is designed to achieve an electrical control of THz transmission at dual channels of 0.96 and 1 THz, leading to a Q factor of 56. This work opens up the possibility for the implementation of tunable THz devices utilizing antiferromagnetic spin resonance.

关键词： Spin optoelectronics Spintronics Antiferromagnets Magnetic multilayers Optical sources & detectors Transition metal oxides Terahertz sources Terahertz spectroscopy

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Real-Time Fish Detection and Tracking using Ensemble based Deep Learning Approach for Aquatic Species Monitoring 8

Real-Time Fish Detection and Tracking using Ensemble based D...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Nishanth, R. Sasank, Sai sudhindra V. Dash, Sriya Simran Yadav, Vinod Kumar Thangavel, Senthil Kumar Somasundaram, K. Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Fisheries Economics Extension & Statistics Division (FEESD) ICAR-Central Institute of Fisheries Education Mumbai 400061 India Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Tamil Nadu India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331512248

This study presents an advanced deep learning framework for real-time fish detection, classification, and motion tracking in underwater environments. YOLOv8 is utilized for fast and accurate fish detection, while motion-based keyframe extraction and optical flow algorithms like Lucas-Kanade and Farneback enhance tracking performance. ByteTrack ensures consistent object tracking across frames, and classification models, including SVM, Random Forest, and CNN, improve species identification. To optimize real-time performance, the system applies preprocessing techniques such as Gaussian blurring and contrast enhancement, along with post-processing methods like Non-Maximum Suppression (NMS) to refine detections. Designed for live underwater monitoring, this approach enhances fish behavior analysis, ecological research, and fisheries management. © 2025 IEEE.

关键词： ByteTrack computer Vision Convolutional Neural Networks (CNN) Deep Learning Ecological Research Fish Detection Fisheries Management Motion Analysis Non-Maximum Suppression (NMS) Object Tracking Optical Flow Real-Time Monitoring Recurrent Neural Networks(RNN) Species Classification YOLOv8

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