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An Introduction to Coding Theory
Приєднався 23 гру 2016
Відео
Automatic Repeat reQuest (ARQ) Schemes
Переглядів 4,4 тис.7 років тому
Automatic Repeat reQuest (ARQ) Schemes
Decoding of Low Density Parity Check Codes-I
Переглядів 18 тис.7 років тому
Decoding of Low Density Parity Check Codes-I
Decoding of Low Density Parity Check Codes-II: Belief Propagation Algorithm
Переглядів 14 тис.7 років тому
Decoding of Low Density Parity Check Codes-II: Belief Propagation Algorithm
Performance Bounds for Convolutional Codes
Переглядів 2,6 тис.7 років тому
Performance Bounds for Convolutional Codes
Introduction to Convolutional Codes-II: State Diagram, Trellis Diagram
Переглядів 9 тис.7 років тому
Introduction to Convolutional Codes-II: State Diagram, Trellis Diagram
Introduction to Convolutional Codes-I: Encoding
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Introduction to Convolutional Codes-I: Encoding
Convolutional Codes:Distance Properties
Переглядів 8 тис.7 років тому
Convolutional Codes:Distance Properties
Convolutional Codes: Classification, Realization
Переглядів 5 тис.7 років тому
Convolutional Codes: Classification, Realization
Decoding of Convolutional Codes-I: Viterbi Algorithm
Переглядів 10 тис.7 років тому
Decoding of Convolutional Codes-I: Viterbi Algorithm
Decoding of Convolutional Codes-II: BCJR Algorithm
Переглядів 11 тис.7 років тому
Decoding of Convolutional Codes-II: BCJR Algorithm
Some Simple Linear Block Codes-II: Reed Muller Codes
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Some Simple Linear Block Codes-II: Reed Muller Codes
Distance Properties of Linear Block Codes-I
Переглядів 7 тис.7 років тому
Distance Properties of Linear Block Codes-I
Distance Properties of Linear Block Codes-II
Переглядів 7 тис.7 років тому
Distance Properties of Linear Block Codes-II
Introduction to Error Control Coding-III
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Introduction to Error Control Coding-III
Syndrome, Error Correction and Error Detection
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Syndrome, Error Correction and Error Detection
Introduction to Linear Block Codes, Generator Matrix and Parity Check Matrix
Переглядів 33 тис.7 років тому
Introduction to Linear Block Codes, Generator Matrix and Parity Check Matrix
Very nicely explained
He's like average sarkari karamchari
19:22 are bhai arrow kr leta hme kese pta ki this se this ho rha hai.... pura din karab kr iss week ne
mt pda es se bdiya to
great lecture, thanks you .
What will move here and there , the pointer is not even visible really annoying. Try to focus there as well also teach like this in classrooms also
13:40, bit metric need to be M[r_l^(i) given x_l^(i)]
Joydeep
at 9:09, sum_i [lambda_i * (x_i dot g_i)] need to be sum_i [lambda_i * (x dot g_i)]
It tells me why even parity check bit is added leftside of the codewords
dude, you're a perfect teacher... just control your level of voice for God's sake if you see this comment... probably you did so far it's been 6 years.... Thanks anyways
Nice Sir
this is most needed.... there should be at least 2 -4 problem solving sessions after each week
Reed Muller majority decision fails when equal 1's and 0' appear. What to do then?
Recall that a distance ‘d’ RM code can correct up to (d-1)/2 errors. As long as the number of errors is in this limit, the majority decision will never give equal number of 1’s and 0’s. The procedure only fails when the no. of errors exceeds the (d-1)/2 condition. You can try a codeword of the RM(2,4) example shown in the video. Its distance is 4, so the majority decision never gives equal 0’s and 1’s for single bit error, but can fail when you have errors in two or more bits.
Reed Muller majority decision fails when equal 1's and 0' appear. What to do then?
The intro is soo asthetic
Excellent video…..
Unbeatable
Why does he only decode 3 bits? The trellis has 4 stages. He should have decoded 4 bits right?
Last bit in the trellis is the tail bit, not the information bit.
I can also read the slides. Can i be a professor in IIT?
👍
Great lecture!
Thank you for the video sir. Can you please explain the computation part of qi
Iam a 2017 ECE graduate can I know what this topic is about is this course for ppl applying for jobs or for students
Thank you!
Very good lecture, thank you
great lecture!!
Thank you sir, it was very helpful for my seminar on this topic.
I do not understand here about the column permutation which has been done. Could anyone help me to explain please?
Hatsoff sir u are my inspiration 🙏🙏🙏
Great video, thank you! Do we have to take into consideration the unit matrix that will be existing in the actual code as well?
Sphere covering bound is left , I think
thank you very much for the nice presentation. stay bless
Stammering a lot
Stages in error correction?
Thank you very much for the great lecture!
Prof. Banerjee, all your lections are great, thank you very much for giving to us all those great explanations!
Thanks
Would be Better if the professor pitch is a little low
great teaching Sir very nicely explained. Helped me a lot. Thankyou
"constraints": say it LOUDLY, pal. If you swallow the word, everyone is left guessing!
Hey any good youtube video to learn ldpc code ?
Where did you learn ldpc code @Aero Dynamico
Sir, thanks a lot. It really helped me. 😊
Terrible lecture. The parts that need explaining are explained too little, the parts that don't need explaining (e.g. how to count the columns of a matrix on paper) take up most of the time...
very insightful explanation. This helped me a lot. thanks Sir!
Can we do coding theory here after 12 th
bhosrike ho kya? iye engg ka topic he, communication systems
Good morning sir
hello all, Can you please if anyone know about equalizer and wireless communication help me to solve this question Consider an 8-tap FIR equalizer. (a) When the channel coefficients are [1, 0.8] (delays separated by a symbol period), determine the optimum equalizer weight vector. You can either derive it from the z-transform or from the MMSE approach. (b) Plot the real part of the weight vector and comment on its shape and how it is related to the channel coefficients Thank you very much and I appreciate your help
Thank you Sir, Great VIdeo!
hello sir. can you please share your whatsapp no. i have issue in ldpc .
very handsome
Bhai qa ant.shant padata h
he explains it very well in a easy manner