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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Pangestu, Agung Sodikin, Iing Yusro, Muhammad Sapundani, Rum Al Hakim, Rosyid Ridlo Wilyanti, Sinka Jakarta Global University Faculty of Engineering and Computer Science Electrical Engineering Department Depok Indonesia State University of Jakarta Faculty of Engineering Department of Electronic Engineering Jakarta Indonesia University of Muhammadiyah Purwokerto Electromedical Engineering Technology Faculty of Health Science Purwokerto Indonesia Jakarta Global University Graduate Program in Electrical Engineering Faculty of Engineering and Computer Science Depok Indonesia

ISBN: (纸本)9781665453899

Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). The MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. The Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1-the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2-the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-Time via a smartphone using the IoT-based. © 2022 IEEE.

关键词： Thermometers

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Multi-dimensional Attention Spiking Transformer for Event-based Image Classification

Multi-dimensional Attention Spiking Transformer for Event-ba...

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Lin Li Yang Liu Faculty of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Image classification is a vital research area in deep learning. However, the use of Artificial Neural Networks (ANNs) in conventional approaches requires vast computational power and memory. As a potential energy-efficient alternative, Spiking Neural Networks (SNNs) leverage temporal information and low-power sensors. Nonetheless, extracting spatio-temporal features from event-based image sequences for improved classification accuracies in SNNs poses a significant challenge. To address this, we propose a Multi-Dimensional Attention Spiking Transformer (MAST) model that integrates attention mechanisms and SNNs to capture spatio-temporal features in event-based image sequences. Consequently, the MAST model achieves state-of-the-art performance in various classification tasks, as shown by the evaluations on the CIFAR, DVS128 Gesture, and CIFAR10-DVS datasets. Overall, MAST exhibits promise in event-based image classification tasks, providing a new perspective on the integration of attention mechanisms and SNNs for improved image classification.

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New Speed Sensorless ANN-IFOC Flux Hybrid Estimator Controller for Induction Motor 3

New Speed Sensorless ANN-IFOC Flux Hybrid Estimator Controll...

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3rd IEEE Industrial electronics and Applications Conference, IEACon 2022

作者： Sepeeh, Muhamad Syazmie Zulkifli, Shamsul Aizam Sim, Sy Yi Chiu, Huang-Jen Jumaat, Siti Amely Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronics Engineering Department of Electrical Power Engineering Johor Batu Pahat Malaysia Faculty of Engineering Technology Department of Electrical Engineering Technology Johor Pagoh Malaysia National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipe Taiwan

ISBN: (数字)9781665480062

ISBN: (纸本)9781665480062

This paper is to suggest a new version of sensorless control for induction motor using flux estimator generation to represent the speed input for speed control. The induction motor that been used is been presented as small EV motor in order to replicate a motor EV. This flux-speed hybrid controller is used with ANN-IFOC to replace the actual speed sensor in order to increase the tracking speed and trajectory effect while reduce the cost operation based on flux estimation speed equation. The flux parameter is been selected because any changes on the load the flux will automatically change simultaneously that is good to be applied to the EV motor. The speed controller is still needed in after the speed signal has been generate at ANN-IFOC for flux parameter but without speed sensor. At the end, all the results show, this new sensorless improved version has better time response to track the speed reference even though the load is changes. © 2022 IEEE.

关键词： Speed control

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Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1308-1322页

作者： Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still *** exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this *** the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral ***,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature ***,the semantic,spatial and spectral features are combined by the transformer for *** with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关键词： image classification machine learning

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Consecutive Irradiation and Thermal Annealing of Commercial P-Channel Power VDMOSFETs 33

Consecutive Irradiation and Thermal Annealing of Commercial ...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Mitrovic, N. Guirado, D. Dankovic, D. Palma, A.J. Ristic, G. Carvajal, M.A. University of Niš Faculty of Electronic Engineering Department of Microelectronics Niš18000 Serbia Hospital Universitario San Cecilio Radiophysics Unit Granada Spain University of Granada Department of Electronics and Computer Technology Granada Spain

ISBN: (纸本)9798350347760

This paper presents research on the effects caused with consecutive irradiation and thermal annealing of two commercial models of p-channel power VDMOSFETs IRF9520 and IRF9530. Threshold voltage shift, which is marked as the most important parameter for both dosimetry application, as well as reliability assesment is obtained and discussed. Experiment setup, as well as the measuring procedures are explained in more detail. Irradiation degradation causes threshold voltage shift, while the thermal annealing causes recovery. The average sensitivity of models for consecutive irradiation sessions is 9.54 ± 0.04 mV Gy-1, where averagely 91.88 % is recovered through thermal annealing. Results point out that p-channel power VDMOSFET devices could be used in some dosimetry applications. © 2023 IEEE.

关键词： Irradiation

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Deep Learning Techniques in Mobile Edge Computing for Internet of Medical Things 3

Deep Learning Techniques in Mobile Edge Computing for Intern...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Alawadhi, Abdulwadood Ahmad, R. Badlishah Almogahed, Abdullah Abrar, Ahmad Department of Computers and Information Technology Yemen Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Perlis Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Parit Raja Malaysia School of Computing Universiti Utara Malaysia InterNetWorks Research Laboratory Sintok-Kedah Malaysia

ISBN: (纸本)9798350305333

The fast expansion of the Internet of Medical Things (IoMT) has resulted in a ubiquitous home health diagnostic network. High patient demand results in high costs, short latency, and communication overload. As a result, 6G is the next generation of IoT, IoMT, and cellular networks, intending to considerably improve the quality of smart healthcare services through high throughput and decreased latency. So far, adopting cloud computing for time-critical applications and decreasing access delays to resources is difficult. Deep learning has been extensively employed to extract characteristics from complicated networks as artificial intelligence technology has advanced. On the other hand, deep learning models are often run in cloud computing data centres with tremendous processing resources. Conventional cloud computing systems primarily rely on the network, which has significant latency and security and privacy issues. One of the successful solutions is the Mobile Edge Computing (MEC) paradigm, which brings cloud computing services closer to the edge network and uses available resources. Mobile edge computing places computer and storage nodes near mobile devices at the Internet's edge, leading to considerable savings in system operating time, memory cost, and power usage. Deep learning is used in mobile edge computing to forecast changes in demand based on daily patient behaviours. It also prepares the network by scaling up network resources as required. This study aims to discuss IoMT and identfy the problems in deep learning for mobile edge computing technology and its applications. © 2023 IEEE.

关键词： Deep learning

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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

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computer Systems Science & engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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Classification of Football Player Actions Using Sensing Data

Classification of Football Player Actions Using Sensing Data

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Image Processing and Robotics (ICIP), International Conference on

作者： Yukito Hirasawa Yuichiro Kinoshita Latifah Munirah Kamarudin Ammar Zakaria Department of Computer Science and Engineering University of Yamanashi Yamanashi Japan Faculty of Electronic Engineering and Technology Universiti Malaysia Perlis Perlis Malaysia

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

This study classifies the actions of football players using sensing data acquired from wearable sensors attached to players and the ball. More than 800 sensing data with the labels of five types of player actions were created as a dataset. The neural networks were trained using 19 input items created by considering time-series variations in player and ball locations. The trained neural network model demonstrated a classification accuracy of $84.0 \%$. The model successfully obtained sufficient accuracy for all types of *** results demonstrate that the sensing data and created input items can be effectively utilized for classifying the actions of football player.

关键词： Robot kinematics Image processing Neural networks Detectors Robot sensing systems Sensors Recording

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GPRS Based Health Monitoring System 1

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2nd International Conference on Advances in Information and Communication technology, ICTA 2023

作者： Deepa, A.R. Hoang, Quang-Trung Bui, Van-Tung Tran, Duc-Tan Ramaswamy, V. Sheela, C. J. Jeba Chowdhury, S. Tran, Duc-Nghia Department of Computer Science and Engineering Koneru Lakshmaih Educational Guntur Foundation India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen2400 Viet Nam Department of PG Computer Science Nesamony Memorial Christian College Marthandam India Chittoor AP Murukambattu India Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam

ISBN: (数字)9783031508189

ISBN: (纸本)9783031508172

The idea and prototype of an Internet of Things (IoT) based health monitoring gadget are introduced in this paper. We concentrate on leveraging the cloud and IoT to monitor temperature an coronary heart rate in real-time. This design can be utilized for the early diagnosis of heart illnesses given the rising number of cardiac patients throughout the world. Contrary to the majority of the current monitoring methods and systems, a variety of wireless technologies are available, but their range is constrained. IoT is being used in this session to monitor. There is no distance restriction with IoT. Through the internet, we can test a patient's coronary heart rate and temperature from anywhere. Our strategy involves using the cloud to track the temperature and heart rate in real time. Through the use of IoT technology, the recorded temperature and heart rate may be saved and instantly evaluated for prognosis and diagnosis. An integrated GPRS microcontroller on a single chip makes up the suggested hardware architecture for connecting to the internet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Crowd-Counting of Images in Rainy Weather Based on GAN-U-Net Model and Multi-column CNN Counting Approaches 3

Crowd-Counting of Images in Rainy Weather Based on GAN-U-Net...

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3rd IEEE International Conference on electronic engineering, ICEEM 2023

作者： Saad, Rasha A. Abd El-Samie, Fathi E. Abdel-Azim, Mohammed El-Hoseny, Heba M. Obour Higher Institute for Engineering and Technology Department of Electronics and Electrical Communications Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Egypt Mansoura University Faculty of Engineering Department of Electronics and Electrical Communications Egypt The Higher Future Institute for Specialized Technological Studies Department of Computer Science Egypt

ISBN: (纸本)9798350323511

A count of crowds tries to determine the total number of persons in crowded places in order to avoid disruption of the safety system and maintain crowd safety. Accurate estimation of the size of crowds is a challenging process because of numerous challenges like occlusion, complicated backgrounds, varying sizes, uneven distribution, alteration of lighting, and adaptable weather. In this study, we propose a solution to two crowd counting challenges: poor weather such as rain and image scaling. We investigate crowd counting in the rainy Shanghai-Tec images. We adopt the GAN-U-Net de-raining model and the SAS-Net [1] and SGA-Net [2] crowd-counting models in our study. © 2023 IEEE.

关键词： computer vision

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