Sathyanarayan S. Rao

Increasing the speed of convergence of the constant modulus algorithm for blind channel equalization

The constant modulus algorithm (CMA) is an excellent technique for blind channel equalization. A signed error version of CMA (SE-CMA) and dithered signed error version (DSE-CMA) have been proposed which afford overall computational efficiency. We propose three different error functions for faster convergence. This would be essential for communication systems, which cannot afford a high startup delay or for systems, where the channels impulse response is rapidly fluctuating. One of these algorithms relies on the idea of a variable step size, which increases the rate of convergence.

Wavelets for waveform coding of digital symbols

A wavelet-based coder-decoder (codec) structure is defined for baseband waveform coding. Numerical results for bandwidth efficiency are given, and a comparison between several different wavelets is presented. Moreover, it is shown that wavelets obey the Nyquist pulse shaping condition and provide a unified framework for analog pulse shaping concepts of communications.

Constrained ECG compression using best adapted wavelet packet bases

The main goal of any electrocardiogram (ECG) compression algorithm is to reduce the bit rate while keeping the signal distortion at a clinically acceptable level. Percentage root mean square difference (PRD), the commonly used figure of merit, does not directly reveal whether the clinically significant ECG waveform information is preserved or not. We present the results of a study of ECG compression using an upper bound on the PRD. This bound is based on the initial performance of the algorithm and could be specified by the clinician after correlating the quality of the compressed versions of the ECG and the resulting PRD.

Neural detectors for signals in non-Gaussian noise

The authors demonstrate that a neural network can be trained for the purpose of detecting a known signal corrupted by additive Gaussian as well as non-Gaussian noise of the impulse type. It is shown that, in the presence of Gaussian noise, the performance of a properly trained neural network is very similar to that of the optimum matched filter detector. In the presence of non-Gaussian noise, however, neural detectors are shown to perform better than both the matched filter and locally optimum detectors.<<ETX>>

Design of discrete coefficient FIR filters by simulated evolution

The article presents a new algorithm, simulated evolutionary optimization, a multi-agent stochastic search method for optimizing the coefficients of a FIR digital filter in the powers-of-two space by minimizing a sum of two cost functions, namely the squared error between discrete filter frequency response and desired frequency response and the squared error between discrete and infinite precision coefficients. The proposed algorithm requires less computation time than existing techniques based on linear programming and simulated annealing and differs from the tree search based algorithms in optimizing the coefficients simultaneously. The effectiveness of the method is demonstrated through a low-pass filter example.

On waveform coding using wavelets

We consider a novel baseband waveform coding technique based on wavelets. Wavelets are recognized for their temporal and spectral localization and for their orthogonality across scale and location. We exploit these fundamental properties of wavelets and propose a wavelet-based modulator-demodulator structure to improve communication efficiency. Numerical results for bandwidth occupancy and bandwidth efficiency are given, and a detailed comparison between different families of wavelets is presented.<<ETX>>

Optimization of bilinear time series models using fast evolutionary programming

This letter presents a new algorithm, fast evolutionary programming (FEP), for determining the model orders and parameters of reduced parameter bilinear (RPBL) models used for predicting nonlinear and chaotic time series. FEP is a variant of the conventional evolutionary programming (EP) algorithm with a new mutation operator. This new mutation operator enhances EPs ability to escape from local minima resulting in a significantly faster convergence to the optimal solution. Both the model order and the parameters are evolved simultaneously. Experimental results on the sunspot series and Mackey-Glass series show that FEP is capable of determining the optimal model order and, in comparison with conventional evolutionary programming, evolves models with lower normalized mean squared error.

Some detection and resolution properties of maximum entropy spectrum analysis

Abstract An experimental comparison between conventional spectral estimation techniques and a Maximum Entropy Spectral Analysis (MESA) algorithm is made. Three factors in the experimentation make the results of considerable interest to workers in acoustic signal processing, especially sonar and surveillance. These are the range of signal-to-noise ratio (SNR) studied, the comparisons based equal length observation intervals and the use of ensemble averaging after maximum entropy analysis. Results are presented, for both resolution and peak signal response, which tend to indicate that the Maximum Entropy Method (MEM) offers considerable promise in achieving the detection performance of long observation interval discrete Fourier transform (DFT) analysis at a much reduced length of observation time.

Gaining Insight into Evolutionary Programming Through Landscape Visualization: An Investigation into IIR Filtering

Evolutionary programming (EP) has been used for the adaptation (optimization) of IIR filters. In a previous study [1], the rate of optimization using EP was shown to be dependent on the structure of the filter used during realization. Furthermore, this dependency changes with the filter order. In this paper, the reasons for such a dependence are investigated. Gradient-based algorithms are also affected by the filter realization, which determines the nature of the mean squared error surface. EP is robust to the presence of local minima and while ensuring the stability of the generated solution offers provable global convergence in the limit. The error surfaces, as seen by EP, while modeling these IIR filters in various realizations, namely, direct, cascade, parallel, and lattice form are analyzed. Experimental results show that ‘gradient friendly’ error surfaces, corresponding to favorable realizations when using gradient based techniques, are not necessarily ‘EP friendly’ and vice versa.

A new error function for fast phase recovery of QAM signals in CMA blind equalizers

A modification to the well-known constant modulus algorithm (CMA) for blind channel equalization is proposed. The conventional CMA has rotational ambiguity and hence cannot achieve carrier phase recovery. It happens because the cost function is made up of the absolute value of the equalizer output and phase information is not used. In this paper, we slightly modify the cost function and provide a different error function that restores the phase of the carrier. Simulations studies are shown for the case of 16-QAM signal constellation.