The heart is one of the crucial parts of a human being. The heart produces electrical signals and these cycles of electrical signals are called as cardiac cycles. The graphical recording of the cardiac cycle produced by an Electrocardiograph is called as Electro cardio gram (ECG) signal. The Electrocardiogram signal is used to diagnose the irregularity in heart beat. Automatic classification of ECG signals has applications in human-computer interaction, as well as in clinical application such as detection of key indicators of the onset of the certain illness. In this work an algorithm has been develop to detect the five abnormal beat signals includes Left bundle branch block beat (LBBB), Right bundle branch block beat (RBBB), Premature Ventricular Contraction (PVC), Atrial Premature Beat (APB) and Nodal (junction) Premature Beat(NPB) along with the normal beat. In order to prepare an appropriate input vector for the neural classifier several pre processing stages have been applied. Harr Wavelet Transform (HWT) is used in order to extract features from the ECG signal. Preprocessing and the classification of ECG signals is done using Forward Feed Neural Network. Finally, the MIT-BIH [10] database is used to evaluate the proposed algorithm.

In this article, a Quasi Z-source inverter (QZSI)-based unified power quality conditioner (UPQC) backed by the solar photovoltaic (SPV) is presented in order to enhance power quality. The UPQC consists of converters connected in parallel and series. The active power filters (APFs) connected in parallel and series is one of the versatile custom power circuitries to reduce current and voltage instabilities. The main functions of QZSI can increase the variable direct current (DC) voltage to any given alternating current (AC) output voltage, reduce the necessary elements, and alleviate harmonic content. The UPQC’s compensation function primarily relies on the control system used for the generation of reference current and voltage. The enhanced second order generalized integrator (ESOGI) is used in this proposed system to extract the reference current of QZSI-UPQC. The proposed UPQC uses the SPV system, which has an energy storage unit to offset long-term current and voltage disruptions and fulfill the active power demands of the grid. The experimental results confirm that the proposed SPV-supported QZSI-UPQC generates sinusoidal grid currents of about 1.2% of total harmonic distortion (THD), thus increasing the power efficiency of the interconnected SPV power distribution network.

No abstract

No abstract

The medical field has been revolutionized by the medical imaging system, which plays a key role in providing information on the early life-saving detection of dreadful diseases. Diabetic retinopathy is a chronic visual disease that is the primary reason for the vision loss in most of the patients, who left undiagnosed at the initial stage. As the count of the diabetic retinopathy affected people kept on increasing, there is a necessity to have an automated detection method. The accuracy of the diagnosis of the automatic detection model is related to image acquisition as well as image interpretation. In contrast to this, the analysis of medical images by using computerized models is still a limited task. Thus, different kinds of detection methods are being developed for early detection of diabetic retinopathy. Accordingly, this paper focuses on the various literature analyses on different detection algorithms and techniques for diagnosing diabetic retinopathy. Here, it reviews several research papers and exhibits the significance of each detection method. This review deals with the analysis on the segmentation as well as classification algorithms that are included in each of the researches. Besides, the adopted environment, database collection and the tool for each of the research are portrayed. It provides the details of the performance analysis of the various diabetic detection models and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be accomplished by future researchers in the detection of diabetic retinopathy.