In this paper, we propose improved versions of normalized least mean square (NLMS) algorithm: single and multiple -variable step size normalized least mean square (VSSNLMS) algorithms for echo cancellation. The presented algorithms exhibit faster convergence rate in comparison to NLMS algorithm. Simulation results employing standard figure of merits show how the algorithms perform better than N...

This paper presents an efficient architecture of the Least Mean Square (LMS) adaptive algorithm implemented as a FIR filter on a reconfigurable platform. The architecture of the adaptive filter was developed with a general structure so it can be used in many applications with minimum limitations. Besides general use, the architecture has the advantage of using optimal hardware resources on the ...

Considering a large-scale energy-harvesting wireless sensor network (EH-WSN) measuring compressible data, sparse random projections are feasible for data well-approximation, and the sparsity of random projections impacts the mean square error (MSE) as well as the system delay. In this paper, we propose an adaptive algorithm for sparse random projections in order to achieve a better tradeoff bet...

In this paper, the comparison between Hybrid Image Compressions methods and Fuzzy logic based image Compression is discussed. The Hybrid Comparison Method is a combination of both the DCT and DWT Image Compression method. When more than one compression technique are applied to compressed one image for high value of PSNR (peak signal to noise ratio) and CR (compression ratio) this process is cal...

The steady-state performance of adaptive equalizers can significantly vary when they are implemented in finite precision arithmetic, which makes it vital to analyze their performance in a quantized environment. In this paper we present a fixed point analysis for the steady-state mean square error (MSE) of a blind adaptive equalizer and the optimal value of the step-size that minimizes this MSE....

Audio compression is designed to reduce the transmission bandwidth requirement of digital audio streams and storage size of audio files. Audio compression has become one of the basic technologies of the multimedia age to achieve transparent coding of audio and speech signals at the lowest possible data rates. This paper presents a comparative analysis of audio signal compression using transform...

We propose a new method for evaluating the mean square error (mse) of a possibly biased estimator 1̂ θ , or, rather, the class of estimators to which it belongs. The method uses confidence intervals c of a corresponding unbiased estimator θ̂ and makes its assessment based on the extent to which c includes 1̂ θ . The method does not require an estimate, implicit or explicit, of the bias of 1̂ θ , is...

The present research work is about to disaster mitigation using the applications of ANN. The ANN is used in the number of diverse fields due to its ability to model non linear patterns and self adjusting (learning) nature to produce consistent output when trained using supervised learning. This study utilizes Backpropagation Neural Network to train ANN models to mitigation of disaster through f...

Performance of equalizers depends on the discrete time model of the input signal and noise. The use of higher sampling rate results in colored noise when the bandwidth of the noise-limiting prefilter is not sufficiently large. It is shown that the mean square error (MSE) performance of linear equalizers becomes sensitive to the decision delay when the input noise is colored, and by using the ap...

This paper proposes a novel linear estimator named stochastic MV-PURE estimator, developed for the stochastic linear model, and designed to provide improved performance over the linear minimum mean square error (MMSE) Wiener estimator in cases prevailing in practical, real-world settings, where at least some of the second-order statistics of the random vectors under consideration are only imper...