High dimensional data are nowadays encountered in various branches of science. Variable selection techniques play a key role in analyzing high dimensional data. Generally two approaches for variable selection in the high dimensional data setting are considered — forward selection methods and penalization methods. In the former, variables are introduced in the model one at a time depending on th...

We formulate the problem of finding a statistical representation of shape as a best basis selection problem in which the goal is to choose the basis for optimal shape representation from a very large library of bases. In this work, our emphasis is on applying this basis selection framework using the wavelet packets library to estimate the probability density function of a class of shapes from a...

We study the performance of differential spacetime codes with linear constellation precoding (LCP) for orthogonal frequency division multiplexing (OFDM) cooperative networks over frequency selective channels. Through exploiting the unitary structure of the orthogonal STBCs, we design a low complexity differential STBC-LCP-OFDM receiver for cooperative networks. We assume the amplify-and-forward...

The orthogonal wavelet lowpassed filters coefficients with arbitrary length are constructed in this paper. When N=2k and N k = − 1 2 , the general analytic constructions of orthogonal wavelet filters are put forward, respectively. The famous Daubechies filter and many other wavelet filters are tested by the proposed novel method, which is very useful for wavelet theory research and many applica...

In this paperwe analyze the Stieltjes functions defined by the Szegő inverse transformation of a nontrivial probabilitymeasure supported on the unit circle such that the corresponding sequence of orthogonal polynomials is defined by either backward or forward shifts in their Verblunsky parameters. Such polynomials are called anti-associated (respectively associated) orthogonal polynomials. Thus...

This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to...

We examine the problem of assigning and reassigning orthogonal variable spreading factor (OVSF) codes at the forward link in 3G mobile communications systems employing WCDMA. Efficient channelization code management results in high code utilization and increased system capacity. We introduce a new code selection scheme, which, by means of a very simple measure, attempts to mitigate the problem ...