Delves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.
Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.
Explores analog-to-digital conversion, neural signal optimization, multichannel architectures, and on-chip compression techniques in neuroengineering.
Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.
Covers the concepts of compression and prediction using prefix-free codes and distributions.
