Main Article Content
The problem of blind source separation (BSS) has been intensively studied by the signal processing community. The first solutions to deal with BSS were proposed in the 1980's and are founded on the concept of independent component analysis (ICA). More recently, aiming at tackling some limitations of ICA-based methods, much attention has been paid to alternative BSS approaches. In this tutorial, in addition to providing a brief review of the classical BSS framework, we present two research trends in this area, namely source separation over Galois fields and sparse component analysis. For both subjects, we provide an overview of the main criteria, highlighting scenarios that can benefit from these more recent BSS paradigms.
How to Cite
Silva, D., Duarte, L., & Attux, R. (2016). Blind Source Separation: Fundamentals and Perspectives on Galois Fields and Sparse Signals. Journal of Communication and Information Systems, 31(1). https://doi.org/10.14209/jcis.2016.16
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