Course Details
Contents
Introduction to recent topics and research trends in signal processing and analysis, with particular emphasis on theory. Topics to be covered in the first session of this course include graph signal processing, graph learning, sparse signal processing, machine learning and optimization for signal processing, advanced topics in Fourier analysis, including algorithms for sparse FFT, sampling theory for discrete signals, etc. Most of the references will be from recent publications in the field, with emphasis on tutorial papers. The list below is for reference and includes only a fraction of the papers expected to be covered.
References
1. Ortega, Antonio, et al. "Graph signal processing: Overview, challenges, and applications." Proceedings of the IEEE 106.5 (2018): 808-828.
2. Dong, Xiaowen, et al. "Learning graphs from data: A signal representation perspective." IEEE Signal Processing Magazine 36.3 (2019): 44-63.
3. Gilbert, Anna C., et al. "Recent developments in the sparse Fourier transform: A compressed Fourier transform for big data." IEEE Signal Processing Magazine 31.5 (2014): 91-100.
4. Chi, Chong-Yung, Wei-Chiang Li, and Chia-Hsiang Lin. Convex optimization for signal processing and communications: from fundamentals to applications. CRC press, 2017.
5. Donoho, David L. "Compressed sensing." IEEE Transactions on information theory 52.4 (2006): 1289-1306.