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Information Theory and Coding Notes give aspirants a head start as they will also acquire the latest Syllabus, Reference Books, and Important Questions List for Information Theory and Coding Notes over regular notes. Information Theory and Coding Lecture Notes and Study Material PDF Free Download.
Participants can benefit from the Information Theory and Coding Notes PDFs and Reference Books from this article and ace the preparation methods with the best and updated study resources and achieve better grades.
 Information Theory and Coding Reference Books
 Information Theory and Coding Curriculum
 List of Information Theory and Coding Important Questions
 FAQs on Information Theory and Coding Notes
 Conclusion
Introduction to Information Theory and Coding Notes
Data is the wellspring of a correspondence framework, regardless of whether it is simple or computerized. Data hypothesis is a numerical way to deal with the investigation of coding of data alongside the evaluation, stockpiling, and correspondence of data.
States of Occurrence of Events
 On the off chance that we think about a function, there are three states of event.
 On the off chance that the function has not happened, there is a state of vulnerability.
 On the off chance that the function has quite recently happened, there is a state of shock.
 On the off chance that the function has happened, a period back, there is a state of having some data.
Consequently, these three happen at various occasions. The distinction in these conditions assists us with having information on the probabilities of event of functions.
1. Entropy
At the point when we watch the conceivable outcomes of event of a function, regardless of whether shock or dubious it would be, it implies that we are attempting to have a thought on the normal substance of the data from the wellspring of the function.
Entropy can be characterized as a proportion of the normal data content per source image. Claude Shannon, the “father of the Information Theory”, has given an equation for it as
$$H = – \sum_{i} p_i\log_{b}p_i$$
Where $p_i$ is the likelihood of the event of character number I from a given stream of characters and b is the base of the calculation utilized. Thus, this is additionally called as Shannon’s Entropy.
The measure of vulnerability staying about the channel contribution subsequent to watching the channel yield is called as Conditional Entropy. It is signified by $H(x \arrowvert y)$
2. Discrete Memory less Source
A source from which the information is being radiated at progressive stretches, which is free of past qualities, can be named as discrete memory less source.
This source is discrete as it isn’t considered for a constant time stretch, yet at discrete time spans. This source is memory less as it is new at every moment of time, without thinking about the past qualities.
Source Coding
As per the definition, “Given a discrete memory less wellspring of entropy $H(\delta)$, the normal codeword length $\bar{L}$ for any source encoding is limited as $\bar{L}\geq H(\delta)$”.
In less difficult words, the codeword (For instance: Morse code for the word QUEUE is – . .. . .. . ) is consistently more prominent than or equivalent to the source code (QUEUE in model). Which implies, the images in the code word are more noteworthy than or equivalent to the letters in order in the source code.
3. Channel Coding
The divert coding in a correspondence framework, presents repetition with a control, to improve the unwavering quality of the framework. Source coding decreases excess to improve the effectiveness of the framework.
 Channel coding comprises of two parts of activity.
 Planning approaching information grouping into a channel input arrangement.
 Back tracking the channel yield arrangement into a yield information succession.
 The last objective is that the general impact of the channel commotion ought to be limited.
 The planning is finished by the transmitter, with the assistance of an encoder, while the converse planning is done at the beneficiary by a decoder
Information Theory and Coding Notes and Study Material PDF Free Download
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Candidates can download the study material and notes and refer to them whenever during the preparation process. Use of the Information Theory and Coding Notes and Study Materials as a reference will help candidates get a better understanding of the concepts and change their score chart.
Here, is a list of a few important notes for a thorough preparation of the Information Theory and Coding course program
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Information Theory and Coding Reference Books
Books are a rich source of information and students should refer to books that provide excellent conceptual background. Candidates can avail the best books for Information Theory and Coding as recommended by the experts of the subject.
Pupils can refer and read through the Information Theory and Coding Books and other Study Sources during your preparation.
The list of best and highly recommended books for Information Theory and Coding preparation are as follows, and candidates can choose the book that meets their knowledge and prepare accordingly.
 “Information Theory and Coding” by N Abramson
 “Information Theory” by R B Ash
 “Error Control Coding” by Shu Lin and D J Costello
 “Information Theory and Coding Basics and Practices” by Veluswamy S
 “Information Theory and Coding” by Muralidhar Kulkarni and K S Shivaprakasha
 “TwoDimensional Information Theory and Coding – With Applications to Graphics Data and HighDensity Storage Media” by Jorn Justesen and Soren Forchhammer
 “Fundamentals in Information Theory and Coding” by Borda
 “Information Theory, Coding and Cryptography” by BOSE
 “Information Theory and Coding by Example” by Mark Kelbert Michael Kelbert Yuri Suhov
 “Fundamentals of Information Theory and Coding Design” by Jacob J S Sullum and Christopher J S Desilva
Information Theory and Coding Curriculum
The best way to make your preparation effective is with an initial idea and an outline of the Information Theory and Coding Syllabus. Keeping in mind every student’s requirements, we have provided a detailed view of the Information Theory and Coding curriculum.
Information Theory and Coding Course Curriculum will give students a clear idea of what to study, and the unitwise break up gives topics under each unit carefully and allot time to each topic.
Students must cover all the topics before attempting the Information Theory and Coding exam so that the paper is reasonably comfortable at the time of the exam. Candidates must ensure awareness of the Information Theory and Coding Syllabus as it prevents you from wasting unnecessary time on redundant topics.
The updated unitwise breakup of the Information Theory and Coding Syllabus is as follows
Unit  Topics 
UNIT I: Information Theory 

UNIT IILinear Block Codes 

UNIT III Cyclic Codes 

UNIT IV Structure and Properties of Convolutional Codes 

UNIT V Decoding of Convolutional Codes 

UNIT VI Automatic Repeat Request Strategies  Basic Techniques
Hybrid ARQ 
UNIT VII Introduction to Cryptography 

UNIT VIII Perfect Secrecy 

UNIT IX Secret and Public Key Encryption 

List of Information Theory and Coding Important Questions
Candidates studying Information Theory and Coding can go through the list of essential questions mentioned below for the Information Theory and Coding course programme. All the given review questions aim to help the candidates to excel in the examination.
 What is entropy?
 What is channel redundancy?
 Name the two source coding techniques.
 Write the expression for code efficiency in terms of entropy.
 What is a memoryless source? Give an example.
 Explain the significance of the entropy H(X/Y) of a communication system where X is the transmitter and Y is the receiver.
 What is information theory?
 Explain ShannonFano coding.
 Define bandwidth efficiency.
 Define the channel capacity of the discrete memoryless channel.
Frequently Asked Questions on Information Theory and Coding Notes
Question 1.
What is the prefix code?
Answer:
In prefix code, no codeword is the prefix of any other codeword. It is variable length code. The binary digits (codewords) are assigned to the messages as per their probabilities of occurrence.
Question 2.
Define the information rate.
Answer:
Information rate(R) is represented in average number of bits of information per second. It is calculated as,
R = r H Information bits / sec
Question 3.
Calculate the entropy of the source with a symbol set containing 64 symbols each with a probability pi = 1/ 64 .
Answer:
Here, there are M = 64 equally likely symbols. Hence entropy of such source is given as,H = log 2 M = log 2 64 = 6 bits / symbol
Question 4.
State the channel coding theorem for a discrete memoryless channel.
Answer:
Statement of the theorem:
Given a source of ‗M‘equally likely messages, with M >>1, which is generating information at a rate. Given channel with capacity C. Then if, R ≤ C
There exists a coding technique such that the output of the source may be transmitted over the channel with a probability of error in the received message which may be made arbitrarily small.
Explanation: This theorem says that if R ≤ C; it is possible to transmit information without any error even if noise is present. Coding techniques are used to detect and correct the errors.
Conclusion
The information on Information Theory and Coding Notes is genuine and reliable and the above mentioned Books and Study Materials aim to help and enhance student’s knowledge and understanding of the subject during preparations and at the time of examination. Students can refer and practice from the provided Information Theory and Coding Books, Study materials, and Important Questions from this article.