This is not something you see everyday!

From [smd] ROSES-14 Amendment 4: Eligible TRLs changed for C.12, PICASSO

ROSES-14 Amendment 4 narrows the range of entry technology readiness levels eligible for submission to C.12, Planetary Instrument Concepts for the Advancement of Solar System Observations (PICASSO). The PICASSO Program supports the development of spacecraft-based instrument systems that show promise for use in future planetary missions. The goal of the program is to conduct planetary and astrobiology science instrument feasibility studies, concept formation, proof of concept instruments, and advanced component technology development to the point where they may be proposed in response to the Maturation of Instruments for Solar System Exploration (MatISSE) Program, C.13 of ROSES. Therefore, the proposed instrument system or advanced components must address specific scientific objectives of likely future planetary science missions. The PICASSO Program is intended to enable timely and efficient technology infusion into the MatISSE Program and eventually into flight missions. As such, the entry technology readiness level (TRL) that PICASSO supports is 1-3. Proposals where the entry TRL is 4 or higher are not appropriate for the PICASSO, but should be submitted to C.13, the MatISSE program. The due dates remain unchanged. Step-1 proposals are due September 15, 2014, and Step-2 proposals are due November 14, 2014. On or about March 5, 2014, this Amendment to the NASA Research Announcement "Research Opportunities in Space and Earth Sciences (ROSES) 2014" (NNH14ZDA001N) will be posted on the NASA research opportunity homepage at http://nspires.nasaprs.com/ and will appear on the RSS feed at:http://nasascience.nasa.gov/researchers/sara/grant-solicitations/roses-2014

If you wonder what TRL 1 through 3 means check the wikipedia page.

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On Domain Knowledge Jumps

How do you go from a field in which you are a specialist to a new one without starting from scratch ? The issue here is that most of the time, you are bound to go back to New Area 101 and fast forward on the sub-areas you know everything about while slowing down dramatically to learn the new lingo and even the community. It can take some time: Time you don't have. What are the shortcuts ?

One can read what researchers in your field have done in this new area, but it exposes you to two drawbacks: their biases become your bias and the low hanging fruits have already been picked. One can always knock on the door of a faculty/researcher in another department. It really is a good way to have access to domain knowledge and reasoning as to why certain things are done a certain way. A top concern is to understand the constraints of the field by asking not-so-dumb dumb questions. But what happens if the subject is too new or that something is not investigated in your area or that your questions are really that dumb ? Things can become real awkward...

Libraries, Wikipedia, the blogs are your only hope after that. It will take time to make of sense of the new area of interest by first going through reviews then through specialized knowledge. What is really likely to happen is that the search will be molded by what has already been done, not by what is needed.

All is not lost, a good shortcut is provided by the reports issued by the National Academies Press. In the U.S., this organization is used by different stakeholders such as government agencies ( NSF, NASA, DOE) or the Office of Science and Technology Policy to provide some summaries or blue sky assessment of generic or detailed subjects of importance to them. One of the intent is to provide the generic constraints of these new subjects of science and technology to lawmakers and/or the executive branch so that they can efficiently allocate funding in the future. In short, these assessments pretty much work like the Technology readiness Level at a meta-level. While the TRL scale enables the evaluation of a specific technology with regards to its maturity and its attendant funding level requirement, the NAP reports provide a similar evaluation of subject areas and provide stakeholders an idea of what needs or doesn't need sustained funding effort.

In particular, and this is something I did not understand until I was involved in the plutnonium disposition program, these agencies or stakeholders pay the National Academy of Sciences or  the National Academy of Engineering to organize one or several workshops on a specific topic of interest to them. Once the contract is in place, the academy, in connection with the funder, use its prestigious network to identify key people who will make that workshop relevant. One of the end products -besides the obvious networking of scientists who come to these meetings- are workshop reports that provide enough insight for specialists of other areas to make more rapidly the connection between their subject areas and this new topic or problematic. Those reports are also very useful even for specialists as it provides them with a template for the Big Picture.

Over the years while reading this blog, you may have noticed my mentioning of the following reports to provide some context while highlighting specific issues:

• The Management and Disposition of Excess Weapons Plutonium: Reactor-Related Options 
• The International Space Station, 
• Mathematics and Physics of Emerging Biomedical Imaging, 
• Assessing the Reliability of Complex Models: Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification

Here are some new ones:

• The Science and Applications of Synthetic and Systems Biology: Workshop Summary
• NAKFI Synthetic Biology: Building a Nation's Inspiration: Interdisciplinary Research Team Summaries

But if you are interested in other areas, you may want to search the National Academies Press website directly.

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Going up the Technology Readiness Level ladder with CS-MUVI

The world does not change at 30Hz! Aswin Sankaranarayanan just sent me the following:

Hi Igor 

We recently finished up a completely new video CS algorithm for the single pixel camera.

Here is a link to the project page: http://www.ece.rice.edu/~as48/research/csmuvi/

A key realization behind the algorithm in the paper is the fact that, when we sense time-varying scenes with a SPC, each compressive measurement is obtained from a slightly-different scene. We cannot ignore this fact especially for scenes with fast moving objects. Many (including me) have papers on video CS where we simulated CS data from 30 fps videos. This creates quite a mismatch from reality --- as we are simulating a world that abruptly changes only once every 1/30 second. In reality, the scene is constantly changing --- and addressing this is key if we want to get something that works in practice.

With Kevin Kelly's group, we now have this algorithm working on the SPC hardware and have been getting very nice results on real data; we'll be releasing that very soon. In all, quite excited about this. On a partially related note, I wanted to touch-base on a post from a month back

http://nuit-blanche.blogspot.com/2012/02/whose-heart-doesnt-sink-at-thought-of.htmli think, we can move the SPC a notch-up the ladder ;-)

And finally, as always, your time and effort on Nuit Blanche is highly appreciated.

-as

Aswin, maybe you moved up the TRL ladder, but you are also probably changing some of our readers' thinking when they talk about compressive sensing system and videos.Here is the introduction of the page:

CS-MUVI

Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive. While existing recovery algorithms for SMCs perform well for static images, they typically fail for time-varying scenes (videos). We propose a novel CS multi-scale video (CS-MUVI) sensing and recovery framework for SMCs. Our framework features a co-designed video CS sensing matrix and recovery algorithm that provide an efficiently computable low-resolution video preview. We estimate the scene's optical flow from the video preview and feed it into a convex-optimization algorithm to recover the high-resolution video. We demonstrate the performance and capabilities of the CS-MUVI framework for different scenes.

CS-MUVI: Video Compressive Sensing for Spatial-Multiplexing Cameras by  Aswin Sankaranarayanan , Christoph Studer, and Richard Baraniuk. The abstract reads:

Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive. While existing recovery algorithms for SMCs perform well for static images, they typically fail for time-varying scenes (videos). In this paper, we propose a novel CS multi-scale video (CS-MUVI) sensing and recovery framework for SMCs. Our framework features a co-designed video CS sensing matrix and

recovery algorithm that provide an efficiently computable low-resolution video preview. We estimate the scene’s optical flow from the video preview and feed it into a convexoptimization algorithm to recover the high-resolution video. We demonstrate the performance and capabilities of the CSMUVI framework for different scenes.

Thanks Aswin !


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What does a compressive sensing approach bring to the table ?

Following up on yesterday's rant, let me give some perspective as to what compressive sensing brings to the table through a new chosen crop of papers from Arxiv in the past two weeks.

Clearly the next two studies fall into the new algorithmic tools section with the second one making an inference that was not done before by specialists in the field. in other words, CS provides a new insight in an older problem that was not recognized by a dedicated community. 

1.Semi-Quantitative Group Testing by Amin Emad, Olgica Milenkovic. The abstract reads:

We consider a novel group testing procedure, termed semi-quantitative group testing, motivated by a class of problems arising in genome sequence processing. Semi-quantitative group testing (SQGT) is a non-binary pooling scheme that may be viewed as a combination of an adder model followed by a quantizer. For the new testing scheme we define the capacity and evaluate the capacity for some special choices of parameters using information theoretic methods. We also define a new class of disjunct codes suitable for SQGT, termed SQ-disjunct codes. We also provide both explicit and probabilistic code construction methods for SQGT with simple decoding algorithms.

2. Casual Compressive Sensing for Gene Network Inference by Mo Deng, Amin Emad, Olgica Milenkovic. The abstract reads:

We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered.

This next paper describes a new imaging system which is clearly at low technology readiness level. It has some potential.

3. Single photon counting imaging system via compressive sensing by Wen-Kai Yu, Xue-Feng Liu, Xu-Ri Yao, Chao Wang, Shang-Qi Gao, Guang-Jie Zhai, Qing Zhao, Mo-Lin Ge. The abstract reads:

An imaging system based on single photon counting and compressive sensing (ISSPCCS) is developed to reconstruct a sparse image in absolute darkness. The single photon avalanche detector and spatial light modulator (SLM) of aluminum micro-mirrors are employed in the imaging system while the convex optimization is used in the reconstruction algorithm. The image of an object in the very dark light can be reconstructed from an under-sampling data set, but with very high SNR and robustness. Compared with the traditional single-pixel camera used a photomultiplier tube (PMT) as the detector, the ISSPCCS realizes photon counting imaging, and the count of photons not only carries fluctuations of light intensity, but also is more intuitive.

The following paper shows a change in the software used in the data processing chain taking place after data has been acquired. There is no change in hardware here even though it might ultimately lead to one.

4. Compressed Beamforming in Ultrasound Imaging by Noam Wagner, Yonina C. Eldar, Arie Feuer, Zvi Friedman. The abstract reads:

Emerging sonography techniques often require increasing the number of transducer elements involved in the imaging process. Consequently, larger amounts of data must be acquired and processed. The significant growth in the amounts of data affects both machinery size and power consumption. Within the classical sampling framework, state of the art systems reduce processing rates by exploiting the bandpass bandwidth of the detected signals. It has been recently shown, that a much more significant sample-rate reduction may be obtained, by treating ultrasound signals within the Finite Rate of Innovation framework. These ideas follow the spirit of Xampling, which combines classic methods from sampling theory with recent developments in Compressed Sensing. Applying such low-rate sampling schemes to individual transducer elements, which detect energy reflected from biological tissues, is limited by the noisy nature of the signals. This often results in erroneous parameter extraction, bringing forward the need to enhance the SNR of the low-rate samples. In our work, we achieve SNR enhancement, by beamforming the sub-Nyquist samples obtained from multiple elements. We refer to this process as "compressed beamforming". Applying it to cardiac ultrasound data, we successfully image macroscopic perturbations, while achieving a nearly eight-fold reduction in sample-rate, compared to standard techniques.

The next paper falls under the new conceptual study for a new architecture:

5. Secure Compressed Reading in Smart Grids by Sheng Cai, Jihang Ye, Minghua Chen, Jianxin Yan, Sidharth Jaggi. The abstract reads:

Smart Grids measure energy usage in real-time and tailor supply and delivery accordingly, in order to improve power transmission and distribution. For the grids to operate effectively, it is critical to collect readings from massively-installed smart meters to control centers in an efficient and secure manner. In this paper, we propose a secure compressed reading scheme to address this critical issue. We observe that our collected real-world meter data express strong temporal correlations, indicating they are sparse in certain domains. We adopt Compressed Sensing technique to exploit this sparsity and design an efficient meter data transmission scheme. Our scheme achieves substantial efficiency offered by compressed sensing, without the need to know beforehand in which domain the meter data are sparse. This is in contrast to traditional compressed-sensing based scheme where such sparse-domain information is required a priori. We then design specific dependable scheme to work with our compressed sensing based data transmission scheme to make our meter reading reliable and secure. We provide performance guarantee for the correctness, efficiency, and security of our proposed scheme. Through analysis and simulations, we demonstrate the effectiveness of our schemes and compare their performance to prior arts.

Finally, the last paper shows an improvement in the reconstruction rather than in the actual hardware.

6. Compressive Sensing Could Accelerate 1H MR Metabolic Imaging in the Clinic.by Geethanath S, Baek HM, Ganji SK, Ding Y, Maher EA, Sims RD, Choi C, Lewis MA, Kodibagkar VD.. The abstract reads:

Purpose: To retrospectively evaluate the fidelity of magnetic resonance (MR) spectroscopic imaging data preservation at a range of accelerations by using compressed sensing. Materials and Methods: The protocols were approved by the institutional review board of the university, and written informed consent to acquire and analyze MR spectroscopic imaging data was obtained from the subjects prior to the acquisitions. This study was HIPAA compliant. Retrospective application of compressed sensing was performed on 10 clinical MR spectroscopic imaging data sets, yielding 600 voxels from six normal brain data sets, 163 voxels from two brain tumor data sets, and 36 voxels from two prostate cancer data sets for analysis. The reconstructions were performed at acceleration factors of two, three, four, five, and 10 and were evaluated by using the root mean square error (RMSE) metric, metabolite maps (choline, creatine, N-acetylaspartate [NAA], and/or citrate), and statistical analysis involving a voxelwise paired t test and one-way analysis of variance for metabolite maps and ratios for comparison of the accelerated reconstruction with the original case. Results: The reconstructions showed high fidelity for accelerations up to 10 as determined by the low RMSE (, 0.05). Similar means of the metabolite intensities and hot-spot localization on metabolite maps were observed up to a factor of five, with lack of statistically significant differences compared with the original data. The metabolite ratios of choline to NAA and choline plus creatine to citrate did not show significant differences from the original data for up to an acceleration factor of five in all cases and up to that of 10 for some cases. Conclusion: A reduction of acquisition time by up to 80%, with negligible loss of information as evaluated with clinically relevant metrics, has been successfully demonstrated for hydrogen 1 MR spectroscopic imaging.

In all what do we see ? A compressive sensing approach currently either:

• provide a means of designing a low TRL hardware  [3]
• provide a means of defining new architectures [5]
• provide a means of changing the current computational chain yielding gains at operational level [1, 4,6] (high TRL) and even permit discovery [2]! At this stage there is no change in hardware but it is likely the first step before new hardware gets to be complemented in view of the new data chain pipeline [4].

In short, at this stage, either the changes possible due to compressive sensing are invisible or very few (only one new sensor [3]) at a very low technology maturity level.

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Request for Expression of Interest: The Qualcomm Tricorder X Prize

The Qualcomm Tricorder X Prize was announced at CES this week. The goal of the competition is to build a "thing" that can be

".....capable of capturing key health metrics and diagnosing a set of 15 diseases. Metrics for health could include such elements as blood pressure, respiratory rate, and temperature. Ultimately, this tool will collect large volumes of data from ongoing measurement of health states through a combination of wireless sensors, imaging technologies, and portable, non-invasive laboratory replacements...".

I say a "thing" because it does not have to be just a portable device as one could envision by remembering the original Tricorder. The current description of the prize is pretty light on the details for the moment and the FAQ is not extraordinarily helpful....yet. For instance, right now we don't know if the 15 diseases are already known or are just up to the team to define. 

Let us look at some of the response of the FAQ to get a sense of what this challenge is really about.. First of all, are we talking about Star Treck type of technology, where most of the technology either does not exist or is very low in the TRL scale ? Probably not:

"....What's new about the technology? Doesn't most of this already exist?
Yes, some of the technology exists today. However, the the teams in this competition will pull this all together in one seamless system. The resulting instrument will also push the sensing component of technology in different ways: Smaller, lighter, cheaper, faster, better. Integration of these many different components is expected to be very challenging...."

While the emphasis is on diagnostics, I note the importance of continuous monitoring

What will the Device actually do?

• Diagnose diseases

• Provide ongoing metrics of health (vitals)

• Allow monitoring or continuous use of sensors to diagnose and measure health

• Provide awareness of health state

• Give confirmation that everything is ok with a consumer

• Notify that something is not ok (a "check engine light")

 Of related interest, there seems to be an interest for non invasive capabilities, this one is tough one.

Do the sensors have to be wireless?
No; however, due to consumer experience requirements it's unlikely a non-wireless sensor will be successful.


Can the sensors be invasive? (What is "invasive?")
There is no requirement or limit on sensing; we define a grand challenge and let teams find the best, innovative new solutions. "Invasive" means it punctures the skin. The competition allows this but it's very unlikely this would be acceptable to a consumer. For example, drawing blood is invasive but the accelerometer in your phone is non-invasive.

Finally, when they talk about sensing, they really mean sensing and make sense of it:

What is the difference between sensors and sensing? (What is "sensing?")
Sensors are generally physical hardware. These are used to collect health metrics and data about a person. The sensor can collect data for a short or long period of time. Sensing is the process of taking the data and interpreting it for patterns. These patterns can be analyzed to show unusual variations within one person, or compared to other people.

From the compressive sensing standpoint, there are obvious subjects of interest in this description, some of which have somehow already been implemented by some research teams. However, besides ECG, EEG, there might be some trickier inverse problems if we want to avoid the issue of non invasiveness. In particular, there may be a need for the fusion of inverse problems that generally are not considered together.

I would be interested in being part of a team that competes in this challenge. I may contact some of you in the future on the matter but if you want to just talk about it, we can do that as well. Obviously, all these discussions will remain private unless we, both parties, agree to communicate on these matters. Wave me in if interested.

.




Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

Request for Expression of Interest: The Qualcomm Tricorder X Prize

The Qualcomm Tricorder X Prize was announced at CES this week. The goal of the competition is to build a "thing" that can

".....capable of capturing key health metrics and diagnosing a set of 15 diseases. Metrics for health could include such elements as blood pressure, respiratory rate, and temperature. Ultimately, this tool will collect large volumes of data from ongoing measurement of health states through a combination of wireless sensors, imaging technologies, and portable, non-invasive laboratory replacements...".

I say a "thing" because it does not have to be just a portable device as one could envision by remembering the original Tricorder. The current description of the prize is pretty light on the details for the moment and the FAQ is not extraordinarily helpful....yet. For instance, right now we don't know if the 15 diseases are already known or are just up to the team to define. 

Let us look at some of the response of the FAQ to get a sense of what this challenge is really about.. First of all, are we talking about Star Treck type of technology, where most of the technology either does not exist or is very low in the TRL scale ? Probably not:

"....What's new about the technology? Doesn't most of this already exist?
Yes, some of the technology exists today. However, the the teams in this competition will pull this all together in one seamless system. The resulting instrument will also push the sensing component of technology in different ways: Smaller, lighter, cheaper, faster, better. Integration of these many different components is expected to be very challenging...."

While the emphasis is on diagnostics, I note the importance of continuous monitoring

What will the Device actually do?

• Diagnose diseases

• Provide ongoing metrics of health (vitals)

• Allow monitoring or continuous use of sensors to diagnose and measure health

• Provide awareness of health state

• Give confirmation that everything is ok with a consumer

• Notify that something is not ok (a "check engine light")

 Of related interest, there seems to be an interest for non invasive capabilities, this one is tough one.

Do the sensors have to be wireless?
No; however, due to consumer experience requirements it's unlikely a non-wireless sensor will be successful.


Can the sensors be invasive? (What is "invasive?")
There is no requirement or limit on sensing; we define a grand challenge and let teams find the best, innovative new solutions. "Invasive" means it punctures the skin. The competition allows this but it's very unlikely this would be acceptable to a consumer. For example, drawing blood is invasive but the accelerometer in your phone is non-invasive.

Finally, when they talk about sensing, they really mean sensing and make sense of it:

What is the difference between sensors and sensing? (What is "sensing?")
Sensors are generally physical hardware. These are used to collect health metrics and data about a person. The sensor can collect data for a short or long period of time. Sensing is the process of taking the data and interpreting it for patterns. These patterns can be analyzed to show unusual variations within one person, or compared to other people.

From the compressive sensing standpoint, there are obvious subjects of interest in this description, some of which have somehow already been implemented by some research teams. However, besides ECG, EEG, there might be some trickier inverse problems if we want to avoid the issue of non invasiveness. In particular, there may be a need for the fusion of inverse problems that generally are not considered together.

I would be interested in being part of a team that competes in this challenge. I may contact some of you in the future on the matter but if you want to just talk about it, we can do that as well. Obviously, all these discussions will remain private unless we, both parties, agree to communicate on these matters. Wave me in if interested.

.

This is the answer I gave on Quora to the question "Has Compressed Sensing peaked ?"

I wrote the more verbose answer about two months ago, but if you have been reading the blog during this time period, you may have seen some of the arguments expanded one way or another. Without further due, here it is:

First let me just say that about every two weeks, on average, I find a good paper that is either strictly CS or an extension of it (low rank problems,...).that clearly advances the subject in one shape or another. One recent example [as of January 12th, 2011] of a good paper is the one confirming that a CS encoding could be used in a critical system on a space mission (encoding of the PACS camera on the Herschel Space telescope). This was in the pipeline since the Herschel spacecraft launched but it took some time to figure out the quirks of how to restore images (colored noise...). In effect, even if the hardware was ready to use, it took a little while to get good data out that system. But Herschel is a peculiar example of a scientific instrumentation that was built before the CS algorithm was devised (they were expecting to use a lesser encoding algorithm in the first place). [Is it a paradigm shift where sensor are built first and then encoding designed later ?]
Second let me give some context as to why the field seems to have peaked and why questions such as the one I am responding to, gets to be asked.

The initial success of CS comes from its utilization in hardware that could already produce samples in the right space (Fourier space in MRI). Once again even if the hardware is spitting out the right type of data, it took some good algorithm development (mostly in the reconstruction stage) to get to the point where now MRI is being speed tracked by most MRI makers. This case is fascinating because a naive implementation of CS just would not be directly competitive with a whole slew of empirical methods devised for the past twenty years. In short, CS is not magic when it gets directly implemented in a technology that has already been exploited. It has however brought something extremely important: CS has accelerated investigation in a certain part of the phase/parameter space (including untouched parameter spaces) and has given good reasons as to why other part of the parameter space should not be important to investigate. Both the positive and negative insights are extremely important when making decisions on whether a certain technology climbs the Technology Readiness Level (TRL) ladder.

The reason people seem to think the subject has peaked is because they feel that besides MRI, there has not been other instrumentation providing similar success. This is indeed the case, most other hardware relying on CS are merely at the very low end of the Technology Readiness Level chart i.e. most are in the prototype stage (CS camera at Rice for instance) since common sensors in those fields do not provide the right type of sampling. This nurturing stage fueled by academic research allows one to investigate a different phase space than what other similar instrumentation can reach. In the end, there will be sensors that will not provide additional information or very little compared to existing technology. From that point of view, it may take a little while and some perseverance before we have other winners out of this list of hardware.
Even if none of the current hardware yield acceptable sensors (which I think is a far stretch), I can still see how CS can be used as just an encoding layer (see Herschel, but also the seismic work at UBC and Georgia Tech) which really put a good framework in what used to be called group testing. I also think that either sparsity or some other feature enforced a la CS, will do wonders on calibration issues thereby making these methods central for any type of sensor development.
Let us not forget that while low TRL insturmentation have appeared in the lab (see list), CS also could enable some new kind of instrumentation that could only be construed as futuristic (check "These Technologies Do Not Exist" or the recent idea of using the USPS fleet as a sensor network)
Finally, while the low maturity on the TRL scale is an important factor, there is probably another structural phenomenon at play: An explosion of different concepts enabled by CS across many branches of science and engineering has effectively diluted the core subject into domain specific areas. This fragmentation has really decreased the critical mass of efforts from one specific subject into less visible smaller subjects of inquiries. The core focus featured in the reconstruction solvers is likely to stay and should provide enough support to people designing new instrumentation or group testing procedures though.

Other related entries on the subject can be found here:

• Never, Ever Give In
• "...I found this idea of CS sketchy,..."
• Islands of Knowledge
• The Dip
• This is not a hardware-only or solver-only problem ...
• What Island Is Next ?

Doesn't the Fort being destroyed every night and reconstructed everyday by kids remind you not of a story we all share in this quest to make CS work ?

What Are You Waiting For ?

Bob Cringely reminded me of this defining moment when two DEC executives looked at a dismantled PC on their desk:

...But the most telling story about Olsen that Avram tells isn’t in his blog.  It was about how he and Ken Olsen bought one of the first IBM PCs and disassembled it on a table in Olsen’s office.
“He was amazed at the crappy power supply,” Avram said, “that it was so puny.  Olsen thought that if IBM used such poor engineering then Digital didn’t have anything to worry about.”
Clearly Olsen was wrong.
I find this little story very telling because it shows Olsen in 1980 very much stuck in the late 1960s when it came to what mattered and what didn’t in a computer.  Yes, having a robust power supply was good for computer reliability, but not as important as having a great operating system and applications. But that’s not the way Olsen saw it.  In a world that had to that point been dominated by computer companies building expensive products aimed primarily at engineers, he simply had no concept of a computer as a consumer product....

What would somebody making expensive hyperspectral imagers say about a webcam and some prisms ? what would an optical engineer say about a microscope with no lens ? Very likely something along the lines of what was said in that DEC office. I have actually asked that question to some specialists, a shrug is a nice way of framing their answers. Not to avail I like being the candid ones in these discussions. But going back to the issue of making it in the real world, one has to face the unbearable sluggishness of how things move along.  More precisely, Jerry Weinberg pointed out that

Most of the time, for most of the world, no matter how hard people work at it, nothing of significance happens.

And as much as what Hollywood wants to convince you, in the journey of maturing a technology there are few aha moments. You're in the dip and the reality of it is that most technologies take some extreme combativeness to be born in reality. More precisely, most of them don't survive the climb on the TRL scale.

When John Mankins created a scale of Technology Readiness Levels, he was mainly trying to rationalize the different investments NASA was undertaking in a wide series of technologies. Now we know that NASA funds more in the realm of TRL 3-6 for its R\&;D, while other agencies like the NSF focus on TRL 0-1 and maybe 2 (if you're lucky). John did not mention TRL 0 as it could be a placeholder for theoretical work that does not precisely hinges on an actual technology. Using this scale here is how I see the life and death of technologies and some examples related to Compressive Sensing.

All the theoretical work fits into the left hand side of the graph. In order to prove to the world that CS is real, you can always point to both the TRL 9 MRI example or the Herschel PACS camera. However, those examples are just accidents in that either the space camera was built to be underused in the first place or that in MRI you were already sampling in the right phase space. Aside from these accidents, one has to push things to the right. Most other technologies described in the hardware page go from 1 to 3-4 and some won't survive the process because they are in direct competition with other technologies. Let's take the example of the single pixel camera. One of the reasoning for using it is that the detecting piece is expensive (say IR or Teraherz sensors), some technology breakthrough could change that and make irrelevant a different scanning systems such as that enabled by compressive sensing. What is truly amazing with compressive sensing is the ability to use an extraordinary set of tools such as the many reconstruction solvers, dictionary learning and the Donoho-Tanner phase transition to see if there is a fighting chance that the technology can go the next level.  What are you waiting for ?

CS: Bombshell announcement from Space: Compressive Sensing IS Relevant in TRL9 mission critical systems: Feasibility and performances of compressed-sensing and sparse map-making with Herschel/PACS data

Forget the Arsenide based life form found in some pond in California, this is bigger. You've watched the launch, you've waited a long time (see the P.S. here, see also here , here, here, here or here) and today is the culmination of all this wait: A Compressed sensing based encoding system can replace other compression encodings used in mission critical TRL9 systems such as those in space exploration. This is big in many respects, one of them is that now our collective narrative  can say that MRI is not the only application in which compressive sensing is highly relevant and mission critical Woohoo! If you know somebody who works in CS and does not read the blog as often as you do, you want to forward this information out to her/him. Without further due, here is the paper, enjoy:

Feasibility and performances of compressed-sensing and sparse map-making with Herschel/PACS data by Nicolas Barbey, Marc Sauvage,Jean-Luc Starck, Roland Ottensamer, Pierre Chanial. The abstract reads:

The Herschel Space Observatory of ESA was launched in May 2009 and is in operation since. From its distant orbit around L2 it needs to transmit a huge quantity of information through a very limited bandwidth. This is especially true for the PACS imaging camera which needs to compress its data far more than what can be achieved with lossless compression. This is currently solved by including lossy averaging and rounding steps on board. Recently, a new theory called compressed-sensing emerged from the statistics community. This theory makes use of the sparsity of natural (or astrophysical) images to optimize the acquisition scheme of the data needed to estimate those images. Thus, it can lead to high compression factors.
A previous article by Bobin et al. (2008) showed how the new theory could be applied to simulated Herschel/PACS data to solve the compression requirement of the instrument. In this article, we show that compressed-sensing theory can indeed be successfully applied to actual Herschel/PACS data and give significant improvements over the standard pipeline. In order to fully use the redundancy present in the data, we perform full sky map estimation and decompression at the same time, which cannot be done in most other compression methods. We also demonstrate that the various artifacts affecting the data (pink noise, glitches, whose behavior is a priori not well compatible with compressed-sensing) can be handled as well in this new framework. Finally, we make a comparison between the methods from the compressed-sensing scheme and data acquired with the standard compression scheme. We discuss improvements that can be made on ground for the creation of sky maps from the data.

CS: Multiplexing in Interferometry - Diophantine Optics

I mentionned the work of Daniel Rouan before. His main problem is to acquire some signal in some analog fashion, multiplex it and then remove some pattern from it (within the analog process). He cannot digitized the signal because it would require him to deal with technology that still does not exist or is at a very low TRL.

As you might have guessed, Daniel Rouan does interferometry. He is interested in exoplanet detection through the removal of light coming from a star while observing/keeping the much lower (by 7 to 10 orders of magnitude) brightness of a nearby planet. He does this by devising nulling interferometers (nulling because it cancels out the main star) that implement a Prouhet-Tarry-Escott [2] (PTE) series in hardware so that the analog signal uses the cancellation property of that series as explained in Diophantine Optics explanation:

Each of the beams of light interact with a paved lattice. The two paved lattices implement each one side of the PTE series thereby producing beams that are then added together producing the nulling effect.

Different kinds of pavement can fulfill this condition, so additional constraints are added to avoid shadowing between tiles.... An example of a working tiling is shown below.

There are other examples of diophatine optics applied to inteferometry here. Back in 2004 when I first mentioned his work, he was mostly presenting the algebra of ultra-supressing inteferometers:

The last tiling cannot not remind some of you of similar tilings found in CS. The PTE series might well be very appropriate for star shining nulling, but one wonders if using other kinds of tilings and CS reconstruction techniques, one cannot get other type of results, i.e. not just nulling effects.

Sources:

[1] Diophatine Optics, Daniel Rouan
[2] Weisstein, Eric W. "Prouhet-Tarry-Escott Problem." From MathWorld--A Wolfram Web Resource.http://mathworld.wolfram.com/Prouhet-Tarry-EscottProblem.html

France: Technologies de rupture et quantification de la maturite d'une technologie.

Lors de ma visite au Salon Europeen de la recherche, j'ai parle avec certaines personnes de OSEO, anciennement ANVAR (plus une autre entite dont je ne me rappelle plus le nom). Lors de la discussion, nous sommes arrives au sujet des baremes/echelles qui sont utilises en France de facon a quantifier le niveau de maturite d'une technologie. C'est important car du point de vue programmatique, les administrations et autres donneurs d'ordres prives doivent etre capable de dire aux chercheurs leurs besoins dans des termes qui sont simples. Cela permet de ne pas perdre son temps sur des technologies qui ne sont pas avancees ou qui le sont trop. Il semble que ce processus d'identification se fait au sein d'OSEO grace a l'utilisation d'experts. C'est interessant mais ce n'est pas le plus important. Il y a beaucoup de technologies que meme les experts ne peuvent juger correctement soit de par leur formation ou a cause d'une connaissance trop profonde des choses qui se font maintenant dans leur domaine. Il y a un vrai risque que nous passions, en France, a cote de technologies de rupture. Bien que ce mot soit a la mode, il est utilise, avec en tete, la definition de Clayton Christensen qui a defini le concept avec son livre "The Innovator's Dilemna" dont le premier chapitre se trouve ici. En resume, les technologies de rupture sont souvent des technologies que les experts ne considerent pas comme viable mais qui est capable d'avoir des parts de marche tres importantes dans des marches "exotiques". Cela leur permet de survivre et de s'affiner jusqu'au jour ou elles supplantent les technologies qui sont deja sur les marches plus traditionnels.

Pour en revenir a l'evaluation des technologies, il y a ce qu'on appelle le niveau de maturite d'une technologie, ou ce que l'on appelle en Americain: Technology Readiness Level (TRL). C'est un concept qui permet aux decideurs techniques, economiques et politiques de mieux cerner les differents niveaux d'avancement ou de maturite de certaines technologies de facon a permettre de repondre a certains besoins. Par exemple, la NASA ne finance en ce moment que des technologies de niveau TRL 8 a 9 pour une majorite de systemes qui iront sur la station spatiale alors que la NSF est dans le financement de technologies de niveaux TRL 1 a 4 (au maximum). Ce tableau est issue d'une traduction de l'entree de TRL sur wikipedia que j'ai modifie (je ne suis pas expert en traduction donc je suis ouvert a tout changement). Il est assez recent et a ete compose par John Mankins parcequ'il y avait beaucoup de confusion au sein de la NASA sur le choix des technologies a developer.

Niveaux de maturite des technologies a la NASA (Source : Mankins (1995), niveaux de maturite des technologies : Un livre blanc)

Niveau de maturite des technologie	Description
TRL 1. Principes de base observés et rapportés	C'est le « niveau le plus bas » de maturite d'une technologie. À ce niveau, la recherche scientifique commence à être traduite en recherche et développement appliqués. This is the lowest "level" of technology maturation. At this level, scientific research begins to be translated into applied research and development.
TRL 2. Concept et/ou application de technologie formulés	Une fois qu'on observe les principes physiques de base de cette technologie, des applications pratiques de ces caractéristiques peuvent « être inventées » ou identifiées au prochain niveau de maturite. À ce niveau, l'application de la technologie est encore spéculative : il n'y a pas de preuve expérimentale ou d'analyse détaillée pour soutenir la conjecture. Once basic physical principles are observed, then at the next level of maturation, practical applications of those characteristics can be 'invented' or identified. At this level, the application is still speculative: there is not experimental proof or detailed analysis to support the conjecture.
TRL 3. Fonction critique analytique et expérimentale et/ou preuve caractéristique du concept	À cette étape dans le processus de maturation, la recherche et le développement actifs (R&D) sont lancés. Ceci doit inclure des études analytiques pour placer la technologie dans un contexte approprié et des études en laboratoire pour valider physiquement que les prévisions analytiques sont correctes. Ces études et expériences devraient constituer la preuve de la validation des applications et des concepts formulés niveau precedent (TRL 2). At this step in the maturation process, active research and development (R&D) is initiated. This must include both analytical studies to set the technology into an appropriate context and laboratory-based studies to physically validate that the analytical predictions are correct. These studies and experiments should constitute "proof-of-concept" validation of the applications/concepts formulated at TRL 2.
TRL 4. Validation de composant et/ou en prototype dans l'environnement du laboratoire	Après avoir valider les applications et les concepts formules au niveau TRL2, des éléments technologiques de base doivent être intégrés de facon a établir que chacun des « morceaux » de la technologie travailleront bien ensemble. Ceci afin de documenter et prouver des niveaux de performance d'un composant et/ou d'un prototype. Cette validation doit être conçue pour soutenir le concept qui a été formulé plus tôt, et devrait également adherer aux conditions des applications potentielles de système. La validation est relativement de « basse fidélité » comparée au système final : elle pourrait se composer de composants mis en place ensemble dans un laboratoire. Following successful "proof-of-concept" work, basic technological elements must be integrated to establish that the "pieces" will work together to achieve concept-enabling levels of performance for a component and/or breadboard. This validation must be devised to support the concept that was formulated earlier, and should also be consistent with the requirements of potential system applications. The validation is relatively "low-fidelity" compared to the eventual system: it could be composed of ad hoc discrete components in a laboratory.
TRL 5. Validation de composant et/ou du prototype dans l'environnement approprié	À ce niveau de maturite, la fidélité du composant et/ou du prototype au produit final doit avoir augmenter de manière significative. Les éléments technologiques de base doivent être intégrés avec des éléments de support raisonnablement réalistes de sorte que toutes les applications (niveau composant, niveau de sous-ensemble, ou niveau système) puissent être examinées dans un environnement « simulé » ou quelque peu réaliste. At this level, the fidelity of the component and/or breadboard being tested has to increase significantly. The basic technological elements must be integrated with reasonably realistic supporting elements so that the total applications (component-level, sub-system level, or system-level) can be tested in a 'simulated' or somewhat realistic environment.
6. Système/modèle de sous-ensemble ou démonstration de prototype dans un environnement approprié (sur terre ou dans l'espace)	Une étape importante au niveau de la fidélité de la démonstration de la technologie suit l'accomplissement du niveau TRL 5. Au niveau TRL 6, un système représentatif de modèle ou de prototype ou du système - qui dépasseraient bien un agencement ad hoc de composants ou un prototype avec des composants simple non integres - serait examinée dans un environnement approprié. À ce niveau, si le seul « environnement approprié » est l'environnement de l'espace, alors le modèle/prototype doit être démontré dans l'espace. A major step in the level of fidelity of the technology demonstration follows the completion of TRL 5. At TRL 6, a representative model or prototype system or system - which would go well beyond ad hoc, 'patch-cord' or discrete component level breadboarding - would be tested in a relevant environment. At this level, if the only 'relevant environment' is the environment of space, then the model/prototype must be demonstrated in space.
TRL7. Démonstration de prototype de système dans un environnement de l'espace	Le niveau TRL 7 est une étape significative au delà du niveau TRL 6, qui exige une démonstration réelle de prototype de système dans un environnement de l'espace. Le prototype devrait être près d'un niveau opérationnel et la démonstration de cette technologie doit avoir lieu dans l'espace. TRL 7 is a significant step beyond TRL 6, requiring an actual system prototype demonstration in a space environment. The prototype should be near or at the scale of the planned operational system and the demonstration must take place in space.
TRL8. Système réel accompli et « vol qualifié » par l'essai et la démonstration (sur la terre ou dans l'espace)	Dans presque tous les cas, ce niveau est la fin du « développement d'un système technologique» pour la plupart des éléments de cette technologie. Ce niveau pourrait etre l'intégration de cette nouvelle technologie dans un système existant. In almost all cases, this level is the end of true 'system development' for most technology elements. This might include integration of new technology into an existing system.
TRL9. Système réel « vol prouvé » par des opérations réussies de mission	Dans presque tous les cas, la fin des aspects de réparation de dernier « bogue » du « développement du systeme technologique » final. Ce niveau pourrait inclure l'intégration de cette nouvelle technologie dans un système existant. Ce niveau de maturite n'inclut pas l'amélioration des systèmes en operation ou réutilisables. In almost all cases, the end of last 'bug fixing' aspects of true 'system development'. This might include integration of new technology into an existing system. This TRL does not include planned product improvement of ongoing or reusable systems.

Il y a un tableau similaire pour l'armee (Air Force).

[ PS: Bien que cette table existe depuis 1995 et est utilise au sein de la NASA depuis 1998, il se trouve que les administrations et organismes d'etats francais commencent seulement a l'utiliser: Exemple les recentes traductions du CNES ou dans le document 2006 de la politique et objectifs scientifiques de la DGA

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