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Double difference algorithm (Waldhauser and Ellsworth, 2000) is a widely used method to improve the seismicity pattern precision in seismology. However, the raw data downloaded from networks can not be used as the inputs directly due to formats, info sequence, missing data, redundant info etc.. Here, we present codes to deal these problems (use Spainish local network catalog as an example) and the resulted two files can be directly used as inputs of double difference relocation (hypoDD).
Maxime Vono, Nicolas Dobigeon, Pierre Chainais, Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler, MLSP, 2018.
Code associated with the paper 'Directional Sinogram Inpainting for Limited Angle Tomography' currently on arXiv.
This repository is a 2D travel-time tomography seismic using MATLAB which I build and my friend Rinta in order to complete my Final Projects. The forward modelling is resolved using Fast Marching Method (FMM) with finite difference approximation and the raytracing is resolved based on John Vidale paper, Finite difference calculation of travel times. This code can use two method in inversion part, Least Square and Pseudo-Inverse where the input data is only needed travel time and the location of station (in UTM, both easting and northing). You can also set some parameters which could affects the tomography result, such as the number of iteration, displaying forward modelling or inverse modelling to track your data, save your model or not and etc.
Implementation of Mapping and Characterizing Endometrial Implants by Registering 2D Transvaginal Ultrasound to 3D Pelvic Magnetic Resonance Images
这是一个开源程序,里面包含了三维各项同性正演模拟和全波形反演[translation: This is an open source program that contains 3D isotropic forward modeling and full waveform inversion.]
3DUSCT data access example script and visualization
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.
Project developed with the collaboration of Gianpaolo Alessiani and Givanni Vacirca for the course of Wireless Communications at Politecnico di Milano
Adaptive Beamforming techniques can be enhanced using Machine Learning Algorithms.
Repository for Matlab simulations of adaptive digital beamforming project
Matlab simulation for beamforming on ad hoc OFDM network
Simulation code for "X. Wu, Y. Cai, M. Zhao, R. C. de Lamare and B. Champagne, "Adaptive Widely Linear Constrained Constant Modulus Reduced-Rank Beamforming," in IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 1, pp. 477-492, Feb. 2017, doi: 10.1109/TAES.2017.2650838."
MATLAB code for Alternating Projections based gridless direction of arrival (DOA) estimation
Projection matrix design in Compressed Sensing
To overcome computational challenges in traditional optimization algorithms, developed an Iterative L1 Regularized Limited Memory Stochastic BFGS algorithm which drops the hyperparameters iteratively at certain rate as the algorithm proceeds providing exact sparse solutions to big data machine learning optimization problems using significantly less computational memory.
Android - Remote Access Trojan List
A library of MATLAB code for analysing post-migration pre-stack seismic data.
Anisotropic seismic modeling in time domain.
Acoustic tomography of the atmosphere
An improved algorithm for Threshold Sparse Bayesian algorithm can automatically adjust the best Threshold value.(YUAN YAO spotted it.)
Reproducibility materials for "Bayesian Hierarchical Varying-sparsity Regression Models with Application to Cancer Proteogenomics" by Yang Ni, Francesco C. Stingo, Min Jin Ha, Rehan Akbani & Veerabhadran Baladandayuthapani
Spearmint integrated Bayesian Optimization for hyper parameter tuning of Auto sparse encoder embedded with softmax Classifier for MNIST digit Classification.
This repository contains functions for obtaining posterior samples of allocation variables in multiple Bayesian over-fitted (sparse finite) mixed-scale mixture models. Mixture models included: 1) Bayesian Tensor Mixture of Product Kernels model (BayesTMPK), 2) Modularized Tensor Factorizations (MOTEF), 3) Bayesian Mixture of Product Kernels (BayesMPK), 4) Bayesian Mixture of Multivariate Gaussians (BayesMixMultGauss). Functions 1 and 2 include ability to model compositional data with essential zeros. Functions 3 and 4 include ability to model non-zero compositional data.
Matlab source code to compute dynamically updated seismic hazard
Bayesian Convolutional Neural Networks for Compressed Sensing Restoration
Bayesian Compressive Sensing and Multi-task Compressive Sensing
Scripts for beamforming seismic data
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.