In telecommunication and information theory, the code rate (or information rate) of a forward error correction code is the proportion of the data-stream that is useful (non-redundant). That is, if the code rate is for every k bits of useful information, the coder generates a total of n bits of data, of which are redundant. If R is the gross bit rate or data signalling rate (inclusive of redundant error coding), the net bit rate (the useful bit rate exclusive of error correction codes) is . For example: The code rate of a convolutional code will typically be 1⁄2, 2⁄3, 3⁄4, 5⁄6, 7⁄8, etc., corresponding to one redundant bit inserted after every single, second, third, etc., bit. The code rate of the octet oriented Reed Solomon block code denoted RS(204,188) is 188/204, meaning that 204 − 188 = 16 redundant octets (or bytes) are added to each block of 188 octets of useful information. A few error correction codes do not have a fixed code rate—rateless erasure codes. Note that bit/s is a more widespread unit of measurement for the information rate, implying that it is synonymous with net bit rate or useful bit rate exclusive of error-correction codes. (Wikipedia).
This is a short video tutorial on unit ratios...also called unit rates. For interactive applets, worksheets, and more videos go to http://www.mathvillage.info
From playlist All about ratios and proportions
From playlist a. Numbers and Measurement
This video defines a rate and a unit rate. Several examples are provided. http://mathispower4u.wordpress.com/
From playlist Ratios and Rates
Setting up related rates equation for word sums.
From playlist Differentiation
Introduction to Rates of Range: Average Rate of Change and Instantaneous Rate of Change
Introduction to Rates of Range: Average Rate of Change and Instantaneous Rate of Change
From playlist Calculus 1 Exam 2 Playlist
Example 1: Determine Unit Rate (MPH)
This video provides an example of how to determine a unit rate in miles per hour. Complete Video List at http://www.mathispower4u.com
From playlist Ratios and Rates
Average Rate of Change Examples
In this video we see two examples of word problems involving the average rate of change. Remember the average rate of change formula: (f(b) - f(a))/(b-a)
From playlist Calculus
Locally testable and locally correctable codes approaching the GV bound - Shubhangi Saraf
Computer Science/Discrete Mathematics Seminar I Topic: Locally testable and locally correctable codes approaching the Gilbert-Varshamov bound Speaker: Shubhangi Sara Affiliation: Rutgers University Date: November 27, 2017 For more videos, please visit http://video.ias.edu
From playlist Mathematics
List decodability of randomly punctured codes - Mary Wootters
Mary Wootters University of Michigan March 24, 2014 We consider the problem of the list-decodability of error correcting codes. The well-known Johnson bound implies that any code with good distance has good list-decodability, but we do not know many structural conditions on a code which gu
From playlist Mathematics
Local Correctability of Expander Codes - Brett Hemenway
Brett Hemenway University of Pennsylvania April 14, 2014 An error-correcting code is called locally decodable if there exists a decoding algorithm that can recover any symbol of the message with high probability by reading only a small number of symbols of the corrupted codeword. There is
From playlist Mathematics
Lifting small locally testable codes (LTCs) to large LTCs via HDXs - Prahladh Harsha
Computer Science/Discrete Mathematics Seminar I Topic: Lifting small locally testable codes (LTCs) to large LTCs via HDXs Speaker: Prahladh Harsha Affiliation: Tata Institute of Fundamental Research Date: November 25, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Live Day 1-Live Session On EDA And Feature Engineering- Zomato Dataset
Join the community session https://courses.ineuron.ai/Mega-Community-Live . Here All the materials will be uploaded. Download The Dataset: https://github.com/krishnaik06/5-Days-Live-EDA-and-Feature-Engineering The Oneneuron Lifetime subscription has been extended. In Oneneuron platform yo
From playlist Brief Introduction to Data Science
Nexus Trimester - John Walsh (Drexel University)
Rate Regions for Network Coding: Computation, Symmetry, and Hierarchy John Walsh (Drexel University) February 17, 2016 Abstract: This talk identifies a number of methods and algorithms we have created for determining fundamental rate regions and efficient codes for network coding proble
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme
Introduction to Coding Neural Networks with PyTorch and Lightning
Although we've seen how to code a simple neural network with PyTorch, we can make our lives a lot easier if we add Lightning to the mix. It makes writing the code easier, makes it portable to different computing environments and can even find the learning rate for us! TRIPLE BAM!!!! Spani
From playlist StatQuest
Nexus trimester - Michael Langberg (SUNY at Buffalo)
A reductionist view of network information theory Michael Langberg (SUNY at Buffalo) February 08, 2016 Abstract: The network information theory literature includes beautiful results describing codes and performance limits for many different networks. While common tools and themes are evi
From playlist Nexus Trimester - 2016 - Distributed Computation and Communication Theme
Coding Challenge #70.1: Nearest Neighbors Recommendation Engine - Part 1
In this coding challenge, I create a movie recommendation engine using the a "nearest neighbor" algorithm. In Part 1, I demonstrate you how to calculate a similarity score between two data points. This video is part of session 3 of my Spring 2017 ITP "Intelligence and Learning" course. 💻C
From playlist Session 3 - Intro to Machine Learning - Intelligence and Learning
Professor Peter O’Hearn: "Reasoning with Big Code"
The Turing Lectures: Computer Science - Professor Peter O’Hearn: "Reasoning with Big Code" Click the below timestamps to navigate the video. 00:00:10 Welcome by Professor Jon Crowcroft 00:02:51 Speaker introduction by Professor Jon Crowcroft 00:03:38 Professor Peter O’H
From playlist Turing Lectures
Lesson: Calculate a Confidence Interval for a Population Proportion
This lesson explains how to calculator a confidence interval for a population proportion.
From playlist Confidence Intervals