BCH Code Simulation in MATLAB
A custom MATLAB simulation program for BCH codes featuring encoding and decoding main programs with corresponding execution results and algorithm explanations.
Multi-Class Support Vector Machine with Error Correcting Output Codes
Error Correcting Output Codes multi-class SVM implementation with custom-designed codebook using completely random encoding, demonstrating excellent performance for 7-15 class classification problems
Simulation of Convolutional Code Encoding and Viterbi Decoding
Performance comparison through simulation of convolutional code encoding and Viterbi decoding algorithms, with code implementation insights
MATLAB-Based OFDM Development System
This provides a complete MATLAB-based OFDM development system covering the entire communication chain - from encoding and channel modeling to demodulation, synchronization establishment, and Viterbi decoding. It serves as excellent learning material with comprehensive technical coverage.
Cubic Spline Algorithm in Image Encoding and Decoding
The cubic spline algorithm demonstrates excellent performance in both image encoding and decoding operations, although its computational intensity reduces overall efficiency. We propose a fast algorithm that maintains quality while optimizing processing speed through improved interpolation calculations and boundary condition handling.
JPEG Compression Implementation Using MATLAB
A MATLAB-based JPEG compression program that processes grayscale BMP images and encodes them into binary (0101 format) compressed data. The implementation follows standard JPEG encoding pipeline: color space conversion from RGB to YCbCr, chroma subsampling exploiting human visual system's lower sensitivity to color variations, block-based Discrete Cosine Transform (DCT), quantization preserving low-frequency components, and entropy coding using Run-Length Encoding and Huffman coding for high compression ratios.
MATLAB Image Processing Implementation with Code Examples
MATLAB Image Processing: Implementing Huffman coding, wavelet transform, and edge detection using Sobel and Laplacian-Gaussian methods for grayscale images. Code demonstrations include image compression techniques and feature extraction algorithms.
Improved Implementation of Convolutional Code Decoding Algorithm for Conv(2,1,9)
Implementation of Conv(2,1,9) convolutional code encoding and soft-decision sliding window Viterbi decoding with generator polynomials G0=561 (octal) and G1=753 (octal) using BPSK modulation over AWGN channel, comparing performance impact of different decoding depths through efficient algorithm implementation
Relationship Between Throughput and Bit Error Rate for Enhanced Type-II Chase Combining HARQ Mechanism
MATLAB simulation implementation analyzing the throughput vs. bit error rate relationship for an improved Type-II selective repeat HARQ mechanism. The simulation features simplified encoding/decoding processes: simulating erroneous bits probabilistically based on BER, with receiver-side error detection based on correctable error thresholds. Each transmitted codeword undergoes error checking using bit error count information - error-free codewords are accepted while erroneous ones trigger retransmission. The algorithm employs an alternating transmission scheme (via alternate value) between information codewords and parity codewords. When parity codewords arrive, the combined codeword (original + parity) undergoes error correction capability verification - successful correction leads to acceptance, otherwise retransmission is requested.
Programming Implementation of Pulse Code Modulation (PCM) Technology
Implementation of PCM technology through three processes: sampling, quantization, and encoding. Sampling: Low-pass continuous signal sampling demonstrated using x=sin(200*t), m=x./(200*t), m=m.*m to illustrate sampling theorem with time/frequency domain plots; Band-pass continuous signal sampling using x=sin(20*t), m=x./t to demonstrate band-pass sampling theorem with corresponding plots. Quantization: Uniform quantization implemented for sinusoidal signals with 64 quantization levels; Non-uniform quantization applied to sinusoidal signals input to A-law PCM encoder, showing sampling sequence and output code sequence. Encoding: Implementation of A-law 13-segment approximation and international standard PCM logarithmic A-law quantization coding.