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...

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...

Recent years have witnessed a controversy over Heisenberg’s famous error-disturbance relation. Here we resolve the conflict by way of an analysis of the possible conceptualizations of measurement error and disturbance in quantum mechanics. We discuss two approaches to adapting the classic notion of root-mean-square error to quantum measurements. One is based on the concept of noise operator; it...

A class of least squares problems that arises in linear Bayesian estimation is analyzed. The data vector y is given by the model y = P(Hθ + η) +w, where H is a known matrix, while θ, η, and w are uncorrelated random vectors. The goal is to obtain the best estimate for θ from the measured data. Applications of this estimation problem arise in multisensor data fusion problems and in wireless comm...

In this study, we present a measurement-based model for path loss prediction in three GSM service areas at 900 MHz . Modified Hata model for rural, suburban, and urban environments were derived in this study on the basis of experimental path loss measurements with the use of least square method. The models developed predicted with reasonable accuracy the path loss of radio networks investigated...

Abstract In this paper, we propose a simple and effective complementary label learning approach to address the noise problem for deep model. Different surrogate losses have been proposed learning, however, are often sophisticated designed, as required satisfy classifier consistency property. We an square loss under unbiased biased assumptions. also show theoretically that our method assurances ...