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Quantizing the Samples: Source Coding Perspective
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Classroom Contents
From Compressed Sensing to Deep Learning - Tasks, Structures and Models
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- 1 Intro
- 2 Data Redundancy
- 3 Digital Information
- 4 Analog Girl in a Digital World...
- 5 Standard Acquisition Systems
- 6 Limitations of Standard Systems
- 7 Task-Based Structured Acquisition
- 8 Advantages of Joint Design
- 9 Streams of Pulses Radar
- 10 Xampling Hardware
- 11 Compressed Sensing Extensions
- 12 Sub-Nyquist Ultrasound Imaging
- 13 Demo Movie
- 14 Deep Adaptive Beamforming
- 15 Channel Data Clinical Forum Improve diagnostics from channel data!
- 16 Sub-Nyquist and Cognitive Radar
- 17 Cognitive Automotive Radar
- 18 Multicoset Sampling
- 19 Xampling: Modulated Wideband Converter
- 20 Sub-Nyquist Cognitive Radio
- 21 Super Resolution Microscopy
- 22 SPARCOM: Super Resolution Correlation Microscopy
- 23 Super Resolution Contrast Enhanced Ultrasound
- 24 SUSHI: Sparsity-Based Ultrasound Super- resolution Hemodynamic Imaging
- 25 Analog to Digital Compression
- 26 Unification of Rate-Distortion and Sampling Theory
- 27 Quantizing the Samples: Source Coding Perspective
- 28 Optimal Sampling Rate
- 29 Metasurfaces for Analog Precoding
- 30 Antenna Selection for Imaging
- 31 Product Arrays
- 32 Spatial Sub-Sampling
- 33 Black-Box Deep Learning
- 34 Model Based Signal Processing
- 35 Model-Based vs. Deep Learning Model-based signal processing
- 36 Model-Based Deep Learning
- 37 Deep Unfolding
- 38 DUBLID: Deep Unrolling for Blind Deblurring
- 39 Deblurring Results
- 40 Super-resolution via Deep Learning
- 41 Data Driven Hybrid Algorithms
- 42 Data-Driven Factor Graph Methods
