Mary Ann George, I. V. L. Durga Bhavani, D. Kamath
{"title":"EX-CCII based FOPID controller for electric vehicle speed control","authors":"Mary Ann George, I. V. L. Durga Bhavani, D. Kamath","doi":"10.1109/DISCOVER50404.2020.9278055","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278055","url":null,"abstract":"The paper presents a fractional analog scheme using an Extra-X second-generation current conveyor (EX-CCII) to realize a fractional-order PID (FOPID) controller. The FOPID controller is designed for an electric vehicle speed control application, considering a second-order model of the electric vehicle. The controller parameters are designed using the Astrom-Hagglund (AH) tuning technique. The order of integrator and differentiator stages are obtained by using the Neider-Mead (NM) optimization technique. The circuit is realized using a single active element and RC network. The controller performance parameters are evaluated using the LTspice simulator for TSMC 0.35 μm CMOS process.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"44 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":"130031858","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}
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}
{"title":"Copyright","authors":"","doi":"10.1109/discover50404.2020.9278063","DOIUrl":"https://doi.org/10.1109/discover50404.2020.9278063","url":null,"abstract":"Copyright","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"156 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":"126971039","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}
{"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}
{"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}
Pradeep A S, G. Bidkar, Thippesha D, Nagaraj, Spurthi M P M, Vishal
{"title":"Design of Compact Beam-Steering Antenna with a Novel Metasubstrate Structure","authors":"Pradeep A S, G. Bidkar, Thippesha D, Nagaraj, Spurthi M P M, Vishal","doi":"10.1109/DISCOVER50404.2020.9278085","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278085","url":null,"abstract":"This paper presents a compact beam-steering antenna in which both beam-steering capability and mutual coupling reduction has been achieved using a novel meta- substrate structure. This novel meta-substrate consists of a 1×2 slotted patch with a split between in which PIN diodes are incorporated and 6×1 complimentary hexagonal split ring resonator (CHSRR) etched on copper plate. It has been observed that by placing this novel meta-substrate structure above the 1×2 patch array antenna at an optimized height of 3 mm produces ±20° beam scanning with around 10 dB mutual coupling reduction and about 20% miniaturization as compared to conventional antenna. All simulations are carried out using Ansoft HFSS EM Simulator and the proposed antenna can be used for ISM band application in a wireless communication system.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"11 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":"122656904","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}
{"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}
Alrida Monteiro, Anjetha J Mathew, Glany V Colaco, Melannie Fernandes, K. R. Fernandes
{"title":"The Mechanism to Combat Data Leakage Trojans in Circuits using Ranomized Encoding","authors":"Alrida Monteiro, Anjetha J Mathew, Glany V Colaco, Melannie Fernandes, K. R. Fernandes","doi":"10.1109/DISCOVER50404.2020.9278053","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278053","url":null,"abstract":"For any system dealing with data, security is an essential factor. With increasing manufacturig and production costs, many companies rely on external facilities. There is a chance of Trojan insertion in such a situation. In the paper, the Randomized Encoding of Combinational Logic for Resistance to Data Leakage (RECORD) technique is explored. Data randomization is carried out in this technique that renders the leaked data useless in the event of a Trojan attack. The percentage of randomization and recovery obtained is taken into consideration to assess the design efficacy. Design analysis on combinational circuits for a given set of inputs provided randomization of 50% and efficient data recovery. The design study of the RECORD method when extended to sequential circuits provides randomization of 40% and 100% recovery for an appropriate sequence of random bit change at every clock cycle.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"6 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":"131053758","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}
{"title":"Priority Based Localization for Anisotropic Wireless Sensor Networks","authors":"Soumya J. Bhat, S. V","doi":"10.1109/DISCOVER50404.2020.9278090","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278090","url":null,"abstract":"Wireless Sensor Networks (WSN) are used in a number of applications like volcanic monitoring, forest fire control systems for monitoring and control of the events. Networks are formed by the random deployment of nodes in the required fields, which are usually anisotropic. Locations of the deployed nodes are essential to act on the collected data. To estimate the locations of nodes in the network, a number of localization algorithms are developed which make use of techniques such as hop distances and centroid to estimate the positions of nodes. These algorithms showed sub-optimal results when tested in anisotropic fields with obstructions. In this work, a priority based localization algorithm is reported, which gives priority to a few reference nodes based on their Average Hop Distances (AHD). Nodes are then localized with weighted centroid method using high priority reference nodes. The simulation results show that the localization results of the reported algorithm is better than the existing weighted centroid methods in anisotropic fields.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"1 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":"133988412","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}
Aishwarya Balakrishnan, Jeevan Medikonda, Pramod K. Namboothiri
{"title":"Analysis of the effect of muscle fatigue on gait characteristics using data acquired by wearable sensors","authors":"Aishwarya Balakrishnan, Jeevan Medikonda, Pramod K. Namboothiri","doi":"10.1109/DISCOVER50404.2020.9278096","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278096","url":null,"abstract":"Parkinson's disease (PD) patients suffer from numerous gait-related disturbances. Various factors contribute to the alteration in gait patterns, among which muscle fatigue plays a significant role. Traditional gait analysis techniques involve laboratory types of equipment that are expensive and require specialized personnel or software tools for analysis. In this paper, a portable wireless data acquisition system embedded with a network of wearable sensors is proposed that can aid real-time gait signal acquisition in an unconstrained environment. Experiments have been carried out to demonstrate the effectiveness of the proposed system and to examine the effect of muscle fatigue in gait monitoring using mechanomyography techniques. Results show distinct variability in mean stride time and cadence with the influence of muscle fatigue.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"1 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":"129810501","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}