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Monitoring the condition of the protective fence above the railway track

IFAC-PapersOnLine 2019年第27期52卷 145-150页

作者： Radovan Hajovsky Martin Pies Jan Velicka Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17.listopadu 15 Ostrava Czech Republic

The paper deals with the description of the development and implementation of a complex monitoring system, which is used to monitor the condition of the protective fence installed above the railway track. The protective fence is used to capture falling rocks and other material from the slope above the track. In doing so, it fulfills a protective safety function against possible danger of passing trains from falling rocks. In a critical case, human health could also be endangered. The monitoring system is based on the use of accelerometer wireless sensors that are located at defined locations on the fence in the MESH network. IQRF technology is used. The data transfer is carried out using the GSM gateway to the SQL database. SW Grafana is used for data visualization.

关键词： Accelerometers Asynchronous Discrete measurements Event systems Environmental stability Failure detection Measuring transducers MEMS sensors Railways Solar cells

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A novel adversarial learning strategy for medical image classification

arXiv

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arXiv 2022年

作者： Fan, Zong Zhang, Xiaohui Gasienica, Jacob A. Potts, Jennifer Ruan, Su Thorstad, Wade Gay, Hiram Song, Pengfei Wang, Xiaowei Li, Hua Department of Bioengineerin University of Illinois Urbana-ChampaignIL United States Carle Illinois College of Medicine ChampaignIL United States Joint Department of Biomedical Engineering University of North Carolina Chapel HillNC United States Equipe Quantif University of Rouen Rouen France Department of Radiation Oncology Washington University St. LouisMO United States Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States Department of Pharmacology and Bioeng University of Illinois ChicagoIL United States Cancer Center at Illinois UrbanaIL United States North Carolina State University RaleighNC United States

Deep learning (DL) techniques have been extensively utilized for medical image classification. Most DL-based classification networks are generally structured hierarchically and optimized through the minimization of a single loss function measured at the end of the networks. However, such a single loss design could potentially lead to optimization of one specific value of interest but fail to leverage informative features from intermediate layers that might benefit classification performance and reduce the risk of overfitting. Recently, auxiliary convolutional neural networks (AuxCNNs) have been employed on top of traditional classification networks to facilitate the training of intermediate layers to improve classification performance and robustness. In this study, we proposed an adversarial learning-based AuxCNN to support the training of deep neural networks for medical image classification. Two main innovations were adopted in our AuxCNN classification framework. First, the proposed AuxCNN architecture includes an image generator and an image discriminator for extracting more informative image features for medical image classification, motivated by the concept of generative adversarial network (GAN) and its impressive ability in approximating target data distribution. Second, a hybrid loss function is designed to guide the model training by incorporating different objectives of the classification network and AuxCNN to reduce overfitting. Comprehensive experimental studies demonstrated the superior classification performance of the proposed model. The effect of the network-related factors on classification performance was investigated. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

arXiv

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arXiv 2020年

作者： Li, Mushu Cheng, Nan Gao, Jie Wang, Yinlu Zhao, Lian Shen, Xuemin Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada State Key Lab. of ISN School of Telecommunications Engineering Xidian University Xian710071 China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada

In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

Research Square

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Research Square 2021年

作者： Soltani, Soheil Ojaghi, Ashkan Qiao, Hui Kaza, Nischita Li, Xinyang Dai, Qionghai Osunkoya, Adeboye O. Robles, Francisco E. Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Automation Tsinghua University Beijing100084 China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30313 United States Departments of Pathology and Urology Emory University School of Medicine AtlantaGA30322 United States Winship Cancer Institute of Emory University AtlantaGA30322 United States

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or "optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel "optical stains" with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis. © 2021, CC BY.

关键词： Grading

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Correction to: Electrospun cellulose Nano fibril reinforced PLA/PBS composite scaffold for vascular tissue engineering

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Journal of Polymer Research 2022年第9期29卷 1-1页

作者： Abdullah, Turdimuhammad Saeed, Usman Memic, Adnan Gauthaman, Kalamegam Hussain, Mohammad Asif Al-Turaif, Hamad Department of Chemical and Materials Engineering Faculty of Engineering King Abdul Aziz University Jeddah Saudi Arabia Center of Nanotechnology King Abdul Aziz University Jeddah Saudi Arabia Center of Excellence in Genomic Medicine Research King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering (Biomedical Engineering Option) King Abdulaziz University Jeddah Saudi Arabia

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Theranostics with radiation-induced ultrasound emission(TRUE)

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Journal of Innovative Optical Health Sciences 2018年第3期11卷 1-7页

作者： Elijah Robertson Liangzhong Xiang School of Electrical and Computer Engineering Institute for Biomedical Engineering Science and Technology University of OklahomaNormanOK 73019USA Stephenson Cancer Center at the University of Oklahoma NormanOK 73019USA

Two novel ultrasound imaging techniques with imaging contrast mechanisms are in the works:X-ray-induced acoustic computed tomography(XACT),and nanoscale photoacoustic tomogra-phy(nPAT).XACT has incredible potential in:(1)biomedical imaging,through which a 3D image can be generated using only a single X-ray projection,and(2)radiation *** a new alternative of super-resolution microscopy can break through the optical difraction limitand is capable of exploring sub-celular structures without reliance on fuorescence *** expect these new imaging techniques to find widespread applications in both pre-clinical andclinical biomedical research.

关键词： X-ray-induced_acoustic computed,tomography(XACT) low-dose,CT three-dimensional(3D)volumetric imaging nanoscale photoacoustic tomography label-fre super-resolution

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Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment

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Brain Informatics 2020年第1期7卷 19页

作者： Rangaprakash, D. Odemuyiwa, Toluwanimi Narayana Dutt, D. Deshpande, Gopikrishna Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States Division of Health Sciences and Technology Harvard University and Massachusetts Institute of Technology CambridgeMA United States Division of Engineering Science Faculty of Applied Science & Engineering University of Toronto TorontoON Canada Department of Electrical Communication Engineering Indian Institute of Science Bangalore India AU MRI Research Center Department of Electrical and Computer Engineering Auburn University 560 Devall Dr Suite 266D AuburnAL36849 United States Department of Psychological Sciences Auburn University AuburnAL United States Alabama Advanced Imaging Consortium University of Alabama Birmingham Alabama United States Center for Health Ecology and Equity Research Auburn University AuburnAL United States Center for Neuroscience Auburn University AuburnAL United States School of Psychology Capital Normal University Beijing China Key Laboratory for Learning and Cognition Capital Normal University Beijing China Department of Psychiatry National Institute of Mental Health and Neurosciences Bangalore India

Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity. © 2020, The Author(s).

关键词： Magnetic resonance imaging

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IMAGING DYNAMICS BENEATH TURBID MEDIA VIA PARALLELIZED SINGLE-PHOTON DETECTION

arXiv

引用

arXiv 2021年

作者： Xu, Shiqi Yang, Xi Liu, Wenhui Jonsson, Joakim Qian, Ruobing Chandra Konda, Pavan Zhou, Kevin C. Kreiss, Lucas Dai, Qionghai Wang, Haoqian Berrocal, Edouard Horstmeyer, Roarke Department of Biomedical Engineering Duke University DurhamNC27708 United States Department of Automation Tsinghua University Beijing100084 China Division of Combustion Physics Department of Physics Lund University 22100 Sweden Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, we utilize a single-photon avalanche diode (SPAD) array camera to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. We then apply a deep neural network to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. We demonstrate the ability to reconstruct images of transient (0.1-0.4s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution, and highlight how our model can flexibly extend to monitor flow speed within buried phantom vessels. © 2021, CC BY.

关键词： Dynamics

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Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

Spectroscopic detection of pigments in tissues: correlation ...

引用

International Conference on Laser Optics

作者： L. M. Oliveira T. M. Gonç alves A. R. Botelho I. S. Martins H. F. Silva I. Carneiro S. Carvalho R. Henrique V. V. Tuchin ISEP Center of Innovation in Engineering and Industrial Technology Porto Portugal Physics Department Polytechnic of Porto - School of Engineering (ISEP) Porto Portugal Department of Electrical and Computer Engineering Porto University - School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Dept. of Pathological Cytological and Thanatological Anatomy Polytechnic of Porto - School of Health (ESS) Porto Portugal Deptartment of Pathology Santa Luzia Hospital (ULSAM) Viana do Castelo Portugal Dept. of Pat hol. and Molecular Immunology Porto University - Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control FRC &#x2018 Saratov Scientific Centre&#x2019 of the Russian Academy of Sciences Saratov Russia

ISBN: (数字)9781665466646

ISBN: (纸本)9781665466653

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans.

关键词： Rabbits Absorption Neurons Lasers Aging Pigments Optics

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Authors' Responses to Peer Reviews of “Supporting Technologies for COVID-19 Prevention: Systemized Review”

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JMIRx Med 2022年第2期3卷

作者： Zhuo Zhao Rui Li Yangmyung Ma Iman Islam Abdul M Azam Rajper WenZhan Song Hongliang Ren Zion Tsz Ho Tse School of Electrical and Computer Engineering University of Georgia Athens GA United States Tandon School of Engineering New York University Brooklyn NY United States Hull York Medical School University of York Heslington York United Kingdom Department of Computer Science University of Georgia Athens GA United States Department of Biomedical Engineering National University of Singapore Singapore Singapore Department of Electronic Engineering University of York York United Kingdom

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