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检索条件"机构=Robotics Engineering Program and Department of Computer Science"

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Sudden Cardiac Arrest Detection Based on Temporal Features of ECG Using Support Vector Machine Classifier

Sudden Cardiac Arrest Detection Based on Temporal Features o...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Banerjee, Prakash Bhattacherjee, Saptak Dasgupta, Kousik Department of Electrical and Electronics Engineering University of Engineering and Management Kolkata India School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Howrah Shibpur India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani India

ISBN: (纸本)9789811670107

Sudden cardiac arrest (SCA) is caused due to malfunctioning in heart rhythm, and the patient needs to be hospitalized as early as possible just after the onset of SCA;otherwise, it can lead to the death of the patient. Therefore, detection of SCA with a very high accuracy and within a very short time after the onset of symptoms is very crucial for patient survival. Several approaches for detection of SCA have been made using temporal and morphological variations in the electrocardiogram (ECG). However, the existing methods suffer from low accuracy or involve long-term monitoring of ECG. Therefore, in the present work, an intelligent algorithm to detect SCA based on the extraction of temporal features like RR interval and the QT interval from ECG and subsequently classifying them using support vector machine (SVM) has been proposed. Experimental evaluation of the proposed method on five SCA and five normal subjects demonstrates a high classification accuracy of 100% which proves the superiority of the proposed algorithm as an effective method for detection of SCA. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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A Convergent Approach of Mathematical Modelling and Deep Learning for Inverse Kinematics Solutions in Stewart Platform Parallel Robots

A Convergent Approach of Mathematical Modelling and Deep Lea...

引用

Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： M. Elseiagy A. El-Sayed M. Elsamanty Mechatronics and Robotics Department Egypt Japan University of Science and Technology Computer Science and Engineering Department Egypt Japan University of Science and Technology Mechanical Department Faculty of Engineering Benha University

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

The Stewart Platform, a sophisticated robotic mechanism known for its precision positioning and orientation in six degrees of freedom (6-DOF), is utilized as a testbed for the proposed solution. The unique design of the platform, combining translational and rotational movements, makes it suitable for applications that demand high accuracy and rapid position and orientation adjustments. It is widely used in sectors like flight simulators, animatronics, underwater research, and medical devices. This research presents an innovative solution for the inverse kinematics problem of the Stewart platform parallel robot by synergistically integrating mathematical modeling and deep learning techniques. The study introduces a fusion of fully connected neural networks and mathematical models, harnessing the computational prowess and adaptability of deep learning and the precision of established mathematical theories.

关键词： Deep learning Parallel robots Adaptation models Technological innovation Accuracy Computational modeling Neural networks

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SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm 12

SoC-Oriented Implementation of Machine Learning Based Breast...

引用

12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Saeed, Abdelrahman Tawfik, Ayman Mostafa, Hassan Khalil, Ahmed Hussein Cairo University Electronics and Communications Engineering Department Giza12613 Egypt Ajman University Department of Electrical and Computer Engineering United Arab Emirates University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza Egypt

ISBN: (纸本)9798350322910

Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU). © 2023 IEEE.

关键词： Convolutional neural networks

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Design Of A ‘ Drive Wave ‘ System based on Communications System Remote Real-time Running Text and Speech to Text Converter using Light Emitting Diode (LED) Display

Design Of A ‘ Drive Wave ‘ System based on Communications ...

引用

computer sciences, engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Juan Felix Christopher Sandy Setiawan Elioenai Sitepu Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Drive Wave is a device that can help drivers communicate with other road users by displaying familiar words and icons as well as a speech to text feature that can make it easier for drivers to communicate with other road users. in this work, a system design prototype consisting of two devices, namely Remote and LED message board. The most spoken words used and icons displayed on the LED message board are controlled wireless using a Remote using the ESP-NOW protocol and Bluetooth Low Energy (Bluetooth LE). The two ways of he LED message board controlled by the remote are manual and automatic speech to text converter system controlled the LED message board displayed. the manual way, the user pressed the button to choice the word to be displayed. in the automatic voiced control, the LED displayed the word uttered by the user. the automatic voiced controlled applied Mel-frequency cepstral coefficients (MFCC) as feature extraction techniques and Long Short-Term Memory (LSTM) method for word identification methods. The results showed that the automatic voice controlled 99.78% and 100% for training and validating accuracy, respectively. the real testing result showed that 24 times testing which have 100% accuracy.

关键词： Meters Pediatrics Roads Drives Light emitting diodes Mel frequency cepstral coefficient Long short term memory Speech to text Vehicles Testing

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Convolutional Neural Network Using Res-Net For Organic and Anorganic Waste Classification 4

Convolutional Neural Network Using Res-Net For Organic and A...

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4th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2022

作者： Girsang, Abba Suganda Yunanda, Rezky Syahputra, Muhammad Edo Peranginangin, Ezra Binus Graduate Program-Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399073

Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparameter which impacts taking much computation time. Genetic algorithm is one method metaheuristic which is robust for choosing the combinatorial possible hyperparameter. This model also uses ResNet-50 which is a pretrained model of convolutional neural network that consists 50 layers. By using a pretrained like ResNet-50, it will increase the performance model. CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly. The best performance model optimized using EGA outperformed the ResNet-50 model and the model optimized using GA and VLGA in classifying organic and inorganic materials. The accuracy value obtained from EGA is 97.53% with a loss of 0.08. © 2022 IEEE.

关键词： Convolutional neural networks

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Feasibility of Spectra-Based Quantification Algorithm for Non-Linear Photoacoustic Contrast Agents

Feasibility of Spectra-Based Quantification Algorithm for No...

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Photons Plus Ultrasound: Imaging and Sensing 2023

作者： Serebrennikova, Liudmila Gao, Shang Murakami, Ryo Boinapally, Srikanth Ray, Sangeeta Pomper, Martin G. Zhang, Haichong K. Biomedical Engineering Department Worcester Polytechnic Institute MA United States Robotics Engineering Department Worcester Polytechnic Institute MA United States Computer Science Department Worcester Polytechnic Institute MA United States Radiology and Radiological Science Johns Hopkins University School of Medicine ME United States

ISBN: (纸本)9781510658639

Photoacoustic (PA) imaging is a technique that visualizes the optical absorption characteristics with ultrasound-like spatial resolution. It has been demonstrated that molecular targeted contrast agents (CAs) have great potential in applying to the early detection of tumor. These CAs attach to the tissues of the subject of interest, i.e., the cancerous cells, and emit PA signals based on the absorbed light. Precise quantification of the CA expression is desired to identify the degree of the malignancies. The current quantification of the CA expression is based on the strength of the PA intensity with the assumption of its linearity between them. However, the estimation accuracy of such a method is limited because the PA intensity is affected by many factors including the light, sound, and tissue interactions and the use of CAs that do not present linearity. Here, we investigate a robust quantification method by using the spectroscopic relationship between the dye concentration and its corresponding PA variation. We introduce a spectroscopic decomposition algorithm considering multiple reference spectra to accommodate the non-linear behavior of sample concentrations. The in vitro validation of the concept was performed using the synthesized PA CA possessing the non-linear property. The concentration of the samples was successfully estimated with the algorithm. The introduced method improved the quantification accuracy by reducing the averaging estimation error from 15.89 μM to 1.80 μM, compared with the conventional estimation. © 2023 SPIE.

关键词： Tissue

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The Indoor People Tracking and Counting System

The Indoor People Tracking and Counting System

引用

2023 International Automatic Control Conference, CACS 2023

作者： Lin, Hao-Pu Han, Chin-Chuan Liao, Jun-Wei Ciou, Chong-Han Hong, Yi-Cheng Tan, Ming-Lun National United University Ph. D. Program in Material and Chemical Engineering MiaoLi Taiwan National United University Department of Computer Science and Information Engineering MiaoLi Taiwan

ISBN: (纸本)9798350306354

COVID-19 outbreaks and becomes serious from 2019 winter. Taking body temperature, wearing masks, recording footprint and avoiding crowds become important tasks and require a lot of manpower support for the prevention of the epidemic in every. In this study, we have integrated the infrared thermometer devices to measure people temperature and the people identities through RGB images at the entrance of buildings. Using the computer vision and deep learning methods, we would like to build a 24/7 and region-wide coverage assistance system to automatically record the footprint of a specific people in the building. We also detect if people taking off their masks in the yard at any time. The processing videos are grabbed from the existing CCTV systems. The developed system also locates the crowd and calculate the number of people in a specified area, and the manager can know the number of people and control the access in real time. It will reduce the burden of manpower. In addition, we have optimized the system performance to handle multiple camera videos and to reduce the hardware cost at the same time. © 2023 IEEE.

关键词： Access control

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Assessing Parkinson's Disease Severity Using Channel Attention and Graph Attention Networks on Kinect Skeleton Images 19

Assessing Parkinson's Disease Severity Using Channel Attenti...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Fadaie, Fatemeh Aghapanah, Hamed Sedighifard, Alireza Shirvan, Hadi Niknam AI and Robotics University of Isfahan Department of Computer Engineering Isfahan Iran School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Department of Bioelectrics and Biomedical Engineering Isfahan Iran Iran University of Science and Technology Department of Artificial Intelligence Isfahan Iran University of Tehran Faculty of New Sciences and Technologies Isfahan Iran

ISBN: (纸本)9798331507565

Parkinson's Disease (PD), a prevalent global neurological disorder, presents with motor symptoms like rigidity and tremors. Accurate severity assessment is vital for intervention evaluation. This study uses computer vision, specifically Kinect cameras and deep learning, to develop a system for assessing the severity of Parkinson's disease. Leveraging Kinect cameras' Precision in tracking key motion patterns, the system utilizes convolutional neural networks, namely Spatial-Temporal Graph Convolutional Network (STGCN) and Two-Stream Adaptive Graph Convolutional Network (2SAGCN), to detect movement anomalies and provide quantitative severity measures. A novel model is proposed, representing an enhanced version of 2SAGCN featuring a channel and graph attention module. Parkinson's severity classification comprises 11 classes based on 9 motion categories, totaling 99 classes. Three-dimensional transfer learning and data augmentation improve model Accuracy. This research advances non-invasive, quantitative Parkinson's assessment using innovative technology and advanced neural network models. The program is hosted on GitHub for implementation and result observation. The 10-fold cross-validation results of the best model, incorporating 2SAGCN structure, Graph, and Channel Attention, yield Accuracy, Precision, Recall, and F1-Score of Confidence Intervals 94.12± 0.14, 99.6 ± 0.20, 98.9 ± 0.10,99.2 ± 0.80 respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Development of a Portable Face Recognition System with GPS Integration Using Raspberry Pi

Development of a Portable Face Recognition System with GPS I...

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computer sciences, engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Muhammad Nurul Puji Winda Astuti Rahmanda Wulandari William Jonathan Edwards Telaumbanua Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Business Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Face recognition systems are a rapidly evolving computer vision technology capable of accurately identifying faces. Additionally, the development of SBCs (Single Board computers) has advanced significantly, enabling fast computational processing. The integration of face recognition systems on SBCs enables the creation of embedded face recognition systems. With the addition of GPS devices, it is possible to develop a mobile embedded face recognition system, which can contribute to enhancing infrastructure security and sustainability. This research demonstrates that A portable face recognition system based on Raspberry Pi 5 and a GPS module has been successfully developed. The system successfully detects target faces and able to send location coordinates through the Telegram application. Testing results show that the system's effective range is 1 to 4 meters, with an effective angle of 80° to 100° (from the left horizontal). The tests also indicate that systems without video output generally have higher FPS compared to systems displaying video.

关键词： Meters computer vision Technological innovation Face recognition Streaming media Real-time systems Sustainable development Optimization Global Positioning System Testing

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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