WEBVTT 00:00:00.660 --> 00:00:03.960 Module 10.2. Some research projects that use the 00:00:03.960 --> 00:00:07.770 techniques you have learned in the digital signal processing class. 00:00:09.050 --> 00:00:11.890 We're going to talk about some current research in the lab. 00:00:11.890 --> 00:00:15.982 There is a whole slew of them, and it's a selection here of interesting research 00:00:15.982 --> 00:00:21.642 projects that we can briefly discuss. The first one is, eFacsimile is a project 00:00:21.642 --> 00:00:26.670 on art work acquisition. The second one is about signal processing 00:00:26.670 --> 00:00:30.170 in sensor networks. Then there is a network science result on 00:00:30.170 --> 00:00:35.020 source localization put in graphs. Then we talk about sampling result, so 00:00:35.020 --> 00:00:38.690 called finite rate of innovation sampling. 00:00:38.690 --> 00:00:42.472 Then we talk again about sampling, that of physical fields, using some new 00:00:42.472 --> 00:00:47.260 techniques for sampling. Then, we have a project on image 00:00:47.260 --> 00:00:52.720 acquisition where we change the sensors used in acquiring images. 00:00:52.720 --> 00:00:55.270 Then, an old classic, predicting the stock market. 00:00:56.550 --> 00:01:00.587 Then, we talk about inverse problems. The three next projects are actually 00:01:00.587 --> 00:01:04.209 inverse problems. The first one is on the diffusion 00:01:04.209 --> 00:01:08.363 equation, the second one is trying to understand the nuclear fall out from 00:01:08.363 --> 00:01:14.400 Fukushima, and last but not least is an inverse problem in acoustics. 00:01:17.460 --> 00:01:20.480 The eFacsimile project. This is a project that we do together 00:01:20.480 --> 00:01:26.800 with Google to try to improve how artwork is represented on the internet. 00:01:26.800 --> 00:01:31.910 it's lead by [INAUDIBLE] researcher Loic Baboulaz and several PhD students are 00:01:31.910 --> 00:01:36.117 involved in this. The questions are how to capture, 00:01:36.117 --> 00:01:40.670 represent, and render artwork as well as possible. 00:01:40.670 --> 00:01:45.415 And to do this, we need some advanced techniques on relighting, manipulation 00:01:45.415 --> 00:01:50.452 of, the so called, light fields that is acquired, and potentially high resolution 00:01:50.452 --> 00:01:57.544 solutions for mobile devices. There are some demos online that I 00:01:57.544 --> 00:02:01.656 encourage you to actually watch. because this really doesn't show the 00:02:01.656 --> 00:02:04.620 idea. This is of course a static version. 00:02:04.620 --> 00:02:08.930 But for example one of the demos is you take a, an oil painting here. 00:02:08.930 --> 00:02:13.208 And you acquire it in such a way that if you show it on a tablet and you move it, 00:02:13.208 --> 00:02:19.140 it will actually exactly look like the original oil painting. 00:02:19.140 --> 00:02:22.450 So you get the illusion that you have actually the oil painting in the hand. 00:02:22.450 --> 00:02:26.550 So if the light changes, the vis, visualization will change. 00:02:26.550 --> 00:02:29.430 If you turn the tablet, the visualization will change. 00:02:29.430 --> 00:02:34.326 so then, what is quite stunning and I suggest you actually watch it, similarly, 00:02:34.326 --> 00:02:39.390 there is another demo which deals with stain glasses. 00:02:39.390 --> 00:02:42.909 So stain glasses are very interesting art objects, but very difficult to render on 00:02:42.909 --> 00:02:46.282 the internet. And so here we will stimulate the stained 00:02:46.282 --> 00:02:49.762 glass, so if you have a tablet in your hand and you move it, it looks like if 00:02:49.762 --> 00:02:56.768 you had, stained glass in your hand. So the tools we use is, we use 00:02:56.768 --> 00:03:01.750 traditional cameras, but we also use so-called light field cameras. 00:03:01.750 --> 00:03:03.930 You might have heard of the light [INAUDIBLE] for example. 00:03:03.930 --> 00:03:07.170 That's a new generation of camera. It's extremely interesting. 00:03:07.170 --> 00:03:11.885 And so we need to fully understand light transport theory and [INAUDIBLE]. 00:03:11.885 --> 00:03:15.420 Which uses sparse recovery methods or compress sensing. 00:03:15.420 --> 00:03:20.798 The website of the project is given here. And as I indicated there is YouTube demo 00:03:20.798 --> 00:03:25.550 that shows quite realistically the demos that we're discussed just in a minute 00:03:25.550 --> 00:03:31.415 ago. So, next project is about wireless sensor 00:03:31.415 --> 00:03:36.510 networks, in particular about monitoring visually in a wireless sensor network. 00:03:36.510 --> 00:03:39.230 So, sensor networks have deployed for many years. 00:03:39.230 --> 00:03:43.860 We have large projects here, in the lab, on environmental monitoring. 00:03:43.860 --> 00:03:48.014 And the current generation is actually equipped with camera, and then you have a 00:03:48.014 --> 00:03:51.880 problem of compression, of representation. 00:03:51.880 --> 00:03:55.479 So even though the trend is towards smaller and smaller devices, they are 00:03:55.479 --> 00:03:59.255 still power hungry and in particular if you have a sophisticated camera, the 00:03:59.255 --> 00:04:03.326 number of images or the number of pixels that is generated might actually overwelm 00:04:03.326 --> 00:04:08.982 the power budget of the system. And so Dr. 00:04:08.982 --> 00:04:13.140 Zichong Chen, who finished his PhD here, and did his post doc, together with 00:04:13.140 --> 00:04:17.430 Guillermo Barrenetxea, are looking at creating large scale sensor networks 00:04:17.430 --> 00:04:22.289 equipped with cameras that are energy efficient. 00:04:24.050 --> 00:04:28.620 So why do we want images? Well, here are a few examples. 00:04:28.620 --> 00:04:33.250 This is from I think a Berkeley project about, monitoring birds' nest. 00:04:33.250 --> 00:04:36.890 Unfortunately a snake is showing up an he's actually eating all the eggs in the 00:04:36.890 --> 00:04:40.380 nest. So that's, monitoring for why life 00:04:40.380 --> 00:04:43.455 protection. Here is an example from the Swiss Alps 00:04:43.455 --> 00:04:48.117 monitoring for for avalanche detection. Here is also an example from the Swiss 00:04:48.117 --> 00:04:51.850 Alps, its monitoring to see weather conditions. 00:04:51.850 --> 00:04:55.810 And, finally, here is monitoring networks that is installed on the PFL campus. 00:04:55.810 --> 00:05:00.990 In all these cases you have many cameras using small communication devices. 00:05:00.990 --> 00:05:04.690 And so compression and representation is extremely critical. 00:05:06.630 --> 00:05:12.619 So there are a number of results that you can find in the thesis of Dr. 00:05:12.619 --> 00:05:17.437 Chi Chong Chang, given here in this, website, and essentially the idea is 00:05:17.437 --> 00:05:21.963 that, cameras can help each other to reduce the amount of information that 00:05:21.963 --> 00:05:26.197 actually has to be sent to the base station or into the cloud, for doing 00:05:26.197 --> 00:05:32.868 efficient monitoring. So a long with signal processing today, 00:05:32.868 --> 00:05:36.275 actually it's moving to single processing on graphs. 00:05:36.275 --> 00:05:39.815 I don't have to explain to you the importance, for example, of social 00:05:39.815 --> 00:05:43.514 networks. And so Pedro Pinto who was involved here 00:05:43.514 --> 00:05:47.610 in the class and is a post doc in the lab, together with Patrick Thiran, has 00:05:47.610 --> 00:05:52.230 worked on the problem of source localization. 00:05:52.230 --> 00:05:57.158 So you have some graph here, let's say social network and somebody launches a 00:05:57.158 --> 00:06:00.850 rumor. Here is the source and the rumor gets 00:06:00.850 --> 00:06:05.252 forwarded along the edges of the graph at different times and you have some 00:06:05.252 --> 00:06:12.570 observers, say green nodes here, that receives a rumor at some instant of time. 00:06:12.570 --> 00:06:15.770 They know where the rumor comes from, you know who told you the gossip, and you 00:06:15.770 --> 00:06:19.976 know when you got the gossip information. So, the question is, you know the 00:06:19.976 --> 00:06:23.000 structure of the graph, or you have an approximation of the structure of the 00:06:23.000 --> 00:06:26.380 graphs. You have these observations. 00:06:26.380 --> 00:06:30.290 Can you figure out who actually, spreads the rumor first. 00:06:30.290 --> 00:06:33.450 It turns out this has, an interesting solution. 00:06:33.450 --> 00:06:38.349 And using only few observers, about 20%, you can achieve a very high accuracy in 00:06:38.349 --> 00:06:43.510 finding the source of a rumor on a large scale network. 00:06:43.510 --> 00:06:46.930 And there are many interesting questions here, to pursue in this source 00:06:46.930 --> 00:06:51.291 localization in social networks. And there was a paper that came out last 00:06:51.291 --> 00:06:55.452 year, Locating the Source of Diffusion in Large-Scale Networks that had quite a bit 00:06:55.452 --> 00:06:59.101 of impact. The project is actually funded by the 00:06:59.101 --> 00:07:02.600 Bill and Melinda Gates Foundation. The reason is that one of the 00:07:02.600 --> 00:07:05.650 applications is to monitor health problems. 00:07:05.650 --> 00:07:10.642 For example, here is a map of Cholera outbreak in Africa, and the map shows the 00:07:10.642 --> 00:07:15.370 river network. Cholera is a water born disease, and so, 00:07:15.370 --> 00:07:19.735 typically Cholera will actually diffuse along waterways. 00:07:19.735 --> 00:07:23.767 But you know when people fell sick at certain locations and then you can infer 00:07:23.767 --> 00:07:27.410 the source of the actual Cholera outbreak. 00:07:27.410 --> 00:07:31.370 There is another example here, which is a simulation of, if you had to figure out 00:07:31.370 --> 00:07:35.150 if there was some pollution or attack on the New York subway, and if you could 00:07:35.150 --> 00:07:39.230 figure out knowing the network of the New York subway and when you start detecting 00:07:39.230 --> 00:07:45.750 the problems where the source of the problem actually was. 00:07:47.310 --> 00:07:51.279 The next project is on sampling, so we have worked on a new theory of sampling 00:07:51.279 --> 00:07:54.681 here called Finite Rate of Innovation Sampling, and it is used in 00:07:54.681 --> 00:07:58.650 communications problems, and in monitoring problems to reduce the number 00:07:58.650 --> 00:08:03.957 of samples being transmitted or acquired. Dr. 00:08:03.957 --> 00:08:08.446 Freris, who is a senior scientist with doctoral students and MS assistants, are 00:08:08.446 --> 00:08:12.868 actually working on doing ECG monitoring at very low power for wireless health 00:08:12.868 --> 00:08:17.002 monitoring. So here is a block diagram, it's 00:08:17.002 --> 00:08:21.032 relatively complicated so let me not get into this, but it uses some fairly 00:08:21.032 --> 00:08:25.186 sophisticated techniques to reduce the sampling rate so as to reduce the energy 00:08:25.186 --> 00:08:33.080 consumption on these wireless devices. So, this project is actually sponsored by 00:08:33.080 --> 00:08:40.165 somebody well known, Qualcomm, interested in the theory of sampling. 00:08:40.165 --> 00:08:43.637 And the extension here for this particular project has been 00:08:43.637 --> 00:08:49.810 generalization of the initial finite rate of innovation sampling methodology. 00:08:49.810 --> 00:08:53.730 To get better compression, and better modelization of the signals. 00:08:53.730 --> 00:08:57.825 So here we have the ECG signal, and then there is sophisticated models that 00:08:57.825 --> 00:09:02.795 allows, to take very few parameters, to model the ECG signal. 00:09:02.795 --> 00:09:06.500 There are a number of papers here, the initial paper on finite rate of 00:09:06.500 --> 00:09:10.920 innovation sampling is this 2002 paper, and the number of recent papers have done 00:09:10.920 --> 00:09:16.940 extension to this theory. So if you like sampling I welcome you to 00:09:16.940 --> 00:09:22.750 actually read up on this stuff, it's one of my favorite research topics. 00:09:24.880 --> 00:09:28.845 When we talk about sampling already in sensor networks we have mentioned that 00:09:28.845 --> 00:09:34.356 placing a sensor is like taking a sample. And so that spatial sampling, now if you 00:09:34.356 --> 00:09:38.596 do spatial sampling, you can also use mobile sensors and Dr. 00:09:38.596 --> 00:09:43.152 Unnikrishnan here, a post doc in the lab, has worked on this or generalization of 00:09:43.152 --> 00:09:47.708 the theory of sampling when you have mobile sensors that can actually go over 00:09:47.708 --> 00:09:56.360 a field in an arbitrary fashion. Then you maybe show this in an example. 00:09:56.360 --> 00:10:00.203 It's again a temperature monitoring example here on the EPFL campus, or you 00:10:00.203 --> 00:10:03.971 have buildings. You have that open space between 00:10:03.971 --> 00:10:07.176 buildings. Those buildings are, of course, hot. 00:10:07.176 --> 00:10:11.327 The open space are cool. And you would like to have monitoring of 00:10:11.327 --> 00:10:16.699 this temperature field not with static spatial sensors, but with people running 00:10:16.699 --> 00:10:23.960 around, having a thermal meter let's say on their mobile phone. 00:10:23.960 --> 00:10:27.854 And the question is, how accurate can you actually measure temperature using a 00:10:27.854 --> 00:10:32.240 device like this? And so, this is being done actually for 00:10:32.240 --> 00:10:37.612 pollution monitoring in the city of Lausanne so there's some equipment put on 00:10:37.612 --> 00:10:44.827 buses to measure pollution parameters. And what we do here is we try to develop 00:10:44.827 --> 00:10:49.909 a theory of how good you can sample when you have these mobile sensors going over 00:10:49.909 --> 00:10:57.310 a surface and measuring a field. The results are very mathematical but are 00:10:57.310 --> 00:11:00.660 interesting because our non-trivial extension of sampling theory through 00:11:00.660 --> 00:11:04.614 multiple dimensions. And a few papers are mentioned here if 00:11:04.614 --> 00:11:11.010 you are interested in more detail. The next project is about a new way of 00:11:11.010 --> 00:11:15.470 doing image acquisition. So in this class, we have seen sampling 00:11:15.470 --> 00:11:20.484 and we have seen quantization. And when we do quantization typically we 00:11:20.484 --> 00:11:25.460 say, let's take [UNKNOWN] samples and then take as many bits as possible. 00:11:25.460 --> 00:11:30.920 Let's say eight bits for speech, 12 bits for images 24 bits maybe for audio, 00:11:30.920 --> 00:11:35.310 etcetera. Now here we took the extreme other 00:11:35.310 --> 00:11:40.332 example we said lets build an image sensor that has many, many, many pixels 00:11:40.332 --> 00:11:46.690 but the pixels only detect either a enough light or not. 00:11:46.690 --> 00:11:49.080 So the pixels are actually binary detectors. 00:11:49.080 --> 00:11:54.610 And so you have a light intensity here. Which changes over space. 00:11:54.610 --> 00:11:58.200 You have a lens that smooths the light intensity. 00:11:58.200 --> 00:12:01.110 So what reaches the camera is this smooth curve here. 00:12:01.110 --> 00:12:05.060 And this smooth curve you sample very, very, very finely. 00:12:05.060 --> 00:12:09.410 But you only decide if it's above or below a certain threshold. 00:12:09.410 --> 00:12:13.480 So the sensor only generates a sequence of binary digits. 00:12:13.480 --> 00:12:17.850 So that's the imaging model. And this has been studied by Dr. 00:12:17.850 --> 00:12:22.008 Feng Yang, did his PhD thesis on this, is now a post-doc working on this project, 00:12:22.008 --> 00:12:27.370 and a whole slew of other people. This was a very extensive project. 00:12:27.370 --> 00:12:32.765 And what is interesting is that this new way of acquiring images, for example, 00:12:32.765 --> 00:12:40.089 allows to do high dynamic range imaging. Here is a simulation of a high dynamic 00:12:40.089 --> 00:12:45.870 range image in a much easier way than with conventional cameras. 00:12:45.870 --> 00:12:49.736 That's one advantage. Another one is that you can have very, 00:12:49.736 --> 00:12:52.615 very cheap sensors. So here's an example of one that was 00:12:52.615 --> 00:12:56.800 built in the lab. And then, you take many, many frames. 00:12:56.800 --> 00:12:59.832 They are extremely noisy. If they look noisy, they are simply 00:12:59.832 --> 00:13:03.660 binary, so you only have zeroes and ones, but you have enough of these, and you do 00:13:03.660 --> 00:13:09.955 an optimal reconstruction method. You actually can recognize here, the logo 00:13:09.955 --> 00:13:15.026 of EPFL. There are publications here that you are 00:13:15.026 --> 00:13:19.380 welcome to look up. And the thesis is online. 00:13:19.380 --> 00:13:23.230 Last but not least Rambus silicon valley company, actually works with us on this 00:13:23.230 --> 00:13:28.620 and has acquired some of the technologies that was developed in this project. 00:13:30.790 --> 00:13:33.600 And old classic is trying to predict the stock market. 00:13:33.600 --> 00:13:38.438 So, we gave it another shot. so Lionel Coulot did his PhD thesis, was 00:13:38.438 --> 00:13:43.100 co-advised with Peter Bossaerts who is at Caltech. 00:13:43.100 --> 00:13:47.316 And we were trying to understand if methods from information theory would 00:13:47.316 --> 00:13:51.668 allow to predict models for the stock market, and that requires statistical 00:13:51.668 --> 00:13:56.234 models for what the stock market might be. 00:13:56.234 --> 00:14:00.264 And what is interesting is that you have to decide between very sophisticated 00:14:00.264 --> 00:14:04.294 models that might be overkill and are hard to estimate, and very simple models 00:14:04.294 --> 00:14:08.200 which might be too simplistic, but which might be very robust to things that 00:14:08.200 --> 00:14:15.118 happen in the stock market. And, in the end we used coding theory and 00:14:15.118 --> 00:14:20.082 classic algorithmic methods like dynamic programming to come up with a method that 00:14:20.082 --> 00:14:24.434 decides what is the correct model at every time of, the observation of the 00:14:24.434 --> 00:14:31.690 stock market. So I'm just going to show a picture. 00:14:31.690 --> 00:14:35.939 And the picture is, is a value on the stock market. 00:14:35.939 --> 00:14:40.412 And the question is, can you detect if the stock market is in a bear market or a 00:14:40.412 --> 00:14:44.562 bull market? So when the stock market goes up it's, 00:14:44.562 --> 00:14:48.130 called bull market. If it goes down, it's a bear market. 00:14:48.130 --> 00:14:53.180 What is very hard is to decide by watching every day what's happening. 00:14:53.180 --> 00:14:56.582 If currently the trend is going up or the trend is going down and you need to do 00:14:56.582 --> 00:15:01.857 this with an online algorithm. Okay, you cannot look into the future and 00:15:01.857 --> 00:15:06.543 this method developed by Lionel allows to do a model fitting and to very quickly 00:15:06.543 --> 00:15:14.050 detect when the stock market changes from a bull market to a bar, bear market. 00:15:15.710 --> 00:15:21.970 The thesis online and this was sponsored by, as you may guess, by a bank. 00:15:21.970 --> 00:15:26.041 And the results are interesting, but we are still having a regular day job so you 00:15:26.041 --> 00:15:29.404 can guess that the method is not completely fool proof to predict the 00:15:29.404 --> 00:15:33.590 stock market. But the methods, the algorithms, and the 00:15:33.590 --> 00:15:39.296 theory behind it is quite cool. The next few projects are so called 00:15:39.296 --> 00:15:42.708 inverse problems. So inverse problems are problems where 00:15:42.708 --> 00:15:46.236 you have some measurements but the measurements do not describe the signal 00:15:46.236 --> 00:15:50.649 you're interested in. But some indirect measurement of the 00:15:50.649 --> 00:15:56.210 signal, so you try to invert the system to go back to the original signal. 00:15:56.210 --> 00:15:59.801 You all know about computerized tomography, a medical image method, where 00:15:59.801 --> 00:16:03.660 you can see inside the body without really going there. 00:16:03.660 --> 00:16:08.004 And that's a typical inverse problem. Here we are interested in inverse 00:16:08.004 --> 00:16:13.110 problems in environmental monitoring. So, the first example is diffusion 00:16:13.110 --> 00:16:16.545 equation. And we have a physical phenomena, for 00:16:16.545 --> 00:16:22.150 example temperature has been discussed, or atmospheric dispersal of pollution. 00:16:22.150 --> 00:16:26.840 We want to measure the field at locations where we can put sensors, and the goal is 00:16:26.840 --> 00:16:32.110 to find where are the sources, for example, of pollution. 00:16:32.110 --> 00:16:36.867 Now this is a hard problem because, you have to model how, for example, pollution 00:16:36.867 --> 00:16:40.867 is being diffused. That depends on weather patterns and so 00:16:40.867 --> 00:16:44.037 on. But the tools we are using are typical 00:16:44.037 --> 00:16:48.945 signal processing tools, for analysis. Sampling theory for exemplifying finite 00:16:48.945 --> 00:16:52.245 rate of innovation sampling or compressive sensing, that has also been 00:16:52.245 --> 00:16:56.860 mentioned earlier. Let's look at the picture. 00:16:56.860 --> 00:17:01.488 That's a very simple example of this. Assume you have two smokestacks and 00:17:01.488 --> 00:17:06.992 inside a factory compound, and the smokestacks produce pollution which 00:17:06.992 --> 00:17:12.210 changes every day. You don't know how much pollution is 00:17:12.210 --> 00:17:16.820 being released, and you're working for an environmental monitoring agency. 00:17:16.820 --> 00:17:21.644 You put sensors outside of the compound and you measure what arrives, in terms of 00:17:21.644 --> 00:17:26.569 pollution, at these sensors. And the goal is to figure out if what 00:17:26.569 --> 00:17:29.985 came out of smoke stack was within the bounds allowed, lets say by z, 00:17:29.985 --> 00:17:35.966 Environmental Protection Agency. So this is a interesting and non-trivial 00:17:35.966 --> 00:17:40.776 problem but there are some interesting results that were produced by Yuri 00:17:40.776 --> 00:17:46.104 Ranieri, whom you all know because he was the famous Master Chief assistant for the 00:17:46.104 --> 00:17:53.992 BSB class. So we are able to recover sparse sources 00:17:53.992 --> 00:17:58.378 using this inversion method. we use this finite rate of innovation 00:17:58.378 --> 00:18:02.630 sampling techniques to actually do it. And here we is a list of publications 00:18:02.630 --> 00:18:10.180 that came out of this research. This problem is also an inverse problem. 00:18:10.180 --> 00:18:13.740 It's a Fukushima inverse problem. It is a PhD project of Marta 00:18:13.740 --> 00:18:19.152 Martinez-Camara, and a few other of us are involved in this, and we collaborate 00:18:19.152 --> 00:18:26.180 with a specialist Andreas Stohl. Who is a specialist of monitoring of 00:18:26.180 --> 00:18:30.610 radioactive diffusion. So what we like to do is figure out how 00:18:30.610 --> 00:18:36.280 much radionuclides were actually released in Fukushima at the time of the of the 00:18:36.280 --> 00:18:42.560 nuclear accident at Fukushima. We have only very few sensors, they are 00:18:42.560 --> 00:18:46.060 located around the world very far away from Fukushima. 00:18:46.060 --> 00:18:51.085 And the question is, is it possible from these few measurements around the world 00:18:51.085 --> 00:18:55.885 taken later, to invert the entire process as I diffuse the initial release of 00:18:55.885 --> 00:19:03.150 radioactive material into the atmosphere. What tools are we using? 00:19:03.150 --> 00:19:06.300 Sparse regularizations, so that's compressed sensing. 00:19:06.300 --> 00:19:09.663 And we need to using atmospheric dispersion model to understand how 00:19:09.663 --> 00:19:13.616 radioactive material from from Fukushima was actually transported across the 00:19:13.616 --> 00:19:19.568 world. So one result that we have and which is 00:19:19.568 --> 00:19:24.260 very interesting is we were able to estimate the emission of Xenons, that's 00:19:24.260 --> 00:19:28.884 radioactive gas that was released at the time of explosions at Fukushima, went up 00:19:28.884 --> 00:19:36.798 into the atmosphere, was transported by weather patterns all over the world. 00:19:36.798 --> 00:19:41.218 And from the measurements all over the world, we were able to pinpoint exactly 00:19:41.218 --> 00:19:48.560 when the Xenon was released, and how much Xenon was released into the atmosphere. 00:19:48.560 --> 00:19:52.272 And it turns out we actually know the total amount of Xenon that was released, 00:19:52.272 --> 00:19:57.960 because after the accident no Xenon was actually left in the nuclear power plant. 00:19:59.890 --> 00:20:03.754 Currently we're trying to go beyond this and estimate the Cesium release, but that 00:20:03.754 --> 00:20:08.518 turns out to be a harder problem. The paper that describes this will be 00:20:08.518 --> 00:20:13.860 published ICASSP this year and is available online here in infoscience. 00:20:16.390 --> 00:20:20.614 Last but not least is a project we call, "Can One Hear the Shape of a Room?" It's 00:20:20.614 --> 00:20:24.574 a PhD project of Ivan Dokmanic and several other people in the lab, in 00:20:24.574 --> 00:20:31.110 particular, Reza Parhizkar, Andreaz Walther, have worked on this. 00:20:31.110 --> 00:20:33.806 And also we have a collaboration with Yue Lu. 00:20:33.806 --> 00:20:38.650 He's now with Harvard. Now you know about this problem because, 00:20:38.650 --> 00:20:43.980 Ivan gave module 512 about gear dereverberation, echo cancellation. 00:20:43.980 --> 00:20:48.628 And, uh,the next step is to say, if I listen to echoes, can I actually 00:20:48.628 --> 00:20:55.008 understand what is a room shape? So if I know the room shape, then I know 00:20:55.008 --> 00:20:59.898 how to generate the echoes. But if you give me the echoes, can I know 00:20:59.898 --> 00:21:03.165 the room shape? It's a classic inverse problem, very cute 00:21:03.165 --> 00:21:05.981 one. And we usually explain it by saying, 00:21:05.981 --> 00:21:10.470 let's say you enter a room, you're blindfolded. 00:21:10.470 --> 00:21:13.820 And so you don't see the room at all. You snap your finger. 00:21:13.820 --> 00:21:19.280 You therefore elicit echoes, you listen very carefully to the echoes. 00:21:19.280 --> 00:21:22.460 Can you exactly see or hear the shape of the room? 00:21:24.390 --> 00:21:27.756 Now this has a beautiful theory, which we won't have time to really explain, but 00:21:27.756 --> 00:21:31.660 that you can read up about because it's published material. 00:21:31.660 --> 00:21:36.490 But if you have a source or receiver you have a direct pass between the source and 00:21:36.490 --> 00:21:41.540 the receiver, and you have echoes given by the walls. 00:21:41.540 --> 00:21:45.895 The echoes given by the walls correspond to so called mirror or image sources, so 00:21:45.895 --> 00:21:49.665 this is the same as if you had a source here and the sound would have gone 00:21:49.665 --> 00:21:54.666 straight here. So if you can locate all these image 00:21:54.666 --> 00:21:58.750 sources, then you can actually locate the room. 00:21:58.750 --> 00:22:02.772 The walls, therefore the room. And this is, you know, in principal 00:22:02.772 --> 00:22:08.590 do-able the question was is it always true that this can be done? 00:22:08.590 --> 00:22:11.501 And is it also realistic to do it in practice? 00:22:11.501 --> 00:22:15.659 So, here are examples of a system with five microforms, you have one source five 00:22:15.659 --> 00:22:19.466 microforms. You have somebody snap his finger and you 00:22:19.466 --> 00:22:23.498 have the echos related to the walls and you see there is a complexity which is, 00:22:23.498 --> 00:22:27.467 these echos come in random orders because different walls are at different 00:22:27.467 --> 00:22:33.798 distances of the microphone. And the question is, can we find out the 00:22:33.798 --> 00:22:37.780 shape from a set of measurements as we see here? 00:22:37.780 --> 00:22:44.050 How many measurements do we need? Can we have a robust algorithm? 00:22:44.050 --> 00:22:48.490 So the answer is summarized in, yes we can. 00:22:48.490 --> 00:22:52.157 And there are some experiments we did, both at the labs. 00:22:52.157 --> 00:22:56.122 So this is one of our seminar rooms we created a, an artificial wall here to 00:22:56.122 --> 00:23:01.330 have different shapes of rooms. So this is a typical shape of room. 00:23:01.330 --> 00:23:06.082 Then in this case, with five microphone and one source, we were able to estimate 00:23:06.082 --> 00:23:12.290 the size, shape of the room very accurately to more, better than 1%. 00:23:12.290 --> 00:23:16.996 And once we had this, we said, well, let's see how robust this is. 00:23:16.996 --> 00:23:21.284 We went to Lausanne Cathedral and that's actually a foyer of the Lausanne 00:23:21.284 --> 00:23:25.840 Cathedral, which is not at all needing the assumptions of the algorithms that 00:23:25.840 --> 00:23:31.677 I've described very briefly here. And it was still possible to see the 00:23:31.677 --> 00:23:36.420 major refractors, meaning the major walls here in the Lausanne Cathedral. 00:23:36.420 --> 00:23:40.200 And so the answer is yes, one can hear the shape of a room. 00:23:40.200 --> 00:23:44.840 And you can visit Ivan's web page to see more details. 00:23:46.980 --> 00:23:50.518 Now these were just a selection of projects, of works that is being done by 00:23:50.518 --> 00:23:55.100 PhDs and post-docs and senior researchers in the lab. 00:23:55.100 --> 00:23:59.126 Please go to the website, as that gives the entire portfolio of research here of 00:23:59.126 --> 00:24:02.010 what the lab is currently doing.