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IEEE International Symposium on Biomedical Imaging

作者： Xuetong Wang Rong Zhou Kanhao Zhao Alex Leow Yu Zhang Lifang He Department of Computer Science and Engineering Lehigh University PA USA Department of Bioengineering Lehigh University PA USA Department of Psychiatry University of Illinois at Chicago IL USA Department of Electrical and Computer Engineering Lehigh University PA USA

Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer’s Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups.

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Development of 5G Cyber-Physical Production System

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International Journal of Networked and Distributed Computing 2023年第1期11卷 9-19页

作者： Huang, Hsuan-Chao Tsai, Chun-Hsien Lin, Hsiung-Cheng Department of Computer Science and Information Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan PhD program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan Department of Electronic Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan

The cyber-physical production system (CPPS) was developed for the interconnection between operational technology (OT) and information and communication technology (ICT) among the machines and decentralized production control. However, OT and ICT are now getting more complex in the modern industry. To enhance industrial management and production process, the CPPS based on the 5G architecture under production environments can provide a more comprehensive solution in the context of Industry 4.0. For this purpose, a 5G CPPS model to improve the interoperability of CPPS in ICT and OT is proposed to build a virtualized Industry 4.0 asset management shell system. First, real-time task artifacts and 5G communication modules are bound in a certain form of virtualization. Second, task artifacts are released to the production line in a real-time scheduling. Third, an order is placed on a production line and split into multiple tasks for production process. Single-machine single-workpiece and multi-machine and multi-workpiece processing cases are used for testing in the production line. Experimental results show that the proposed 5G CPPS can efficiently achieve multi-machine work piece processing. © 2022, The Author(s).

关键词： 5G Cyber-physical production system (CPPS) Digital twin (DT) Industry 4.0 Information technology Operational technology

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Bioinspired UV-Visible Multispectral Imaging Sensor Enabled by Perovskite Nanocrystal Coating

Bioinspired UV-Visible Multispectral Imaging Sensor Enabled ...

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2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Chen, Cheng Wang, Ziwen Wu, Jiajing Deng, Zhengtao Zhang, Tao Zhu, Zhongming Jin, Yifei Lew, Benjamin Srivastava, Indrajit Liang, Zuodong Nie, Shuming Gruev, Viktor Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States College of Engineering and Applied Sciences Nanjing University Jiangsu Nanjing China Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Inspired by the visual system of Papilio xuthus butterfly, w e f abricated a ultraviolet-visible multispectral imager which combines perovskite nanocrystals and vertically stacked silicon photodetectors. High-resoluti... 详细信息

ISBN: (纸本)9781957171289

关键词： Nanocrystals

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Tuning Zinc-Copper Dual-Ion Electrolytes to Extend the Cycle Life of Zinc Ion Capacitors

Tuning Zinc-Copper Dual-Ion Electrolytes to Extend the Cycle...

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International Flexible Electronics Technology Conference (IFETC)

作者： Jinwook Jung Chanho Shin Tse Nga Ng Program in Materials Science and Engineering University of California San Diego La Jolla USA Department of Electrical and Computer Engineering University of California San Diego La Jolla USA

ISBN: (数字)9798331529468

ISBN: (纸本)9798331529475

We investigated the effect of copper ion concentration in zinc-copper dual-ion electrolytes to suppress dendrites and extend the cycle life of zinc ion capacitors. The devices were characterized in terms of changes in microstructure and cycling stability. The nuclei size was decreased in the optimal copper ion concentration to promote lateral deposition and avoid vertical dendrites. The device exhibited stable cycling performance with a capacitance retention of 95% after 10,000 redox cycles, compared to the device with single zinc ion electrolyte which short circuited at around 1,250 redox cycles.

关键词： Performance evaluation Capacitors Redox Ions Electrolytes Dendrites (neurons) Copper Microstructure Zinc Tuning

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Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks

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computer Networks 2024年 253卷

作者： Tu, Yi-Hao Lin, En-Cheng Ke, Chih-Heng Ma, Yi-Wei Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Master Program of Information Technology and Applications National Quemoy University Kinmen892009 Taiwan Department of Computer Science of Information Engineering National Quemoy University Kinmen892009 Taiwan

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum (CWmin) and CW Threshold (CWThreshold), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions. © 2024

关键词： Wireless local area networks (WLAN)

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac...

ISBN: (纸本)9781957171258

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In-situ real-time monitoring of GeSn growth using UHV-CVD to achieve high-quality material with lasing at 2250 nm and 100 K [Invited]

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Optical Materials Express 2025年第6期15卷 1371-1381页

作者： Joshua Grant Sudip Acharya Alexer Golden Enbo Yang Hryhorii Stanchu Guo-En Chang Greg Sun Wei Du Shui-Qing Yu Department of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR 72701 USA Material Science and Engineering Program University of Arkansas Fayetteville AR 72701 USA Institute for Nanoscience and Engineering University of Arkansas Fayetteville AR 72701 USA Department of Mechanical Engineering National Chung Cheng University Chiayi 62102 Taiwan Department of Engineering University of Massachusetts Boston MA 02125 USA

GeSn alloys hold great promise for the development of Si photonics due to its direct bandgap with sufficient high Sn composition, tunable bandgap energies covering broad infrared wavelength range, and compatibility with the complementary metal-oxide-semiconductor (CMOS) process. For the past decades, tremendous efforts have been made to develop material growth recipes to obtain device-level material quality using chemical vapor deposition (CVD) reactors. A common issue in material growth is the formation of Sn droplets (i.e., Sn segregation) that seriously degrades the material quality. However, although industrial CVD has delivered high-quality materials for device demonstration, the dynamic process of material growth remains unavailable, and only ex-situ material characterizations could provide feedback. In this work, a specially designed ultra-high-vacuum CVD (UHV-CVD) was used to study the growth dynamic by employing an in-situ optical system, which enables us to monitor the formation of Sn droplets in real-time. The Sn segregation occurring at the cooling stage after growth completion rather than during material growth was discovered, indicating that a post-growth treatment is needed to minimize the Sn droplet formation. By extending the GeSn growth time and controlling the cooling cycle, the number of Sn droplets dramatically reduced, leading to a high-quality strain-relaxed GeSn layer with 10.2% Sn composition and over 730 nm thickness. Material quality is comparable with those grown using commercial RPCVD in the early stage, which was further confirmed by the demonstration of an optically pumped laser. A threshold of ∼600 kW/cm 2 at 77 K and a maximum operating temperature of 100 K were obtained, with the lasing peak at ∼2250 nm.

关键词： Avalanche photodiodes Fourier transform infrared spectroscopy Laser sources Materials processing Physical vapor deposition Thin films

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Meta-optic accelerators for object classification

Meta-optic accelerators for object classification

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2023 Flat Optics: Components to Systems, FLatOptics 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in high-speed classifiers that are demonstrated for hand... 详细信息

ISBN: (纸本)9781957171289

关键词： Imaging systems Machine vision Neural networks Optical components Optical neural systems Optical systems

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Automatic 2D Material Detection in Optical Images using Deep Learning Based computer Vision 4

Automatic 2D Material Detection in Optical Images using Deep...

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AI and Optical Data sciences IV 2023

作者： Ramezani, Fereshteh Parvez, Sheikh Fix, J. Pierce Battaglin, Arthur Whyte, Seamus Borys, Nicholas J. Whitaker, Bradley Electrical and Computer Engineering Department Montana State University Bozeman United States Department of Physics Montana State University Bozeman United States Materials Science Program Montana State University Bozeman United States Optical Technology Center Montana State University Bozeman United States

ISBN: (纸本)9781510659810

computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. In order to use these materials for research and product development, single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of 2D materials based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectorRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes ( © 2023 SPIE.

关键词： Object detection

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Exploring the science and Art of UAV Light Painting: From Equations and Pixels to Long-Exposure Photography

Exploring the Science and Art of UAV Light Painting: From Eq...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leonardo A. Fagundes-Junior Celso O. Barcelos Raquel F. Vassallo Alexandre S. Brandão Department of Electrical Engineering Robotics Specialization Center Graduate Program on Computer Science Federal University of Viçosa Viçosa MG Brazil Department of Electrical Engineering Federal University of Espirito Santo Vitória ES Brazil

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The use of Unmanned Aerial Vehicles (in short, UAVs, aka drones) for cultural and entertainment purposes, such as drone light shows, has grown exponentially. One such innovative and creative application is the visual arts using drones to explore long-exposure photography. Light painting are generally performed indoors and outdoors in a dedicated space with a human using a moving light source to create spectacular images and save the movement perception in a picture. In this work, we propose a robotic perception system designed to choreograph UAV movements based on time-parametric curves or image edges, serving as reference motions. Our framework begins by processing a digital image, extracting its contours through boundary tracing, and subsequently generating a safe, navigable, and precise path for UAV motion planning. This process involves optimizing waypoints within the UAV workspace to determine a feasible trajectory that encompasses all designated points or computes safe trajectories utilizing established mathematical equations. The validation of the motion planning is performed through light painting, where the UAV can either fly through the motion reference to mimic the original image. The generated trajectories on light painting mode by physical robots are compared against the ground-truth demonstrating the accuracy of the applied control scheme.

关键词： Photography Visualization Art Autonomous aerial vehicles Trajectory Planning Robots

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