A. Sampath Dakshina Murthy ¹, S. Koteswara Rao ², G. Thiagarajan ¹, V. Suresh ³

Affiliations
1 Department of ECE, Vignan’s Institute of Information Technology, Beside VSEZ, Duvvada, Gajuwaka, Visakhapatnam - 530046, Andhra Pradesh, IN
2 Department of ECE, K L University, Green Fields, Vaddeswaram, Guntur District - 522502, Andhra Pradesh, IN
3 Department of CSE, Vignan’s Institute of Information Technology, Beside VSEZ, Duvvada, Gajuwaka, Visakhapatnam - 530046, Andhra Pradesh, IN

Abstract

For processing signals and in control application filters are essential, linear optimums discrete time filters such as wiener filter and Kalman filter are on orthogonal principle. For non stationary cases of having a presence of noise, adaptive wiener filter has to be applied using Monte Carlo Simulation 250 samples were used for 50 runs. Coefficients of linear filter are used to estimate the additive white noise. Error is calculated and RMS value of each error is added to the sample for desired signal. FIR wiener filters of order 6, 12, 24 were chosen for adaptive operators .Simulation results were quite encourage in the sense that noise was suppressed to maximum extends. Adaptive methods noisy higher number of samples and more than 100 runs are linear to yield better results.

Keywords

Noise Cancellation, Monte Carlo Simulation, Wiener Filter, Optimal Filter, Wiener-Hopf Equations, Wide-Sense Stationary Random Processes, Discrete Wiener Filter, Discrete Kalman Filter.

Full Text

   

V. Suresh ¹, S. Koteswarao Rao ², G. Thiagarajan ¹, Rudra Pratap Das ¹

Affiliations
1 Department of ECM, Vignan's Institute of Information Technology, Visakhapatnam - 530046, Andhra Pradesh, IN
2 Department of ECE, K L University, Vaddeswaram - 522502, Guntur, Andhra Pradesh, IN

Abstract

Objective: Noise determination is an important part of any signal processing evaluation .It can influence the output and create faults in the system. Wavelet transforms are used for evaluate noise components in signal and imaging processing. Discrete wavelets are more suitable for denoising. Methods/Analysis: In this work it has been proposed to use DWT having their successive procedures such as signal decomposition, threshold of coefficient and signal reconstruction. Discontinuities are also detected by wavelets. Findings: Synthetic signals have been generated using MATLAB simulation. Attempt has been to reduce the noise in various cases using wavelet analysis. Corresponding results of LPF and HPF have been considered. Novelty/Improvement: Further work can be done by extended the procedure to higher number of filtering operations.

Keywords

Denoising, Discontinuities, Discrete Wavelets, High Pass Filter, Lowpass Filter, Wavelet Transforms.

Full Text