ICA as a preprocessing technique for classification

In this paper we propose the use of the independent component analysis (ICA) [1] technique for improving the classification rate of decision trees and multilayer perceptrons [2], [3]. The use of an ICA for the preprocessing stage, makes the structure of both classifiers simpler, and therefore improves the generalization properties. The hypothesis behind the proposed preprocessing is that an ICA analysis will transform the feature space into a space where the components are independent, and aligned to the axes and therefore will be more adapted to the way that a decision tree is constructed. Also the inference of the weights of a multilayer perceptron will be much easier because the gradient search in the weight space will follow independent trajectories. The result is that classifiers are less complex and on some databases the error rate is lower. This idea is also applicable to regression

A digital holography technique for generating beams with arbitrary polarization and shape

A method for generating beams with arbitrary polarization and shape is proposed. Our design requires the use of a Mach-Zehnder set-up combined with translucent liquid crystal displays in each arm of the interferometer; in this way, independent manipulation of each transverse beam components is possible. The target of this communication is to develop a numerical procedure for calculating the holograms required for dynamically encode any amplitude value and polarization state in each point of the wavefront. Several examples demonstrating the capabilities of the method are provided.

Process variability-aware proactive reconfiguration technique for mitigating aging effects in nanos scale SRAM lifetime

Postprint (published version)