Calculating the Lyapunov Exponent for Logistic Map to Analyze Chaotic Behavior
Determining whether a sequence exhibits chaotic properties by computing the Lyapunov exponent of the logistic map, with implementation insights for numerical analysis
Cross-Correlation Function Calculation for Two Sequences: Implementation and Noise Analysis
Computational methods for cross-correlation functions between two sequences and auto-correlation functions for single sequences, with demonstrations of effects under varying noise power levels using MATLAB implementations.
Autocorrelation Operation for Sequence Analysis
Autocorrelation program designed to compute autocorrelation operations on sequences, featuring robust performance and practical application value
Calculating Kolmogorov Entropy and Lyapunov Exponents for Chaotic Sequence Identification
Implementation of Kolmogorov entropy calculation and Lyapunov exponent algorithms for determining whether a sequence exhibits chaotic behavior
MATLAB-based Huffman Encoding and Decoding Program for Images and Sequences
Implementation of Huffman encoding and decoding algorithms for images and data sequences using MATLAB
Generating Sequences with Fixed Autocorrelation Coefficients in MATLAB
Implementing fixed autocorrelation coefficient sequence generation using MATLAB and analyzing the corresponding power spectral density through built-in signal processing functions.
Generation of Zadoff-Chu (ZC) Sequences
This program generates Zadoff-Chu (ZC) sequences, which are utilized in LTE's Random Access Channel (RACH) procedure and downlink synchronization process. The implementation follows the mathematical formulation of ZC sequences with configurable root index and sequence length parameters.
MATLAB Implementation of GPS PRN Generator
GPS PRN generator that takes satellite ID as input and outputs corresponding GPS PRN sequence with code implementation details.
Power Spectrum Estimation of Sequences Using the Periodogram Method
Estimating the power spectrum of sequences using the periodogram method with a Hamming data window. The scenario involves resolving three sinusoidal signals of different frequencies embedded in white noise, where phases are independent random variables uniformly distributed over 2π, and amplitude corresponds to unit white noise. A collection of 50 sample sequences, each of length N=512, is generated for analysis.
Pseudo-Random Code or PN Code
Pseudo-random codes, also known as PN codes, are essential sequences in spread spectrum communication. Spread spectrum communication fundamentally operates by modulating original signals with highly correlated pseudo-random sequences, causing the signal to manifest according to the sequence's characteristics. The implementation involves generating m-sequences using linear feedback shift registers (LFSRs) with specific primitive polynomials. The MATLAB code below demonstrates m-sequence generation where different polynomial configurations produce sequences of varying orders, with key parameters including tap positions and initial states determining correlation properties and sequence length.