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arXiv 2023年

作者： Ebrahimi-Fard, Kurusch Steven Gray, W. Venkatesh, G.S. Trondheim7491 Norway Centre for Advanced Study [CAS Drammensveien 78 Oslo0271 Norway Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States

The main objective of this work is to show that the single-input, single-output (SISO) affine feedback group, a transformation group in the context of the affine feedback interconnection of Chen–Fliess series, is a p... 详细信息

关键词： Feedback control

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Unsupervised Adaptive Lesion Segmentation with Similarity Based Cross Pseudo Supervision

Unsupervised Adaptive Lesion Segmentation with Similarity Ba...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Yuxin Zhu Shuhao Li Aodi Yang Mei Feng Xiaorong Pu Yazhou Ren School of Computer Science and Engineering University of Electronic Science and Technology of China Department of Medical Oncology The Third People’s Hospital of Sichuan China School of Computer Science and Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Notable advancement has been made in lesion segmentation based on deep learning. Nonetheless, the effectiveness of deep learning heavily relies on the availability of a substantial amount of high-quality labeled data, which is labor-intensive and expensive to require. Moreover, when applied to a different domain, deep learning-based lesion segmentation methods often experience a decline in performance due to variations in device vendors and patient populations. The unsupervised domain adaptation method is proposed to tackle the domain discrepancy problem. There are certain similarities between semi-supervised learning and unsupervised domain adaptation. From an application perspective, semi-supervised learning models can be used to solve unsupervised domain adaptation *** this end, we introduce the cross-pseudo-supervision mechanism in the semi-supervised field for unsupervised adaptive lesion segmentation. However, the method ignores the similar characteristics in medical images. To fully leverage the semantic similarity between domains, a novel similarity-based cross-pseudo-supervised lesion segmentation model is proposed. The proposed model employs a U-Net encoder, trained on source domain, to extract features from both the source and target domains. Subsequently, cosine similarity is computed based on these extracted features. The obtained similarity scores are then employed to weigh the source samples during the training process. The effectiveness of the proposed method is demonstrated through cross-domain experiments conducted on two publicly available COVID-19 lesion segmentation datasets.

关键词： Deep learning Training Adaptation models Image segmentation Computational modeling Semisupervised learning Feature extraction

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Enhancing Power Quality Event Classification with AI Transformer Models

arXiv

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arXiv 2024年

作者： Saber, Ahmad Mohammad Youssef, Amr Svetinovic, Davor Zeineldin, Hatem Kundur, Deepa El-Saadany, Ehab Advanced Power and Energy Center EECS Department Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering University of Toronto TorontoON Canada Concordia Institute for Information Systems Engineering MontrealQC Canada

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%-91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency. © 2024, CC BY-NC-ND.

关键词： Signal to noise ratio

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Nonlinear optical imaging by detection with optical parametric amplification(invited paper)

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Journal of Innovative Optical Health Sciences 2023年第1期16卷 5-14页

作者： Yi Sun Haohua Tu Stephen A.Boppart Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL 61801USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL 61801USA Carle Illinois College of Medicine University of Illinois at Urbana-Champaign UrbanaIL 61801USA

Nonlinear optical imaging is a versatile tool that has been proven to be exceptionally useful in various research ***,due to the use of photomultiplier tubes(PMTs),the wide application of nonlinear optical imaging is limited by the incapability of imaging under am-bient *** this paper,we propose and demonstrate a new optical imaging detection method based on optical parametric amplification(OPA).As a nonlinear optical process,OPA in-trinsically rejects ambient light photons by coherence *** poled lithium niobate(PPLN)crystals are used in this study as the media for *** to bulk nonlinear optical crystals,PPLN crystals support the generation of OPA signal with lower pump ***,this characteristic of PPLN crystals is particularly beneficial when using high-repetition-rate lasers,which facilitate high-speed optical signal detection,such as in spec-troscopy and imaging.A PPLN-based OPA system was built to amplify the emitted imaging signal from second harmonic generation(SHG)and coherent anti-Stokes Raman scattering(CARS)microscopy imaging,and the amplified optical signal was strong enough to be detected by a biased photodiode under ordinary room light *** OPA detection,ambient-light-on SHG and CARS imaging becomes possible,and achieves a similar result as PMT detection under strictly dark *** results demonstrate that OPA can be used as a substitute for PMTs in nonlinear optical imaging to adapt it to various applications with *** ing conditions.

关键词： Nonlinear optical microscopy optical parametric amplification optical detection

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Parallel Repetition of k-Player Projection Games

arXiv

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arXiv 2023年

作者： Bhangale, Amey Braverman, Mark Khot, Subhash Liu, Yang P. Minzer, Dor Department of Computer Science and Engineering University of California Riverside United States Department of Computer Science Princeton University United States Department of Computer Science Courant Institute of Mathematical Sciences New York University United States School of Mathematics Institute for Advanced Study PrincetonNJ United States Department of Mathematics Massachusetts Institute of Technology United States

We study parallel repetition of k-player games where the constraints satisfy the projection property. We prove exponential decay in the value of a parallel repetition of projection games with value less than 1. ©...

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Imaging atomic-scale chemistry from fused multi-modal electron microscopy

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npj Computational Materials 2022年第1期8卷 164-171页

作者： Jonathan Schwartz Zichao Wendy Di Yi Jiang Alyssa J.Fielitz Don-Hyung Ha Sanjaya D.Perera Ismail El Baggari Richard D.Robinson Jeffrey A.Fessler Colin Ophus Steve Rozeveld Robert Hovden Department of Materials Science and Engineering University of MichiganAnn ArborMIUSA Mathematics and Computer Science Division Argonne National LaboratoryLemontILUSA Advanced Photon Source Facility Argonne National LaboratoryLemontILUSA Dow Chemical Co. MidlandMIUSA Department of Material Science and Engineering Cornell UniversityIthacaNew YorkUSA School of Integrative Engineering Chung-Ang UniversitySeoulRepublic of Korea Department of Physics Cornell UniversityIthacaNYUSA The Rowland Institute at Harvard CambridgeMAUSA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMIUSA National Center for Electron Microscopy Lawrence Berkeley National LaboratoryMolecular FoundryBerkeleyCAUSA Applied Physics Program University of MIchiganAnn ArborMIUnited States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic ***,fused multi-modal electron microscopy offers high signal-to-noise ratio(SNR)recovery of material chemistry at nano-and atomic-resolution by coupling correlated information encoded within both elastic scattering(high-angle annular dark-field(HAADF))and inelastic spectroscopic signals(electron energy loss(EELS)or energy-dispersive x-ray(EDX)).By linking these simultaneously acquired signals,or modalities,the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector *** many cases,the dose requirements can be reduced by over one order of *** high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.

关键词： materials chemistry fused

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A Novel Design of Thin Flexible Force Myography Sensor Using Weaved Optical Fiber: A Proof-of-Concept study

A Novel Design of Thin Flexible Force Myography Sensor Using...

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International Conference on Ubiquitous Robots (UR)

作者： Chongyoung Chung Heeju Mun S. Farokh Atashzar Ki-Uk Kyung Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Mechanical and Aerospace Engineering New York University (NYU) New York NY USA Department of Electrical and Computer Engineering Mechanical and Aerospace Engineering and Biomedical Engineering New York University (NYU) New York NY USA NYU WIRELESS Center and NYU CUSP Brooklyn NY USA Mechanical Engineering Department Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Electrical and Computer Engineering New York University (NYU) New York NY USA

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

Motion recognition and tracking is one of the crucial features for intuitive and direct human-robot interaction (HRI), prompting researchers to explore various sensor types. In this paper, we proposed a sleeve-type force myography (FMG) system using new type of thin and flexible FMG sensor utilizing weaved optical fiber. This study introduces a novel design for a thin and flexible FMG sensor using weaved plastic optical fibers for motion recognition and tracking. The proposed sensor demonstrates a compact form factor (15 mm width, 25 mm height, and 2 mm thickness) with high flexibility, making it suitable for embedding in clothing without causing discomfort. Evaluations confirm its high sensitivity, wide force sensing range (>10 N). Accuracy of the estimating force using the proposed sensor was approximately 99.17% or higher and the response time of 85 ms ensures its effectiveness in real-time applications, emphasizing its potential for applications like prosthetics and virtual reality (VR) interactions. To conduct the proof of concept for the FMG sensor, elbow flexion angle estimation was performed focusing solely on the bicep muscle, and high-precision flexion angle tracking was achieved with 94.27% of correlation coefficient. Overall, the proposed FMG sensor presents a promising solution for intuitive and accurate motion recognition in various HRI applications.

关键词： Accuracy Sensitivity Tracking Force Human-robot interaction Virtual reality Robot sensing systems

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Classification of Infant Sleep/Wake States: Cross-Attention among Large Scale Pretrained Transformer Networks using Audio, ECG, and IMU Data

arXiv

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arXiv 2023年

作者： Chang, Kai Chieh Hasegawa-Johnson, Mark McElwain, Nancy L. Islam, Bashima Department of Electrical and Computer Engineering University of Illinois Urbana Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States Department of Human Development and Family Studies University of Illinois Urbana-Champaign United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute United States

Infant sleep is critical to brain and behavioral development. Prior studies on infant sleep/wake classification have been largely limited to reliance on expensive and burdensome polysomnography (PSG) tests in the laboratory or wearable devices that collect single-modality data. To facilitate data collection and accuracy of detection, we aimed to advance this field of study by using a multi-modal wearable device, LittleBeats (LB), to collect audio, electrocardiogram (ECG), and inertial measurement unit (IMU) data among a cohort of 28 infants. We employed a 3-branch (audio/ECG/IMU) large scale transformer-based neural network (NN) to demonstrate the potential of such multi-modal data. We pretrained each branch independently with its respective modality, then finetuned the model by fusing the pretrained transformer layers with cross-attention. We show that multi-modal data significantly improves sleep/wake classification (accuracy = 0.880), compared with use of a single modality (accuracy = 0.732). Our approach to multi-modal mid-level fusion may be adaptable to a diverse range of architectures and tasks, expanding future directions of infant behavioral research. © 2023, CC BY.

关键词： Electrocardiograms

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Impact of nanopillars on phonon dispersion and thermal conductivity of silicon membranes

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Nanoscale 2023年第5期15卷 2248-2253页

作者： Anufriev, Roman Ohori, Daisuke Wu, Yunhui Yanagisawa, Ryoto Jalabert, Laurent Samukawa, Seiji Nomura, Masahiro Institute of Industrial Science The University of Tokyo Tokyo153-8505 Japan Institute of Fluid Science Tohoku University Sendai980-8577 Japan The University of Tokyo Tokyo153-8505 Japan Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Joint Research Center of National Yang Ming Chiao Tung University Tohoku University National Yang Ming Chiao Tung University Hsinchu300 Taiwan

The performance of silicon-based thermoelectric energy generators is limited by the high thermal conductivity of silicon. Theoretical works have long proposed reducing the thermal conductivity by resonant phonon modes in nanopillars placed on the surface of silicon films. However, these predictions have never been confirmed due to the difficulty in the nanofabrication and measurements of such nanoscale systems. In this work, we report on the fabrication and measurements of silicon films with nanopillars as small as 12 nm in diameter. Our Brillouin light scattering spectroscopy experiments revealed that nanopillars indeed host resonant phonon modes. Yet, our thermal measurements using the micro time-domain thermoreflectance technique showed only a statistically insignificant difference between the thermal properties of silicon membranes with and without nanopillars. Results of this work contrast with the predictions of a substantial reduction in the thermal conductivity due to nanopillars and suggest refining the simulations to account for realistic experimental conditions. © 2023 The Royal Society of Chemistry.

关键词： Phonons

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Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

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computers, Materials & Continua 2021年第1期66卷 141-156页

作者： Dildar Hussain Muhammad Adnan Khan Sagheer Abbas Rizwan Ali Naqvi Muhammad Faheem Mushtaq Abdur Rehman Afrozah Nadeem School of Computational Sciences Korea Institute for Advanced StudySeoul02455Korea Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan

The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries,including the transportation *** worldwide transport departments face many obstacles following the implementation and integration of different vehicle *** of these tasks is to ensure that vehicles are autonomous,intelligent and able to grow their repository of *** learning has recently been implemented in wireless networks,as a major artificial intelligence branch,to solve historically challenging problems through a data-driven *** this article,we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging *** Extreme Learning Machine(DELM)framework is introduced in this article to be incorporated in vehicles so they can take human-like *** present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain *** leads to the concept of vehicle controller making *** proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route *** overcomes inadequacy of the current in-vehicle route-finding technology and its *** the relevant route-related information for the ride will be provided to the user based on its *** the DELM method,a high degree of precision in smart decision taking with a minimal error rate is *** investigation,it has been observed that proposed framework has the highest accuracy rate with 70%of training(1435 samples)and 30%of validation(612 samples).Simulation results validate the intelligent prediction of the proposed method with 98.88%,98.2%accuracy during training and validation respectively.

关键词： DELM ANN IoT feedforward route decision prediction smart city

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