This paper provides an identification method for three-parameter models i.e. first order with dead time models and second order with dead time models. The proposed identification method is based on step response and can be easily implemented using digital microprocessors. The proposed method first identifies the order of the plant i.e. first order or second order from the behavior of the plant with constant input. After the order of the plant is determined, a test step input is applied to the system and the three parameters of the plant are obtained from the corresponding response of the plant. The output of the plant need not to be zero when the test signal is applied. The efficacy of proposed algorithms is verified through simulation and experiment.
Millions of people throughout the world suffer with Parkinson's disease (PD), severely reducing their quality of life. With the symptoms when we detect Parkinson disease automatically, it could provide insights to the disease's early stages of development, enhancing the patients' projected clinical results through correctly focused therapies. This potential has prompted numerous academics to explore ways for measuring and quantifying the existence of PD symptoms using commercially available sensors. In this paper, we offer an overview of some recent scientific articles on several machine learning techniques that assist physiologists in detecting PD early. In addition, a comparative study between traditional machine learning (TML) algorithms and deep learning (DL) architectures based on the scope of their appropriate usage for classifying PD effectively has been discussed. Based on the comparison on detecting the PD from previous works, this paper concludes that deep learning models are more efficacious than traditional machine learning algorithms.
The T wave is the most important portion of the Electrocardiogram (ECG) since it detects abnormalities. Various ECG leads produce different T waves. In research, various waveform morphologies may present as an indication of benign or clinically significant injury or insult to the myocardium. The frequency of the QT interval and the shape of the electrocardiographic T-wave are both signs of abnormal ventricular repolarization, according to many scientific and clinical investigations. Furthermore, it is still unclear if T wave inversion has any clinical value in the ECG diagnosis of coronary artery disease (CAD). To obtain the accurate results of ECG of patients with CAD, this study aims to analyze the correlation using the right triangle hypotenuse for T wave detection. In this paper, we have proposed an algorithm for detecting T waves in ECG for all types of leads. We use the right triangle hypotenuse to determine T wave start and end points. Moreover, we determine the T wave upslope or downslope, as well as up or down peak value. An extensive experiment is performed on 53 datasets, including 18 databases from the MIT-BIH ST database and 35 databases from the European ST-T database with long duration, which exhibits a promising result.
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