检索结果-内蒙古大学图书馆

Portable Multiparameter biomedical Sensor Fusion for Home Healthcare 2

Portable Multiparameter Biomedical Sensor Fusion for Home He...

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Vidyasagar, K.E.Ch Manchikanti, Venkata Jaideep Datta Kanduri, Harika Saikia, Manob Jyoti University of Memphis Biomedical Sensors and Systems Lab MemphisTN38152 United States Osmania University Department of Biomedical Engineering Hyderabad500007 India University of Memphis Electrical and Computer Engineering Department MemphisTN38152 United States

ISBN: (纸本)9798350366570

The ever-increasing focus on preventative healthcare and remote patient monitoring has driven the need for innovative solutions. Traditionally, measuring vital signs in both clinical and home settings has relied on a collection of individual devices, often leading to cluttered setups, patient discomfort, and potential inaccuracies due to user error. This paper proposes a multi-parameter acquisition system designed to address these limitations. The system integrates the measurement of essential parameters into a single, user-friendly device. This streamlined approach not only reduces measurement complexity but also improves patient comfort by eliminating the need for multiple devices and cumbersome wires. The design of the system, including its sensors, hardware components, and data processing unit is presented in detail. The device serves a dual purpose. Firstly, it empowers individuals at home by providing them with a single device to monitor their vital signs. Secondly, for hospitals, the device offers a streamlined and less cumbersome way to visualize essential patient parameters, allowing for improved monitoring efficiency. © 2024 IEEE.

关键词： Remote patient monitoring

来源：评论

学校读者我要写书评

暂无评论

De-convolution and De-noising of SAR Based GPS Images Using Hybrid Particle Swarm Optimization

引用

Chinese Journal of Electronics 2023年第1期32卷 166-176页

作者： Rizwan Sadiq Muhammad Bilal Qureshi Muhammad Mohsin Khan Dept.of Electrical and Electronic Engineering Koc University Dept.of Electrical and Computer Engineering COMSATS University Dept.of Radiology and Biomedical Imaging Yale University Sino-Pak Center for Artificial Intelligence (SPCAI) Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology (PAF-IAST)

Synthetic aperture radar(SAR) imaging is an efficient strategy which exploits the properties of microwaves to capture images. A major concern in SAR imaging is the reconstruction of image from back scattered signals in the presence of noise. The reflected signal consist of more noise than the target signal and it is a challenging problem to reduce the noise in the collected signal for better reconstruction of an image. Current studies mostly focus on filtering techniques for noise removal. This can result in an undesirable point spread function causing extreme smearing effect in the desired image. In order to handle this problem, a computational technique, particle swarm optimization(PSO) is used for de-noising purpose and later the target performance is further improved by an amalgamation of Wiener ***, to improve the de-noising performance we have exploited the singular value decomposition based morphological filtering. To justify the proposed improvements we have simulated the proposed techniques and results are compared with the conventional existing models. The proposed method revealed considerable decrease in mean square error compared to Wiener filter and PSO techniques. Quantitative analysis of image restoration quality are also presented in comparison with Wiener filter and PSO based on the improvement in signal to noise ratio and peak signal to noise ratio.

关键词： Radar remote sensing Wiener filters Filtering Statistical analysis Radar polarimetry Particle swarm optimization Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

引用

Iran Journal of computer Science 2023年第4期6卷 387-396页

作者： Saad, Mohamed N. Zareef, Galena W. Ibrahim, Fatma S. Said, Ashraf M. Hamed, Hisham F. A. Biomedical Engineering Department Faculty of Engineering Minia University Minia Egypt Biomedical Engineering Department Misr University for Science and Technology 6th of October City Egypt Bioinformatics Department Faculty of Computer and Information Minia University Minia Egypt Computer and Software Engineering Department Misr University for Science and Technology 6th of October City Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Electrical and Electronics Engineering Department Faculty of Engineering Minia University Minia Egypt

This article provides a detailed description, analysis, and visualization of a case–control genome-wide genotypic dataset from the North American Rheumatoid Arthritis Consortium (NARAC). The data is presented in terms of the number of females and males in both cases and controls, as well as the percentage of missing data. The number of alleles and genotypes is also counted, and the minor allele frequency (MAF) is calculated for each single nucleotide polymorphism (SNP). The data is further classified into four categories based on the SNP's MAF, namely, very rare, rare, low frequency, and common SNPs. The regions of these categories in the chromosome are investigated to determine the proportion of SNPs in coding locations and other regions. It is observed that each category has a different proportion in each region of consequence annotation. The data composition in terms of alleles and genotypes is found to be greatly disproportionate. The results present clear insights into the data and its MAF, which can be compared with other datasets. These findings can aid researchers in gaining a comprehensive understanding of such case–control datasets and bring accurate insights into the data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Genome-wide association study Genome-wide exploratory analysis Minor allele frequency Single nucleotide polymorphism Variant annotation

来源：评论

学校读者我要写书评

暂无评论

Gamified Rehabilitation and Physiotherapy Using MediaPipe for Posture and Hand Gesture Recognition 2

Gamified Rehabilitation and Physiotherapy Using MediaPipe fo...

引用

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Vidyasagar, K.E. Ch Sai, G. N. K. Anantha Ruchita, Munagala Saikia, Manob Jyoti University of Memphis Biomedical Sensors and Systems Lab MemphisTN38152 United States Osmania University Department of Biomedical Engineering Hyderabad500007 India University of Memphis Electrical and Computer Engineering Department MemphisTN38152 United States

ISBN: (纸本)9798350366570

Introduction: Rehabilitation for conditions affecting mobility, such as stroke, muscle atrophy, and others, poses significant challenges in traditional physiotherapy practices. Engaging patients in enjoyable exercises is crucial for maintaining compliance with rehabilitation protocols. In response, this paper introduces a pioneering game-based approach to rehabilitation, leveraging posture detection technology. Materials and Methods: A game-based rehabilitation system was developed utilizing deep learning techniques through the MediaPipe framework for posture and hand gesture recognition. The system analyzes user posture in real time via a webcam and translates these movements into game controls, allowing patients to interact with popular games like Subway Surfers to simulate physical activities such as running and jumping. Latency between patient movements and in-game responses was measured across three hardware configurations (8GB, 12GB, and 16GB RAM) to evaluate system performance and responsiveness. Results and Discussion: Latency decreased with higher RAM, with the lowest measured at 430 ms on a 16GB configuration. This real-time responsiveness enhances patient engagement, making the rehabilitation process more immersive and effective. By leveraging posture detection technology, this innovative system bridges the gap between the necessity of rehabilitation exercises and the appeal of gaming, offering a promising solution for improving treatment outcomes across diverse age groups and conditions. © 2024 IEEE.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

Transi-Net: An Explainable Deep Learning Model Ensemble For Prostate's Transition Zone Segmentation 23

Transi-Net: An Explainable Deep Learning Model Ensemble For ...

引用

23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos S. Kalantzopoulos, Charalampos Marias, Kostas Tsiknakis, Manolis Koutsouris, Dimitrios D. Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical & Computer Engineering Biomedical Research Institute National Technical University of Athens FORTH Athens Ioannina Greece Biomedical Research Institute FORTH Ioannina Greece Computational Biomedicine Laboratory FORTH Heraklion Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Biomedical Research Institute Unit of Medical Technology and Intelligent Information Systems FORTH University of Ioannina Ioannina Greece

ISBN: (纸本)9798350393118

The identification of the location of prostate cancer is of paramount importance for improved treatment. This process is strictly bonded with the accurate segmentation of the prostate gland and its zones, on MR images. In the present study, an ensemble of 3 deep learning models along with a Meta-learner module able to refine the outcomes of the models, is proposed (Transi-Net) to segment the prostate's transition zone. A method to quantify the model's uncertainty is introduced to measure the confidence of an architecture with respect to its final decision. The backbone of Transi-Net consist the original U-net, Dense2U-net and Bridged U-net models. The proposed model showcased significant improvement in comparison with its base components as well as an independent model, the USE-Net, while it was proven more confident about its decision. The proposed model resulted in an improvement of 5%, 3%, 3% and 4% for Sensitivity, Balanced Accuracy, Dice Score and Rand Error Index respectively, compared to the second best, USE-Net. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

UNETVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTM 22

UNETVL: Enhancing 3D Medical Image Segmentation with Chebysh...

引用

22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Guo, Xuhui Dam, Tanmoy Dhamdhere, Rohan Modanwal, Gourav Madabhushi, Anant University of Michigan Electrical Engineering and Computer Science Department Ann Arbor United States Georgia Institute of Technology Emory University Wallace H. Coulter Department of Biomedical Engineering Atlanta United States

ISBN: (纸本)9798331520526

3D medical image segmentation has progressed considerably due to Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), yet these methods struggle to balance long-range dependency acquisition with computational efficiency. To address this challenge, we propose UNETVL (U-Net Vision-LSTM), a novel architecture that leverages recent advancements in temporal information processing. UNETVL incorporates Vision-LSTM (ViL) for improved scalability and memory functions, alongside an efficient Chebyshev Kolmogorov-Arnold Networks (KAN) to handle complex and long-range dependency patterns more effectively. We validated our method on the ACDC and AMOS2022 (post challenge Task 2) benchmark datasets, showing a significant improvement in mean Dice score compared to recent state-of-the-art approaches, especially over its predecessor, UNETR, with increases of 7.3% on ACDC and 15.6% on AMOS, respectively. Extensive ablation studies were conducted to demonstrate the impact of each component in UNETVL, providing a comprehensive understanding of its architecture. Our code is available at https://***/tgrex6/UNETVL, facilitating further research and applications in this domain. © 2025 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

引用

IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

引用

biomedical engineering Frontiers 2022年第1期3卷 31-47页

作者： Lingyi Zhao Muyinatu A.Lediju Bell Department of Electrical and Computer Engineering Johns Hopkins UniversityBaltimoreUSA Department of Computer Science Johns Hopkins UniversityBaltimoreUSA Department of Biomedical Engineering Johns Hopkins UniversityBaltimoreUSA

The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore,understand,and develop new techniques for diagnosis and *** lung ultrasound imaging is a less established approach when compared to other medical imaging modalities such as X-ray and CT,multiple studies have demonstrated its promise to diagnose COVID-19 *** the same time,many deep learning models have been built to improve the diagnostic efficiency of medical *** integration of these initially parallel efforts has led multiple researchers to report deep learning applications in medical imaging of COVID-19 patients,most of which demonstrate the outstanding potential of deep learning to aid in the diagnosis of *** invited review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition,associated datasets,deep learning models,and comparative performance.

关键词： diagnosis lung Lung

来源：评论

学校读者我要写书评

暂无评论

Hand Movements Detection Using EMG Signals for Human-computer Interface and convolution neural network 20

Hand Movements Detection Using EMG Signals for Human-Compute...

引用

20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nazari, Jamal Nosheri, Azar Ghamangiz Mousavi, Seyed Amirhossein Islamic Azad University Qazvin Branch Department of Electrical Engineering and Mechatronics Engineering Qazvin Iran Islamic Azad University Mashhad Branch Department of Computer Engineering Mashhad Iran Islamic Azad University Mashhad Branch Department of Biomedical Engineering Mashhad Iran

ISBN: (纸本)9798350383942

This article presents a study on the effectiveness of a Convolutional Neural Network -based approach for hand movement detection using electromyography (EMG) signals. The study involved 40 participants and used Myo armbands to record EMG signals. The CNN model consisted of three convolutional layers, three max-pooling layers, and two fully connected layers. The model achieved an F1-score of 0.981, precision of 0.981, and recall of 0.981, outperforming two other machine learning algorithms, SVM and RF. The study's results demonstrate the potential of using CNNs for detecting hand movements using EMG signals, which can be applied in prosthetics and robotics for controlling prosthetic limbs and robotic arms. The study's findings suggest that CNN-based approaches have great potential for future research in the field of EMG-based hand movement detection. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Optimized Substrate Integrated Waveguide (SIW) Antenna for Wearable Health Monitoring Applications 5

Optimized Substrate Integrated Waveguide (SIW) Antenna for W...

引用

5th International Conference on Smart Sensors and Application, ICSSA 2024

作者： Saranya, S. Sharmila, B. Shashank Kanna, R. Nesasudha, M. Karthikeyan, T.A. Department of Electronics and communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Biomedical Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electrical Engineering and Computer Science Monash University Malaysia Department of ECE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354294

Around 50 million people in the world are affected by chronic seizures and epilepsy. Seizures are usually diagnosed by CT scans, MRI scans, EEGs, and blood tests. However, there is a need for devices that help people to detect and receive alerts before the onset of a seizure episode. This paper addresses the need to design a seizure detection device that works on the principle of changing muscle permittivity. As chronic seizures can be life-threatening, there is a need for devices that detect seizures before their onset, to help a person take precautionary measures. Though there are a few devices that work for this application, there is always room for improvement and innovation. Therefore, we have proposed a novel microstrip patch antenna that acts as a miniaturised biosensor. One of the major symptoms of seizure is the change in muscle stiffness. Considering this factor, our antenna is designed to detect the change in the permittivity caused due to the change in muscle tension which is reflected through the S parameter graph that gives us the information to detect a seizure attack beforehand. The return loss of the antenna is improved - 27.596 dB by introducing the concept of substrate integrated waveguides. The antenna will be integrated into an embedded system design to enable it to function as a wearable device. Also, the SAR properties are analysed and optimized as per the FCC standards as it would be modelled as a wearable device. The antenna has been designed, simulated, and analysed using the ANSYS HFSS tool. Further, a comparative study on the various patch shapes, substrate materials, and SIWs was also made using ANSYS HFSS. © 2024 IEEE.

关键词： Wearable antennas

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：