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Concept

Convolutional code

Related lectures (28)

Convolutional Codes: Mapping Symbols and Paths

Covers Convolutional Codes, focusing on mapping symbols and paths.

Designing Convolutional Codes

Covers the art of designing convolutional codes to improve communication rate.

Convolutional Coder Introduction

Covers the introduction of Convolutional Coder, explaining its structure and operation.

Convolutional Codes: Encoding and Decoding

Explains the encoding and decoding process of convolutional codes using constellation prints.

Convolutional Codes: Decoding and Performance Analysis

Covers the decoding and performance analysis of convolutional codes in communication systems.

Convolutional Codes: Basics and Applications

Explores the basics and applications of convolutional codes in communication systems.

Convolutional Codes: Decoding and Eye Diagrams

Covers the decoding process of convolutional codes and the analysis of eye diagrams.

Linear Codes: Systematic vs Non-Systematic

Explores the creation of systematic linear codes and the transformation of non-systematic codes, showcasing their equivalence and importance in simplifying encoding and decoding processes.

Linear Block Codes: Basics

Covers the basics of linear block codes and systematic codes over a field F.

Data Compression and Shannon's Theorem: Entropy Calculation Example

Demonstrates the calculation of entropy for a specific example, resulting in an entropy value of 2.69.

Convolutional Codes: Analysis and Applications

Delves into the analysis and applications of convolutional codes, highlighting the significance of correct implementation for optimal results.

Binary Parity Check Code

Explores binary parity check codes, systematic generator matrix, and Gaussian elimination.

Data Compression and Shannon's Theorem Summary

Summarizes Shannon's theorem, emphasizing the importance of entropy in data compression.

Hidden Markov Models (HMM): Theory

Covers Hidden Markov Models (HMM) for modeling time series data and decoding using the Viterbi Algorithm.

If-elif-else: Python Control Flow Statements

Explores Python's if-else statements, demonstrating their syntax and usage through examples.

Three definitions of rate code MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Discusses three definitions of rate code in computational neuroscience, emphasizing temporal averaging, interspike intervals, and FANO factor.

Conditional Branching in Java

Covers the basics of conditional branching in Java using if-else statements and emphasizes the importance of code blocks.

Escape noise MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores escape noise in computational neuroscience, covering stochastic intensity, interspike intervals, likelihood functions, noise model comparison, and rate versus temporal codes.

Variability of Spike Trains MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores the variability of spike trains in computational neuroscience, covering experiments, sources of variability, and stochastic spike arrival and firing.

Entropy and Compression I

Explores entropy theory, compression without loss, and the efficiency of the Shannon-Fano algorithm in data compression.