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Apr 20, 2024
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ECE 6381 - Sparse Representations for Signal Processing Credit Hours: 3
Lecture Contact Hours: 3 Lab Contact Hours: 0
Prerequisite: The students are expected to have basic knowledge of Digital Signal Processing or material covered in an equivalent course. Students are also expected to have basic Matlab knowledge.
The course will focus on foundations of multi-resolution analysis and wavelet theory for signal representation. Additionally, the general framework of sparsity (a foundational tool for applications such as compressive sensing, denoising and classification) and structured sparsity will be presented. The course will have a rigorous theoretical component and a hands-on project component where students will apply these techniques to a real-world image analysis problem.
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