New paper accepted in IEEE Transactions on Signal Processing

A few days ago (16/11/2010) I was notified that my paper "Extension of Wirtinger’s Calculus to Reproducing Kernel Hilbert Spaces and the Complex Kernel LMS" has been accepted for publication in the March or April 2011 issue of the IEEE Transactions on Signal Processing.

You may find the preprint version at Arxiv.
If you are interested in Wirtinger's Calculus you may find usefull my detailed notes on the subject.

ICPR 2010 Best Scientific Paper Award

My paper, "Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces" (with K. Slavakis and S. Theodoridis)  has been selected for the Best Scientific Paper Award (Track III: Signal, Speech, Image and Video Processing) at the 20th International Conference on Pattern Recognition (ICPR 2010).

http://www.icpr2010.org/awards.php

New paper to be presented in MLSP 2010 - EXTENSION OF WIRTINGER CALCULUS IN RKH SPACES AND THE COMPLEX KERNEL LMS

Today, I have received the letter of acceptance for the manuscript "EXTENSION OF WIRTINGER CALCULUS IN RKH SPACES AND THE COMPLEX KERNEL LMS". This paper will be presented at the 2010 IEEE Machine Learning for Signal Processing Workshop (MLSP 2010). Below you can read the abstract:

Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. However, so far, the emphasis has been on batch techniques. It is only recently, that online adaptive techniques have been considered in the context of signal processing tasks. To the best of our knowledge, no kernel-based strategy has been developed, so far, that is able to deal with complex valued signals. In this paper, we take advantage of a technique called complexification of real RKHSs to attack this problem. In order to derive gradients and subgradients of operators that need to be defined on the associated complex RKHSs, we employ the powerful tool ofWirtinger’s Calculus, which has recently attracted much attention in the signal processing community. Writinger’s calculus simplifies computations and

offers an elegant tool for treating complex signals. To this end, in this paper, the notion of Writinger’s calculus is extended, for the first time, to include complex RKHSs and use it to derive the Complex Kernel Least-Mean-Square (CKLMS) algorithm. Experiments verify that the CKLMS can be used to derive nonlinear stable algorithms, which offer significant performance improvements over the traditional complex LMS orWidely Linear complex LMS (WL-LMS) algorithms, when dealing with nonlinearities.

New paper accepted in the IEEE Transactions of Image Processing

A few days ago I received the letter of acceptance for the manuscript "Adaptive Kernel-based Image Denoising employing Semi-Parametric Regularization".
In my home page (http://users.uoa.gr/~ldalla/pantelis/news.html) you can find the preprint version of the article, as well as the code (in C) and the examples used in the paper.

First

I decided to create a simple blog to post news about my research work.