**[The Compressive Sensing Blog][Compressive Sensing: The Big picture] **

**[Compressive Sensing 2.0 Community]
[Compressive Sensing 2.0][Compressive Sensing Videos]**

**[The blog] ****[Compressed Sensing: The Big picture] ****[Local CS codes] [List of entries in the blog]**** **

Most of the material listed is a little outdated.I have used only about the first 100 entries on CS. As of Today, the current number of entries has gone beyond 220. For a better description of the on-going developement in compressive sensing, I have set up a living document on Compressed Sensing that can be found here. Also, since the current layout of Blogger does not allow much summarizing of the entries and since Compressed Sensing is such a dynamic framework, I have found it useful to try to parse of these entries on Compressed Sensing in the following list (the summary of all these entries is in Compressed Sensing: The Big Picture).

- 1. Tutorials
- 1.1 Videos
- 1.2 Presentations
- Heavy hitters/sketching

- 1.3 Examples
- ~ Compressed Sensing: The Framework

- 2. Short Reviews
~ CS is not just Compressed Sampling nor Compressed Sensing.

~ Smashed filters and Bayesian approach to Compressed Sensing among others

~ Compressed Sensing: High Resolution Radar, Identification of Sparse Matrices

~ Compressed Sensing: Extending Reed-Solomon codes.

~ Compressed Sensing: A Method for Large-Scale l1-Regularized Least Squares

~ Compressed Sensing: Sparse Measurement Matrices

~ Compressed Sensing: Fast Compressive Sampling with Structurally Random Matrices

~ Compressed Sensing: Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors

~ Compressed Sensing: Optimal non-linear models for sparsity and sampling

- 3. Synthesis thoughts/ Theory
~ Compressed Sensing: Random Thought on a Low Dimensional Embedding

~ Compressed Sensing: Reweighted L1 meets Europa

On L1

~ It's not about the sensor, it's about the target

~ Compressed Sensing Illustrated

~ Why Compressed Sensing is important when detecting movement

Random matrices

Greedy algorithms

3.2 Historical perspective

- ~ Nothing short of a revolution, part 3 : It does not need to be nonlinear
- ~ Nothing short of a revolution. Part deux: Pursuing a dream
- ~ Nothing short of a revolution. Part I: When a scam can kill you

~ Compressed Sensing: How could Hardware implementations become disruptive technologies ?

~ Compressed Sensing: Would Hierarchical Compressed Sensing Make Sense ?

- 4. Hardware
- 4.1 Rice Single Pixel Camera
- 4.2 My implementation
- 4.3 Other Implementations
~ Compressed Sensing Hardware Implementations

~ Compressed Sensing Hardware Implementations (part deux)

~ Compressed Sensing: Hardware Implementation, Part III, The explosive birth of coded aperture.

~ Compressed Sensing: Single-Pixel Imaging via Compressive Sampling, Stable Manifold Embedding

~ Compressed Sensing: Thoughts on Coded Aperture and Compressed Sensing

~ Compressive Sensing: Hardware Implementation update and some newer developments

- 4.4 Disruptive Technology

- + -
5. Algorithms
- 5.1 Reconstruction Algorithm
- ~ Compressed Sensing: Reweighted L1 and a nice summary on Compressed Sampling
- ~ Compressed Sensing: Reweighted L1 meets Europa
- ~ Compressed Sensing: Reweighted Lp through Least square (L2), Forget Europa, let's shoot for Titan
- ~ Compressed Sensing: SpaRSA and Image Registration using Random Projections
- ~ Compressed Sensing: Solving the Basis Pursuit problem with a Bregman Iterative Algorithm
- ~ Compressed Sensing: A new TwIST

- 5.2 Measurement Algorithm
- 5.3 Software Implementation
~ SPGL1: a solver for large-scale sparse reconstruction problems

~ Sparsify, another reason you should not avoid Compressed Sensing

~ There are now three Compressed Sensing Reconstruction Codes available

~ l1_ls: Simple Matlab Solver for l1-regularized Least Squares Problems (compressed sensing)

Bayesian approach

~ Compressed Sensing: Sparco helps you test all these spiffy reconstruction algorithms

~ Monday Morning Algorithm Part deux: Reweighted Lp for Non-convex Compressed Sensing Reconstruction

~ Compressed Sensing: Sparse PCA and a Compressed Sensing Search Engine

~ Compressed Sensing: Restricted Isometry Properties and Nonconvex Compressive Sensing

~ Compressed Sensing: SpaRSA and Image Registration using Random Projections

~ Compressed Sensing: Utilizing Sparsity in Compressed Sensing Reconstruction Algorithms

- 5.4 Monday Morning Algorithm
- 5.5 Finding Sparse Bases

- 5.1 Reconstruction Algorithm
- + -
6. Applications
- 6.1 Brain / Cognition
- 6.2 Machine Learning
- 6.3 Solving integral equations
- 6.4 SAR
- 6.5 Network
- 6.6 PDE
- 6.7 Other

- 7. Reference Material
- 8. Miscelaneous
~ Traduction de Compressed Sensing en Francais

~ Compressed Sensing: Map of entries from this blog

~ Monday Morning Algorithm Part 1: Fast Low Rank Approximation using Random Projections

~ Trace: What is it good for ? - How Compressed Sensing relates to Dimensionality Reduction

~ Sparsity: What is it good for ?

~ Compressed Sensing: Blog entries and website updates.

~ Compressed Sensing: MCA news and Sublinear Recovery of Sparse Wavelet Signals

~ Compressed Sensing, Compressive Sampling: A Customized Search Engine

~ Sparsity Based Blind Source Separation

Calendar

- Conferences
- Seminar