Module 10.2. Some research projects that use the
techniques you have learned in the digital signal processing class.
We're going to talk about some current research in the lab.
There is a whole slew of them, and it's a selection here of interesting research
projects that we can briefly discuss. The first one is, eFacsimile is a project
on art work acquisition. The second one is about signal processing
in sensor networks. Then there is a network science result on
source localization put in graphs. Then we talk about sampling result, so
called finite rate of innovation sampling.
Then we talk again about sampling, that of physical fields, using some new
techniques for sampling. Then, we have a project on image
acquisition where we change the sensors used in acquiring images.
Then, an old classic, predicting the stock market.
Then, we talk about inverse problems. The three next projects are actually
inverse problems. The first one is on the diffusion
equation, the second one is trying to understand the nuclear fall out from
Fukushima, and last but not least is an inverse problem in acoustics.
The eFacsimile project. This is a project that we do together
with Google to try to improve how artwork is represented on the internet.
it's lead by [INAUDIBLE] researcher Loic Baboulaz and several PhD students are
involved in this. The questions are how to capture,
represent, and render artwork as well as possible.
And to do this, we need some advanced techniques on relighting, manipulation
of, the so called, light fields that is acquired, and potentially high resolution
solutions for mobile devices. There are some demos online that I
encourage you to actually watch. because this really doesn't show the
idea. This is of course a static version.
But for example one of the demos is you take a, an oil painting here.
And you acquire it in such a way that if you show it on a tablet and you move it,
it will actually exactly look like the original oil painting.
So you get the illusion that you have actually the oil painting in the hand.
So if the light changes, the vis, visualization will change.
If you turn the tablet, the visualization will change.
so then, what is quite stunning and I suggest you actually watch it, similarly,
there is another demo which deals with stain glasses.
So stain glasses are very interesting art objects, but very difficult to render on
the internet. And so here we will stimulate the stained
glass, so if you have a tablet in your hand and you move it, it looks like if
you had, stained glass in your hand. So the tools we use is, we use
traditional cameras, but we also use so-called light field cameras.
You might have heard of the light [INAUDIBLE] for example.
That's a new generation of camera. It's extremely interesting.
And so we need to fully understand light transport theory and [INAUDIBLE].
Which uses sparse recovery methods or compress sensing.
The website of the project is given here. And as I indicated there is YouTube demo
that shows quite realistically the demos that we're discussed just in a minute
ago. So, next project is about wireless sensor
networks, in particular about monitoring visually in a wireless sensor network.
So, sensor networks have deployed for many years.
We have large projects here, in the lab, on environmental monitoring.
And the current generation is actually equipped with camera, and then you have a
problem of compression, of representation.
So even though the trend is towards smaller and smaller devices, they are
still power hungry and in particular if you have a sophisticated camera, the
number of images or the number of pixels that is generated might actually overwelm
the power budget of the system. And so Dr.
Zichong Chen, who finished his PhD here, and did his post doc, together with
Guillermo Barrenetxea, are looking at creating large scale sensor networks
equipped with cameras that are energy efficient.
So why do we want images? Well, here are a few examples.
This is from I think a Berkeley project about, monitoring birds' nest.
Unfortunately a snake is showing up an he's actually eating all the eggs in the
nest. So that's, monitoring for why life
protection. Here is an example from the Swiss Alps
monitoring for for avalanche detection. Here is also an example from the Swiss
Alps, its monitoring to see weather conditions.
And, finally, here is monitoring networks that is installed on the PFL campus.
In all these cases you have many cameras using small communication devices.
And so compression and representation is extremely critical.
So there are a number of results that you can find in the thesis of Dr.
Chi Chong Chang, given here in this, website, and essentially the idea is
that, cameras can help each other to reduce the amount of information that
actually has to be sent to the base station or into the cloud, for doing
efficient monitoring. So a long with signal processing today,
actually it's moving to single processing on graphs.
I don't have to explain to you the importance, for example, of social
networks. And so Pedro Pinto who was involved here
in the class and is a post doc in the lab, together with Patrick Thiran, has
worked on the problem of source localization.
So you have some graph here, let's say social network and somebody launches a
rumor. Here is the source and the rumor gets
forwarded along the edges of the graph at different times and you have some
observers, say green nodes here, that receives a rumor at some instant of time.
They know where the rumor comes from, you know who told you the gossip, and you
know when you got the gossip information. So, the question is, you know the
structure of the graph, or you have an approximation of the structure of the
graphs. You have these observations.
Can you figure out who actually, spreads the rumor first.
It turns out this has, an interesting solution.
And using only few observers, about 20%, you can achieve a very high accuracy in
finding the source of a rumor on a large scale network.
And there are many interesting questions here, to pursue in this source
localization in social networks. And there was a paper that came out last
year, Locating the Source of Diffusion in Large-Scale Networks that had quite a bit
of impact. The project is actually funded by the
Bill and Melinda Gates Foundation. The reason is that one of the
applications is to monitor health problems.
For example, here is a map of Cholera outbreak in Africa, and the map shows the
river network. Cholera is a water born disease, and so,
typically Cholera will actually diffuse along waterways.
But you know when people fell sick at certain locations and then you can infer
the source of the actual Cholera outbreak.
There is another example here, which is a simulation of, if you had to figure out
if there was some pollution or attack on the New York subway, and if you could
figure out knowing the network of the New York subway and when you start detecting
the problems where the source of the problem actually was.
The next project is on sampling, so we have worked on a new theory of sampling
here called Finite Rate of Innovation Sampling, and it is used in
communications problems, and in monitoring problems to reduce the number
of samples being transmitted or acquired. Dr.
Freris, who is a senior scientist with doctoral students and MS assistants, are
actually working on doing ECG monitoring at very low power for wireless health
monitoring. So here is a block diagram, it's
relatively complicated so let me not get into this, but it uses some fairly
sophisticated techniques to reduce the sampling rate so as to reduce the energy
consumption on these wireless devices. So, this project is actually sponsored by
somebody well known, Qualcomm, interested in the theory of sampling.
And the extension here for this particular project has been
generalization of the initial finite rate of innovation sampling methodology.
To get better compression, and better modelization of the signals.
So here we have the ECG signal, and then there is sophisticated models that
allows, to take very few parameters, to model the ECG signal.
There are a number of papers here, the initial paper on finite rate of
innovation sampling is this 2002 paper, and the number of recent papers have done
extension to this theory. So if you like sampling I welcome you to
actually read up on this stuff, it's one of my favorite research topics.
When we talk about sampling already in sensor networks we have mentioned that
placing a sensor is like taking a sample. And so that spatial sampling, now if you
do spatial sampling, you can also use mobile sensors and Dr.
Unnikrishnan here, a post doc in the lab, has worked on this or generalization of
the theory of sampling when you have mobile sensors that can actually go over
a field in an arbitrary fashion. Then you maybe show this in an example.
It's again a temperature monitoring example here on the EPFL campus, or you
have buildings. You have that open space between
buildings. Those buildings are, of course, hot.
The open space are cool. And you would like to have monitoring of
this temperature field not with static spatial sensors, but with people running
around, having a thermal meter let's say on their mobile phone.
And the question is, how accurate can you actually measure temperature using a
device like this? And so, this is being done actually for
pollution monitoring in the city of Lausanne so there's some equipment put on
buses to measure pollution parameters. And what we do here is we try to develop
a theory of how good you can sample when you have these mobile sensors going over
a surface and measuring a field. The results are very mathematical but are
interesting because our non-trivial extension of sampling theory through
multiple dimensions. And a few papers are mentioned here if
you are interested in more detail. The next project is about a new way of
doing image acquisition. So in this class, we have seen sampling
and we have seen quantization. And when we do quantization typically we
say, let's take [UNKNOWN] samples and then take as many bits as possible.
Let's say eight bits for speech, 12 bits for images 24 bits maybe for audio,
etcetera. Now here we took the extreme other
example we said lets build an image sensor that has many, many, many pixels
but the pixels only detect either a enough light or not.
So the pixels are actually binary detectors.
And so you have a light intensity here. Which changes over space.
You have a lens that smooths the light intensity.
So what reaches the camera is this smooth curve here.
And this smooth curve you sample very, very, very finely.
But you only decide if it's above or below a certain threshold.
So the sensor only generates a sequence of binary digits.
So that's the imaging model. And this has been studied by Dr.
Feng Yang, did his PhD thesis on this, is now a post-doc working on this project,
and a whole slew of other people. This was a very extensive project.
And what is interesting is that this new way of acquiring images, for example,
allows to do high dynamic range imaging. Here is a simulation of a high dynamic
range image in a much easier way than with conventional cameras.
That's one advantage. Another one is that you can have very,
very cheap sensors. So here's an example of one that was
built in the lab. And then, you take many, many frames.
They are extremely noisy. If they look noisy, they are simply
binary, so you only have zeroes and ones, but you have enough of these, and you do
an optimal reconstruction method. You actually can recognize here, the logo
of EPFL. There are publications here that you are
welcome to look up. And the thesis is online.
Last but not least Rambus silicon valley company, actually works with us on this
and has acquired some of the technologies that was developed in this project.
And old classic is trying to predict the stock market.
So, we gave it another shot. so Lionel Coulot did his PhD thesis, was
co-advised with Peter Bossaerts who is at Caltech.
And we were trying to understand if methods from information theory would
allow to predict models for the stock market, and that requires statistical
models for what the stock market might be.
And what is interesting is that you have to decide between very sophisticated
models that might be overkill and are hard to estimate, and very simple models
which might be too simplistic, but which might be very robust to things that
happen in the stock market. And, in the end we used coding theory and
classic algorithmic methods like dynamic programming to come up with a method that
decides what is the correct model at every time of, the observation of the
stock market. So I'm just going to show a picture.
And the picture is, is a value on the stock market.
And the question is, can you detect if the stock market is in a bear market or a
bull market? So when the stock market goes up it's,
called bull market. If it goes down, it's a bear market.
What is very hard is to decide by watching every day what's happening.
If currently the trend is going up or the trend is going down and you need to do
this with an online algorithm. Okay, you cannot look into the future and
this method developed by Lionel allows to do a model fitting and to very quickly
detect when the stock market changes from a bull market to a bar, bear market.
The thesis online and this was sponsored by, as you may guess, by a bank.
And the results are interesting, but we are still having a regular day job so you
can guess that the method is not completely fool proof to predict the
stock market. But the methods, the algorithms, and the
theory behind it is quite cool. The next few projects are so called
inverse problems. So inverse problems are problems where
you have some measurements but the measurements do not describe the signal
you're interested in. But some indirect measurement of the
signal, so you try to invert the system to go back to the original signal.
You all know about computerized tomography, a medical image method, where
you can see inside the body without really going there.
And that's a typical inverse problem. Here we are interested in inverse
problems in environmental monitoring. So, the first example is diffusion
equation. And we have a physical phenomena, for
example temperature has been discussed, or atmospheric dispersal of pollution.
We want to measure the field at locations where we can put sensors, and the goal is
to find where are the sources, for example, of pollution.
Now this is a hard problem because, you have to model how, for example, pollution
is being diffused. That depends on weather patterns and so
on. But the tools we are using are typical
signal processing tools, for analysis. Sampling theory for exemplifying finite
rate of innovation sampling or compressive sensing, that has also been
mentioned earlier. Let's look at the picture.
That's a very simple example of this. Assume you have two smokestacks and
inside a factory compound, and the smokestacks produce pollution which
changes every day. You don't know how much pollution is
being released, and you're working for an environmental monitoring agency.
You put sensors outside of the compound and you measure what arrives, in terms of
pollution, at these sensors. And the goal is to figure out if what
came out of smoke stack was within the bounds allowed, lets say by z,
Environmental Protection Agency. So this is a interesting and non-trivial
problem but there are some interesting results that were produced by Yuri
Ranieri, whom you all know because he was the famous Master Chief assistant for the
BSB class. So we are able to recover sparse sources
using this inversion method. we use this finite rate of innovation
sampling techniques to actually do it. And here we is a list of publications
that came out of this research. This problem is also an inverse problem.
It's a Fukushima inverse problem. It is a PhD project of Marta
Martinez-Camara, and a few other of us are involved in this, and we collaborate
with a specialist Andreas Stohl. Who is a specialist of monitoring of
radioactive diffusion. So what we like to do is figure out how
much radionuclides were actually released in Fukushima at the time of the of the
nuclear accident at Fukushima. We have only very few sensors, they are
located around the world very far away from Fukushima.
And the question is, is it possible from these few measurements around the world
taken later, to invert the entire process as I diffuse the initial release of
radioactive material into the atmosphere. What tools are we using?
Sparse regularizations, so that's compressed sensing.
And we need to using atmospheric dispersion model to understand how
radioactive material from from Fukushima was actually transported across the
world. So one result that we have and which is
very interesting is we were able to estimate the emission of Xenons, that's
radioactive gas that was released at the time of explosions at Fukushima, went up
into the atmosphere, was transported by weather patterns all over the world.
And from the measurements all over the world, we were able to pinpoint exactly
when the Xenon was released, and how much Xenon was released into the atmosphere.
And it turns out we actually know the total amount of Xenon that was released,
because after the accident no Xenon was actually left in the nuclear power plant.
Currently we're trying to go beyond this and estimate the Cesium release, but that
turns out to be a harder problem. The paper that describes this will be
published ICASSP this year and is available online here in infoscience.
Last but not least is a project we call, "Can One Hear the Shape of a Room?" It's
a PhD project of Ivan Dokmanic and several other people in the lab, in
particular, Reza Parhizkar, Andreaz Walther, have worked on this.
And also we have a collaboration with Yue Lu.
He's now with Harvard. Now you know about this problem because,
Ivan gave module 512 about gear dereverberation, echo cancellation.
And, uh,the next step is to say, if I listen to echoes, can I actually
understand what is a room shape? So if I know the room shape, then I know
how to generate the echoes. But if you give me the echoes, can I know
the room shape? It's a classic inverse problem, very cute
one. And we usually explain it by saying,
let's say you enter a room, you're blindfolded.
And so you don't see the room at all. You snap your finger.
You therefore elicit echoes, you listen very carefully to the echoes.
Can you exactly see or hear the shape of the room?
Now this has a beautiful theory, which we won't have time to really explain, but
that you can read up about because it's published material.
But if you have a source or receiver you have a direct pass between the source and
the receiver, and you have echoes given by the walls.
The echoes given by the walls correspond to so called mirror or image sources, so
this is the same as if you had a source here and the sound would have gone
straight here. So if you can locate all these image
sources, then you can actually locate the room.
The walls, therefore the room. And this is, you know, in principal
do-able the question was is it always true that this can be done?
And is it also realistic to do it in practice?
So, here are examples of a system with five microforms, you have one source five
microforms. You have somebody snap his finger and you
have the echos related to the walls and you see there is a complexity which is,
these echos come in random orders because different walls are at different
distances of the microphone. And the question is, can we find out the
shape from a set of measurements as we see here?
How many measurements do we need? Can we have a robust algorithm?
So the answer is summarized in, yes we can.
And there are some experiments we did, both at the labs.
So this is one of our seminar rooms we created a, an artificial wall here to
have different shapes of rooms. So this is a typical shape of room.
Then in this case, with five microphone and one source, we were able to estimate
the size, shape of the room very accurately to more, better than 1%.
And once we had this, we said, well, let's see how robust this is.
We went to Lausanne Cathedral and that's actually a foyer of the Lausanne
Cathedral, which is not at all needing the assumptions of the algorithms that
I've described very briefly here. And it was still possible to see the
major refractors, meaning the major walls here in the Lausanne Cathedral.
And so the answer is yes, one can hear the shape of a room.
And you can visit Ivan's web page to see more details.
Now these were just a selection of projects, of works that is being done by
PhDs and post-docs and senior researchers in the lab.
Please go to the website, as that gives the entire portfolio of research here of
what the lab is currently doing.