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A quantitative classification of essential and Parkinson's tremor using wavelet transform and artificial neural network on sEMG and accelerometer signals

A quantitative classification of essential and Parkinson's t...

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IEEE International Conference on Networking, Sensing and Control

作者： Santosh Kumar Nanda Wen-Yen Lin Ming-Yih Lee Rou-Shayn Chen PhD Program in Biomedical Engineering Chang Gung University Taiwan Tao-Yuan Taiwan Graduate Institute of Medical Mechatronics Chang Gung University Taiwan Tao-Yuan Taiwan Department of Electrical Engineering Chang Gung University Taiwan Tao-Yuan Taiwan Department of Neurology Chang Gung Memorial Hospital Taiwan Tao-Yuan Taiwan

Correct discrimination of essential tremor from Parkinson's tremor is a major problem in clinical neurology as minor differences in the tremor patterns are hard to distinguish. Mathematical analysis of tremor signals recorded non-invasively has been widely accepted for tremor differentiation. However, classification of tremor signals collected from electromyograph or accelerometer, based on time and frequency domain techniques has limited accuracy because of overlapping frequency range and non-stationary nature of those signals. This paper describes a simple, non-invasive decision making logic method for discrimination of tremor. Wavelet transform based feature extraction technique in combination with feed forward type artificial neural network is proposed. Fractal dimensions of wavelet features of the decomposed detailed coefficients are used as the feature matrix. The neural network classified the tremor sEMG signals with 91.66% accuracy and 100% in case of accelerometer signals. Although, the classification accuracy of sEMG signal is comparable to that of accelerometer but the localized involuntary vibratory nature of tremor at the extremities of human body puts accelerometer as a better option in cases where tremor fails to excite the muscle. This proposed classification algorithm adds strength to the non-invasive signal detection methods at reduced cost and higher sensitivity.

关键词： Accelerometers Artificial neural networks Fractals Wavelet analysis Wavelet transforms Accuracy

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A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh Graduate Degree Program of Robotics National Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an NDT-based radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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Impedance measurement of normal and cancerous human breast cells using a microfluidic tunnel

Impedance measurement of normal and cancerous human breast c...

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IEEE SENSORS

作者： Giseok Kang Sung-Kil Lee Sung Keun Yoo Sung Yang Jong-Hyun Lee Graduate-program of Medical System Engineering Gwangju Institute of Science and Technology Gwangju South Korea School of Information and Mechatronics Gwangju Institute of Science and Technology Gwangju South Korea Department of Nanobio Materials and Electronics Gwangju Institute of Science and Technology Gwangju South Korea Gwangju Institute of Science and Technology Gwangju KR

In this paper, a novel microelectrical impedance spectroscopy (μEIS) with three-dimensional interdigitated electrodes (3D-IDE) is developed to differentiate normal and cancerous cells. The device utilizes a microfluidic tunnel structure, which forces cells to be squeezed. Thus, the enlarged contact area between cells and electrodes allows the device to measure the intrinsic electrical signal of the cells with a higher sensitivity than a noncontact case. The cell squeezing is realized by fabricating smaller microfluidic channel than cell size. The electrical impedances are measured through 3D-IDE. The device well distinguishes normal human breast cell (MCF-10A) and early-stage human breast cancer cell (MCF-7) with a phase difference of 1.42° at 500 kHz. The proposed device also features a high repeatability because the phase change is as small as 0.27° (which is sufficiently smaller than the phase difference between normal and cancer cell) before and after the each cell assay.

关键词： Impedance Electrodes Microfluidics Impedance measurement Breast cancer

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A Multi-hypothesis Sequential Probability Test for Partial Discharges Localization in Power Transformers

A Multi-hypothesis Sequential Probability Test for Partial D...

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International Symposium on mechatronics and its Applications

作者： Wasim M. F. Al-Masri Mamoun F. Abdel-Hafez Ayman El-Hag Mechatronics Engineering Graduate Program American University of Sharjah Sharjah UAE Department of Mechanical Engineering American University of Sharjah Sharjah UAE Department of Electrical Engineering American University of Sharjah Sharjah UAE

ISBN: (纸本)9781467377980

We present a new method to accurately locate partial discharge by using a sequential fault detection and identification (FDI) algorithm for detecting a bias fault in the measurements of partial discharge in transformer insulation system using acoustic signals. In this paper, a novel technique is proposed to identify the possibility of measurement errors generated from acoustic emission sensors during partial discharge localization inside a transformer tank. The technique probabilistically detects and identifies possible bias on the sensors' measurement. This bias is possibly caused by sensor's fault, sensor's aging, or proximity of the PD to a certain sensor in comparison to other sensors. Correct detection of sensors' measurement bias enables high accuracy PD localization as will be demonstrated in this paper. The accuracy and convergence characteristics of the proposed algorithm are verified in a simulation environment. This study is tremendously important for scheduling and starting maintenance/repair actions cost and time efficiently or to perform a risk analysis.

关键词： PD FDI Power Transformers

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GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection

GNN-Based Point Cloud Maps Feature Extraction and Residual F...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei-Hsiang Liao Chieh-Chih Wang Wen-Chieh Lin Graduate Degree Program of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be beneficial to predetermine where targets are more or less likely to appear. Compared with semantic maps, HD maps formed by point clouds provide much richer information from surrounding static objects and scenes. In this work, we construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Our work is based on PVRCNN, the state-of-the-art LiDAR object detection method. With point-wise and voxel-wise features obtained from PVRCNN, residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40–50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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Prediction of anterior cruciate ligament injury from MRI using deep learning

Prediction of anterior cruciate ligament injury from MRI usi...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Truong, Nguyen Khanh Hung Nguyen, Thuan Phuoc Kha, Quang Hien Le, Ngoc Hoang Le, Van Tuan Cao, Thi Luu, Ho Thanh Lam Le, Nguyen Quoc Khanh Kang, Jiunn-Horng International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Orthopedic and Trauma Cho Ray Hospital Ho Chi Minh city Viet Nam Mechatronics Engineering Ho Chi Minh City University of Technology Ho Chi Minh city Viet Nam Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan Orthopedic and Rehabilitation Department University of Medicine and Pharmacy at Ho Chi Minh city Ho Chi Minh city Viet Nam Children's Hospital 2 Ho Chi Minh City Viet Nam Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Knee injury emerges as one of the most common diseases, causing dislocation of knee joints, immobility, etc., in which the anterior cruciate ligament (ACL) injury is the most common one. The development of various artificial intelligence (AI) frameworks gained enormous attention in many areas, including injury prediction and health management via medical image analysis. The objective of the current study is to focus on a comprehensive high accurate prediction of ACL injury based on MRI medical images, and also demonstrate the ability of AI in practical and outline conceptual prediction and diagnosis frameworks for other types of knee injuries in the future. Our dataset comprised of knee MRI reports from Cho Ray Hospital, Vietnam which are composed of ACL and non-ACL injury patients. The MRI images were used as supporting data in the deep learning classification model with DenseNet-121 algorithm. The successful establishment of an ACL injury diagnosis model from MRI will pave the way for us to develop more diagnostic models of other injuries in the body as well as the prediction of bone diseases. © 2021 SPIE.

关键词： Magnetic resonance imaging

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A Modified Radiopropagation Multipath Model for Constant Refractivity Gradient Profiles

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Journal of Microwaves, Optoelectronics and Electromagnetic Applications 2023年第2期22卷 298-312页

作者： Parada, D. Rego, C.G. Guevara, D. Navarro, A. Ramos, G.L. Oliveira, R. Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Av. Antônio Carlos 6627 MG Belo Horizonte Brazil Departament of Electronics Engineering Universidade Federal de Minas Gerais MG Belo HorizonteCEP 31270-901 Brazil Departament of Electricity and Electronics Universidad Francisco de Paula Santander Norte de Santander CúcutaCEP 540003 Colombia Departament of Information and Communication Technologies Universidad ICESI Cali Valle del CaucaCEP 760031 Colombia Departament of Telecommunications and Mechatronics Engineering Federal University of São João del-Rei MG Ouro BrancoCEP 36415-000 Brazil

This paper presents a radiopropagation algorithm based on a Ray Tracing (RT) technique that combines a modified multipath model for constant refractivity gradient profiles and the Uniform Theory of Diffraction (UTD). A novel formulation is proposed by the authors for calculation and ground-reflection analysis of ray paths depending on atmospheric refractivity. The algorithm introduced herein was evaluated in a mixed scenario and in two more realistic case studies, under conditions of constant refractivity gradient and lossy terrain profiles. Pathloss results are obtained and compared with Parabolic Equation (PE) numerical solution results at 2.0 GHz, 3.5 GHz and 5.4 GHz. In such conditions, the modified radiopropagation multipath algorithm with atmospheric refractivity introduced herein showed satisfactory results. © 2023 SBMO/SBMag.

关键词： Refraction

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Enhancement of Self-Trapped Exciton and Near-Infrared Emission in Bi3+/Er3+ Co-Doped Cs2Ag0.4Na0.6InCl6 Double Perovskite for Optical Telecommunications

SSRN

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SSRN 2022年

作者： Dave, Kashyap Huang, Wen-Tse Leśniewski, Tadeusz Lazarowska, Agata Grzegorczyk, Maciej Mahlik, Sebastian Leniec, Grzegorz Kaczmarek, Slawomir M. Liu, Ru-Shi Department of Chemistry National Taiwan University Taipei106 Taiwan Nanoscience and Technology Program Taiwan International Graduate Program Academia Sinica National Taiwan University Taipei115 Taiwan Institute of Experimental Physics Faculty of Mathematics Physics and Informatics University of Gdańsk Gdańsk80-308 Poland Department of Technical Physics Faculty of Mechanical Engineering and Mechatronics West Pomeranian University of Technology in Szczecin Piastow 48 Szczecin70-311 Poland Advanced Research Center of Green Materials Science and Technology National Taiwan University Taipei106 Taiwan

Erbium (Er) complexes are used as optical gain material for signal generation in the telecom C-band at 1540 nm, but they need a sensitizer to enhance absorption. Na+ substitution at Ag+ and Bi3+ doping at the In3+ site are possible strategies to enhance broadband emission in Cs2AgInCl6, which could be used as sensitizers to energy transfer into rare-earth elements. Herein, self-trapped exciton (STE) energy transfer to Er3+ at 1540 nm for low loss optical telecommunication applications is reported. An acid precipitation method is used to synthesize Cs2AgInCl6 and its derivative with Er3+, Bi3+, and Na+. Bare Cs2AgInCl6:Er emission signals are found to be weak at 1540 nm, but Bi3+ doping increased by 12 times, and Bi3+ and Na+ doping increased by up to 25 times signal intensity. Electron paramagnetic resonance spectroscopy characterized a decrease in axial symmetry over the Er3+ ion after substitutions of Na+ and Bi3+ in Cs2AgInCl6 at low temperature ( © 2022, The Authors. All rights reserved.

关键词： Bismuth compounds

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Near-Infrared I/II Nanophosphors with Cr3+/Ni2+ Energy Transfer for Bioimaging

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Advanced Optical Materials 2023年第15期11卷

作者： Satpathy, Aishwarya Huang, Wen-Tse Chan, Ming-Hsien Su, Ting-Yi Kamiński, Mikolaj Majewska, Natalia Mahlik, Sebastian Leniec, Grzegorz Kaczmarek, Slawomir M. Hsiao, Michael Liu, Ru-Shi Department of Chemistry National Taiwan University Taipei106 Taiwan Nanoscience and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Academia Road 128 Nankang Taipei115 Taiwan Genomics Research Center Academia Sinica Academia Road Section 2 Nankang Taipei115 Taiwan Institute of Experimental Physics Faculty of Mathematics Physics and Informatics University of Gdansk Wita Stwosza 57 Gdansk80–308 Poland Department of Technical Physics Faculty of Mechanical Engineering and Mechatronics West Pomeranian University of Technology in Szczecin al. Piastow 19 Szczecin70–310 Poland

In the biomedical field, the use of fluorescence imaging in the second near-infrared (NIR-II) region is growing rapidly because it imparts the advantages of reduced autofluorescence and low photon scattering. The advantage of reduced scattering is that it increases penetration depth in vivo and improves imaging clarity. Herein, this work uses mesoporous silica, a biocompatible template that can be easily modified, functionalized, and loaded with drugs for use in several bioapplications. The ZnGa2O4 spinel oxide system is integrated into mesoporous silica and different concentrations of Cr3+ and Ni2+ are loaded in octahedral sites to obtain the highest emission intensity in the NIR-II region at 1285 nm via energy transfer from Cr3+ to Ni2+. Given that only a few nanophosphor systems with emission in the NIR-II region are available, this work attempts to establish emission in the NIR-II and NIR-I regions to obtain images in vivo with an increased penetration depth to 5 mm and improved clarity for bioimaging purposes. This system will open the door for biomedical research on other NIR-II nanophosphors. © 2023 Wiley-VCH GmbH.

关键词： Bioimaging

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