Dear Colleagues,

Dictionary Learning (DL) is a combination of signal processing and machine learning that aims at finding a dictionary in which some training data permits a sparse representation. DL is a popular technique in signal processing for denoising, enhancement, inpainting, classification, or super-resolution.

In addition, while many real-time and embedded signal processing systems need to process real-time data under strict physical and operational conditions, efficient techniques for reducing unnecessary or redundant data to be processed must be utilized. Some representative examples on this direction are the Orthogonal Matching Pursuit (OMP) algorithm, Matrix Inversion Bypass (MIB), Maximum a Posteriori (MAP), among others. Therefore, there exists a major necessity for signal processing algorithm implementations in VLSI design, especially Field Programmable Gate Array (FPGA), because FPGAs acquire better performance and/or flexibility at a lower cost compared to Digital Signal Processors (DSP).

We invite you to submit high quality papers to this Special Issue on “Circuits and Systems of Dictionary Learning Algorithms and Applications”, with subjects covering all aspects of embedded, HW/SW design and VLSI implementations. The topics include, but are not limited to:

- VLSI implementations of Dictionary Learning (DL) algorithms and techniques;

- efficient FPGA implementations DL algorithms;

- system-on-chips designs of DL algorithms;

- embedded systems for of DL algorithms;

- hardware architectures of DL-based image processing;

- hardware architectures of DL-based speech signal processing;

- low power issues of DL hardware architectures;

- performance enhancement issues of DL hardware architectures;

- implementations of DL applications in signal processing and machine learning.

Prof. Dr. Paris Kitsos
Guest Editor

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