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arXiv 2025年

作者： Asemani, Hamidreza Meemon, Panomsak Barrera, Gilmer Flores Rolland, Jannick P. Parker, Kevin J. Institute of Optics University of Rochester RochesterNY14627 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States School of Physics Institute of Science Suranaree University of Technology Nakhon Ratchasima30000 Thailand Department of Biomedical Engineering University of Rochester RochesterNY14627 United States

Optical coherence elastography (OCE) is a powerful non-invasive imaging technique for high-resolution assessment of tissue elasticity and viscoelasticity. Accurate characterization of viscoelastic properties requires estimating shear wave speed (SWS) across multiple frequencies, as dispersion induces frequency-dependent variations in wave speed. This study introduces a novel multi-frequency reverberant OCE (MFR-OCE) approach to enhance viscoelastic tissue characterization by simultaneously capturing shear wave dynamics over multiple frequencies. We present the theoretical framework, experimental setup, and validation of MFR-OCE through simulations and experiments on gelatin phantoms, ex vivo porcine cornea, and ex vivo bovine liver. Simulation results demonstrate that MFR-OCE estimates SWS with errors below 4% relative to ground truth, and phantom experimental results show that MFR-OCE and single-frequency OCE also yield closely matching SWS estimates, with differences below 3%. Furthermore, the frequency-dependent dispersion coefficients extracted from biological tissues and phantoms align with the theoretical viscoelastic power law model. The gelatin phantoms exhibit a low viscoelastic behavior with an exponent of 0.13 for the power law fit of SWS, while the ex vivo porcine cornea demonstrates intermediate viscoelastic behavior, with a power law exponent of 0.33. The liver tissue shows significant frequency dependence, with a power law exponent of 0.51. These findings demonstrate that MFR-OCE enables a more comprehensive understanding of tissue mechanics and holds the potential for improving diagnostic accuracy in clinical applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Dispersion (waves)

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Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds

arXiv

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arXiv 2022年

作者： Zhou, Haoran Chen, Honghua Zhang, Yingkui Wei, Mingqiang Xie, Haoran Wang, Jun Lu, Tong Qin, Jing Zhang, Xiao-Ping State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Department of Computing and Decision Sciences Lingnan University Hong Kong School of Nursing The Hong Kong Polytechnic University Hong Kong Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting-edge learning-based techniques for shape analysis and generation. In this paper, we propose a normal refinement network, called Refine-Net, to predict accurate normals for noisy point clouds. Traditional normal estimation wisdom heavily depends on priors such as surface shapes or noise distributions, while learning-based solutions settle for single types of hand-crafted features. Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations. To this end, several feature modules are developed and incorporated into Refine-Net by a novel connection module. Besides the overall network architecture of Refine-Net, we propose a new multi-scale fitting patch selection scheme for the initial normal estimation, by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal estimation framework: 1) point normals obtained from other methods can be further refined, and 2) any feature module related to the surface geometric structures can be potentially integrated into the framework. Qualitative and quantitative evaluations demonstrate the clear superiority of Refine-Net over the state-of-the-arts on both synthetic and real-scanned datasets. Our code is available at https://***/hrzhou2/refinenet. Copyright © 2022, The Authors. All rights reserved.

关键词： Domain Knowledge

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Correction: Microstructuring of cobalt (Co) thin films by UV laser irradiation

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Applied Physics A 2023年第1期130卷 1-1页

作者： Itapu, Srikanth Borra, Vamsi Selvendran, S. Department of Electronics and Communication Engineering Alliance University Bengaluru India Electrical and Computer Engineering Program Rayen College of Engineering Youngstown State University Youngstown USA Centre for Healthcare Advancement Innovation and Research (CHAIR) School of Electronics Engineering (SENSE) Vellore Institute of Technology Chennai India

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The Role of Frontal and Occipital Cortices in Processing Sustained Visual Attention in Young Adults with Attention-Deficit/Hyperactivity Disorder:A Functional Near-Infrared Spectroscopy Study

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Neuroscience Bulletin 2020年第6期36卷 659-663页

作者： Ziyan Wu Yuyang Luo Yu Gao Ying Han Kai Wu Xiaobo Li Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA Department of Psychology Brooklyn College and the Graduate Center of the City University of New YorkBrooklynNY 10004USA Department of Neurology XuanWu Hospital of Capital Medical UniversityBeijing 100053China Beijing Institute of Geriatrics XuanWu Hospital of Capital Medical UniversityBeijing 100053China National Clinical Research Center for Geriatric Disorders Beijing 100053China Center of Alzheimer’s Disease Beijing Institute for Brain DisordersBeijing 100069China Department of Biomedical Engineering School of Materials Science and EngineeringSouth China University of TechnologyGuangzhou 510003China Guangzhou Huiai Hospital The Affiliated Brain Hospital of Guangzhou Medical UniversityGuangzhou 510370China Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia Guangzhou 510500China

Dear Editor,Attention deficit/hyperactivity disorder(ADHD),characterized by age-inappropriate symptoms of inattention,hyperactivity,and impulsivity,is a highly prevalent and heritable childhood-onset neurodevelopmental disorder,with impairing symptoms that persist into adulthood in up to 65%of patients[1].Adults with persistent ADHD symptoms since childhood have been found to have neurocognitive impairments in multiple domains,especially in attention and cognitive control,which significantly contribute to an increased risk of social disability,educational and occupational failure,and other comorbid psychiatric disorders,resulting in significant economic burdens to the affected individuals, their families, andsociety [2, 3].

关键词： Corti impairment Infrared

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Enhancing nutritional status prediction through attention-based deep learning and explainable AI

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Intelligence-Based Medicine 2025年 11卷 100255-100255页

作者： Santoso, Heru Agus Dewi, Nur Setiawati Haw, Su-Cheng Pambudi, Arga Dwi Wulandari, Sari Ayu Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Center for Medical Technology Innovation (CEMTI) Faculty of Engineering Universitas Dian Nuswantoro Semarang 50131 Indonesia Nursing Department Faculty of Medicine Universitas Diponegoro Semarang 50275 Indonesia Faculty of Computing and Informatics Multimedia University Cyberjaya 63100 Malaysia Electrical Engineering Department Faculty of Engineering Universitas Dian Nuswantoro Semarang 50131 Indonesia Biomedical Engineering Department Faculty of Engineering Universitas Dian Nuswantoro Semarang 50131 Indonesia

Accurate and interpretable prediction of child malnutrition remains a critical challenge, as existing AI models often lack the transparency needed for clinical adoption. This study introduces a deep learning framework enhanced with Multi-Head Attention (MHA) for nutritional status prediction, offering a novel contribution through the first direct head-to-head comparison of CNN-MHA and LSTM-MHA to evaluate the effectiveness of spatial feature learning versus sequential dependency modeling in structured anthropometric tabular data. Our framework integrates advanced preprocessing techniques, feature selection, and Explainable AI (SHAP), enabling clinically aligned and transparent predictions. Experimental results on a 9605-sample dataset reveal that CNN-MHA achieves superior performance (99.08 % accuracy) compared to LSTM-MHA (98.91 %), confirming that spatial modeling is better suited for this dataset type. SHAP-based feature attribution further validates WHO-standard z-scores as the most influential predictors, enhancing model credibility for clinical application. Additionally, the study introduces an IoT-enabled anthropometric data acquisition system, enhancing real-time monitoring and scalability. This research represents a significant methodological advancement in nutritional status prediction, addressing key gaps in feature prioritization, accuracy, and interpretability. By bridging the gap between high-accuracy AI and clinical transparency, this study advances AI-driven nutritional monitoring and offers a scalable, explainable framework for public health interventions. Future research should explore multi-modal data integration to further enhance generalizability and real-world applicability. © 2025 The Authors

关键词： Deep learning Explainable AI Machine learning in public health Multi-head attention Nutritional status prediction

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Implementation of Generate and Test Algorithm for Junior High School Zoning System in Malang 4

Implementation of Generate and Test Algorithm for Junior Hig...

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4th International Conference on Vocational Education and Training, ICOVET 2020

作者： Ratnasar, Novia Agustin Mawarni, Inggar Tri Nur Handayani, Anik Wibawa, Aji Prasetya Hammad, Jehad A. H Wibowotomo, Budi Sugandi, MacHmud Universitas Negeri Malang Vocational Education Program Department Indonesia Universitas Negeri Malang Electrical Engineering Dept Indonesia Al-Quds Open University Faculty of Technology and Applied Sciences Department of Computer Information Systems Palestine Universitas Negeri Malang Industrial Technology Department Malang65145 Indonesia Universitas Negeri Malang Mechanical ENgineering Department Malang65145 Indonesia

ISBN: (纸本)9781728181318

Admission of new students in 2019 through zoning still faces many obstacles, one of which is the readiness of the application to determine the distance of a student's house from the nearest recommended school. The purpose of this study is to provide better recommendations regarding the shortest and most accurate distance in the zoning system for admission of junior high school students in Malang by using the Generate and Test algorithm without having to do it manually or using other map applications. The results of this study indicate that the distance value obtained from the haversine formula corresponds to the original distance measured through a zoning application that applies the Generate and Test algorithm to measure the closest distance from the student's location. The recommended distance is given through a process of repetition and comparison of nodes. By using the generate and test algorithm for the search process the shortest distance from the user to the destination school has an average time of 6.2MB, the level of accuracy is the same as the Haversine calculation. © 2020 IEEE.

关键词： Students

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Multi-Resolution Data Fusion for Super Resolution Imaging

arXiv

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arXiv 2021年

作者： Reid, Emma J. Drummy, Lawrence F. Bouman, Charles A. Buzzard, Gregery T. The Department of Mathematics Purdue University West LafayetteIN47907 United States The Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson Air Force Base OH45433 United States The Departments of Electrical and Computer Engineering and Biomedical Engineering Purdue University IN47907 United States

Applications in materials and biological imaging are limited by the ability to collect high-resolution data over large areas in practical amounts of time. One solution to this problem is to collect low-resolution data and interpolate to produce a high-resolution image. However, most existing super-resolution algorithms are designed for natural images, often require aligned pairing of high and low-resolution training data, and may not directly incorporate a model of the imaging sensor. In this paper, we present a Multi-resolution Data Fusion (MDF) algorithm for accurate interpolation of low-resolution electron microscope data at multiple resolutions up to 8x. Our approach uses small quantities of unpaired high-resolution data to train a neural network prior model denoiser and then uses the Multi-Agent Consensus Equilibrium (MACE) problem formulation to balance this denoiser with a forward model agent that promotes fidelity to measured data. A key theoretical novelty is the analysis of mismatched backprojectors, which modify typical forward model updates for computational efficiency or improved image quality. We use MACE to prove that using a mismatched back-projector is equivalent to using a standard back-projector and an appropriately modified prior model. We present electron microscopy results at 4x and 8x interpolation factors that exhibit reduced artifacts relative to existing methods while maintaining fidelity to acquired data and accurately resolving sub-pixel-scale features. © 2021, CC BY.

关键词： Interpolation

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Spatio-temporal processing for automatic vehicle detection in wide-area aerial video

arXiv

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arXiv 2020年

作者： Gao, Xin Szep, Jeno Satam, Pratik Hariri, Salim Ram, Sundaresh Rodríguez, Jeffrey J. Department of Electrical and Computer Engineering University of Arizona TucsonAZ United States Department of Radiology Department of Biomedical Engineering University of Michigan Ann ArborMI United States

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatio-temporal processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatio-temporal processing scheme improves average F-score to above 0.8 and achieves an average reduction of 83.8% in false positives. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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Wide-field ophthalmic space-division multiplexing optical coherence tomography

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Photonics Research 2020年第4期8卷 539-547页

作者： JASON JERWICK YONGYANG HUANG ZHAO DONG ADRIENNE SLAUDADES ALEXANDER J.BRUCKER CHAO ZHOU Department of Electrical and Computer Engineering Lehigh UniversityBethlehemPennsylvania 18015USA Department of Biomedical Engineering Washington University in St.LouisSt.LouisMissouri 63130USA Scheie Eye Institute Penn Presbyterian Medical CenterUniversity of PennsylvaniaPhiladelphiaPennsylvania 19104USA Department of Bioengineering Lehigh UniversityBethlehemPennsylvania 18015USA

High-speed ophthalmic optical coherence tomography(OCT)systems are of interest because they allow rapid,motion-free,and wide-field retinal ***-division multiplexing optical coherence tomography(SDMOCT)is a high-speed imaging technology that takes advantage of the long coherence length of micro electro mechanical vertical cavity surface emitting laser sources to multiplex multiple images along a single imaging *** demonstrate wide-field retinal OCT imaging,acquired at an effective A-scan rate of 800,000 A-scans/s with volumetric images covering up to 12.5 mm×7.4 mm on the retina and captured in less than 1 s.A clinical feasibility study was conducted to compare the ophthalmic SDM-OCT with commercial OCT systems,illustrating the high-speed capability of SDM-OCT in a clinical setting.

关键词： Wide-field ophthalmic space-division multiplexing optical coherence tomography

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Linewidth broadening induced by fundamental thermal fluctuations in metallo-dielectric nanolasers

Linewidth broadening induced by fundamental thermal fluctuat...

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CLEO: Applications and Technology, CLEO_AT 2020

作者： Jiang, Sizhu Pan, Si Hui Deka, Suruj S. Chengyi-Fang Chen, Zijun Fainman, Yeshaiahu El Amili, Abdelkrim Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093-0407 United States Department of Physics University of California San Diego San DiegoCA92093-0407 United States Material Science and Engineering Program University of California San Diego San DiegoCA92093-0407 United States

We demonstrate that the fundamental thermal fluctuations can induce significant linewidth broadening effect in metallo-dielectric nanolasers through numerical simulation. Broader linewidths are observed with the decre... 详细信息

ISBN: (数字)9781943580767

ISBN: (纸本)9781557528209

关键词： Linewidth

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