We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithms that utilize the weighted block Newton (WBN) and variable step size WBN (VSSWBN) method, in which the weighting matrix is determined in the form of the inverse of the squared error or as an exponential function with a negative exponent. The proposed WBN and VSSWBN algorithms converge in two iteratio...

A convolutional network architecture termed sparse convolutional neural network (SCNN) is proposed and tested on a real-world classification task (car classification). In addition to the error function based on the mean squared error (MSE), approximate decorrelation between hidden layer neurons is enforced by a weight orthogonalization mechanism. The aim is to obtain a sparse coding of the obje...

We discuss the problem of image restoration from observed images. Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance of the filters, the parameter values should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image ...

An important aspect of estimation theory is characterizing the best achievable performance in a given estimation problem, as well as determining estimators that achieve the optimal performance. The traditional Cramér-Rao type bounds provide benchmarks on the variance of any estimator of a deterministic parameter vector under suitable regularity conditions, while requiring a-priori specification...

The choice of a smoothing parameter or bandwidth is crucial when applying nonparametric regression estimators. In nonparametric mean regression various methods for bandwidth selection exists. But in nonparametric quantile regression bandwidth choice is still an unsolved problem. In this paper a selection procedure for local varying bandwidths based on the asymptotic mean squared error (MSE) of ...

In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addi...

The effects of DC offsets on four variations of the stochastic gradient algorithm are analyzed to determine the most appropriate algorithm for hardware implementation. The output mean squared error (MSE) performance in the presence of DC offsets is evaluated and compared with computer simulations for each of the algorithms assuming a Gaussian input distribution.

We consider monitoring the quality of compressed video transmitted over a packet network from the perspective of a network service provider. We estimate the Mean-Squared Error (MSE) caused by packet loss by examining only the received video bitstream. We describe our FullParse method, which extracts sequence-specific information including spatio-temporal activity and the effects of error propag...