While using the Google, I was just reminded of the following video of Emmanuel Candes' presentation in EE380 at Stanford. I like the first slide.
Blogger, the blogging platform of this site, now provides public stats on pageviews. Since July 1 st, 2010, we've had more than 100,000 pageviews. It looks staggering at first sight....
...and it is still staggering at second sight :-). If you come to the site, you'll also notice the posts that have been most popular in the past seven days.
Add to that, more than 50,000 views of entries as read through the Feedreaders. I may expand on that later but the type of subjects and entries most viewed on the blog and through the feed are markedly different.
Mahesh Shastry, one of the author of one of the first paper using the Donoho-Tanner transition for checking on a potential hardware set-up, just sent me the following:
I am a grad student at Penn State. This email concerns an historic fact that has possible connections to compressive sensing, that I was recently made aware of. My thesis adviser (Ram M. Narayanan) pointed me to a patent (by Alfred A. Wolf) from 1974 which proposes using a type of random projections for speech signal processing. Please find attached. It makes interesting read!
It is here: A speech compression system.by Alfred A. Wolf. The encoding scheme is shown here:
Whereas the reconstruction schematics can be found in patent 3,746,791 by the same author in Speech Synthesizer Utilizing White Noise
Indeed it looks similar, at the very least it looks similar to the 'crazy' random filters in Random Filters For Compressive Sampling And Reconstruction. However, here are several differences:
- the scheme described in the patent does not assume sparsity of the signal (maybe the signal is sparse by default for it to work, so this is not an exclusion property).
- the complexity of the reconstruction does not seem high (which doesn't preclude it from being part of the club), but more importantly
- the random noise that is encoding the signal does not seem to be stored for use in the decoding stage..
And that, my friend, is a killer... I think. Because of this last feature of the system I would not call this system an instance of compressive sensing, and you ? You can answer the poll below.
Thanks Mahesh .
While doing a search I found the following presentation by Zhu Xiaoxiang on Very High Resolution SAR Tomography via Compressive Sensing.
In the past weeks, here are some of the blog entries that got my interest:
- Hal: Comparing Bounds
- Dick: Galactic Algorithms (maybe instead of saying the upper part of the Donoho-Tanner transition is made up of galactic algorithms, what do you think ?)
- Suresh: On outreach to applied communities
- ISW: CMOS Sensors for Astronomy Challenges and Opportunities, Hamamatsu Develops MEMS-based Photomultiplier Tube (please note the question and answer in the comment section, too bad :-()
- Bob: Papers of the Day (Po'D): Music Cover Song Identification Edition, pt. 1, Papers of the Day (Po'D): Concatenative Synthesis Edition, Processing for Artificial Intelligence Programming?
- Sarah: Church: a language for probabilistic computation
- Alex: A probabilistic version of one of Littlewood’s theorems, and the equivalence of the row and column norms of a matrix
- Greg: a UWB Switched Antenna Array Radar Imaging System. I wonder how a CS acquisition could improve either the acquisition time or the clarity of the solution (which looks sparse).
- Terry: Problem solving strategies
- Djalil: Moments de solitude, Back to basics: coupon collector
- Arthur: How to generate variables from a quasi Poisson distribution with overdispersion
- The practical Quant: Compressed Sensing and Big Data
Opening of a PhD position:
"Optical Inverse Problem Solving in 3-D Deflectometry"
(starting date: January 1st, 2011, application deadline: November 19th, 2010. )
* Dr Laurent Jacques (ICTEAM/ELEN, UCL)
* Prof. Philippe Antoine (IMCN/NAPS, UCL)
* Prof. Benoît Macq (ICTEAM/ELEN, UCL)
Improved functional performance is a general trend in ocular surgery today. As an example, multifocal intraocular lens (IOL) achieves different optical powers, as such to enable good near and distant vision. There are two types of multifocal lenses. A refractive multifocal lens is made of concentric rings whose refractive powers alternate from centre to periphery. Diffractive multifocal lens uses light diffraction at an interference grid made of micrometric steps. Such complex surfaces are a real challenge both for manufacturing and for characterization.
This position opening takes place in a 3-year regional project (DETROIT), funded by the Belgian Walloon Region. This project aims at characterizing surfaces by optical deflectometry. The principle is to measure the deviation of the light reflected by each point of the surface. This technique is an interesting alternative to interferometry in order to estimate the surface topography. Indeed measuring the deviation angle instead of the height has several advantages. It is insensitive to vibrations as it is not based on interferences. It is more effective in detecting local details and object contours than height measurement. In deflectometry, the shape of an object is numerically reconstructed from the gradient data with a high accuracy. As an example, 10nm flatness deviation over a 50mm window glass can be observed with high accuracy instrument.
Experimentally, the very short radius of curvature of the IOLs requires the use of wide acceptance optics as such to collect light that is reflected in a large range of angles. The drawback is the very narrow field of view. In order to reduce the acquisition time, a device that images the whole lens shall be preferred but inevitable distortion of the image will be numerically corrected based on the knowledge of instrument response. This solution is challenging but very attractive for industrial perspectives.
Nowadays, assuming that a signal (e.g., a 1-D signal, an image or a volume of data) has a sparse representation, namely that this signal is linearly described with few elements taken in a suitable basis, is an ubiquitous hypothesis validated in many different scientific domains. Interestingly, this sparsity assumption is the heart of methods solving inverse problems, namely those estimating a signal from some linear distorting observations. Sparsity stabilizes (or regularizes) these signal estimation techniques often based on L1-norm (or Total Variation norm) minimization and greedy methods.
This PhD position concerns the application of the sparsity principle for modeling and solving the optical inverse problem described in the previous section, that is, the reconstruction of the undistorted image of the IOL from the experimental measurements. This research will be carried out in collaboration with a postdoctoral researcher hired for one year on the same project.
The student will develop mathematical methods and algorithms for reconstructing the undistorted IOL image from the experimental measurements, taking into account (and modeling) the particularities of the sensing systems. Calibration of the response of the instrument will be carried out by another partner of the project. However, a close collaboration between the two teams is necessary. Moreover, the PhD student will be co-supervised by a postdoctoral researcher hired on the same topic and funded by the same project.
The optical development takes place in the 3-year project DETROIT. It involves two industrial partners, Physiol and Lambda-X and 3 university partners. Physiol is well-known for its development of IOL. Lambda-X has a large experience in optical characterization of optical components by means of deflectometry. The leading academic partner is the Atomic, Molecular and Optical Physics Laboratory (IMCN/NAPS) of University of Louvain (UCL, Louvain-la-Neuve, Belgium), helped by two other Belgian university partners: the Active Structures Laboratory of the University of Brussels (ASL, ULB), in charge of the development of fast adaptive optics, and the Communications and Remote Sensing laboratory TELE (ICTEAM/ELEN, UCL) which is responsible of the IOL image reconstruction and post-processing algorithms.
Research activity will be carried out in the TELE Laboratory, and partly at PAMO and at Lambda-X offices in Nivelles, Belgium.
* M.Sc. in Applied Mathematics, Physics, Electrical Engineering, or Computer Science;
* Knowledge (even partial) in the following topics constitutes assets:
o Convex Optimization methods,
o Signal/Image Processing,
o Classical Optics,
o Compressed Sensing and inverse problems.
* Experience with Matlab, C and/or C++.
* Good communications skills, both written and oral;
* Speaking fluently in English or French is required. Writing in English is mandatory.
* A research position in a dynamic and advanced high-tech environment, working on leading-edge technologies and having many international contacts.
* Funding for 2-3 years, with the possibility to extend it by applying for a Belgian NSF grant.
Applications should include a detailed resume, copy of grade sheets for B.Sc. and M.Sc. Names and complete addresses of referees are welcome.
Please send applications by email to:
Questions about the subject or the position should be addressed to the same email addresses.
Finally, while most imagers pay specific attention to getting the right light into lenses and then onto the imaging dye, you always are dependent on stray light. Here is an Edmund Optics entry on the subject for reducing stray light. I look at it from a different point of view, maybe painting the edge of the lens other than black provide additional "scrambled" information about the scene. I wonder how that could be used for some compressive sensing work (like the weak compressed sensing concept). By the way if you think that stray light is just a problem we have when designing star trackers, think again, it looks like this is the reason you will not see a white iPhone anytime soon.