Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits
Smart Electrical Vehicle
Development of Smart Electronic System to Implement Smart Home
Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment
Photographing a Black Hole
Development of an Intelligent Battery Charging System Based on PIC16F877A Microcontroller
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Fetal ECG Extraction from Maternal ECG using MATLAB
Detection of Phase to Phase Faults and Identification of Faulty Phases in Series Capacitor Compensated Six Phase Transmission Line using the Norm of Wavelet Transform
A Novel Approach to Reduce Deafness in Classical Earphones: MUEAR
A novel mathematical ECG signal analysis approach for features extraction using LabVIEW
Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Application of Polynomial Approximation Techniques for Smoothing ECG Signals
A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets
Wearable Health Monitoring Smart Gloves
PCG signal recordings are so complex and non stationary signal, they are also affected by different kinds of noise the segmentation method followed by the time and frequency domain analysis characterization of some phonocardiogram (PCG) signals. The paper using the Discrete Wavelet Transform (DWT) in decomposition signal. In the segmentation technique, we calculate the signal to noise ratio and peak signal to noise ratio and energy of the details coefficients at each level and threshold it in order to detect the murmur of heart sound signals. The results of the method illustrate clearly the detection of the main components S1, S2, S3, S4 Pathological murmurs and the identification of the valves disease.
Electrocardiogram (ECG) is the recording of the electrical activities of the heart. ECG signals are recorded on the body surface with the help of surface electrodes. While recording, different artifacts get introduced in the signal like; electrode contact noise, motion artifacts, base line drift, base line wander, electrosurgical noise, and power line interferences. Some kind of signal processing is required to get meaningful information from ECG. Now a days digital signal processing is preferred over analog signal processing. Various digital signal processing techniques have been developed over a period of last four decades for removing noise from ECG signal. The curve fitting is a simple and widely used technique for smoothing ECG signal (removing high frequency noise). In this paper we have presented a comparison study of various smoothing filters to filter high frequency noise. To compare performance of various smoothing filters Power Spectral Density (PSD), Average Power and Percent Root mean square Difference (PRD) have been calculated.
Attenuation of sound is the process of decay in the amplitude due to combination of absorption and scattering. Transmitted pulse is influenced by many factors for selecting the center frequency of active sonar. For example, for a fixed size transmitter array with constant radiated power, ambient noise decreases with frequency and the source level increases with frequency. These considerations would suggest the use of high center frequency. On other hand the absorption of the medium increases with frequency Here, in this paper the relationship between the range and optimum frequency is derived which helps in selecting the center frequency.
A wide variety of algorithms have been devised for the compression of ECG signals during last five decades. These techniques have not only brought about a considerable reduction in ECG data volume for storage but also enabled economic and efficient transmission of data for distant analysis. The main purpose of this paper is to present an overview of ECG compression methods especially the direct data compression methods as well as the various performance measures governing the effectiveness of these methods. Broadly ECG compression methods have been classified as direct compression method, transformation method and parameter extraction method. However, this paper addresses the various direct data compression techniques such as AZTEC, Modified AZTEC, Turning Point Technique, CORTES, Fan, SAPA, Entropy Coding, Peak-Picking, Cycle to Cycle compression and ECG data compression by DPCM.
The speech recognition is the most important research area to recognize the speech signal by the computer. To develop the recognition rate of the continuous speech signal, we preferred frontend process such as speech segmentation, feature extraction (MFCC) and clustering techniques i.e., Fuzzy c means clustering is the formation of clusters from the extracted features based on similar sense and form the optimum number of clusters. In speech recognition the acoustic models are the major role to testing the trained data. Here the acoustic models for continuous speech recognition was discussed i.e., The Hidden morkov model (HMM),Gaussian mixture model(GMM) and GMM-UBM(Universal Background Model) are the most suitable acoustic models which are used for train the speech signal and recognize the corresponding text data.