martin's starred repositories
Coded-Compressed-Sensing-for-Unsourced-Multiple-Access
Course Project for CS754 Advanced Image Processing, Spring 2024
FPGA-Implementation-Of-Adaptive-Noise-Cancellation
Speech is the most fundamental method of communication for humans, with a bandwidth of only 4 kHz. It can communicate information with the same emotion as a human voice. The voice signal has certain characteristics, such as being a one-dimensional signal with time as its independent variable. It is unpredictable, non-stationary, and the frequency spectrum does not remain consistent throughout time. Human speech contains substantial frequency components only up to 4 kHz while having an audible frequency range of 20 Hz to 20 kHz. The influence of interfering noise in the signals is the most prevalent issue in voice processing. The presence of background acoustic noise conceals the voice signal, reducing its intelligibility, and also affects speech communication in loud environments. Speech intelligibility is greatly reduced when background noise is present. Background noise is suppressed using noise reduction algorithms, which increase the perceived quality and intelligibility of speech. Due to the unpredictable nature of noise and the inherent complexity of speech, removing various forms of noise is challenging. In most noise reduction approaches, there is a trade-off between the quantity of noise removed and the speech distortions generated as a result of signal processing. In the field of voice enhancement, several approaches have been developed for this goal, including the spectral subtraction approach, wiener filter, and Kalman filter. The quality and intelligibility of the processed speech stream determine how well these approaches function. The goal of most methods is to enhance the voice signal to noise ratio.
FPGA-Video-Capture
2023集创赛紫光同创杯一等奖项目
BRAM_DDR3_HDMI
在FPGA中将图像数据输入到DDR3中，再输送到HDMI接口上进行显示。
Noice-Removal
In this project , I have used matlab to denoise a voice note or an mp3 audio using MATLAB .
LTE-OFDMA-Downlink-Simulation
LTE OFDMA Downlink Simulation
ODFMA-System-Analysis
An OFDMA system with two user downlinks
MIMO-OFDM-Wireless-Communications-with-MATLAB
MATLAB Code for MIMO-OFDM Wireless Communications with MATLAB | MIMO-OFDM无线通信技术及MATLAB实现
FPGA-Build
A novel architectural design for stitching video streams in real-time on an FPGA.
FPGAandImage
image processing based FPGA
CRC-aided_SPARCs_for_URA
This repository contains source codes for simulation results shown in one published paper titled "CRC-aided Sparse Regression Codes for Unsourced Random Access."
t_error_coded_cs
Coded Compressed Sensing With List Recoverable Codes for the Unsourced Random Access
PolarCodeDecodersInMatlab
This is the Matlab realization of Polar Decoders, including CA-SCL, Fast CA-SCL and BP decoder.
otfs-chan-est-and-eq
Simulation codes for "Channel Estimation and Equalization for CP-OFDM-based OTFS in Fractional Doppler Channels"
Channel-Estimation
Simulates an FBMC and OFDM transmission over a doubly-selective channel. Allows to reproduce all figures from "Doubly-Selective Channel Estimation in FBMC-OQAM and OFDM Systems", IEEE VTC Fall, 2018
sparse-plex
A MATLAB library for sparse representation problems
SPAWC2017_CS_robust_sync_errors
This directory contains all the codes required to reproduce the results in our SPAWC 2017 paper titled "A compressive channel estimation robust to synchronization impairments"
Compressive_Sensing_C_and_MATLAB
C and MATLAB implementation of CS recovery algorithm, i.e. Orthogonal Matching Pursuit, Approximate Message Passing, Iterative Hard Thresholding Algorithms
MMV-AMP-Algorithm-for-Massive-Connectivity-with-Massive-MIMO
This code is for paper: L. Liu and W. Yu, "Massive connectivity with massive MIMO-Part I: Device activity detection and channel estimation," IEEE Trans. Signal Process., vol. 66, no. 11, pp. 2933-2946, Jun. 2018. and L. Liu and W. Yu, "Massive connectivity with massive MIMO-Part II: Achievable rate characterization," IEEE Trans. Signal Process., vol. 66, no. 11, pp. 2947-2959, Jun. 2018.
orthogonal-matching-pursuit
An implementation of the Orthogonal Matching Pursuit (OMP) algorithm for recovery of signals in compressive sensing
Massive-MIMO-Precoding
Linear and Nonlinear precoding in downlink Multi-user Massive MIMO systems
ldpc-quant
Quantization for LDPC-decoder node messages