2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)最新文献_第2页

EX-CCII based FOPID controller for electric vehicle speed control 基于EX-CCII的电动汽车速度控制FOPID控制器

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278055

Mary Ann George, I. V. L. Durga Bhavani, D. Kamath

引用次数: 1

Experiential Learning of Physio-Chemical and Bacteriological Properties of Water using Virtual Labs 利用虚拟实验室体验学习水的理化和细菌学特性

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278043

Swathi Shetty, Aishwarya Shetty, A. A, Anusha B Salian, Akshaya, Pruthviraj Umesh, K. Gangadharan

{"title":"Experiential Learning of Physio-Chemical and Bacteriological Properties of Water using Virtual Labs","authors":"Swathi Shetty, Aishwarya Shetty, A. A, Anusha B Salian, Akshaya, Pruthviraj Umesh, K. Gangadharan","doi":"10.1109/DISCOVER50404.2020.9278043","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278043","url":null,"abstract":"Virtual Labs, an initiative by the Government of India under the National Mission on Education through Information and Communication Technology (NMEICT), has revolutionized the teaching and learning process for laboratory courses in the science and engineering disciplines. The web-based laboratories provided by the Virtual Labs project enable personalized learning while being cost effective and highly scalable. This approach helps to quickly learn the fundamental concepts of science and engineering through virtual experimentation, fosters curiosity and innovation among students, and prevents laboratory hazards. In this paper, we describe the design and development of two web-based virtual laboratories that simulate the fundamental concepts of Civil Engineering and Environmental Engineering. The proposed virtual labs provide a detailed explanation of the experiments in the respective engineering domains, and reagents and apparatuses involved while performing the experiments. The outcomes of this work are evaluated by analyzing the feedback collected from the users of these virtual labs, which reveals that the labs are an useful means to provide easy, cost effective and scalable solutions for online experiential learning.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"52 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128967983","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

Copyright 版权

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/discover50404.2020.9278063

引用次数: 0

Development of Prediction and Forecasting Model for Dengue Disease using Machine Learning Algorithms 基于机器学习算法的登革热疾病预测模型的开发

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278079

Swapna Saturi

{"title":"Development of Prediction and Forecasting Model for Dengue Disease using Machine Learning Algorithms","authors":"Swapna Saturi","doi":"10.1109/DISCOVER50404.2020.9278079","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278079","url":null,"abstract":"Globally, Dengue is one of the most quickly spreading vector-borne viral sicknesses with an expanding number of territories in danger. Many researchers have worked on different measures to control and prevent the spread of disease. The main objective of the research is to develop a forecast model to control the outbreak of dengue disease that will give an opportunity for medical professionals in designing, planning and handling the disease at an early stage. Moreover, the improvement of the assortment of strategies for determining and predictive modeling utilizing measurable, numerical examination of machine learning was studied. There are mainly six issues need to be solved in determination of dengue disease, those are exploring data sources, analyzing data sources, techniques for data preparation, data representation, dengue forecasting models and evaluation approaches. A major limitation of the traditional methods is that these methods need large volumes of data for data processing, to improve the dynamic characteristics. From the review of existing methods, it can be clearly stated that the K-means clustering method with fuzzy based system has high accuracy and it significantly improves the analysis/prediction of dengue disease. The k-means clustering algorithm separates the dengue diseased patient records into k divisions. As the dengue dataset were fully clustered, k-means clustering method improves the analysis or prediction of dengue disease. Similarly, the fuzzy based system The input factors and changing over these informational factors into fuzzy membership functions will make a better decision making in predicting dengue forecasting model. Thus, the issues stated from comprehensive research provide a useful platform for public health research and epidemiology.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114397866","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

Computer Aided Tool for diagnosing Epilepsy using Kolmogorov Complexity and Approximate Entropy 基于Kolmogorov复杂度和近似熵的癫痫诊断计算机辅助工具

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278044

Shreya Prabhu K, Roshan Joy Martis

{"title":"Computer Aided Tool for diagnosing Epilepsy using Kolmogorov Complexity and Approximate Entropy","authors":"Shreya Prabhu K, Roshan Joy Martis","doi":"10.1109/DISCOVER50404.2020.9278044","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278044","url":null,"abstract":"One of the most common neurological disorders in human beings is Epilepsy which is known to cause unprovoked seizures and convulsions. Electroencephalogram (EEG) readings which capture the signals that are transmitted between the neurons across various parts of the brain can help in diagnosing Epileptic seizures which is different from normal controls due to topological, structural, and network changes. Features like Approximate Entropy and Kolmogorov Complexity are extracted from the readings captured by EEG electrodes. These readings act as inputs to the five-layered Back Propagation Multi-Layer Perceptron Neural Network in performing training and testing in order to classify the patients suffering from Epilepsy and normal controls. Initially, this methodology is applied to readings from all the 14 electrodes that are available from the database resulting in Accuracy of 96.5 %, Precision of 98.1 %, Sensitivity of 95%, and Specificity of 98% with Area Under the Curve (AUC) of 0.964. Since the data from 14 electrodes consume a lot of storage space and time for calculation and analysis, the subsets of EEG electrodes F7, F8, FC5, FC6, T7, T8 which are placed over the temporal region of the brain which is mainly affected during seizures is considered and when the same methodology is applied on it, results in Accuracy of 97 %, Precision of 95.5 %, Sensitivity of 99 %, and Specificity of 94.5 % with AUC 0.967. In both the cases, their classification performance is almost equal but the storage space and the time taken for calculation in the second case are comparatively lesser than the first case due to less number of EEG electrodes involved. This can help in the faster diagnosis of Epilepsy in patients.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124727224","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

Design of Compact Beam-Steering Antenna with a Novel Metasubstrate Structure 一种新型超基板结构的紧凑型波束导向天线设计

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278085

Pradeep A S, G. Bidkar, Thippesha D, Nagaraj, Spurthi M P M, Vishal

引用次数: 2

Machine Learning Based Decision Support System for Atrial Fibrillation Detection using Electrocardiogram 基于机器学习的心电图房颤检测决策支持系统

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278124

Shrikanth Rao S K, R. J. Martis

{"title":"Machine Learning Based Decision Support System for Atrial Fibrillation Detection using Electrocardiogram","authors":"Shrikanth Rao S K, R. J. Martis","doi":"10.1109/DISCOVER50404.2020.9278124","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278124","url":null,"abstract":"Atrial Fibrillation (AF) is a common sustained arrhythmia encountered in regular clinical practice. In order to diagnose AF, Electrocardiogram (ECG) is used in correlation with clinical symptoms. ECG is noninvasive and cost effective modality in order to diagnose cardiac abnormalities using AF. The complexity of ECG and its interrelationship with other physiological parameters make the AF detection a challenging task in the clinical practice. The traditional practice of diagnosing AF manually by the physician can cause intra physician variability leading to a need for automated algorithm based assisting system to detect AF. In the present methodology, the QRS complex is detected and each beat in the entire signal is segmented, the median beat is calculated for a given signal, the dimensionality is reduced using Principal Component Analysis (PCA) and the resultant components along with energy values are used for classification using decision tree. The methodology provided an improved average accuracy of 85.1 percent which is reasonably high. The system developed can be used in many practical applications and can provide acceptable results in clinical implementations. The developed methodology can be used as an adjunct tool by the physician in his clinical practice.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130596779","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: 3

The Mechanism to Combat Data Leakage Trojans in Circuits using Ranomized Encoding 利用随机化编码对抗电路中数据泄漏木马的机制

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278053

Alrida Monteiro, Anjetha J Mathew, Glany V Colaco, Melannie Fernandes, K. R. Fernandes

引用次数: 1

Priority Based Localization for Anisotropic Wireless Sensor Networks 基于优先级的各向异性无线传感器网络定位

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278090

Soumya J. Bhat, S. V

引用次数: 2

Analysis of the effect of muscle fatigue on gait characteristics using data acquired by wearable sensors 利用可穿戴传感器采集的数据分析肌肉疲劳对步态特征的影响

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278096

Aishwarya Balakrishnan, Jeevan Medikonda, Pramod K. Namboothiri

引用次数: 4