TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)最新文献_第3页

Technology Review of Electric Motor for Hybrid-Electric Vehicle 混合动力汽车电机技术综述

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707371

Y. U. Nugraha, D. A. Asfani, I. M. Y. Negara, Muhammad Aziz, M. N. Yuniarto

引用次数: 3

Effect of different splitting criteria on the performance of speech emotion recognition 不同分割标准对语音情感识别性能的影响

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707265

Bagus Tris Atmaja, A. Sasou

引用次数: 3

Neural Network Architecture for the Classification of Alzheimer's Disease from Brain MRI 基于脑MRI的阿尔茨海默病分类神经网络架构

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707412

Riasat Mahbub, Muhammad Anwarul Azim, Nafiz Ishtiaque Mahee, Zahidul Islam Sanjid, Khondaker Masfiq Reza, M. Parvez

{"title":"Neural Network Architecture for the Classification of Alzheimer's Disease from Brain MRI","authors":"Riasat Mahbub, Muhammad Anwarul Azim, Nafiz Ishtiaque Mahee, Zahidul Islam Sanjid, Khondaker Masfiq Reza, M. Parvez","doi":"10.1109/TENCON54134.2021.9707412","DOIUrl":"https://doi.org/10.1109/TENCON54134.2021.9707412","url":null,"abstract":"Alzheimer's Disease (AD) is a neurological condition in which the decline of brain cells causes memory loss and cognitive decline. Various Neuroimaging techniques have been developed to diagnose AD; among those, Magnetic Resonance Imaging (MRI) is one of the most prominent ones. Historically, expert radiologists were solely responsible for making decisions of a patient's AD situation by manually analyzing brain MR images. However, the recent progress in medical image analysis using deep learning especially has automated this task significantly. Although the state-of-the-art architectures have achieved human-level performance in classifying AD images from Normal Control (NC), they often require predefined Regions of interest as a basis for feature extraction. This condition not only requires specialized domain knowledge of the human brain but also makes the overall design complicated. In this paper, we designed a 14 layer Neural network architecture that can facilitate AD diagnosis without being dependent on any neurological assumption. The network was tested over ADNI-1, a benchmark MRI dataset for AD research, and found an accuracy of 87.06 % $(mathbf{AUC}=mathbf{0. 9 3}.)$","PeriodicalId":405859,"journal":{"name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128473071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ATPG for Incomplete Testing of SOC Considering Bridging Faults 考虑桥接故障的SOC不完全测试的ATPG

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707383

Kunwer Mrityunjay Singh, S. Biswas, J. Deka

{"title":"ATPG for Incomplete Testing of SOC Considering Bridging Faults","authors":"Kunwer Mrityunjay Singh, S. Biswas, J. Deka","doi":"10.1109/TENCON54134.2021.9707383","DOIUrl":"https://doi.org/10.1109/TENCON54134.2021.9707383","url":null,"abstract":"Nowadays System on Chip (SOC) is used widely. Clients require gadgets that can handle several applications progressively. Due to an increase in the number of applications, the number of cores embedded in SOC increased too. Each core has a large number of components which increases the probability of occurring of bridging faults in SOC. Efficient testing of these faults is necessary. Testing larger SOC needs large test data volume (TDV), large test access time (TAT). It is hard to store this large amount of test data. It requires a large amount of time to process this test data which makes the testing sluggish. Testing is more complicated for large SOCs. Various traditional methods for testing bridging faults are already proposed to test SOC thoroughly. These strategies are accurate but more expensive in terms of testing resources and the cost of testing. A large number of cores in SOC leads to long TAT which is infeasible sometimes. In this paper, a method is proposed to test the bridging faults and to reduce the TDV and TAT. We propose an efficient method for incomplete testing of SOC considering bridging faults which affectively reduces the TDV but with a little compromise with the fault coverage. In this method, essential bridging faults are considered and a heuristic optimization technique is utilized to improve the TDV while compromising with the quality of testing.","PeriodicalId":405859,"journal":{"name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127168452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Learning Technique for Electricity Price Forecasting in Consideration of Spikes 一种考虑峰值的电价预测深度学习技术

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707319

Kodai Yamada, H. Mori

引用次数: 1

Towards the Development of a Low-Cost Soil Drying Oven 低成本土壤干燥箱的研制

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707453

P. Chand, Matt Foulkes, Ajay Kumar

引用次数: 0

Quantification of edge effects in capacitive biopotential sensing* 电容式生物电位传感中边缘效应的定量研究*

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707359

Gautam Anand, A. Lowe, Richard Jones, W. Arnold, Anubha Kalra, R. Simpkin, I. Sinno, D. Budgett

{"title":"Quantification of edge effects in capacitive biopotential sensing*","authors":"Gautam Anand, A. Lowe, Richard Jones, W. Arnold, Anubha Kalra, R. Simpkin, I. Sinno, D. Budgett","doi":"10.1109/TENCON54134.2021.9707359","DOIUrl":"https://doi.org/10.1109/TENCON54134.2021.9707359","url":null,"abstract":"Knowledge of capacitance and factors contributing to its variation is important in capacitive biopotential sensing where the dimension of the electrode is much less than the dielectric (skin). As such, this study aimed to quantify the actual capacitance exhibited by an electrode, by accounting for fringe fields (edge effects). This study simulated two different dual-electrode configurations to calculate the capacitance of each electrode in the presence and absence of the other electrode. The results were compared with existing expressions from the literature to investigate their reliability in quantifying edge effects. It was found that the capacitance expression for electrode smaller than the body shows the least difference from simulated capacitance than other expressions. However, the difference increased with increasing airgap. Also, the concentric configuration identified that the outer electrode acts as a guard to the inner electrode and can be approximated with minimum error. This will help estimate the actual effective electrode capacitance in single and multi-electrode systems and allow for efficient measurement circuit design and optimal signal processing of the biopotential of interest.","PeriodicalId":405859,"journal":{"name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130829167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Node Classification Using Graph Convolutional Network with Dropout Regularization 基于Dropout正则化的图卷积网络节点分类

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707262

Bing-Yu Xiao, C. Tseng, Su-Ling Lee

引用次数: 4

Detection and Identification of Abaca Diseases using a Convolutional Neural Network CNN 基于卷积神经网络的Abaca疾病检测与识别

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707337

Lyndon T. Buenconsejo, N. Linsangan

引用次数: 6

Anomaly Detection Using Classification CNN Models: A Video Analytic Approach 使用分类CNN模型的异常检测:一种视频分析方法

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) Pub Date : 2021-12-07 DOI: 10.1109/TENCON54134.2021.9707202

S. Girisha, M. Pai, Ujjwal Verma, R. Pai, Shreesha Surathkal

{"title":"Anomaly Detection Using Classification CNN Models: A Video Analytic Approach","authors":"S. Girisha, M. Pai, Ujjwal Verma, R. Pai, Shreesha Surathkal","doi":"10.1109/TENCON54134.2021.9707202","DOIUrl":"https://doi.org/10.1109/TENCON54134.2021.9707202","url":null,"abstract":"Video anomaly detection has gained much attention in the computer vision community due to its wide applications in security. Specifically, the focus has been on feature extraction and the design of inference algorithms. The extraction of features to model the normality is challenging due to the scarcity of data and supervision. To this end, current computer vision technologies use reconstruction based methods that relied on auto-encoders to reconstruct normal events in an unsupervised manner. Higher reconstruction errors are often used to detect anomalies. However, the use of multiple auto-encoders to extract features (temporal and appearance) is redundant and expensive for videos. In this context, the present study proposes a novel feature extractor that uses a single CNN architecture to extract both temporal and appearance features. Also, this model is trained for classification tasks which are adapted as feature extractors in anomaly detection. The training of this model is easy and can be deployed efficiently due to its lightweight architecture. Further, the proposed model has been quantitatively evaluated on the UCSD ped 2 dataset and found to perform competitively with an AUC of 0.958.","PeriodicalId":405859,"journal":{"name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123203413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1