From Compressed Sensing to Deep Learning - Tasks, Structures and Models
Explore a comprehensive IEEE Signal Processing Society lecture on signal processing advancements, from compressed sensing to deep learning. Delve into task-based structured acquisition, sub-Nyquist sampling techniques, and cognitive radar applications. Discover innovative approaches in ultrasound imaging, super-resolution microscopy, and analog-to-digital compression. Examine the unification of rate-distortion and sampling theory, and investigate the interplay between model-based signal processing and deep learning. Learn about cutting-edge algorithms like SPARCOM, SUSHI, and DUBLID, and their applications in various fields. Gain insights into data-driven hybrid algorithms and factor graph methods, bridging traditional signal processing with modern machine learning techniques.