Durofy

Many audio systems in the market are classified as "Hi-Fi" Systems. Hi-Fi refers to High Fidelity - which is a measure of the degree of faithful reproduction of sound. Systems with high faithfulness are called Hi-Fi systems. In these systems, the output audio is an exact replica of the input audio.

There are certain requirements to be met by an audio system for it to be classified as a Hi-Fi system.Note that Hi-Fi systems are not ideal systems but a good approximation of ideal systems. These are given below with the corresponding requirements for ideal systems.

Virtual Reality systems like Microsoft Kinect work on the basis of tracking motion in the physical world and then reflecting the changes in the virtual world. Hence, it is important to accurately detect and capture movements of the player in the physical world.

The methods for motion capture explained here only need a camera which finds the the motion by finding the difference between the current position of the user and the initial position of the user. Hence, there will be some delay in reflecting the motion in the virtual world. The less this delay, the better is the Virtual Reality system.

Method 1 - Find difference between frames. Calculate change in pixels.

In this method, we take two images of the user(player) at a short time interval and then find the difference(Difference is a Image Processing Operation) of the two images. The resulting image - which is the difference - gives the area of motion(change).

For example, consider the images below taken from the movie Limitless :

Then, the difference of the two images (generated using Paint.NET) looks as follows:

You can see that in both the images the background is the same but Cooper has moved one step further - which is the difference of the two images. This difference comes in white in the output difference image as shown above. Hence, all we need is a stationary camera to keep the backgrounds same and find the changes or movements of the users.

Method 2 - Construct motion graph using displacement of nodes

In this method, we mark various parts of the body, usually joints like knees, feet, etc as nodes and the displacement of the nodes is considers using image processing operations and on the basis of these movements - the virtual world is reconstructed.
However, essentially, there is only one method for motion capture which is Image Processing. Both the above methods and based on Image Processing applications.

The next component of a virtual reality system is Speech Recognition.

Essentially, there are only three components of a Virtual Reality System - Face Recognition, Motion Recognition & Voice Recognition. Using these, it can achieve all of it's functionality.

Remember that now, all we have is a camera and a microphone. So, how do we achieve the above using only a cam and a mic? This is where image processing comes in.

Basically, a set of Image Processing algorithms are programmed into the virtual reality system for Face Detection, Face Recognition & Gesture/Motion Detection. Further, algorithms are implemented to achieve voice recognition. A number of algorithms are further used to increase the efficiency of the above.

We'll now look into each of the individual components and how they work. Let's start with Face Detection & Recognition.

A 3 Step Process :

We first need to look into Face Detection before Recognition. Face Detection involves finding the position of the faces in a given image. Once the faces are found they will be recognized by matching them to faces in a database using face templates, which we will discuss subsequently.

So face detection only gives us the answer to the question - "Where are the faces in the image?". Such algorithms are also used in digital cameras along with smile detection algorithms. The resulting image looks somewhat like this:

The output image has the faces marked with a green square outline. You must have noticed Facebook introduced Face Detection which they call their Auto-Tagging feature wherein, when you batch upload images, your friends are already tagged-

Algorithm - The method described here is called The Viola-Jones Method for Face Detection-or rather, for object detection. In general, the algorithm can be used to find any desired object-of-interest which in our case, happens to be face.

Note that there are many different algorithms to do the same. This is just one of them - A popular, simple and efficient one.

Viola-Jones method uses detectors to find faces in an image. A detector exploits the basic characteristics of faces in general to detect the faces in an image. Assume that you take a rectangular image of only your eyes. Then you take another image of the are below your eyes - the upper region of the cheeks. Then, if we compare the two images in terms of intensities (assume that the images are black and white - ie - they consists only of variations of grey from white to black). We find that the sum of intensities of all pixels of the first image is greater than the sum of intensities of all pixels of the second image.

That is, sum of intensities of pixels of the eye region is greater than the sum of intensities of the cheek region. This fact is utilized by the first Viola Jones Detector. We use a detector which has two rectangles - the upper has a higher sum of intensities than the lower one. We then move this detector on the image horizontally and vertically until it fits in on the image.

That is, it finds a region where the intensity of the above rectangle is greater than the lower rectangle. This place where it fits in is a probable face. This process can be explained below. The first step before using our detector is to convert the image to black and white:

We then apply the detector explained above to find a possible face. If it finds the area that fits in the intensities of the rectangle, then the output for the detector is 1, else the output is 0.

Like the above detector, the Viola Jones method uses a number of different detectors. For example, another detector uses the fact that the bridge of the nose if lighter than the eyes. That is, the sum of intensities of the eyes will be greater than the area between the eyes.

Again, if the face found by the first detector gives an output 1 and the second detector also gives an output 1 for the same image face sub-region, only then the sub-region goes to the next detector.

This process continues for a number of detectors and the image sub-region which gives an output = 1 for all the detectors is then classified as a face. If at any stage (ANY DETECTOR), the output is 0, the image sub-region is classified as "not a face". This makes Viola Jones an efficient algorithm for face detection.

In the past, there have been news of face recognition not working for black people. And that the companies were being racist. But perhaps, they might be using the Viola Jones method and the sum of intensities of the black and white images of black people could not be detected by the Viola Jones detector. So, it might be a purely technical reason (a limitation of the algorithm) and not racism.

Once we have detected the face in the image, face recognition is just finding the face template that matches with the found face. Face recognition uses pre-defined face templates for a database of people. They define major nodal points on the face and the distances between them - since, these distances are different for different people. For example, the distance between the eyes, etc.

An example of a template created using such distances on the face is shown in the image. The algorithm for face recognition searches the database of people's face templates with the image under consideration. If a template exists in the database with similar nodal distances as in the image, then the face is a match for the person in the database. This process can be summarized as follows:

Face recognition is used in Virtual Reality systems like Microsoft Kinect to recognize users and also to create avatars of the users apart from other applications in Security (Biometrics), etc.

The next component of Virtual Reality Systems is Motion Detection/Motion Capture.

The Virtual Reality System works on the following principle - It tracks the physical movements in the real world, then a rendering computer redraws the virtual world to reflects those movements. The updated virtual world is sent to the output (to the user in the real world).

In this case, the output is sent back to a head mounted display. Hence, The user feels "immersed" in the virtual world - as if she was in the virtual world itself as all she can see is her rendered movements in the virtual world.

However, to really be able to relate to the concept, we need to look for something from our real lives that works on this concept. In 2010, Microsoft introduced Kinect for Xbox 360. This is essentially a virtual reality system which does not need any equipment on the user - no head mounted display, no equipment on hands or body to track movements. Everything is done by a camera & a microphone on the device itself.

If you're not familiar with Kinect, please watch the following video before you continue to read:

This should definitely remind of the film Ra One where Ra One was meant to be a Virtual Reality System (as a game) but it eventually gets integrated into the real world using holography. So, they've basically tried to combine VR & Holography.. But failed to impress.

The next section describes the components/functionality used to make up a Virtual Reality System - System Components

Structure of the text:

This text is a basic introduction to Virtual Reality for absolute beginners. In addition, it provides things you could work on if you find the topic interesting.

Here's how we'll go about it - We shall first define Virtual Reality in a broad sense to get an idea of all the stuff it could refer to. We will then identify the major technical components of this technology and discuss how these components work individually. Eventually, we will integrate these components to form what we can call a Virtual Reality System.

Introduction to Virtual Reality:

Above is a beautiful image by dgator77 of the setting sun... Or is it? Is the sun really about to set?

Did you know the light of the sun takes 8 minutes (approx) to reach the earth? So the sun is no longer there where you see it. It is now displaced, maybe it has already set. When you look at the image or in the sky, you "see" the sun. Your eyes see it. But it is just a virtual image created of, in this case, a previous reality. Since reality is determined by our senses, we can generate a virtual impression of something that is not a reality which is what we can call - a Virtual Reality.

"What is real? How do you define real?
If you're talking about what you can hear, what you can smell, taste & feel, then real is simply electrical signals interpreted by your brain." - Morpheus (The Matrix)

So essentially, it turns out that our definition of reality depends largely on our senses and how we perceive things around us. Virtual Reality aims at stimulating the senses to create a virtual world which is indistinguishable from the “real” world.

We can then go one step further and provide a greater ranges of the various senses than what humans usually have. This could be to allow them to see with bird’s UV vision or hear with whale’s ultra-low frequencies. But hey, let's not get ahead of ourselves.

Let's first look at how a virtual reality system works - Virtual Reality > Basic Principle

Satellite Systems can be classified based upon their orbits as low earth orbit, medium earth orbit & geostationary earth orbit systems. Geostationary is also the highest earth orbit and hence, also provides the greatest visibility using only a few satellites. The coverage region of a satellite is called its footprint. This is the region from which the satellite is visible. Three geostationary satellite footprints ensure complete coverage of the earth as shown:

Hence, there is permanent or 24 hour visibility of geostationary satellites without the need of handoffs. While LEO & MEO satellites do not have 24 hour visibility as the satellites have smaller footprints since they are closer to the earth (low satellite height). Hence, a larger number of satellites are needed to cover the earth. Also, since each satellite has a small footprint, handoffs are also required between satellites.

Major differences between LEO, MEO & GEO satellite systems:

Note -

• HEO refers to highly elliptical orbits which have a visibility of about 12 hours.
• ICO or intermediate circular orbit is an example of MEO.
• GPS satellites are not in Geostationary orbits but instead, they orbit twice for every rotation of the earth at a height of 20, 000 km.
• Handheld terminals have low sending power are hence use LEO for mobile communication. LEO are also closest to the earth, have better signal strength and less time delay.

LED vs LASER

Major differences between LED & LASER optical sources for optic-fiber communication:

WCDMA vs CMDA2000

Major differences between WCDMA (3GPP) & CDMA2000 (3GPP2) standards for CDMA-based 3G implementations:

FTP vs TFTP

Major differences between FTP(File Transfer Protocol) & TFTP(Trivial File Transfer Protocol) Application layer protocols for file transfer: