Information theory and source coding-application

In summary, this course is difficult for me because of the transformation of random variables. I am not sure what it has to do with communications.
  • #1
Bassalisk
947
2
So I am enrolled into this course.

And we are learning probability and statistics and all that good stuff.

But one thing bothers me VERY much.

I lie, a lot of things bother me about this course but here is the first one:

Transformation of random variables.

So far we have been only doing that pure, raw unattractive math. And I cannot swallow the point of that.

Says here in the book, you have a voltage in input and you get something else out...

But still I cannot wrap my head around it.

How does this apply to communications?! This transformation of random variable?!

LTI systems I understand. But we get precise inputs and precise outputs. What role does a random variable take in my learning?!
 
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  • #2
A study of random variables will teach you how to model noise, which one of the most important (and deep) concepts in communication theory.

- Warren
 
  • #3
chroot said:
A study of random variables will teach you how to model noise, which one of the most important (and deep) concepts in communication theory.

- Warren

Please go on. I am dying over here.
 
  • #4
Well, noise is a random process, and random variables provide a formal, analytical means of analyzing noise and its effects on a system. This alone is incredibly important.

Random variables can also be used to model higher-level concepts, like the arrival time of packets on a communications network (Poisson random variables). They can be used to model the behavior of simple receivers, inter-symbol interference, and all kinds of other phenomena.

Random variables are one of the most important mathematical tools in the study of signal processing and communications. They're pretty fun, too, once you understand them. Keep at it; it's worth it.

- Warren
 
  • #5
chroot said:
Well, noise is a random process, and random variables provide a formal, analytical means of analyzing noise and its effects on a system. This alone is incredibly important.

Random variables can also be used to model higher-level concepts, like the arrival time of packets on a communications network (Poisson random variables). They can be used to model the behavior of simple receivers, inter-symbol interference, and all kinds of other phenomena.

Random variables are one of the most important mathematical tools in the study of signal processing and communications. They're pretty fun, too, once you understand them. Keep at it; it's worth it.

- Warren
"Once you understand them". Now there's an interesting idea :rofl:I usually have trouble understanding math. Why? Because mainly, in materials that I have, we only get the definition. What is random variable(strict mathematical definition) and no examples.

Took me a week to truly understand (using loosely "truly" here) random variables. First I thought random variables map a number to a probability. Then after that week, I saw that I was waaaaaaay off. Transformation of random variable, is something I find so so so hard to swallow. Why? Because we only get strict mathematical definition of it and barely any examples. We got one on recitation, and I couldn't conclude everything from that.Do you suggest that I try using Matlab for this? Will I only get lost?
My main tool for understanding random processes was Matlab. And, I am self taught. We learned very few in recitation. They focus us on math so much...
 
  • #6
Bassalisk, I feel your pain. It sounds like you do not have a particularly good teacher. Take a look at this Wikipedia page on RVs; it's a good place to start.

http://en.wikipedia.org/wiki/Random_variable

Also, here's an interesting handout from a political science class at Stanford. It explains random variables in the context of social sciences. I think the examples are particularly concrete and easy to grasp.

http://www.stanford.edu/class/polisci100a/newprob2.pdf

Feel free to ask any specific questions you might have on RVs, and I'll do my best to answer.

- Warren
 
  • #7
chroot said:
Bassalisk, I feel your pain. It sounds like you do not have a particularly good teacher. Take a look at this Wikipedia page on RVs; it's a good place to start.

http://en.wikipedia.org/wiki/Random_variable

Also, here's an interesting handout from a political science class at Stanford. It explains random variables in the context of social sciences. I think the examples are particularly concrete and easy to grasp.

https://www.physicsforums.com/showthread.php?t=549482

Feel free to ask any specific questions you might have on RVs, and I'll do my best to answer.

- Warren

My teacher is good if you like math :D. He explained it us as a mathematician and not engineer. Why? Because he is mathematician and he has PhD in math.

Thank you for your resource, I will definitely check that stanford out. wiki gave me the base intuition in the start.

I won't hesitate to ask any questions. Thank you very much kind sir.
Currently we are trying to make me understand this thread:

https://www.physicsforums.com/showthread.php?t=549482EDIT: wrong thread. this one is mine
 
Last edited:

1. What is information theory and source coding?

Information theory is a branch of mathematics that deals with the quantification, storage, and communication of information. Source coding, also known as data compression, is a technique used to reduce the amount of data needed to represent information.

2. Why is information theory important?

Information theory is important because it provides a mathematical framework for understanding and analyzing communication systems. It also allows us to determine the maximum possible data transfer rate and the amount of data that can be reliably transmitted.

3. How is information theory and source coding used in real-world applications?

Information theory and source coding are used in various real-world applications such as data compression in audio and video files, image compression in digital cameras, and data transmission in wireless communication systems. They are also used in error correction codes to ensure reliable data transmission.

4. What are some common techniques used in source coding?

Some common techniques used in source coding include Huffman coding, arithmetic coding, run-length encoding, and dictionary encoding. These techniques aim to reduce the redundancy in the data to be transmitted and achieve higher compression ratios.

5. How does source coding affect the quality of the transmitted information?

Source coding can affect the quality of the transmitted information as it involves discarding some data to achieve compression. Depending on the compression technique used, this can result in a loss of information and affect the quality of the transmitted data. However, advanced compression techniques have been developed to minimize the loss of information and maintain high-quality data.

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