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
One of the most important problems in many application areas is to extract the signal of interest from background noise. In background noise, the occurrence of signal and the behaviour of signal are random. Therefore, it is reasonable to deal with the signal extraction problem using methods based on probability theory and statistical estimation. That is to say that signal detection and parameter estimation problems are statistical hypothesis testing problems in mathematical statistics. In this paper, we examine signal and noise environments encountered in active sonar using CW and LFM pulses. The optimum receiver is presented for range-Doppler-shift processing in a background-noise-limited environment. FFT based implementation for detection of CW and LFM active sonar target has been shown here. By following this method both range and Doppler resolution can be achieved.
The Electro-CardioGram (ECG) is a graphical representation of electro-mechanical activities of the heart. It reflects the state of heart, and is very much useful in disease diagnosis. Since, the ECG signal contains high frequency noise, is well known as power line interference. Hence, it must be removed for further processing. This paper presents an algorithm, developed for denoising high frequency noise from ECG signal which is based on a simple averaging and a moving averaging filter. The filtering process is followed by an algorithm for smoothing the ECG signal using polynomial curve fitting. It’s denoising performance is implemented, smoothened, and compared in the C++ environment. The proposed algorithm does require redundant preprocessing steps, thus allowing a simple architecture for its implementation as well as low computational cost.
A novel method for Multiple target localization based on linear canonical transform is presented in this paper. It involves a modal preprocessing step to transform the signals received at the sensor array into signals at different modes, where narrowband techniques for DOA estimation can be applied. The incorporation of the fractional Fourier transform in the proposed method makes it possible to estimate the parameters of multiple targets even in challenging scenarios such as low SNR and closely spaced targets. Detection and estimation of Range, Velocity and Direction Of Arrival (DOA) of multiple far field targets using wideband chirp signals using ROOT-MUSIC algorithm reduces the error for DOA, Range and Velocity. The proposed method is better than the existing method. While comparing Raleigh channel with Root- MUSIC algorithm where errors like PAPR, CFO and STO are minimized. Proposed method gives more accurate results with low Root-Mean-Square Errors in the parameters estimated under complex conditions such as closely spaced targets and low Signal-to-Noise-Ratio.
All around the world there are various diseases acquired by human beings. These diseases are of various kinds and it affects almost all parts of the human body. Heart diseases are nowaday's becoming very painstaking part that needs to be taken much care. The major part of solving such problems involves a considerable amount of work to identify the disease. As the heart is the most complex and delicate structure of the human body, it is very difficult to deal with it physically. The area of biomedical signal processing is vast and very useful to accurately analyze and detect the disease. We calculate the normalised root mean square of the detailed coefficients at each level and threshold it in order to detect the murmur of heart sound signals. The result of this method clearly illustrates the detection of the main components S1, S2, S3, S4 Pathological murmurs and the identification of the disease.
The primary objective of this paper is to present a simulation scheme to simulate an adaptive filter using Least Mean Square, and Normalized Least Mean Square adaptive filtering algorithms for system identification and echo cancellation. The objective of echo cancellation is to estimate the unknown system response that is system identification. With the help of system identification and adaptive filtering algorithms Mean Square Error (MSE) can be minimized and hence echo free signal can be obtained. This method uses a primary input signal that contains speech signal and a reference input signal containing noise. The estimated signal is obtained by subtracting adaptively filtered reference input signal from the primary input signal. In this method, the desired signal corrupted by an additive echo can be recovered by an adaptive echo canceller using LMS, and NLMS algorithms. This adaptive echo canceller is useful in minimizing the MSE and to improve the SNR. Here the estimation of the adaptive filtering is done using MATLAB environment.
Noise can limit the extraction of some basic and vital peculiarities from biomedical signals and thus makes it impossible to perform exact analysis of these signals. EMG (Electromyography) signals is one such case, which can be affected by number of factors. For example, power line noises, noises caused by electrical and electronic equipments, inherent semiconductor devices noises, etc. Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. This paper presents an independent component analysis approach for removing noise from raw EMG signals. As the base of the presented systems is independent component analysis, but the technique also uses a multistep approach of filtering and combining the signals to recover the lost components also. The simulation results show that the proposed algorithm removes the noise without compromising the useful information of signal.