Dear Colleagues,

Recent advances in signal processing and signal representation have brought to the forefront the need for a new understanding of the information content of signals and signal models. Progress, for instance, in sparsity and compressive has led to countless advances in signal processing.  However, a deficit in understanding both the information content of the innovative signal processing algorithms and the relations among information content and signal acquirement complexity still exists.

At the same time, the latest developments in information theory (IT) have proven its usage in a broader variety of research areas, far from its common uses in communications. Likewise, IT outlooks have powered new methods of both clustering and dimensionality reduction. The algorithm methods predominant in error improvement have led to advanced sparse signal retrieval methods, encouraging the use of IT tools in other emerging fields.

Signal processing in IT framework is mainly related to procedures and algorithms to accomplish IT standards. In any case, information is practically always characterized as some type of signal, hence making any employment of IT procedures irremovable from signal processing. The novel challenges developing from the new big data era are changing fields like statistics, machine learning, and data mining as information processing methods. Moreover, there are several fields central to signal processing that are of growing importance in the IT community, like machine learning.

The aim of this Special Issue is to bring together the signal processing, machine learning, and information theory communities. Thus, we encourage researchers to submit their latest works in IT for digital signal processing. Potential subjects include but are not limited to the following:

• Signal processing, e.g., interference alignment, interference cancellation, and other multiuser capability accomplishing methods; full-duplex communication; and information acquisition.
• Statistics and machine learning, e.g., computational efficiency and high-dimensional statistical theory.

Prof. Dr. Jorge Rocha
Prof. Dr. José António Tenedório
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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