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 ...

This paper investigates the mean square error (MSE)-optimal conditional estimator (CME) in one-bit quantized systems context of channel estimation with jointly Gaussian inputs. We analyze relationship generally nonlinear CME to linear Bussgang estimator, a well-known method based on Bussgang's theorem. highlight novel observation that is equal for different special cases, including case univari...