The performance of the Constant Modulus Algorithm can su er because of the existence of local minimawith large Mean Squared Error(MSE). This paper presents a new way of obtaining the optimumMSE over all delays using a second equalizer under a mixed Constant Modulus and Cross Correlation Algorithm (CM-CCA). Proof of convergence is obtained for the noiseless case. Simulations demonstrate the pote...

We propose a simple and efficient method to compute the weighted mean square error for a biorthogonal M-channel wavelet coder for multidimensional signals. Indeed, biorthogonal filters weight the amount of quantization error which appears on the reconstructed output.We show that the mean square error of a reconstructed signal, resulting from the quantization errors of the M cosets provided by a...

Best mean square prediction for moving average time series models is generally non-linear prediction, even in the invertible case. Gaussian processes are an exception, since best linear prediction is always best mean square prediction. Stable numerical recursions are proposed for computation of residuals and evaluation of unnormalized conditional distributions in invertible or non-invertible mo...

We consider the compressed sensing problem, where the object x0 ∈ R is to be recovered from incomplete measurements y = Ax0 + z; here the sensing matrix A is an n × N random matrix with iid Gaussian entries and n < N . A popular method of sparsity-promoting reconstruction is `-penalized least-squares reconstruction (aka LASSO, Basis Pursuit). It is currently popular to consider the strict spars...

We derive a simple general parametric representation of the rate–distortion function of a memoryless source, where both the rate and the distortion are given by integrals whose integrands include the minimum mean square error (MMSE) of the distortion ∆ = d(X, Y ) based on the source symbol X , with respect to a certain joint distribution of these two random variables. At first glance, these rel...

Some connections between linear minimum mean square error estimators, maximum output SNR filters and the least square solutions are presented. The notes have been prepared to be distributed with EE 503 (METU, Electrical Engin.) lecture notes. 1 Linear Minimum Mean Square Error Estimators The following signal model is assumed: r = Hs + v (1) Here r is a N × 1 column vector denoting the observati...

parameter estimation of the nonlinear muskingum model is a highly nonlinear optimization problem. although various techniques have been applied to optimize the coefficients of the nonlinear muskingum flood routing models, but an efficient method for this purpose in the calibration process is still lacking. the accuracy of artificial bee colony (abc) algorithm is investigated in this paper to op...

This report summarizes the understanding I have gained in my studies for the independent studies course ENEE 699 up to 15th April, 2000. It includes the derivation of an extension of a result of Wong and Brockett on the behaviour of scalar quantizers. 1 Control under limited communication Let, ẋ(t) = f(x(t), u(t)) + n(t) (1) be a controlled dynamical system where the state x(·) ∈ R, the control...