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-matlab-data-cleansing-before-double-difference-relocation

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).

2018-mlsp-sparse-bayesian-logistic-regression

Maxime Vono, Nicolas Dobigeon, Pierre Chainais, Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler, MLSP, 2018.

2018_directional_inpainting_for_limited_angle

Code associated with the paper 'Directional Sinogram Inpainting for Limited Angle Tomography' currently on arXiv.

2d-tomography

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.

3d-2d-mri-tvus-image-registration

Implementation of Mapping and Characterizing Endometrial Implants by Registering 2D Transvaginal Ultrasound to 3D Pelvic Magnetic Resonance Images

3d-iso-fd-fwi-mpich

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-script

3DUSCT data access example script and visualization

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.

acoustic-shadowing

adaptive-beamforming

Project developed with the collaboration of Gianpaolo Alessiani and Givanni Vacirca for the course of Wireless Communications at Politecnico di Milano

adaptive-beamforming-using-machine-learning-in-matlab

Adaptive Beamforming techniques can be enhanced using Machine Learning Algorithms.

adaptive-dbf

Repository for Matlab simulations of adaptive digital beamforming project

adaptive-ofdm-array

Matlab simulation for beamforming on ad hoc OFDM network

adaptive-widely-linear-constrained-constant-modulus-reduced-rank-beamforming

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."

alternating_projections_gridless_doa_estimation

MATLAB code for Alternating Projections based gridless direction of arrival (DOA) estimation

an-efficient-method-for-robust-projection-matrix-design

Projection matrix design in Compressed Sensing

an-iterative-l1-regularized-limited-memory-stochastic-bfgs-algorithm

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-ratlist

Android - Remote Access Trojan List

aps_release

A library of MATLAB code for analysing post-migration pre-stack seismic data.

asofi3d

Anisotropic seismic modeling in time domain.

atom

Acoustic tomography of the atmosphere

automatic-threshold-sparse-bayesian-algorithm

An improved algorithm for Threshold Sparse Bayesian algorithm can automatically adjust the best Threshold value.(YUAN YAO spotted it.)

bayesian-hierarchical-varying-sparsity-regression-models-with-application-to-cancer-proteogenomics

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

bayesian-optimization-for-hyper-parameter-tuning

Spearmint integrated Bayesian Optimization for hyper parameter tuning of Auto sparse encoder embedded with softmax Classifier for MNIST digit Classification.

bayesian_ovfit_mixedscale_compositionaldata_mixturemodels

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.

bayesianpsha

Matlab source code to compute dynamically updated seismic hazard

bcnn

Bayesian Convolutional Neural Networks for Compressed Sensing Restoration

bcs

Bayesian Compressive Sensing and Multi-task Compressive Sensing

beamform

Scripts for beamforming seismic data

beamforming