检索结果-内蒙古大学图书馆

IEEE/RAS-EMBS International Conference on biomedical Robotics and Biomechatronics (BioRob)

作者： Ali Marjaninejad Francisco J. Valero-Cuevas Biomedical Engineering Department University of Southern California Electrical and Computer Engineering Department Division of Biokinesiology and Physical Therapy University of Southern California

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Robots will achieve true autonomy in controlling their bodies and interacting with their environments only when they are able to learn within the constraints of their designs or environments with minimal to no prior information and can learn from and adapt to new tasks and conditions without experiencing catastrophic forgetting of prior ones. The autonomous learning of control is of an especial important in the case of tendon-driven robots, which offer a wide set of advantages such as flexibility in the placement of actuators, but also introduce further challenges to its modeling and control. Here, we have extended our bio-inspired learning algorithm, General-to-Particular, to a quadruped system and have shown its aptitude in learning to control the system within only a few seconds and performing functional movements with continuous learning. Most importantly, we have successfully utilized simple tactile sensory information to enable the system to distinguish between changes in the amount of load it carries and achieve lifelong-learning without catastrophic forgetting (continually learn without overwriting the previous skills).

关键词： Actuators Biological system modeling Neural networks Kinematics Biomechatronics Robot sensing systems Motors Control systems Quadrupedal robots Load modeling

来源：评论

学校读者我要写书评

暂无评论

Transabdominal fetal signals measurement using fiber-based frequency-modulated continuous-wave near-infrared spectroscopy in a fetal lamb model

Transabdominal fetal signals measurement using fiber-based f...

引用

Bio-Optics: Design and Application, BODA 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Liu, Shing-Jiuan Lee, Su Yeon Pivetti, Christopher Kulubya, Edwin Wang, Aijun Farmer, Diana L. Ghiasi, Soheil Yang, Weijian Department of Electrical and Computer Engineering University of California at Davis DavisCA95616 United States Department of Surgery University of California at Davis SacramentoCA95817 United States Department of Biomedical Engineering University of California at Davis DavisCA95616 United States

We propose a novel transabdominal fetal pulse oximetry using frequency-modulated continuous-wave near-infrared spectroscopy. By measuring the time-resolved reflectance of light shined on pregnant sheep’s abdomen, we ... 详细信息

ISBN: (纸本)9781957171210

关键词： Infrared devices

来源：评论

学校读者我要写书评

暂无评论

Single Capture Quantitative Phase Imaging with Tomographic Sectioning 9

Single Capture Quantitative Phase Imaging with Tomographic S...

引用

Quantitative Phase Imaging IX 2023

作者： Costa, Paloma Casteleiro Guang, Zhe Kaza, Nischita Robles, Francisco E. School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States

ISBN: (纸本)9781510658837

Quantitative Phase Imaging (QPI) has become a mainstay imaging technique in the biomedical sciences to study cells and other biological processes. Traditional QPI techniques are transmission-based and, thus, limited to thin samples. Over the past few years, multiple 3D QPI tools have emerged attempting to overcome this limitation and provide cross-sectional phase information of thicker samples. However, most of these techniques remain transmission-based, which constrains their ability to image samples thicker than a few mean free scattering lengths. Recently, we have developed quantitative oblique back-illumination microscopy (qOBM) as an epi-mode technique that enables label-free quantitative phase imaging of thick samples with tomographic cross-sectioning. Like in most 3D QPI instances, qOBM requires multiple captures to render a quantitative phase image. Specifically, qOBM requires four raw captures, obtained by illuminating the sample obliquely from four different directions, to reconstruct the quantitative phase. This muti-capture scheme hinders qOBM’s ability to investigate valuable fast dynamic processes, such as blood flow, as well as its usability in some in-vivo applications. Here, we present a deep-learning enabled single-capture version of qOBM that quadruples the system’s imaging speed and prevents motion artifacts. To this end, we have trained a U-Net GAN to learn the qOBM reconstruction from a single capture obtained with oblique illumination. We show the capabilities and limitations of this approach, as well as some of the novel applications that this system enables, such as in-vivo high-resolution non-invasive blood flow quantitative phase imaging. © 2023 SPIE.

关键词： Computational Imaging

来源：评论

学校读者我要写书评

暂无评论

Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography

引用

光：科学与应用（英文版） 2025年第2期14卷 490-508页

作者： Shiyi Cheng Shuaibin Chang Yunzhe Li Anna Novoseltseva Sunni Lin Yicun Wu Jiahui Zhu Ann C.McKee Douglas L.Rosene Hui Wang Irving J.Bigio David A.Boas Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Department of Computer Science Boston UniversityBostonMA 02215USA Boston University Alzheimer's Disease Research Center and CTE Center Boston University School of MedicineBostonMA 02118USA Department of Neurology Boston University School of MedicineBostonMA 02118USA VA Boston Healthcare System U.S.Department of Veteran AffairsBostonMA 02130USA Department of Pathology and Laboratory Medicine Boston University School of MedicineBostonMA 02118USA Department of Anatomy & Neurobiology Boston University School of MedicineBostonMAUSA Department of Radiology Athinoula A.Martinos Center for Biomedical ImagingMassachusetts General HospitalBostonMA 02129USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Neurophotonics Center Boston UniversityBostonMA 02215USA

A major challenge in neuroscience is visualizing the structure of the human brain at different *** histology reveals micro-and meso-scale brain features but suffers from staining variability,tissue damage,and distortion,which impedes accurate 3D *** emerging label-free serial sectioning optical coherence tomography(S-OCT)technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical ***,we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining(DS)*** enhanced imaging modality integrates high-throughput 3D imaging,low sample variability and high interpretability,making it suitable for 3D histology *** develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver *** demonstrate DS on various human cerebral cortex samples,achieving consistent staining quality and enhancing contrast across cortical layer ***,we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks,allowing for visualization of meso-scale vessel networks in the white *** believe that our technique has the potential for high-throughput,multiscale imaging of brain tissues and may facilitate studies of brain structures.

关键词： tomography serial imaging digital human brain coherence enhanced multiscale optical

来源：评论

学校读者我要写书评

暂无评论

Fully-automated Alveolar Bone Level Measurements in Adolescents via Landmark Localization in Intraoral Ultrasound Videos

Fully-automated Alveolar Bone Level Measurements in Adolesce...

引用

2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Kumaralingam, Logiraj Dinh, Hoang B. V. Nguyen, Kim-Cuong T. Punithakumar, Kumaradevan Kaipatur, Neelambar R. Lou, Edmond H. M. Major, Paul W. Le, Lawrence H. University of Alberta Department of Electrical and Computer Engineering EdmontonAB Canada University of Alberta Department of Radiology and Diagnostic Imaging EdmontonAB Canada University of Alberta School of Dentistry EdmontonAB Canada University of Alberta Department of Biomedical Engineering EdmontonAB Canada

ISBN: (纸本)9798350346459

Accurate and automatic assessment of alveolar bone level in ultrasound videos is crucial for orthodontic treatment and diagnosis, as manual interpretation is time-consuming and clinicians exhibit substantial interobserver variation. A systematic approach for quantifying alveolar bone loss involves the direct measurements of the alveolar bone level (ABL), the distance between the cementoenamel junction and alveolar bone crest. In this paper, we propose an end-to-end landmark localizing network by combining a convolutional neural network and Swin-transformer architecture to effectively localize the cementoenamel junction and alveolar bone crest in intraoral ultrasound videos and automatically measure the ABL. In addition, key frames and non-key frames are identified and discarded based on an uncertainty prior. This study used ultrasound videos of 147 teeth acquired from 20 orthodontic adolescent patients. Experimental studies have been performed and compared with state-of-the-art models to prove the feasibility of the proposed architecture. © 2023 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

引用

computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

来源：评论

学校读者我要写书评

暂无评论

Wireless Beamforming for Electromagnetic Field Focusing in Brain Tissue

Wireless Beamforming for Electromagnetic Field Focusing in B...

引用

European Conference on Microwave

作者： Woojun Lee Pouya Faeghi Alan D. Dorval Jeffrey S. Walling Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University USA Department of Biomedical Engineering University of Utah USA

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

This work explores the potential of wireless deep brain stimulation (DBS) through the use of a phased antenna array in a hemisphere configuration to focus electromagnetic fields into targeted zones within the brain. This study introduces various optimization methods to enhance the focality and effectiveness of wireless stimulation by adjusting electromagnetic fields emitted from a hemispherically deployed phased array with both a computer program and a commercial electromagnetic solver. By varying frequencies and configurations, this work provides insights into the interaction between electromagnetic waves and a human head. Simulation results demonstrate the enhanced performance of the newly proposed optimization methods in focusing electric fields within the brain without densely populated sampling points.

关键词： Wireless communication Phased arrays Nonuniform electric fields Field emitter arrays Simulation Focusing Optimization methods Surgery Europe Microwave theory and techniques

来源：评论

学校读者我要写书评

暂无评论

Fully Unsupervised Deep-Learning with Dimension Expansion for Nanophotonic Design

Fully Unsupervised Deep-Learning with Dimension Expansion fo...

引用

2024 Frontiers in Optics, FiO 2024

作者： Huang, Shuo Torfeh, Mahsa Shi, Yonggang Hsu, Chia Wei Stevens Neuroimaging and Informatics Institute University of Southern California Los AngelesCA90033 United States Department of Biomedical Engineering University of Southern California Los AngelesCA90089 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

We propose a fully unsupervised deep-learning approach using dimension expansion for nanophotonic design. The neural network generates binary broadband integrated Y-splitters with average efficiency 98% and splitting error 2%, without any pre-generated dataset. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词：

来源：评论

学校读者我要写书评

暂无评论

DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance 11

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

引用

11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Rando Setiawan, Noor Akhmad Permanasari, Adhistya Erna Rulaningtyas, Riries Suksmono, Andriyan Bayu Sitanggang, Imas Sukaesih Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Biomedical Engineering Program Dept. of Physics Universitas Airlangga Surabaya Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Ipb University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781665460309

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%. © 2022 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Weak and Extremely Low Frequency Magnetic Fields Alter the Growth Rates of HT-1080 Fibrosarcoma

Weak and Extremely Low Frequency Magnetic Fields Alter the G...

引用

US National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)

作者： Nhat Dang Jason Keller Marek Bajtos Hakki Gurhan Frank Barnes Department of Electrical Computer and Energy Engineering College of Engineering and Applied Science University of Colorado Boulder Boulder Colorado USA Department of Electromagnetics and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (数字)9781946815200

ISBN: (纸本)9798331513979

This study presents experimental results showing that weak, extremely low frequency electromagnetic (EM) fields influence the growth rates of HT-1080 fibrosarcoma cells in vitro and offers a theoretical explanation for these effects. The results demonstrate that weak magnetic fields both increase and decrease the growth rates of fibrosarcoma cells by altering the mitochondrial superoxide $(\mathrm{O}^{2-})$ and calcium ions $(\text{Ca}^{2+})$ concentrations in living cells. Over a 4-day exposure period, fibrosarcoma growth rates were modulated in the presence of low-level alternating current (AC) magnetic fields at a $10 \mu \mathrm{T}$ amplitude at 13.5 Hz to 30 Hz frequency, superimposed on a $45 \mu \mathrm{T}$ static background field. Effects due to changes in amplitude from 10 to $30 \mu \mathrm{T}$ at selected frequencies are also investigated with the same superimposed static field.

关键词： Frequency modulation Calcium Ions Magnetic fields In vitro Electromagnetics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：