Computationally efficient algorithms for the two-dimensional Kolmogorov–Smirnov test

A Comparative Study of Exact Algorithms for the Two Dimensional Strip Packing Problem

In this paper we consider a two dimensional strip packing problem. The problem consists of packing a set of rectangular items in one strip of width W and infinite height. They must be packed without overlapping, parallel to the edge of the strip and we assume that the items are oriented, i.e. they cannot be rotated. To solve this problem, we use three exact methods: a branch and bound method, a...

Computationally-Efficient DNLMS-Based Adaptive Algorithms for Echo Cancellation Application

This paper investigates the application of the delayed normalized least mean square (DNLMS) algorithm to echo cancellation. In order to reduce the amount of computations, DNLMS is modified by using computationallyefficient techniques including the M-Max algorithm, a Stopand-go (SAG) algorithm, and Power-of-two (POT) quantization. For the SAG algorithm, a new stopping criterion related to the re...

Computationally efficient algorithms for third order adaptive Volterra filters

The input autocorrelation matrix for a third order (cubic) Volterra adaptive filter for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efftcient fast adaptive algorithm. When the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of the cubic filter inherits a block diagonal structure, wit...

Computationally Efficient Adaptive Algorithms for Multicarrier Physical Layer

Computationally efficient algorithms for statistical image processing. Implementation in R

Abstract: In the series of our earlier papers on the subject, we proposed a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We developed algorithms that allowed to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of unknown distribution. No b...