Elias S. Manolakos

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We investigate the application of Hopfield neural networks (HNN's) to the problem of multiuser detection in spread spectrum/CDMA (code division multiple access) communication systems. It is shown that the NP-complete problem of minimizing the objective function of the optimal multiuser detector (OMD) can be translated into minimizing an HNN "energy"(More)
Nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms,(More)
The k-nearest neighbor (k-NN) is a popular non-parametric benchmark classification algorithm to which new classifiers are usually compared. It is used in numerous applications, some of which may involve thousands of data vectors in a possibly very high dimensional feature space. For real-time classification a hardware implementation of the algorithm can(More)
One of the most commonly used methods for protein separation is 2-DE. After 2-DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.e. without introducing distortions(More)
A novel formulation of the important DNA sequence base-calling problem as well as algorithms for its solution are introduced. The proposed approach is to bring DNA base-calling within the framework of a powerful statistical learning paradigm, which allows the incorporation of prior knowledge about the structure of the problem directly into the base-calling(More)
We have designed a modular SOM systolic architecture that can classify data vectors with thousands of elements in real time. The architecture is described as a soft IP core in synthesizable VHDL. The SOM neural network size, the input data vectors dimension, the weight and data element bitwidth precision etc. are all designer tunable parameters. Several SOM(More)
Many scientists and engineers have signal and image processing applications that involve large data sets and could benefit from parallel processing on readily-available clusters of workstations (COWs). Unfortunately these applications often exist as legacy code, such as Matlab functions, which are not easily parallelizable. The goal of the JavaPorts project(More)
The detection of protein spots in 2DGE images is one of the most important tasks in a proteomics data analysis workflow. All subsequent steps of differential expression analysis for biomarkers discovery depend on its effectiveness. In this study, we introduce the use of Active Contours without Edges coupled with Contourlet Transform - based image(More)