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00_separation-free-super-resolution

Codes associated with Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach

40-algorithms-every-programmer-should-know

40 Algorithms Every Programmer Should Know, published by Packt

acoustic-direction-finding-using-single-acoustic-vector-sensor-under-high-reverberation

We propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the “clean” time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.

admm-net-for-mimo-detection

ambient-backscatterers-using-fm-broadcasting-for-low-cost-and-low-power-wireless-applications

Hardware and Software files of IEEE publication: Ambient Backscatterers using FM Broadcasting for Low Cost and Low Power Wireless Applications

approximate-2d-rf-imaging

This repository is for imaging by inverse reconstruction of the image by received RF signal using different methods: Compressed sensing, Maximum Entropy, and Total Variation. The data generated here is using ray-tracing based models, not as accurate as CST simulation generated data.

bayesian-sparse-deep-learning

Code for An Adaptive Empirical Bayesian Method for Sparse Deep Learning (NeurIPS'19)

bcs

Bayesian Compressive Sensing and Multi-task Compressive Sensing

bsbl-fm-sbl-02

A fast implementation of the Block Sparse Bayesian Learning algorithm

cs-recovery-algorithms

cuda-programming

Sample codes for my CUDA programming book

cuda-programming-with-python

关于书籍CUDA Programming使用了pycuda模块的Python版本的示例代码

cuda-samples

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

cuda_extensionops

Some C++/C/CUDA Extension

cudacode-samples

Source code examples from the Parallel Forall Blog

cudasteps

基于《cuda编程-基础与实践》（樊哲勇 著）的cuda学习之路。

cv-surveys

计算机视觉相关综述。包括目标检测、跟踪........

deep-admm-net

downkyi

哔哩下载姬downkyi，B站视频下载工具，支持批量下载，支持8K、HDR、杜比视界，提供工具箱（音视频提取、去水印等）。

ecgcs

ecg compressed sensing

engg4801-passive-radar-detection

Final Year thesis project 2014. Investigating Passive Radar detection methods and implementing a new algorithm, Range-Doppler transformation

errorfunction4sparsity

A Novel Regularization Based on the Error Function for Sparse Recovery

fastsbl

A fast sparse Bayesian learning algorithm based on gaussian scale mixture model for regression problem

fista

FISTA implementation in MATLAB (recently updated FISTA with backtracking)

frank-wolfe-based-method-for-sd-mmv

We solve separable NMF problem under convex relaxation in the form of self-dictionary multiple measurement vectors (SD-MMV), using Frank-Wolfe algorithm. It possesses desirable identifiability properties even under noisy case while only requires a linear growth to data size in memory.

gamp_sbl

Computationally Efficient Sparse Bayesian Learning via Generalized Approximate Message Passing

gmmv-lim

Linear Inversion Method based on the Generalized Multiple Measurement Vectors model

gtc2017-numba

Numba tutorial for GTC 2017 conference

gtc2018-numba

Numba tutorial for GTC 2018

gtc2019-numba

Numba tutorial for GTC2019