In this paper we present a visual learning approach that uses non-parametric probability estimators. We use entropy analysis over the training set in order to select the features that best represent the pattern class of faces, and set up discrete probability models. These models are tested in the context of maximum likelihooddetection of faces. Excellent results are reported in terms of the cor...

It is well-known that the optimum multiuser detector achieves the global maximum likelihood (GML) detection in the entire set of hypotheses and several other iterative detectors such as the SAGE detector achieve the local maximum likelihood (LML) detection in the smallest neighborhood. In this paper, we generalize the local maximum likelihood detection into any size of neighborhood. The charact...

The problem of multiuser detection in asynchronous Direct-Sequence Code-Division Multiple-Access systems with residual errors in timing and amplitude estimation is addressed. Prior work has shown that the maximum-likelihood (ML) solution is sensitive to timing errors as small as a fraction of a chip period. In this paper, residual errors in both synchronization and fading coeecient estimation a...

Recent advances in quantum technologies pave the way for noisy intermediate-scale (NISQ) devices, where approximation optimization algorithm (QAOA) constitutes a promising candidate demonstrating tangible advantages based on NISQ devices. In this paper, we consider maximum likelihood (ML) detection problem of binary symbols transmitted over multiple-input and multiple-output (MIMO) channel, fin...