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arXiv 2021年

作者： Ma, Pingli Li, Chen Rahaman, Md Mamunur Yao, Yudong Zhang, Jiawei Zou, Shuojia Zhao, Xin Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group Medicine and Biological Information Engineering College Northeastern University China Department of Electrical and Computer Engineering Stevens Institute of Technology United States Biomedical Information College University of Luebeck Germany

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields. © 2021, CC BY.

关键词： Microorganisms

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A convoy of magnetic millirobots transports endoscopic instruments for minimally-invasive surgery

arXiv

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arXiv 2025年

作者： Jeong, Moonkwang Tan, Xiangzhou Fischer, Felix Qiu, Tian Cyber Valley group - Biomedical Microsystems Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart70569 Germany Department of General Surgery Xiangya Hospital Central South University Changsha410008 China International Joint Research Center of Minimally Invasive Endoscopic Technology Equipment & Standards Changsha410008 China Site Dresden Blasewitzer Str. 80 Dresden01307 Germany Faculty of Engineering Sciences University of Heidelberg Heidelberg Germany Faculty of Medicine Carl Gustav Carus Technical University Dresden Germany Faculty of Electrical and Computer Engineering Technical University Dresden Germany

Small-scale robots offer significant potential in minimally-invasive medical procedures. Due to the nature of soft biological tissues, however, robots are exposed to complex environments with various challenges in locomotion, which is essential to overcome for useful medical tasks. A single mini-robot often provides insufficient force on slippery biological surfaces to carry medical instruments, such as a fluid catheter or an electrical wire. Here, for the first time, we report a team of millirobots (TrainBot) that can generate around two times higher actuating force than a TrainBot unit by forming a convoy to collaboratively carry long and heavy cargos. The feet of each unit are optimized to increase the propulsive force around three times so that it can effectively crawl on slippery biological surfaces. A human-scale permanent magnetic set-up is developed to wirelessly actuate and control the TrainBot to transport heavy and lengthy loads through narrow biological lumens, such as the intestine and the bile duct. We demonstrate the first electrocauterization performed by the TrainBot to relieve a biliary obstruction and open a tunnel for fluid drainage and drug delivery. The developed technology sheds light on the collaborative strategy of small-scale robots for future minimally-invasive surgical procedures. © 2025, CC BY.

关键词： Endoscopy

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Two-step electronic response to magnetic ordering in a van der Waals ferromagnet

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Physical Review B 2024年第4期109卷 045416-045416页

作者： Han Wu Jian-Xin Zhu Lebing Chen Matthew W. Butcher Ziqin Yue Dongsheng Yuan Yu He Ji Seop Oh Bin Gao Jianwei Huang Shan Wu Cheng Gong Yucheng Guo Sung-Kwan Mo Jonathan Denlinger Donghui Lu Makoto Hashimoto Matthew B. Stone Alexander I. Kolesnikov Songxue Chi Junichiro Kono Andriy H. Nevidomskyy Robert J. Birgeneau Pengcheng Dai Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Department of Applied Physics Yale University New Haven Connecticut 06511 USA Department of Electrical and Computer Engineering and Quantum Technology Center University of Maryland College Park Maryland 20742 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Material Science and Nanoengineering Rice University Houston Texas 77005 USA Department of Materials Science and Engineering University of California Berkeley California 94720 USA

The two-dimensional material Cr2Ge2Te6 is a member of the class of insulating van der Waals (vdW) magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te p-orbital-dominated bands to undergo changes at the Curie transition temperature TC while the Cr d-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets.

关键词： Electronic structure Magnetic order Magnetic phase transitions Van der Waals systems Angle-resolved photoemission spectroscopy

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Enhanced nonlinearity in a functionalized whispering gallery mode microcavity

Enhanced nonlinearity in a functionalized whispering gallery...

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2020 Frontiers in Optics Conference, FiO 2020

作者： Pampel, Steven K. Bae, Kyuyoung Horning, Thomas M. Zohrabi, Mo Grayson, Michael Park, Wounjhang Gopinath, Juliet T. Department of Physics University of Boulder Colorado BoulderCO80309 United States Department of Electrical Computer and Energy Engineering University of Boulder Colorado BoulderCO80309 United States Materials Science and Engineering Program University of Colorado BoulderCO80309 United States

We report a ~3x increase in third-harmonic generation conversion efficiency using indium tin oxide (ITO) nanoparticles on the surface of a silica microsphere. This establishes ITO-microresonators as a promising tool f... 详细信息

ISBN: (纸本)9781943580804

关键词： Microresonators

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Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context ⁎

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IFAC-PapersOnLine 2020年第2期53卷 7248-7255页

作者： A. Pozzi X. Xie D.M. Raimondo R. Schenkendorf Institute of Energy and Process Systems Engineering TU Braunschweig 38106 Braunschweig Germany Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy

Battery management systems may rely on mathematical models to provide higher performance than standard charging protocols. Electrochemical models allow us to capture the phenomena occurring inside a lithium-ion cell and therefore, could be the best model choice. However, to be of practical value, they require reliable model parameters. Uncertainty quantification and optimal experimental design concepts are essential tools for identifying systems and estimating parameters precisely. Approximation errors in uncertainty quantification result in sub-optimal experimental designs and consequently, less-informative data, and higher parameter unreliability. In this work, we propose a highly efficient design of experiment method based on global parameter sensitivities. This novel concept is applied to the single-particle model with electrolyte and thermal dynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. The proposed method avoids the simplifying assumption of output-parameter linearization (i.e., local parameter sensitivities) used in conventional Fisher information matrix-based experimental design strategies. Thus, the optimized current input profile results in experimental data of higher information content and in turn, in more precise parameter estimates.

关键词： parameter identification global parameter sensitivities uncertainty quantification design of experiments lithium-ion batteries

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Automated radiomic-based prognostication for pancreatic adenocarcinoma

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Pancreatology 2024年 24卷 e55-e56页

作者： Vezakis, Ioannis Skreka, Androniki-Maria Chardalias, Leonidas Vezakis, Antreas Polydorou, Antreas Vezakis, Antonios Matsopoulos, George Biomedical Engineering Laboratory School of Electrical and Computer Engineering National and Technical University of Athens Athens Attiki Greece 2nd Department of Surgery Aretaieion Hospital National and Kapodistrian University of Athens Athens Attiki Greece

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Motion artifact reduction in quantitative susceptibility mapping using deep neural network

arXiv

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arXiv 2021年

作者： Li, Chao Zhang, Hang Zhang, Jinwei Spincemaille, Pascal Nguyen, Thanh D. Wang, Yi Department of Applied and Engineering Physics Cornell University IthacaNY United States Department of Radiology Weill Medical College of Cornell University New YorkNY United States Department of Electrical and Computer Engineering Cornell University IthacaNY United States Department of Biomedical Engineering Cornell University IthacaNY United States

An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles to simulate motion-corrupted QSM images. The simulated QSM image is paired with its motion-free reference to train a neural network using supervised learning. The trained network is tested on unseen simulated motion-corrupted QSM images, in healthy volunteers and in Parkinson's disease patients. The results show that motion artifacts, such as ringing and ghosting, were successfully suppressed. © 2021, CC BY.

关键词： Deep neural networks

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Reconfigurable Integrated Photonic Chips as Dual-Purpose Neuromorphic Accelerators and Physical Unclonable Functions

arXiv

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arXiv 2025年

作者： Sarantoglou, George da Ros, Francesco Sozos, Kostas Bogris, Adonis Mesaritakis, Charis Department of Biomedical Engineering University of West Attica Agiou Spiridonos Egaleo12243 Greece Department of Electrical and Photonics Engineering Technical University of Denmark Ørsetds Plads Building 343 Kgs. LyngbyDK-2800 Denmark Department of Informatics & Computer Engineering University of West Attica Agiou Spiridonos 12243 Egaleo Athens Greece

In this work, we experimentally validate the dual use of a reconfigurable photonic integrated mesh as a neuromorphic accelerator, targeting signal equalization, and as a physical unclonable function offering authentication at the hardware level. The processing node is an optical spectrum slicing self-coherent transceiver targeting the mitigation of dispersion impairments of an intensity detected QPSK signal, after 25 km of transmission at 32 Gbaud. Unavoidable fabrication related imperfections of the nodes, such as waveguide roughness, can act as "fingerprints" of the device, and, during neuromorphic processing, result in unique weights at the digital back-end during signal equalization. Extracted security metrics offer low false positive/negative probability -8 for the generated responses, confirming un-clonability, whereas bit-error-ratio for the QPSK equalization task was always below the hardware forward error correction limit. The experimental results substantiate the capability of the proposed scheme to simultaneously act as an accelerator and as a security token. Copyright © 2025, The Authors. All rights reserved.

关键词： Reconfigurable hardware

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Depression Severity Identification Based on Shallow 2d Self-Attention-Cnn Using Eeg Functional Connectivity Network

SSRN

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SSRN 2024年

作者： Zhou, Yihan Yu, Xiaokang Lin, Huiping Li, Rihui Liang, Jiuxing Shi, Xue Luo, Yuxi School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518100 China Center for Cognitive and Brain Sciences Department of Electrical and Computer Engineering University of Macau Macau S.A.R Taipa China Key Laboratory of Brain Cognition and Education Science Ministry of Education Institute for Brain Research and Rehabilitation Guangdong Key Laboratory of Mental Health and Cognitive Science South China Normal University China Department of Neurology Shenzhen People's Hospital The Second Clinical Medical College Jinan University The First Affiliated Hospital Southern University of Science and Technology Shenzhen China Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province Sun Yat-sen University Guangdong Shenzhen518100 China

Background and objectives: Depression inflicts significant harm on both society and family. Previous studies have indicated that the functional network of EEG signals worked well in recognizing major depression. This study aims to further identify depression severities and characterize their EEG functional network difference by designing a deep learning strategy and a corresponding visualization ***: Rest state EEG recordings from 30 healthy controls, 35 mild depressed and 26 severe depressed patients were included. Weighted phase lag indexes were computed across four frequency sub-bands to delineate the functional connectivity of EEG networks, serving as the input matrix. To better adapt volume conduction effects, a shallow CNN-based incorporated with 2D Self-Attention architecture was designed, enabling the model to capture information across diverse spans and scales within the functional connectivity (FC) matrix. Leveraging the Grad-CAM algorithm, the model highlighted crucial FCs and corresponding EEG pairs for classification. Finally, the changes of EEG FC network across depression severities were statistically analyzed and ***: An accuracy of 89.2% was achieved in tri-classification of 10-second EEG segments using 52 EEG channels, remaining high at 84.1% with 30 selected channels. Notably, the investigation revealed that significant FC changes from mild to severe depression did not exhibit a simple or monotonous ***: This research presented a directly measured methodology to identify depression severity, vital for informing prevention and therapeutic interventions. Furthermore, the findings shed light on the evolving patterns of brain function as depression progresses. The proposed deep learning model and channel selection algorithm offer potential applications beyond this study, promising broader utility in EEG-based research endeavors. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Polarized reflectance texture distinguishes superficial damage of bovine articular cartilage explants

Polarized reflectance texture distinguishes superficial dama...

引用

Frontiers in Optics, FIO 2019 - Part of Frontiers in Optics + Laser Science APS/DLS

作者： Huynh, Ruby Pesante, Benjamin Nehmetallah, George Raub, Christopher Department of Biomedical Engineering Catholic University of America 620 Michigan Ave NE. WashingtonDC20064 United States Electrical Engineering and Computer Science Catholic University of America 620 Michigan Ave NE. WashingtonDC20064 United States

Polarized reflectance maps of bovine articular cartilage are rich in texture content. Image texture analysis extracts quantitative parameters useful to assess intact cartilage versus cartilage with mechanical surface ... 详细信息

ISBN: (纸本)9781943580675

关键词： Cartilage

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