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1
Intro
2
Data Redundancy
3
Digital Information
4
Analog Girl in a Digital World...
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Standard Acquisition Systems
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Limitations of Standard Systems
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Task-Based Structured Acquisition
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Advantages of Joint Design
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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
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Cognitive Automotive Radar
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Multicoset Sampling
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Xampling: Modulated Wideband Converter
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Sub-Nyquist Cognitive Radio
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Super Resolution Microscopy
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SPARCOM: Super Resolution Correlation Microscopy
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Super Resolution Contrast Enhanced Ultrasound
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SUSHI: Sparsity-Based Ultrasound Super- resolution Hemodynamic Imaging
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Analog to Digital Compression
26
Unification of Rate-Distortion and Sampling Theory
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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
Description:
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.

From Compressed Sensing to Deep Learning - Tasks, Structures and Models

IEEE Signal Processing Society
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#Engineering #Electrical Engineering #Signal Processing #Computer Science #Deep Learning #Compressed Sensing #Beamforming
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Intro