Thank you !

You know what they say about traditions in Aggieland: "Do something twice and it's a tradition." Mirroring last year's Thank you post, here is this year's. First, in 2009 the readership has increased and has mostly shifted from direct hits on the website to feedreaders.

The tone of the blog has probably changed a little as I mostly show new preprints without discussing them. As it stands, I try to balance the need to have a place where the latest and greatest is shared rapidly throughout the community and my need to digest all this information by trying to understand what is new and what is not. In order to digest this information, y'all are being amazingly kind to me with my dumb questions. I framed these into Q&A's and posted them on the blog, here is the list of the people who kindly obliged in this exercise:

• Laurent Jacques, Yves Wiaux and Pierre Vandergheynst, in CS: SparSA, Q/A, CS and Random Arrays, KF for reconstruction, Inferring Rankings Under Constrained Sensing, Post-silicon timing, a course and SSP09
• Jared Tanner and Remi Gribonval in CS: RIP vs NSP, Clarifications by Jared Tanner and Remi Gribonvall
• Dror Baron in CS: A small discussion with Dror Baron
• Andres Asensio Ramos in CS: Compressive Sensing for Spectroscopy and Polarimetry, Q&A with Andres Asensio Ramos, Some news from HERSCHEL/PACS
• Dror Baron and Dongning Guo in CS: LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing, A Q&A with Dror Baron and Dongning Guo/ Two jobs
• Michael Lustig in CS: Combining Parallel Imaging and Compressed Sensing, A short discussion with Michael Lustig, and CS: Q/A, Accelerating SENSE Using CS, Regularized SENSE thru Bregman Iterations, SparseSENSE, Bregmanized Nonlocal Regularization for Deconvolution
• Waheed Bajwa in CS: Answering a DSN question, Distilled Sensing, Modified-CS: Modifying CS for Problems with Partially Known Support, L1 and Hamilton-Jacobi

Something new and unexpected happened this past year as well and no it wasn't a list of people interested in compressive sensing on Twitter but rather the ability of the blog to provide some new thrust of thoughts as witnessed in the Ghost Imaging experiment of Ori Katz and Yaron Bromberg leading to the Compressive Ghost Imaging paper or in the inclusion of some thoughts in the review entitled: Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? by Xiaochuan Pan, Emil Sidky and Michael Vannier. As it happens, Richard Gordon the inventor of ART for CT-scanners responded to that review in this entry: MIA'09 and Richard Gordon's ART CT algorithm. I am sure this is the beginning of a long discussion. Thank you Xiaochuan Pan, Emil Sidky and Michael Vannier for getting the ball rolling. I hope that entries featured in the "These technologies do not exist" series will provide similar impetus.

Others have contributed to the blog and made it better as a result, thank you to them. Here is a list (in no particular order):

• Laurent Jacques in CS: Inline hologram reconstruction with sparsity constraints, Reading the Book of Memory, CS: LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing, A Q&A with Dror Baron and Dongning Guo/ Two jobs, CS: Wavefront Coding for Random Lens Imagers ?, CS: Sparse Reconstruction of Complex Signals in Compressed Sensing Terahertz Imaging, Compressive Sensing for Sparsely Excited Speech Signals
• Mário Figueiredo in CS: SparSA, Q/A, CS and Random Arrays, KF for reconstruction, Inferring Rankings Under Constrained Sensing, Post-silicon timing, a course and SSP09, CS: Robust PCA, C-SALSA is released, KAUST Jobs, CS: Some basic CS examples, C-SALSA, Distributed Sensing, Dictionary learning, Replica Method and Applications to CS, SLEP
• Angshul Majumdar in CS: Different codes, Recovery of clustered sparse signals, Theory of CS, Stagewise Polytope Faces Pursuit, 2-D tomography, CS:OLS/ROLS/StOLS, Group sparsity, Poisson CS, CS of parameterized shapes, IT meets free discontinuity, An homotopy algorithm for the Lasso, CS: MIA, More SpaRSA, Kalman Filter-CS, Compressed sensing and sparse recovery in exploration seismology, Solving Helmholtz, CS: LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing, A Q&A with Dror Baron and Dongning Guo/ Two jobs
• Jon Dattorro in CS: Convex iteration method, Statistical Estimation in High Dimension,, Super-resolution far-field ghost imaging via compressive sampling
• Yaniv Erlich in CS: Compressed Genotyping, DNA Sudoku - Harnessing high throughput sequencing for multiplexed specimen analysis
• David Gross in CS: Recovering low-rank matrices from few coefficients in any basis, Probability of Unique Integer Solution to a System of Linear Equations
• Alejandro Weinstein in CS: Some basic CS examples, C-SALSA, Distributed Sensing, Dictionary learning, Replica Method and Applications to CS, SLEP
• Pierre Borgnat in CS: ExCoV: Expansion-Compression Variance-component Based Sparse-signal Reconstruction, CS approach to time-frequency localization
• Brien Housand in CS: Dequantizing Compressed Sensing with Non-Gaussian Constraints, A request for Collaboration.
• Noam Shental in CS: Rare-Allele Detection Using Compressed Se(que)nsing, Shifted Transversal Design smart-pooling for high coverage interactome mapping
• Yi Ma in CS: A Matrix Completion Entry
• Ben Moran in CS: This week's long entry
• Tanish Agrawal in CS: This week's long entry
• Wenlin Gong in CS: Recovering low-rank matrices from few coefficients in any basis, Probability of Unique Integer Solution to a System of Linear Equations
• Petros Boufounos in CS: Sparsity, Randomness and Compressed Sensing, Spatio‐Temporal Compressive Sensing
• Jean-Luc Starck in CS: Compressive Sensing for Spectroscopy and Polarimetry, Q&A with Andres Asensio Ramos, Some news from HERSCHEL/PACS
• Giuseppe Paleologo in CS: Food for thoughts for the week-end.
• Wotao Yin in CS: ISD, A New Compressed Sensing Algorithm via Iterative Support Detection (and NSP vs RIP)
• Lianlin Li in CS: The Compressed-Sampling Filter (CSF)
• Daryl Lim, in CS: This week's long entry
• Michael Friedlander in Spot: A Linear Operator Toolbox
• Ewout van der Berg in Spot: A Linear Operator Toolbox
• Lei Yu, in CS: LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing, A Q&A with Dror Baron and Dongning Guo/ Two jobs
• Dirk Lorenz in CS: LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing, A Q&A with Dror Baron and Dongning Guo/ Two jobs
• Alexandros Dimakis in CS: LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing, A Q&A with Dror Baron and Dongning Guo/ Two jobs
• Julien Mairal, in CS:SPAMS, SPArse Modeling Software, a dictionary building facility and more...
• Jerome Darbon in CS: Update on "A Simple Compressive Sensing Algorithm for Parallel Many-Core Architectures", Saturn Equinox
• Sean O'Connor in CS: Various thresholds for $\ell_1$-optimization in CS, Radial K-t FOCUSS MRI, BBCS algorithm, block-sparse CS, Non Convex CS, OCT
• Tomoya Sakai in CS: CSBP, CoSAMP, Freedom through Imperfection: Exploiting the flexibility offered by redundancy in signal processing, Lossy Speech Compression Via CS
• Pawan Baheti in CS: Irregular tiling in retina, Recognition using information-optimal adaptive feature-specific imaging, other CS hardware encoding.
• Matthew Herman in CS: An answer to a question, Statistical RIP and Semi-Circle Distribution of Incoherent Dictionaries, Learning with Structured Sparsity, CS: Compressive Sensing Hardware Update, Compressive estimation , General Deviants, a conference, Adaptive CS, 2 graduate asstships.
• Piotr Indyk in CS: Sequential Sparse Matching Pursuit, Compressed Blind De-convolution
• Martin Braun in CS: Spectral Estimation and Compressive Sensing GNU Radio release, BCS New Release, CS can make you rich
• Dirk Lorenz in CS: KF-CS code, Weighted l1 with prior, Comment on CGD, Cognitive Radio, a Postdoc, TSW-CS, Impact and Trends and a Video, CS: Different codes, Recovery of clustered sparse signals, Theory of CS, Stagewise Polytope Faces Pursuit, 2-D tomography
• Gabriel Peyré in CS: Compressive dual photography, Image reconstruction by deterministic CS, Nesterov algorithm, CS: Chambolle's Algorithm for the Resolution of Compressed Sensing with TV regularization
• Thomas Strohmer, CS: calendar, CS Block Map-LMS Adaptive Filter, Democracy in Action, and more, CS: CS-GPR prototype, CS-CT, SML presentations related to CS,
• Jong Chul Ye in CS: Various thresholds for $\ell_1$-optimization in CS, Radial K-t FOCUSS MRI, BBCS algorithm, block-sparse CS, Non Convex CS, OCT
• David Hammond in CS: Basis Pursuit DeQuantizer, BPDQ Toolbox now available.
• Weiyu Xu in CS: Analysis for Iterative Reweighted l_1, Projection proximal-point algorithm,Subsampling Algorithms, Imaging of moving targets with multi-static SAR
• Waheed Bajwa in CS: On the value of Distributed Sensor Networks, More on the Labview Reconstruction algorithm
• Andrew Yagle in CS: Matrix Completion with Noise, Computational Ghost Imaging and CS ?, On uncertainty principles in the finite dimensional setting
• Jarvis Haupt in CS: Answering a DSN question, Distilled Sensing, Modified-CS: Modifying CS for Problems with Partially Known Support, L1 and Hamilton-Jacobi, CS: Bayesian Compressive Sensing using Laplace Priors, Distilled Sensing
• Shiqian Ma in CS: A question in DSN, a talk, FPCA code, Near-Oracle Performance of Basis Pursuit Under Random Noise
• Derin Babacan in CS: Bayesian Compressive Sensing using Laplace Priors, Distilled Sensing
• Mark Iwen in CS: A Theoretical Analysis of Joint Manifolds and the release of the Ann Arbor Fast Fourier Transform
• Sina Jafarpour in CS: Construction of a Large Class of Deterministic Sensing Matrices that Satisfy a Statistical Isometry Property
• Moshe Mishali, CS: Xampling, Ghost imaging via compressive sampling, Low-rank Matrix Recovery, Volume Anomaly Detection
• Nicolas Thierry-Mieg, CS: Compressed Genotyping, DNA Sudoku - Harnessing high throughput sequencing for multiplexed specimen analysis
• Hadi Zayyani in CS: calendar, CS Block Map-LMS Adaptive Filter, Democracy in Action, and more.
• Andriyan Suksmono in CS: Some open questions, Talk slides, Tutorials, Split Bregman Methods, Different coded apertures
• Rahul Garg in CS: Question about CS and MPI and RFIDs, Matrix Completion, CS in Traffic, Generalizations of the Linearized Bregman Method
  
• Andrea Montanari in CS: Question about CS and MPI and RFIDs, Matrix Completion, CS in Traffic, Generalizations of the Linearized Bregman Method
• Leon Palafox in CS: Question about CS and MPI and RFIDs, Matrix Completion, CS in Traffic, Generalizations of the Linearized Bregman Method
• David Skinner in CS: Question about CS and MPI and RFIDs, Matrix Completion, CS in Traffic, Generalizations of the Linearized Bregman Method
• Alex Conrad in CS: Fourier-transform Ghost Imaging, Message Passing Algorithms for Compressed Sensing
• Andy in CS: Inline hologram reconstruction with sparsity constraints, Reading the Book of Memory
• David in CS: An announcement and a plea. Videos of CS particle Filter results
• Georgos Tzagkarakis in CS: YALL1, fitness function Eureqa, Data Driven CS, Matlab, Python, NIPS 2009 papers, Bayesian statistics in AI and Social Sciences
• Michael in Imaging with Nature (part 3)
• Sanjay in CS: On the value of Distributed Sensor Networks, More on the Labview Reconstruction algorithm
• KFC from the Arxiv blog in CS: Wavefront Coding for Random Lens Imagers ?
• An anonymous reader in CS: Questions on IST, Audio CS and anoter GPR

Also, Thanks to the folks who mentions Nuit Blanche as an acknowledgment in their papers:

• Emmanuel Candes and Terry Tao in the Dec. '08 issue of the IEEE Information Theory Society Newsletter
• Ori Katz, Yaron Bromberg, and Yaron Silberberg in their Ghost Imaging paper and
• Wenlin Gong and Shensheng Han in their paper.

Finally, the Duke Compressive Sensing Workshop contributed to much interest in the topic of compressive sensing this year. Thank you to the organizers of the meeting: Larry Carin, Gregory Arnold, David Brady, Mauro Maggioni, Xiaobai Sun, and Rebecca Willett for making it happen and for putting all the talks on video. The stats of the Compressive Sensing Video page has witnessed a bump since the meeting.

Thank you also to Gabriel Peyré , Laurent Cohen and Frédéric Barbaresco for organizing MIA09.

A big kudos goes to Eva Dyer and Mark Davenport for having attended and regularly updated the Rice Compressive Sensing site.

Thank you, y'all.