Much work has been done to optimize wavelet transforms for SIMD extensions of modern CPUs. However, these approaches are mostly restricted to the vertical part of 2-D transforms w...
— We make use of discrete wavelets to extract distinguishing features between normal and cancerous human breast tissue fluorescence spectra. These are then used in conjunction wi...
Bhadra Mani, C. Raghavendra Rao, P. Anantha Lakshm...
— This paper introduces an expectation-maximization (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband OFDM based UWB commun...
In this paper, a digital watermarking algorithm for copyright protection based on the concept of embed digital watermark and modifying frequency coefficients in discrete wavelet tr...
: We propose a wavelet multiscale decomposition based autoregressive approach for the prediction of one-hour ahead ahead load based on historical electricity load data. This approa...
D. Benaouda, Fionn Murtagh, Jean-Luc Starck, O. Re...
While there is a very long tradition of approximating a data array by projecting row or column vectors into a lower dimensional subspace the direct approximation of a data matrix ...
Background: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical m...
: Compression is the process of representing information in a compact form so as to reduce the bit rate for transmission or storage while maintaining acceptable fidelity or data qu...
The interest in inference in the wavelet domain remains vibrant area of statistical research because of needs of scientific community to process and explore massive data sets. Prim...
Ilya Lavrik, Yoon Young Jung, Fabrizio Ruggeri, Br...
With the increasing growth of technology and the entrance into the digital age, we have to handle a vast amount of information every time which often presents difficulties. So, the...