Theory and Applications of Information Processing Algorithms

Dear Colleagues,

During the last decades of research, we have witnessed a progressive consolidation of the concept of information at the inner core of the design and evaluation of many modern algorithmic procedures for the processing of the observed data. Information measures and statistical divergences have revealed themselves as transversal tools whose widespread use tends to blur some of the already diffuse boundaries between interrelated research fields such as artificial intelligence, cybernetics, statistical signal processing, communications, multimedia processing and biomedical signal analysis.

In this special issue, we encourage researchers to present original results in the use of information and divergence measures as building blocks for both the principles and criteria that drive the processing of the observations and, also, their associated performance evaluation. Possible topics include, but are not limited to, advances in the theory and applications of machine learning for signal processing, shallow and deep learning methods, estimation and detection techniques, compression, model selection or comparison. Furthermore, we also welcome exceptional review contributions covering the state-of-the-art research areas that fall within the scope of this special issue.

Prof. Dr. Sergio Cruces
Dr. Iván Durán-Díaz
Dr. Rubén Martín-Clemente
Prof. Dr. Andrzej Cichocki
Guest Editors

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