Module 10.3, examples of start-ups that 
use signal processing as a core

technology. 
Earlier on in this class somebody asked

in the forum, if I follow digital signal 
processing class, can I get a job in the

start-up and what sort of start-ups? 
So this brought us to think, well maybe

we can describe a few start-ups that came 
out of research done in the lab.

And our four that we discuss here, there 
are actually more that are active, but

four will be discussed here are 
Illusonic, Quividi, Sensorscope and

Vidinoti. 
So the first start up I want to discuss

is called Illusonic. 
It was started by Cristof Faller who did

his PhD thesis on a time as a 
[INAUDIBLE], and was interested in

acoustical signal processing. 
And in particular in multi channel audio.

So if you do acoustical signal 
processing, there are questions like

beamforming, echo control, we just 
discussed this earlier.

We used a project of can you hear, the 
shape of a room.

When you want to do spatial audio 
processing, you want to generate audio

for many channels. 
Either for headphones or for multichannel

loudspeaker systems, you may want to do 
upmix or you take a stereo signal and you

would like to render it as a 5:1 signal 
or as a 17:1 signal.

And, there are tools, of course, where, 
you can use signal processing techniques,

for example, to de-noise Music or 
de-reverb, recording of, person singing.

So the tools that are used at Illusonic 
are classic digital signal processing

tools, plus what was discussed briefly, 
when I talk about the class on audio and

acoustic signal processing. 
Again, perceptual models are extremely

important because the human auditory 
system is a very sophisticated signal

processing device, and if you try to fool 
that device you better need to understand

how it works. 
Here is an example of cool application,

so let's say you have your home cinema 
and you have a stereo recording that you

would like to listen to. 
So the home cinema has actually in this

case one, two, three, four, five, six, 
seven, eight, nine plus probably two base

booster somewhere, so it's probably an 
eleven channel system, so you would do

enough mix from a stereo signal, let's 
say from your MP3 player...

To this eleven channel spatial audio 
system, and you would like to make it so

that it sounds really like you're in the 
concert hall.

And so even sony has a very cool 
technology to do this, and not only do

they have the technology, they actually 
sell a box that will do this at

professional quality level. 
So the company is it's a small company

about five people, half a dozen people, 
it licenses technology, state of the art

stuff, to other, companies, and it has 
custom technologies that it develops.

For specific applications and as I 
mentioned it has this very cool Immersive

Audio Processor that was just launched 
this year and please visit our website

and see this cool stuff and if you want 
to buy one of these Immersive Audio

Processors, I can tell you it sounds 
incredibly beautiful.

The next company I want to describe is 
Quividi.

Now this is a very important company in 
its class because its a company of Palo

Prandoni. 
So when he's not teaching on Coursera and

playing his his guitar To explain signal 
processing.

He's actually the CTO of a company in 
Paris, called Quividi.

And Quividi does a full length thing in 
environments where you have cameras and

you have digital signage. 
So we have advertisements on screens or

you have information on screens, then 
Quividi clearly allows you to monitor who

is actually watching what you are 
showing.

So if you have a bunch of people in front 
of this camera, it will identify also

people it will say oh, here is a lady, 
here's ladies, you also got, a few of the

people are guys. 
It will make some statistics, how long

the people actually watch for example in 
advertisement, where they look on the

screen and so on. 
And this entire system is distributed in

the cloud, and Allows you to do a 
dashboard, a so-called dashboard, of how

your advertisement is being seen in these 
public screens, or in the malls where the

screens are being shown. 
And at latest, they have 150 networks of

measurements that are deployed all across 
the world as you can see.

And there are some very famous names that 
show up and so they essentially can do

monitoring of the quality of 
advertisement for all of these companies

essentially in real time and provide 
reports to the effectiveness of using

advertising on screens in public spaces. 
Okay, that's the story of Quividi.

It's cool technology. 
It uses computer vision, image

processing, the, it also uses a lot of, 
you know, state of the art, algorithmic

and machine learning technology. 
Please visit their website if you want to

know more about this one. 
The third company is called Sensorscope.

It grew out of all the efforts of doing 
environmental monitoring and various

projects here at DPFL. 
So, if you want to do environmental

monitoring, it's cool if you can do real 
time visualization of what is happening

there. 
an application where people are very

interesting is so-called precision 
agriculture, so we want to control the

quality, let's say for example, of water 
systems.

you also want to detect you know, certain 
weather patterns and so on and then

optimize crop production thanks to this 
monitoring.

So the company does large scale sensor 
networks, deployments and data

management. 
So you need wireless sensor networks.

So these are small stations that talk to 
each other in an ad hoc fashion.

So self organize sensor networks. 
Then you need signal and image

processing. 
The usual stuff that you have learned

here in the class. 
And of course radio communication

technology. 
So here would be a typical example.

you build a monitoring station. 
We have seen such monitoring stations at

class when we have discussed sampling 
issues with respect to rain monitoring.

So you take state-of-the-art, 
off-the-shelf sophisticated monitoring

communication and so on. 
You build sensor stations.

You deploy them in a self-organized 
network.

Then from a bay station you talk to the 
cloud.

On the cloud, you have all this data, and 
people that are interested in monitoring

this sort of deployment get access, 
privileged access to this data and can

take statistics and, you know, decide 
what to do, for example, about their

precision agriculture project. 
It's a small company, half a dozen

people, and probably its main market is 
precision all, agriculture, even if it

started, also from a, academic point of 
view, mostly about environmental

monitoring. 
And you can watch their website here, you

can also watch all the data that is 
online at climaps.com.

So all the deployments that have ever 
been done by the company and by the lab

are actually available here on, on this 
website, and you can also use this data

and you know, do some further signal 
processing if you're actually interested

by this topic. 
The fourth company here is called

Vidinoti. 
It's a recent start up from the lab and

it works in augmented reality, in 
particle augmented reality on mobile

devices. 
So, the core technologies image

recognition, computer vision. 
But in a ways that is robust and then to

also do all these processing in real time 
on small devices, like mobile phones and

decide how much processing you do on a 
mobile phone, how much you do in the

Cloud or on the server. 
And Vidinoti has a bunch of state of the

art. 
Algorithms on the one side, to do

tracking and recognition, and also a 
number of cool ideas on how to do

augmented reality based on these 
methodologies.

So, the technology is essentially 
cloud-based.

But there is an iPhone application that 
you can download, and then you can

annotate your favorite pictures or 
newspapers or whatever with augmented

reality, and this is actually being used 
in particular in the newspaper industry

to sort of bring digital content in a, 
you know, in a funny or attractive way

onto a medium. 
Mainly the newspaper that is being

challenged by, of course by Internet 
currently.

It is a small company, less than 10 
people currently.

It has you know, a strong research and 
development.

It has also a strong intellectual 
portfolio based on patterns that has been

generated over the year around Augmented 
Reality.

And if you want to know more, here's a 
web site, and here is interactive

application for the iPhone currently, it 
will be ported to Android within a couple

of months as well. 
So, these were example of start-ups that

used state of the art signal processing, 
image processing, computer vision, and so

on. 
And bring it to the real world, in very

concrete applications, from audio, to 
sensor networks, to augmented reality, to

monitoring of audience in, advertising.