2021 Emerging Trends in Industry 4.0 (ETI 4.0)最新文献_第5页

Performance Assessment of Two Stage Operational Transconductance Amplifier in 180nm and 130nm Technology with Optimised Compensation Capacitance 基于优化补偿电容的180nm和130nm两级运算跨导放大器性能评估

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619398

Anand Krisshna P, Archana R Nair, P. R. Sreenidhi

{"title":"Performance Assessment of Two Stage Operational Transconductance Amplifier in 180nm and 130nm Technology with Optimised Compensation Capacitance","authors":"Anand Krisshna P, Archana R Nair, P. R. Sreenidhi","doi":"10.1109/ETI4.051663.2021.9619398","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619398","url":null,"abstract":"This paper illustrates the performance assessment and design of CMOS Two Stage OTA under 130nm and 180nm technology nodes focusing on optimization in compensation capacitance, reduction in power dissipation. The designed circuit operates at two different supply voltages of 1.2V and 1.8V and the input relay is dependent on bias current. In this paper, the device parameters such as AC-Gain, Phase margin, Slew rate, CMRR, ICMR, Output offset voltage, Gain bandwidth, Noise and Power dissipation are theoretically calculated and analysed using LT spice software for 130nm and 180nm technology for given specifications. As the power is a major design parameter, the bias current and supply voltage is varied within the range of respective technology nodes to achieve a minimum power dissipation design. For minimum power design, reduction in bandwidth and stability of the system are major trade-offs. The designed circuit uses a specific compensation methodology for implementing the compensation called Miller compensation. It is used for improving the bandwidth and slew rate of the designed system for various capacitive loads.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332909","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 CPW Feed Orthogonal Wideband Quad-Port Conformal MIMO Antenna for Satellite Applications 卫星用CPW馈电正交宽带四端口共形MIMO天线

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619207

G. Raviteja, K.S.Rama Praveen, K.Anisha Keerthi, R. Abhishek, V. Sarvari

引用次数: 0

Performance Analysis of Deep Convolutional Features using Support Vector Machines for COVID-19 Diagnosis on X-ray Images 基于支持向量机的深度卷积特征在x射线图像上诊断COVID-19的性能分析

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619357

Z. Rustam, S. Hartini

{"title":"Performance Analysis of Deep Convolutional Features using Support Vector Machines for COVID-19 Diagnosis on X-ray Images","authors":"Z. Rustam, S. Hartini","doi":"10.1109/ETI4.051663.2021.9619357","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619357","url":null,"abstract":"Since the first case of COVID-19 appeared in Wuhan city, China, in December 2019, the disease has affected more than millions of people worldwide. Therefore, early detection of COVID-19 is important to prevent transmission to more people. One method widely used to detect COVID-19 through X-ray images is Convolutional Neural Networks (CNN). However, CNN needs large amounts of image data to build models with high accuracy, while the medical image has limited amounts of data. To overcome this problem, transfer learning technique where CNN is used as a feature extraction method is usually be chosen as an alternative. However, most studies use the extraction results of the final layers such as fully connected layer or the last convolutional layer. In this study, all layers will be used by turns to analyze how the extraction results affect the performance of classification method. The CNN models used are pre-trained models VGG16 and VGG19, while the classification method used is Support Vector Machines (SVM). Based on the results of the study, the extraction results by the initial layer gave a better performance on SVM compared to the layers that are deeper in the selected CNN architecture. Several layers in CNN model did not analyze due to limited source capability in doing computation. Therefore, as the future work, the rest layers of CNN in this study can be analyzed as well as the other CNN models and the classification method.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134300314","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

Segmented Region based Feature Extraction for Image Classification 基于分割区域的图像分类特征提取

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619353

Lipismita Panigrahi, K. Verma

{"title":"Segmented Region based Feature Extraction for Image Classification","authors":"Lipismita Panigrahi, K. Verma","doi":"10.1109/ETI4.051663.2021.9619353","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619353","url":null,"abstract":"Reliability and accuracy is the key concern of an automated image classification process. However, the impact of background or surrounding area is very less in compared to object features, which create ambiguity while assigning the appropriate class label and reduce the classification accuracy. This paper presents a new model to address this issue which select the relevant features from the segmented images based on the inner and outer regions. The key idea of this model is that the texture features inside the objects are more relevant than the surrounding or outside region of the objects. The proposed model applying a segmentation method for automated segment the image. These segmented images are further partition into two parts (i.e. inner and outer). The 463 shape and texture features are extracted from the inner, outer parts of the segmented images and also from the whole image. Next, these extracted features are used to train the classifier using support vector machine (SVM). A database of 644 images that consists of 8 classes is used to verify the efficacy of the proposed model. The result proves the efficacy of the proposed model which achieves classification accuracy up to 97.79 % from the inner part of the image. The classification accuracy of inner features is increased by 9.58% from surroundings features.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"62 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134543629","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

Identifying Lung Cancer and Chronic Obstructive Pulmonary Diseases using Residual Neural Network 残差神经网络识别肺癌和慢性阻塞性肺疾病

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619350

Asha Sara Thomas, E. Sasikala

{"title":"Identifying Lung Cancer and Chronic Obstructive Pulmonary Diseases using Residual Neural Network","authors":"Asha Sara Thomas, E. Sasikala","doi":"10.1109/ETI4.051663.2021.9619350","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619350","url":null,"abstract":"In the last ten years, Lung Cancer and Chronic Obstructive Pulmonary Disease (COPD) have become two major diseases in the category of Respiratory Diseases which have lead to a large number of death rates in India and also in other countries. The main reason for the increase in these cases is due to the excessive smoking habit among youngsters and adults. Thus, proper diagnosis of both lung cancer and COPD are important in order to save human life. A fast and effective method to do this is to differentiate accurately among both diseases and provide the required treatment. This paper focuses on efficiently differentiating among chest pathologies in chest X-Ray using different artificial neural networks, machine learning, and deep learning approaches. It shows how an artificial neural network can be used in the prediction of diseases based on the image sets. ResNets help in better feature extraction of the image sets that lead to the correct classification of diseases. The model achieves a better performance in evaluating chest radiograph datasets that depicts the changes caused in a person's lungs when compared to normal lung images such as the formation of small lobes (or) the enlarged arteries in lungs and so on..","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132189994","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

Binary Equilibrium Optimizer Based Weak bus Constrained PMU Placement 基于二元平衡优化器的弱总线约束PMU布局

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619191

P. M. Joshi, H. Verma

引用次数: 1

Hydrodynamic Performance of an OWC Device under the Action of Oblique Incident Waves 斜入射波作用下OWC装置的水动力性能

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619316

Kshma Trivedi, S. Koley

引用次数: 0

Prediction of Heart Disease using Random Forest 使用随机森林预测心脏病

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619208

Nagaraj M. Lutimath, Neha Sharma, B. K. Byregowda

引用次数: 3

Generative Adversarial Networks based method for Generating Photo-Realistic Super Resolution Images 基于生成对抗网络的逼真超分辨率图像生成方法

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619393

Darshana A. Naik, V. Sangeetha, G. Sandhya

引用次数: 0

Design of Full Adder and Parity Generator Based on Reversible Logic 基于可逆逻辑的全加法器和奇偶校验器的设计

2021 Emerging Trends in Industry 4.0 (ETI 4.0) Pub Date : 2021-05-19 DOI: 10.1109/ETI4.051663.2021.9619268

Sunakshi Sharma, V. Sharma

引用次数: 4