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Codes associated with Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach
40 Algorithms Every Programmer Should Know, published by Packt
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.
Hardware and Software files of IEEE publication: Ambient Backscatterers using FM Broadcasting for Low Cost and Low Power Wireless Applications
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.
Code for An Adaptive Empirical Bayesian Method for Sparse Deep Learning (NeurIPS'19)
Bayesian Compressive Sensing and Multi-task Compressive Sensing
A fast implementation of the Block Sparse Bayesian Learning algorithm
Sample codes for my CUDA programming book
关于书籍CUDA Programming使用了pycuda模块的Python版本的示例代码
Samples for CUDA Developers which demonstrates features in CUDA Toolkit
Some C++/C/CUDA Extension
Source code examples from the Parallel Forall Blog
基于《cuda编程-基础与实践》(樊哲勇 著)的cuda学习之路。
计算机视觉相关综述。包括目标检测、跟踪........
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ecg compressed sensing
Final Year thesis project 2014. Investigating Passive Radar detection methods and implementing a new algorithm, Range-Doppler transformation
A Novel Regularization Based on the Error Function for Sparse Recovery
A fast sparse Bayesian learning algorithm based on gaussian scale mixture model for regression problem
FISTA implementation in MATLAB (recently updated FISTA with backtracking)
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.
Computationally Efficient Sparse Bayesian Learning via Generalized Approximate Message Passing
Linear Inversion Method based on the Generalized Multiple Measurement Vectors model
Numba tutorial for GTC 2017 conference
Numba tutorial for GTC 2018
Numba tutorial for GTC2019
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.