plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | Sage-Bionetworks/PDScores-master | fastdfa.m | .m | PDScores-master/bridge_ufb (for code generation)/fastdfa.m | 1,815 | utf_8 | 194ca754ba619f99878eac03559221d7 | % Performs fast detrended fluctuation analysis on a nonstationary input signal to
% obtain an estimate for the scaling exponent.
%
% Useage:
% [alpha, intervals, flucts] = fastdfa(x)
% [alpha, intervals, flucts] = fastdfa(x, intervals)
% Inputs
% x - input signal: must be a row vector
% Optional inp... |
github | Sage-Bionetworks/PDScores-master | mfcc.m | .m | PDScores-master/bridge_ufb (for code generation)/mfcc.m | 3,003 | utf_8 | f8a176478643ab178a95b5ce7000903c | % Computes mel-frequency cepstral coefficients (MFCCs) for an audio signal.
% Inputs:
% samples - mono audio signal
% Outputs:
% cepstra - MFCC matrix
%
% (CC BY-SA 3.0) Max Little, 2014
function cepstra = mfcc(samples)
% Sample rate
sr = 44100;
% MFCC parameters
wintime = 0.020; % Frame durat... |
github | Sage-Bionetworks/PDScores-master | swipep.m | .m | PDScores-master/bridge_ufb (for code generation)/swipep.m | 9,513 | utf_8 | 04dedf86521511a76a1af2141ba8a3ed | function [p,t,s] = swipep(x,fs,plim,dt,dlog2p,dERBs,woverlap,sTHR)
coder.extrinsic('exist','warning');
% SWIPEP Pitch estimation using SWIPE'.
% P = SWIPEP(X,Fs,[PMIN PMAX],DT,DLOG2P,DERBS,STHR) estimates the pitch
% of the vector signal X every DT seconds. The sampling frequency of
% the signal is Fs (in H... |
github | Sage-Bionetworks/PDScores-master | features_bpa.m | .m | PDScores-master/bridge_ufb (for code generation)/features_bpa.m | 1,518 | utf_8 | 7bf05c9503e5ce379a0b86fff9320d0a | % Computes basic posture test features.
% Inputs:
% post - posture accelerometry vector: post(:,1) - time points,
% post(:,2:4) - X,Y,Z acceleration data
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bpa(post)
% Output feature vector
ft = NaN(1,3);
% Ignore zero-length inputs
N = siz... |
github | Sage-Bionetworks/PDScores-master | features_bvav2.m | .m | PDScores-master/bridge_ufb (for code generation)/features_bvav2.m | 1,842 | utf_8 | 37504a770d7c8b4ae293453b43b50175 | % Computes basic phonation test features.
% Inputs:
% audio - mono floating-point, [-1,+1] normalized phonation
% signal
% srate - sample rate in Hz
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bvav2(audio,srate)
% Output feature vector
ft = NaN(1,13);
% Ignore zero-length inputs
... |
github | Sage-Bionetworks/PDScores-master | extractaudiophon.m | .m | PDScores-master/bridge_ufb (for code generation)/extractaudiophon.m | 769 | utf_8 | 639f4c97d5fa2b8ee53b7409ab675831 | % Finds the best segment of a phonation recording.
% Inputs:
% rawaudio - mono floating-point, [-1,+1] normalized phonation recording
% Outputs:
% audiout - best phonation signal
% indout - segment index of phonation signal
%
% (CC BY-SA 3.0) Max Little, 2014
function [audioout, indout] = extractaudiophon(r... |
github | Sage-Bionetworks/PDScores-master | features_bga.m | .m | PDScores-master/bridge_ufb (for code generation)/features_bga.m | 1,832 | utf_8 | f7c50e3ba1dd0de8730496008809af81 | % Computes basic gait test features.
% Inputs:
% gait - gait accelerometry vector: gait(:,1) - time points,
% gait(:,2:4) - X,Y,Z acceleration data
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bga(gait)
% Output feature vector
ft = NaN(1,7);
% Ignore zero-length inputs
N = size(gait... |
github | Sage-Bionetworks/PDScores-master | features_bta.m | .m | PDScores-master/bridge_ufb (for code generation)/features_bta.m | 817 | utf_8 | dc4c989b60fc80651eeafd0d120762fd | % Computes basic tapping test features.
% Inputs:
% tap - tapping data vector: tap(:,1) - time points,
% tap(:,2:3) - X,Y touch screen coordinates
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bta(tap)
% Output feature vector
ft = NaN(1,2);
% Ignore zero-length inputs
N = size(tap,1);... |
github | Sage-Bionetworks/PDScores-master | vadsplitphon.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/vadsplitphon.m | 3,353 | utf_8 | 0cddac4a77beadfd15cfb6e72cf5dc84 | % Segment a phonation recording, based on an attack-sustain-release state
% machine model.
% Inputs:
% x - mono floating-point, [-1,+1] normalized phonation recording
% Outputs:
% y - phonation segments
% i - sample index of start of each segment
%
% (CC BY-SA 3.0) Max Little, 2014
function [y,i] = vadsplit... |
github | Sage-Bionetworks/PDScores-master | features_ufb.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/features_ufb.m | 915 | utf_8 | 385004ac2dfad9cc0d81bed49a0759a7 | % Receives raw features, normalization parameters and feature weights, and
% computes user feedback value.
% Inputs:
% ftvec - feature vector (1xN)
% wvec - weight vector (Nx1)
% ilog - list of features to apply logarithm (variance stabilization)
% ftmin - feature normalization vector minimum (1xN)
% ftma... |
github | Sage-Bionetworks/PDScores-master | fastdfa.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/fastdfa.m | 1,815 | utf_8 | 194ca754ba619f99878eac03559221d7 | % Performs fast detrended fluctuation analysis on a nonstationary input signal to
% obtain an estimate for the scaling exponent.
%
% Useage:
% [alpha, intervals, flucts] = fastdfa(x)
% [alpha, intervals, flucts] = fastdfa(x, intervals)
% Inputs
% x - input signal: must be a row vector
% Optional inp... |
github | Sage-Bionetworks/PDScores-master | mfcc.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/mfcc.m | 3,003 | utf_8 | f8a176478643ab178a95b5ce7000903c | % Computes mel-frequency cepstral coefficients (MFCCs) for an audio signal.
% Inputs:
% samples - mono audio signal
% Outputs:
% cepstra - MFCC matrix
%
% (CC BY-SA 3.0) Max Little, 2014
function cepstra = mfcc(samples)
% Sample rate
sr = 44100;
% MFCC parameters
wintime = 0.020; % Frame durat... |
github | Sage-Bionetworks/PDScores-master | swipep.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/swipep.m | 9,513 | utf_8 | 04dedf86521511a76a1af2141ba8a3ed | function [p,t,s] = swipep(x,fs,plim,dt,dlog2p,dERBs,woverlap,sTHR)
coder.extrinsic('exist','warning');
% SWIPEP Pitch estimation using SWIPE'.
% P = SWIPEP(X,Fs,[PMIN PMAX],DT,DLOG2P,DERBS,STHR) estimates the pitch
% of the vector signal X every DT seconds. The sampling frequency of
% the signal is Fs (in H... |
github | Sage-Bionetworks/PDScores-master | features_bpa.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/features_bpa.m | 1,518 | utf_8 | 7bf05c9503e5ce379a0b86fff9320d0a | % Computes basic posture test features.
% Inputs:
% post - posture accelerometry vector: post(:,1) - time points,
% post(:,2:4) - X,Y,Z acceleration data
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bpa(post)
% Output feature vector
ft = NaN(1,3);
% Ignore zero-length inputs
N = siz... |
github | Sage-Bionetworks/PDScores-master | features_bvav2.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/features_bvav2.m | 1,842 | utf_8 | 37504a770d7c8b4ae293453b43b50175 | % Computes basic phonation test features.
% Inputs:
% audio - mono floating-point, [-1,+1] normalized phonation
% signal
% srate - sample rate in Hz
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bvav2(audio,srate)
% Output feature vector
ft = NaN(1,13);
% Ignore zero-length inputs
... |
github | Sage-Bionetworks/PDScores-master | extractaudiophon.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/extractaudiophon.m | 769 | utf_8 | 639f4c97d5fa2b8ee53b7409ab675831 | % Finds the best segment of a phonation recording.
% Inputs:
% rawaudio - mono floating-point, [-1,+1] normalized phonation recording
% Outputs:
% audiout - best phonation signal
% indout - segment index of phonation signal
%
% (CC BY-SA 3.0) Max Little, 2014
function [audioout, indout] = extractaudiophon(r... |
github | Sage-Bionetworks/PDScores-master | features_bga.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/features_bga.m | 1,832 | utf_8 | f7c50e3ba1dd0de8730496008809af81 | % Computes basic gait test features.
% Inputs:
% gait - gait accelerometry vector: gait(:,1) - time points,
% gait(:,2:4) - X,Y,Z acceleration data
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bga(gait)
% Output feature vector
ft = NaN(1,7);
% Ignore zero-length inputs
N = size(gait... |
github | Sage-Bionetworks/PDScores-master | features_bta.m | .m | PDScores-master/copy-contents-to-desktop/bridge_ufb/features_bta.m | 817 | utf_8 | dc4c989b60fc80651eeafd0d120762fd | % Computes basic tapping test features.
% Inputs:
% tap - tapping data vector: tap(:,1) - time points,
% tap(:,2:3) - X,Y touch screen coordinates
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bta(tap)
% Output feature vector
ft = NaN(1,2);
% Ignore zero-length inputs
N = size(tap,1);... |
github | Sage-Bionetworks/PDScores-master | vadsplitphon.m | .m | PDScores-master/bridge_ufb (original)/vadsplitphon.m | 3,060 | utf_8 | 3bbd3fb2673c398be740d1c13815fea2 | % Segment a phonation recording, based on an attack-sustain-release state
% machine model.
% Inputs:
% x - mono floating-point, [-1,+1] normalized phonation recording
% Outputs:
% y - phonation segments
% i - sample index of start of each segment
%
% (CC BY-SA 3.0) Max Little, 2014
function [y,i] = vadsplit... |
github | Sage-Bionetworks/PDScores-master | features_ufb.m | .m | PDScores-master/bridge_ufb (original)/features_ufb.m | 915 | utf_8 | 385004ac2dfad9cc0d81bed49a0759a7 | % Receives raw features, normalization parameters and feature weights, and
% computes user feedback value.
% Inputs:
% ftvec - feature vector (1xN)
% wvec - weight vector (Nx1)
% ilog - list of features to apply logarithm (variance stabilization)
% ftmin - feature normalization vector minimum (1xN)
% ftma... |
github | Sage-Bionetworks/PDScores-master | fastdfa.m | .m | PDScores-master/bridge_ufb (original)/fastdfa.m | 1,298 | utf_8 | 57274a6313658c9e7df8fc5255626731 | % Performs fast detrended fluctuation analysis on a nonstationary input signal to
% obtain an estimate for the scaling exponent.
%
% Useage:
% [alpha, intervals, flucts] = fastdfa(x)
% [alpha, intervals, flucts] = fastdfa(x, intervals)
% Inputs
% x - input signal: must be a row vector
% Optional inp... |
github | Sage-Bionetworks/PDScores-master | mfcc.m | .m | PDScores-master/bridge_ufb (original)/mfcc.m | 2,509 | utf_8 | 1d64161450f4a228d48a084dba569ce6 | % Computes mel-frequency cepstral coefficients (MFCCs) for an audio signal.
% Inputs:
% samples - mono audio signal
% Outputs:
% cepstra - MFCC matrix
%
% (CC BY-SA 3.0) Max Little, 2014
function cepstra = mfcc(samples)
% Sample rate
sr = 44100;
% MFCC parameters
wintime = 0.020; % Frame duratio... |
github | Sage-Bionetworks/PDScores-master | swipep.m | .m | PDScores-master/bridge_ufb (original)/swipep.m | 7,959 | utf_8 | 2f4f1d118e1dbdb3de56f07007174134 | function [p,t,s] = swipep(x,fs,plim,dt,dlog2p,dERBs,woverlap,sTHR)
% SWIPEP Pitch estimation using SWIPE'.
% P = SWIPEP(X,Fs,[PMIN PMAX],DT,DLOG2P,DERBS,STHR) estimates the pitch
% of the vector signal X every DT seconds. The sampling frequency of
% the signal is Fs (in Hertz). The spectrum is computed using ... |
github | Sage-Bionetworks/PDScores-master | features_bpa.m | .m | PDScores-master/bridge_ufb (original)/features_bpa.m | 1,427 | utf_8 | 60817100e9ac327b170da27b72dc44ee | % Computes basic posture test features.
% Inputs:
% post - posture accelerometry vector: post(:,1) - time points,
% post(:,2:4) - X,Y,Z acceleration data
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bpa(post)
% Output feature vector
ft = NaN(1,3);
% Ignore zero-length inputs
N = siz... |
github | Sage-Bionetworks/PDScores-master | features_bvav2.m | .m | PDScores-master/bridge_ufb (original)/features_bvav2.m | 1,599 | utf_8 | 4cb533680a514c40ad83600bb1834bfd | % Computes basic phonation test features.
% Inputs:
% audio - mono floating-point, [-1,+1] normalized phonation
% signal
% srate - sample rate in Hz
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bvav2(audio,srate)
% Output feature vector
ft = NaN(1,13);
% Ignore zero-length inputs
... |
github | Sage-Bionetworks/PDScores-master | extractaudiophon.m | .m | PDScores-master/bridge_ufb (original)/extractaudiophon.m | 666 | utf_8 | 243688bd45dc54007cd7825b33650dba | % Finds the best segment of a phonation recording.
% Inputs:
% rawaudio - mono floating-point, [-1,+1] normalized phonation recording
% Outputs:
% audiout - best phonation signal
% indout - segment index of phonation signal
%
% (CC BY-SA 3.0) Max Little, 2014
function [audioout, indout] = extractaudiophon(r... |
github | Sage-Bionetworks/PDScores-master | features_bga.m | .m | PDScores-master/bridge_ufb (original)/features_bga.m | 1,747 | utf_8 | 74ef69f9d4ce8d65dee072a8bfc25c83 | % Computes basic gait test features.
% Inputs:
% gait - gait accelerometry vector: gait(:,1) - time points,
% gait(:,2:4) - X,Y,Z acceleration data
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bga(gait)
% Output feature vector
ft = NaN(1,7);
% Ignore zero-length inputs
N = size(gait... |
github | Sage-Bionetworks/PDScores-master | features_bta.m | .m | PDScores-master/bridge_ufb (original)/features_bta.m | 817 | utf_8 | dc4c989b60fc80651eeafd0d120762fd | % Computes basic tapping test features.
% Inputs:
% tap - tapping data vector: tap(:,1) - time points,
% tap(:,2:3) - X,Y touch screen coordinates
%
% (CC BY-SA 3.0) Max Little, 2014
function ft = features_bta(tap)
% Output feature vector
ft = NaN(1,2);
% Ignore zero-length inputs
N = size(tap,1);... |
github | fryjustinc/FRCTracker-master | demsvm2.m | .m | FRCTracker-master/svm/demsvm2.m | 10,878 | utf_8 | 36427c53822acf405dc1c62730ebe38a | function demsvm2()
% DEMSVM2 - Demonstrate advanced Support Vector Machine features
%
% DEMSVM2 demonstrates the classification of a simple artificial data
% set by a Support Vector Machine classifier. The features of the SVM
% routines that make it useful for large data sets are shown.
%
% See also
% SVM, S... |
github | fryjustinc/FRCTracker-master | normalizeFeatures01.m | .m | FRCTracker-master/svm/normalizeFeatures01.m | 1,174 | utf_8 | 16f5c22e2890b3752a74a4ae67e6f1a6 | % This function normalizes the features to the range [0,1]. For each feature type,
% for example, Lmean, the min becomes 0 and max becomes 1. This isn't that robust, because
% a single outlier could compress the rest of the data too much.
% The data is assumed to be in the the format specified in the paper.
% Featur... |
github | fryjustinc/FRCTracker-master | svmtrain.m | .m | FRCTracker-master/svm/svmtrain.m | 21,731 | utf_8 | 85340357d47285ad2f64255366ba67d7 | function net = svmtrain(net, X, Y, alpha0, dodisplay)
% SVMTRAIN - Train a Support Vector Machine classifier
%
% NET = SVMTRAIN(NET, X, Y)
% Train the SVM given by NET using the training data X with target values
% Y. X is a matrix of size (N,NET.nin) with N training examples (one per
% row). Y is a column vect... |
github | SteveGoldstein/condorWorkshop-master | Independent.m | .m | condorWorkshop-master/flockMatlab/indir/Independent.m | 911 | utf_8 | 3f37d996366df7ea84e96da5355ffe35 | %
% run.m Run the next reaction method on the system
%
% 0 -> M, M -> 0, M -> M + P, P -> 0, 2P -> D
%
% with rates c1 = 25, c2 = .1, c3 = 1000, c4 = 1, c5 = .001
%
% usage: Independent(n,tol)
%
% n = number of times to run to get stats.
% tol = tolerance... |
github | SteveGoldstein/condorWorkshop-master | nrm_dimer.m | .m | condorWorkshop-master/flockMatlab/indir/nrm_dimer.m | 1,795 | utf_8 | 570729cc70db2cc167c1368dcb475b18 | %
% run.m Using next reaction method for
%
% 0 -> M, M -> 0, M -> M + P, P -> 0, 2P -> D
%
% with rates c1 = 25, c2 = .1, c3 = 1000, c4 = 1, c5 = .001
%
% usage: nrm_dimer(tend,initial)
%
% tend = max time for the program to run
%
% initial = initial cond... |
github | QuantumUtils/quantum-utils-mathematica-master | M2MTests.m | .m | quantum-utils-mathematica-master/test/M2MTests.m | 3,573 | utf_8 | 2b3ad7f8fb6a64e4c0242b95e8f46119 | (* ::Package:: *)
(* ::Title:: *)
(*QuantumUtils for Mathematica*)
(*M2M Unit Tests*)
(* ::Subsection::Closed:: *)
(*Copyright and License Information*)
(* ::Text:: *)
(*This package is part of QuantumUtils for Mathematica.*)
(**)
(*Copyright (c) 2015 and later, Christopher J. Wood, Christopher E. Granade, Ian N. ... |
github | mdoescher/Computer_Vision-master | align_images.m | .m | Computer_Vision-master/align_images.m | 3,294 | utf_8 | f9e6e0b7de941443b3c4c3f2f7c9c9ff | function[images]=align_images(input_images, depth)
% Align images using Ward's Medium Threshold bitmap (MTB) technique
% input_images is a cell array containing the images to be aligned
% grab the middle image and use this as the reference image to which the
% other images will be aligned.
n=length(input_images);
refe... |
github | sibbi77/openair4G-master | gen_7_5_kHz.m | .m | openair4G-master/openair1/PHY/MODULATION/gen_7_5_kHz.m | 3,298 | utf_8 | a08e730b234a112cbf6aac5b44c3af8b |
function [] = gen_7_5_kHz()
[s6_n2, s6_e2] = gen_sig(6);
[s15_n2, s15_e2] = gen_sig(15);
[s25_n2, s25_e2] = gen_sig(25);
[s50_n2, s50_e2] = gen_sig(50);
[s75_n2, s75_e2] = gen_sig(75);
[s100_n2, s100_e2] = gen_sig(100);
fd=fopen("kHz_7_5.h","w");
fprintf(fd,"s16 s6n_kHz_7_5[%d]__attribute__((aligned(16))) = {",lengt... |
github | sibbi77/openair4G-master | f_tls_diag.m | .m | openair4G-master/targets/PROJECTS/TDDREC/f_tls_diag.m | 1,272 | utf_8 | 443132469284a3d4d0b38bd5fb7d0522 | %
% PURPOSE : TLS solution for AX = B based on SVD assuming X is diagonal
%
% ARGUMENTS :
%
% A : observation of A
% B : observation of B
%
% OUTPUTS :
%
% X : TLS solution for X (Diagonal)
%
%**********************************************************************************************
% ... |
github | sibbi77/openair4G-master | f_tls_ap.m | .m | openair4G-master/targets/PROJECTS/TDDREC/f_tls_ap.m | 1,368 | utf_8 | 223603e551ebede67ff13452e95097b1 | %
% PURPOSE : TLS solution for AX = B based on alternative projection
%
% ARGUMENTS :
%
% A : observation of A
% B : observation of B
%
% OUTPUTS :
%
% X : TLS solution for X
%
%**********************************************************************************************
% ... |
github | sibbi77/openair4G-master | f_ofdm_rx.m | .m | openair4G-master/targets/PROJECTS/TDDREC/f_ofdm_rx.m | 1,545 | utf_8 | ade01596524abbe660fc84064cbb4724 | %
% PURPOSE : OFDM Receiver
%
% ARGUMENTS :
%
% m_sig_R : received signal with dimension ((d_N_FFT+d_N_CP)*d_N_ofdm) x d_N
% d_N_FFT : total carrier number
% d_N_CP : extented cyclic prefix
% d_N_OFDM : OFDM symbol number per frame
% v_active_rf : active RF antenna indicator
%
% OUTPUTS :
%
% m_sym_R ... |
github | sibbi77/openair4G-master | f_ch_est.m | .m | openair4G-master/targets/PROJECTS/TDDREC/f_ch_est.m | 1,711 | utf_8 | f583032bb1c37167a7ff2a029a629de9 | %
% PURPOSE : channel estimation using least square method
%
% ARGUMENTS :
%
% m_sym_T : transmitted symbol, d_N_f x d_N_ofdm x d_N_ant_act x d_N_meas
% m_sym_R : received symbol, d_N_f x d_N_ofdm x d_N_ant_act x d_N_meas
% d_N_meas : number of measurements
%
% OUTPUTS :
%
% m_H_est : estimation o... |
github | sibbi77/openair4G-master | f_ofdm_tx.m | .m | openair4G-master/targets/PROJECTS/TDDREC/f_ofdm_tx.m | 1,997 | utf_8 | 042917cd72b493f1384cbc5fa2fa2de5 | %
% PURPOSE : OFDM Transmitter
%
% ARGUMENTS :
%
% d_M : modulation order
% d_N_f : carrier number carrying data
% d_N_FFT : total carrier number
% d_N_CP : extented cyclic prefix
% d_N_OFDM : OFDM symbol number per frame
% v_active_rf : active RF antenna indicator
% d_amp : amplitude
%
% O... |
github | sibbi77/openair4G-master | f_ofdm_rx.m | .m | openair4G-master/targets/PROJECTS/TDDREC/v4_CH_EST/f_ofdm_rx.m | 1,545 | utf_8 | 798a54f027b266189ab1fdc57569dac1 | %
% PURPOSE : OFDM Receiver
%
% ARGUMENTS :
%
% m_sig_R : received signal with dimension ((d_N_FFT+d_N_CP)*d_N_ofdm) x d_N
% d_N_FFT : total carrier number
% d_N_CP : extented cyclic prefix
% d_N_OFDM : OFDM symbol number per frame
% v_active_rf : active RF antenna indicator
%
% OUTPUTS :
%
% m_sym_R ... |
github | sibbi77/openair4G-master | f_ch_est.m | .m | openair4G-master/targets/PROJECTS/TDDREC/v4_CH_EST/f_ch_est.m | 1,708 | utf_8 | ad1741afb57ea0bbc0da1f1f0d410ce6 |
% PURPOSE : channel estimation using least square method
%% ARGUMENTS :
%
% m_sym_T : transmitted symbol, d_N_f x d_N_ofdm x d_N_ant_act x d_N_meas
% m_sym_R : received symbol, d_N_f x d_N_ofdm x d_N_ant_act x d_N_meas
% d_N_meas : number of measurements
%
% OUTPUTS :
%
% m_H_est : estimation of s... |
github | sibbi77/openair4G-master | f_ofdm_tx.m | .m | openair4G-master/targets/PROJECTS/TDDREC/v4_CH_EST/f_ofdm_tx.m | 1,997 | utf_8 | 52b00cfe39a99e4f6d079fcc3cdad1f8 | %
% PURPOSE : OFDM Transmitter
%
% ARGUMENTS :
%
% d_M : modulation order
% d_N_f : carrier number carrying data
% d_N_FFT : total carrier number
% d_N_CP : extented cyclic prefix
% d_N_OFDM : OFDM symbol number per frame
% v_active_rf : active RF antenna indicator
% d_amp : amplitude
%
% O... |
github | sibbi77/openair4G-master | genorthqpskseq.m | .m | openair4G-master/targets/PROJECTS/TDDREC/v0/genorthqpskseq.m | 1,143 | utf_8 | 330b0dc2a723aa7a373d8a0cd01fcabb | # % Author: Mirsad Cirkic
# % Organisation: Eurecom (and Linkoping University)
# % E-mail: mirsad.cirkic@liu.se
function [carrierdata, s]=genorthqpskseq(Ns,N,amp)
if(N!=512*150)
error('The sequence length must be 76800.');
endif
s = zeros(N,Ns);
H=1; for k=1:log2(128) H=[H H; H -H]; end; H=H(:,1:120);
i=1; while... |
github | sibbi77/openair4G-master | genrandpskseq.m | .m | openair4G-master/targets/PROJECTS/TDDREC/v0/genrandpskseq.m | 776 | utf_8 | 5cb33d8e20311847ffaa652cf70a4865 | % Author: Mirsad Cirkic
% Organisation: Eurecom (and Linkoping University)
% E-mail: mirsad.cirkic@liu.se
function [carrierdata, s]=genrandpskseq(N,M,amp)
if(mod(N,640)~=0)
error('The sequence length must be divisible with 640.');
end
s = zeros(N,1);
MPSK=exp(sqrt(-1)*([1:M]*2*pi/M+pi/M));
% OFDM sequence with ... |
github | sibbi77/openair4G-master | rfldec.m | .m | openair4G-master/targets/ARCH/EXMIMO/USERSPACE/OCTAVE/rfldec.m | 363 | utf_8 | 24448e69682b1f6a6ea652c2426b08f7 | ## Decodes rf_local values: [ txi, txq, rxi, rxq ] = rfldec(rflocal)
## Author: Matthias Ihmig <ihmig@solstice>
## Created: 2012-12-05
function [ txi, txq, rxi, rxq ] = rfldec(rflocal)
txi = mod(floor( rflocal /1 ), 64)
txq = mod(floor( rflocal /64), 64)
rxi = mod(floor( rflocal /4096), 64)
rxq = mo... |
github | sibbi77/openair4G-master | rfl.m | .m | openair4G-master/targets/ARCH/EXMIMO/USERSPACE/OCTAVE/rfl.m | 225 | utf_8 | 9ad201a94d01db3e65ecedb02252ca19 | ## Composes rf_local values: rfl(txi, txq, rxi, rxq)
## Author: Matthias Ihmig <ihmig@solstice>
## Created: 2012-12-05
function [ ret ] = rfl(txi, txq, rxi, rxq)
ret = txi + txq*2^6 + rxi*2^12 + rxq*2^18;
endfunction
|
github | OSGeoLabBp/tutorials-master | ellipse.m | .m | tutorials-master/english/data_processing/lessons/code/ellipse.m | 1,339 | utf_8 | d1a9b51cd38578543ec5ecb347a7085a | % regression
version = '1.0 alpha';
global eps = 1e-4; % limit for parameter change in iteration
global max_iter = 1000; % maximal number of iteration
% regression circle
function [x0, y0, r] = circle(points)
res=[points(:,1) points(:,2) ones(rows(points),1)] \ [-(points(:,1).^2+points(:,2).^2)];
x0 = -0.5 ... |
github | OSGeoLabBp/tutorials-master | regression.m | .m | tutorials-master/english/data_processing/lessons/code/regression.m | 4,052 | utf_8 | 0ae1245580530320ccbe5dc5c386ef99 | % regression
version = '1.0 alpha';
global eps = 1e-4; % limit for parameter change in iteration
global max_iter = 1000; % maximal number of iteration
pkg load statistics
% function to mimic ternary operator
% param expr - boolean expression
% true_val - returned value if expr true
% false_val- ret... |
github | OSGeoLabBp/tutorials-master | section.m | .m | tutorials-master/english/data_processing/lessons/code/section.m | 5,493 | utf_8 | 0a85b6302da08f7726dacdb25feb972b | % Section of a point cloud
% (c)Varga Timea, Siki Zoltan 2017
%
% commandline parameters:
% txt_point_cloud - path to the point cloud file
% section_type - 1/2/3 horizontal/vertical/general section
% for horizontal section:
% elevation - section elevation
% tolerance - tolerace for section
% ... |
github | OSGeoLabBp/tutorials-master | rigid_transform_3D.m | .m | tutorials-master/english/data_processing/lessons/code/rigid_transform_3D.m | 1,284 | utf_8 | 022be3b1623948a5ae0eacfbb71d9473 | % This function finds the optimal Rigid/Euclidean transform in 3D space
% It expects as input a Nx3 matrix of 3D points.
% It returns R, t
% original work of Nghia Ho (http://nghiaho.com/?page_id=671)
% corrected by Zoltan Siki
% You can verify the correctness of the function by copying and pasting these commands:
%{
... |
github | LeeTaewoo/fast_sound_source_localization_using_TLSSC-master | ld_vadlist.m | .m | fast_sound_source_localization_using_TLSSC-master/2_data_preproceesing/ld_vadlist.m | 3,238 | utf_8 | 0595174381b22b8a75402096681d3a1a | % Release date: May 2015
% Author: Taewoo Lee, (twlee@speech.korea.ac.kr)
%
% Copyright (C) 2015 Taewoo Lee
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or... |
github | LeeTaewoo/fast_sound_source_localization_using_TLSSC-master | VAD.m | .m | fast_sound_source_localization_using_TLSSC-master/2_data_preproceesing/VAD.m | 5,757 | utf_8 | 53ec59ec3abd3f7265a474d49b4c0c59 | % Voice Activity Detector (VAD)
% with MMSE a posteriori noise estimation and Decision-Directed SNR estimation
%
% Usage:
% results = VAD(wav_file, threshold, win_dur, hop_dur, num_noise, argin)
%
% Input arguments with typical values:
% wav_file = 'speech.wav' % Your speech file in WAV format
% threshold =... |
github | LeeTaewoo/fast_sound_source_localization_using_TLSSC-master | ISM_RoomResp.m | .m | fast_sound_source_localization_using_TLSSC-master/1_gen_simul_data/ISM_RoomResp.m | 11,228 | utf_8 | 27ca356f0d951e2e6aea01d8e430af95 | function [RIRvec] = ISM_RoomResp(Fs,beta,rt_type,rt_val,X_src,X_rcv,room,varargin)
%ISM_RoomResp RIR based on Lehmann & Johansson's image-source method
%
% RIR = ISM_RoomResp(Fs,BETA,RT_TYPE,RT_VAL,SOURCE,SENSOR,ROOM)
% RIR = ISM_RoomResp( ... ,'arg1',val1,'arg2',val2,...)
%
% This function generates the room impulse... |
github | LeeTaewoo/fast_sound_source_localization_using_TLSSC-master | ld_RIRlist.m | .m | fast_sound_source_localization_using_TLSSC-master/1_gen_simul_data/ld_RIRlist.m | 3,112 | utf_8 | d6888c5246c09bac53ebf29cfe338ea0 | % Release date: May 2015
% Author: Taewoo Lee, (twlee@speech.korea.ac.kr)
%
% Copyright (C) 2015 Taewoo Lee
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or... |
github | IlyaLab/CombiningDependentPvaluesUsingEBM-master | EmpiricalBrownsMethod.m | .m | CombiningDependentPvaluesUsingEBM-master/Matlab/EmpiricalBrownsMethod.m | 2,827 | utf_8 | 06c316265035690bc3b0b8fb96074507 | % Copyright 2015, Institute for Systems Biology.
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% Unless required by applicable la... |
github | IlyaLab/CombiningDependentPvaluesUsingEBM-master | KostsMethod.m | .m | CombiningDependentPvaluesUsingEBM-master/Matlab/KostsMethod.m | 2,451 | utf_8 | bb3acf03ad16d6e7faa569ea51c44a25 | % Copyright 2015, Institute for Systems Biology.
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% Unless required by applicable la... |
github | gpeyre/2015-AOS-AdaptiveWigner-master | poissrnd.m | .m | 2015-AOS-AdaptiveWigner-master/code/toolbox/poissrnd.m | 10,053 | utf_8 | 64b00f80e484b0c01bfd50a87756f587 | function r = poissrnd(lambda,m,n)
%POISSRND Random matrices from Poisson distribution.
% R = POISSRND(LAMBDA) returns a matrix of random numbers chosen
% from the Poisson distribution with parameter LAMBDA.
%
% The size of R is the size of LAMBDA. Alternatively,
% R = POISSRND(LAMBDA,M,N) returns an M by N... |
github | gpeyre/2015-AOS-AdaptiveWigner-master | gamrnd.m | .m | 2015-AOS-AdaptiveWigner-master/code/toolbox/gamrnd.m | 10,861 | utf_8 | 9b8a786919b4ddfbebdf74985b19ebb2 | function r = gamrnd(a,b,m,n);
%GAMRND Random matrices from gamma distribution.
% R = GAMRND(A,B) returns a matrix of random numbers chosen
% from the gamma distribution with parameters A and B.
% The size of R is the common size of A and B if both are matrices.
% If either parameter is a scalar, the size of ... |
github | gpeyre/2015-AOS-AdaptiveWigner-master | binornd.m | .m | 2015-AOS-AdaptiveWigner-master/code/toolbox/binornd.m | 9,362 | utf_8 | 0ca5d51eb461d57552c90487eab9946d | function r=binornd(n,p,mm,nn)
% BINORND Random matrices from a binomial distribution.
% R = BINORND(N,P,MM,NN) is an MM-by-NN matrix of random
% numbers chosen from a binomial distribution with parameters N and P.
%
% The size of R is the common size of N and P if both are matrices.
% If either parameter is a scalar, ... |
github | matthijsvk/numerieke_taak1-master | bisectionKdeEigval.m | .m | numerieke_taak1-master/matlab/2_Eigval/bisectionKdeEigval.m | 944 | utf_8 | 477232023b37b680931b000364ad4a44 | % Eigenvalue of tridiagonal matrix using the bisection method
function [E,residue,i] = bisectionKdeEigval(A,k,tol,maxIter)
n = size(A,1);
% eigA = eig(A);
% bisection itself for locating k'th eigenvalue
xl = -max(max(A))^2; % x lower
xu = max(max(A))^2; % xupper
i = 1;
% exactLambda = eigA(k); % used fo... |
github | matthijsvk/numerieke_taak1-master | divcon.m | .m | numerieke_taak1-master/matlab/matlab_geg/divcon.m | 7,414 | utf_8 | 069f7c405ff28b5d5e0a6168cc611808 | % function [lam,U] =divcon(A)
%
% Verdeel- en heersmethode om de eigenwaarden en eigenvectoren van een
% symmetrische, tridiagonale matrix te berekenen
%
% Invoer:
% A een symmetrisch, tridiagonale matrix
%
% Uitvoer:
% lam eigenwaarden
% U eigenvectoren
function [lam,U] =... |
github | matthieu637/lhpo-master | load_dirs.m | .m | lhpo-master/utils/load_dirs.m | 3,140 | utf_8 | 1d26c0ec9b17e7f56959cdfa8b165a54 |
function final = load_dirs (beforef,endf, colm, sb, hb=1, discre=-1, im=50, debug=0)
if (nargin < 4 || not(ischar(beforef)) || not(ischar(endf)))
printf('usage : load_dirs (path, file, column, save_best, higher_better)\n');
printf("X=load_dirs ('.', '[0-9.]*learning.data', 6, 1, 0);\n");
printf("X=load_dirs... |
github | matthieu637/lhpo-master | best_param.m | .m | lhpo-master/utils/best_param.m | 4,306 | utf_8 | efe21e7fb2eb60588944cb091e32ea94 | #!/usr/bin/octave -qf
arg_list = argv ();
file_to_load=arg_list{1};
plotme=str2num(arg_list{2});
column=str2num(arg_list{3});
save_best=str2num(arg_list{4});
higher_better=str2num(arg_list{5});
ymin=str2num(arg_list{6});
ymax=str2num(arg_list{7});
xmax=str2num(arg_list{8});
split_long_rows(0);
fid = fopen ("rules.ou... |
github | matthieu637/lhpo-master | one_by_one.m | .m | lhpo-master/utils/one_by_one.m | 1,091 | utf_8 | 65d7518962f6d2f8cfe6c76953f80bfa | #!/usr/bin/octave -qf
arg_list = argv ();
if length(arg_list) == 4+3
X=load_dirs('', arg_list{1}, str2num(arg_list{2}), str2num(arg_list{3}), str2num(arg_list{4}));
s=plotMedianQ(X, 'r');
[uu,vv]= max(median(X));
printf('\t -> %f\t %f \t %f \t %f \t %f\n', uu, vv, median(median(X)), mean(median(X)), mean(statistic... |
github | matthieu637/lhpo-master | best_param_plot.m | .m | lhpo-master/utils/best_param_plot.m | 2,143 | utf_8 | a9fe8b0c48f39d42153f7beb18974ed5 | #!/usr/bin/octave -qf
arg_list = argv ();
file_to_load=arg_list{1};
plotme=str2num(arg_list{2});
column=str2num(arg_list{3});
save_best=str2num(arg_list{4});
higher_better=str2num(arg_list{5});
id=str2num(arg_list{6});
ymin=str2num(arg_list{7});
ymax=str2num(arg_list{8});
%plotme=2;
fid = fopen ("rules.out");
line =... |
github | matthieu637/lhpo-master | save_best_policy.m | .m | lhpo-master/utils/save_best_policy.m | 313 | utf_8 | a7d9872c06c86790c042f4bdeb59341a |
function final = save_best_policy(X, higher_better=1)
final = zeros(size(X));
for ind=1:size(X,1)
for ep=1:size(X,2)
if higher_better
final(ind,ep) = max(X(ind,1:ep));
else
final(ind,ep) = min(X(ind,1:ep));
endif
endfor
endfor
endfunction
|
github | matthieu637/lhpo-master | moving_max_policy.m | .m | lhpo-master/utils/moving_max_policy.m | 350 | utf_8 | 08525aab05d520ceeda558e698d25d0d |
function final = moving_max_policy(X, higher_better=1, inter)
final = zeros(size(X));
for ind=1:size(X,1)
for ep=1:size(X,2)
if higher_better
final(ind,ep) = max(X(ind,max(1, ep-inter):ep));
else
final(ind,ep) = min(X(ind,max(1, ep-inter):ep));
endif
e... |
github | DavidMercier/Matlab_functions-master | Hashin_ElasticModulus.m | .m | Matlab_functions-master/ruleMixtures/Hashin_ElasticModulus.m | 1,071 | utf_8 | 86e0196ce4886560064d3a0b256f9382 | %% Copyright 2014 MERCIER David
function HashinAverage = Hashin_ElasticModulus(E_M, E_F, Poisson_M, ...
Poisson_F, volumeFraction, varargin)
%% Function giving the Hashin average
% From D.M. Karpinos, Composite Materials, Handbook. Kiev: Naukova Dumka 1985
% author: david9684@gmail.com
% Calculation of Young's mo... |
github | DavidMercier/Matlab_functions-master | HalpinTsaiAverage.m | .m | Matlab_functions-master/ruleMixtures/HalpinTsaiAverage.m | 658 | utf_8 | cc766c7042c915502acc461f2ae537ba | %% Copyright 2014 MERCIER David
function halpintsaiAverage = HalpinTsaiAverage(propM, propF, volumeFraction, shapeRatio, varargin)
%% Function giving the Halpin- Tsai average
% From F. Akhtar, Canadian Metallurgical Quarterly 2014 VOL 53 NO 3 253
% author: david9684@gmail.com
if nargin < 4
shapeRatio = 1; % in %
... |
github | DavidMercier/Matlab_functions-master | VegardLaw.m | .m | Matlab_functions-master/ruleMixtures/VegardLaw.m | 614 | utf_8 | d8bc02d4b7f99073d0ad2dc2bb422e09 | %% Copyright 2017 MERCIER David
function paramAlloy = VegardLaw(xFraction_A, paramA, paramB, varargin)
%% Vegard's law is the empirical heuristic that the lattice parameter of a
% solid solution of two constituents is approximately equal to a rule of
% mixtures of the two constituents' lattice parameters at the same t... |
github | DavidMercier/Matlab_functions-master | VoigtAverage.m | .m | Matlab_functions-master/ruleMixtures/VoigtAverage.m | 543 | utf_8 | 840851b4497fb49510a9776f3f92ab9d | %% Copyright 2014 MERCIER David
function voigtAverage = VoigtAverage(property, volumeFraction, varargin)
%% Function giving the Voigt's average
% From "Mechanical Behavior of Materials", by Meyers M and Chawla K.,
% 2nd edition, 2008.
% author: david9684@gmail.com
if nargin < 1
property = [10 20 30]; % in the uni... |
github | DavidMercier/Matlab_functions-master | VoigtReussHillAverage.m | .m | Matlab_functions-master/ruleMixtures/VoigtReussHillAverage.m | 592 | utf_8 | fdfa8771d7e73015ffd202277662ae88 | %% Copyright 2014 MERCIER David
function voigtReussHillAverage = VoigtReussHillAverage(property, volumeFraction, varargin)
%% Function giving the Voigt-Reuss-Hill's average
% author: david9684@gmail.com
if nargin < 1
property = [10 20 30]; % in the unit of the property
end
if nargin < 2
l_p = length(property... |
github | DavidMercier/Matlab_functions-master | ReussAverage.m | .m | Matlab_functions-master/ruleMixtures/ReussAverage.m | 550 | utf_8 | 701e2fe6678df7d4c83372444b2d56b8 | %% Copyright 2014 MERCIER David
function reussAverage = ReussAverage(property, volumeFraction, varargin)
%% Function giving the Reuss's average
% From "Mechanical Behavior of Materials", by Meyers M and Chawla K.,
% 2nd edition, 2008.
% author: david9684@gmail.com
if nargin < 1
property = [10 20 30]; % in the uni... |
github | DavidMercier/Matlab_functions-master | strainMultiLayer.m | .m | Matlab_functions-master/mechanic/strainMultiLayer.m | 1,963 | utf_8 | 17cf872d573901b7491d8de94cbaa6b3 | %% Copyright 2016 MERCIER David
function strainML = strainMultiLayer(E, nu, t, z_ind, R, varargin)
%% Function giving the Reuss's average
% From S. Logothetidis, "Handbook of Flexible Organic Electronics: Materials,
% Manufacturing and Applications", Elsevier, Cambridge, 2014.
% Default values from Gerthoffer A. et al.... |
github | DavidMercier/Matlab_functions-master | MT_homoScheme.m | .m | Matlab_functions-master/mechanic/MT_homoScheme.m | 3,719 | utf_8 | 7dee4210cd35b5076abdbc3fbd2505e3 | %% Copyright 2014 MERCIER David
function [E_homo, nu_homo] = MT_homoScheme(modulusMatrix, modulusParticle, ...
coeffPoissonMatrix, coeffPoissonParticle, volFracParticle, varargin)
%% Function to apply Mori-Tanaka homogenization scheme
% From J. Nemecek, Habilitation thesis, "Nanoindentation of Heterogeneous
% Struc... |
github | DavidMercier/Matlab_functions-master | trueStressStrain.m | .m | Matlab_functions-master/mechanic/trueStressStrain.m | 1,110 | utf_8 | 55d941f3b26f61ceccc866442e836697 | %% Copyright 2014 MERCIER David
function [true_strain, true_stress] = trueStressStrain(...
engineering_stress, engineering_strain, varargin)
%% Function giving the true stress-strain curve
% author: david9684@gmail.com
if nargin < 2
engineering_strain = [0 5 10 15 20 25 30 35 40]/100; % in %
end
if nargin < ... |
github | DavidMercier/Matlab_functions-master | stressSingularityExponent.m | .m | Matlab_functions-master/mechanic/cracking_decohesion_thinFilms/stressSingularityExponent.m | 1,186 | utf_8 | ff009644468c7de8647c776e7c4b6c10 | %% Copyright 2014 MERCIER David
function s = stressSingularityExponent
%% Calculation of critical film cracking number in film cracking
% From J. L. Beuth., "Cracking of thin bonded films in residual tension",
% International Journal of Solids and Structures, 29(13), 1657-1675, 1992.
% See also A. Favache et al., "Frac... |
github | DavidMercier/Matlab_functions-master | Zcoeff_film.m | .m | Matlab_functions-master/mechanic/cracking_decohesion_thinFilms/crack_ThinFilm/Zcoeff_film.m | 1,757 | utf_8 | d22a3be0270a810edc503c93f01d9041 | %% Copyright 2014 MERCIER David
function Z = Zcoeff_film(Ef, Es, nuf, nus, varargin)
%% Calculation of critical film cracking number in film cracking
% From A. G. Evans et al., "The cracking and decohesion of thin films",
% Journal of Materials Research, 3(5), 1043-1049, 1988.
% From J. L. Beuth., "Cracking of thin bon... |
github | DavidMercier/Matlab_functions-master | Zcoeff_interface.m | .m | Matlab_functions-master/mechanic/cracking_decohesion_thinFilms/crackInterface_ThinFilmSub/Zcoeff_interface.m | 1,415 | utf_8 | 9822c4bb745dd0c08a15b80f9f047357 | %% Copyright 2014 MERCIER David
function Z = Zcoeff_interface(Ef, Es, nuf, nus, varargin)
%% Calculation of decohesion number in film decohesion
% From A. G. Evans et al., "The cracking and decohesion of thin films",
% Journal of Materials Research, 3(5), 1043-1049, 1988.
% Z: Adimensonnal parameter for toughness/rel... |
github | DavidMercier/Matlab_functions-master | CMYK2RGB.m | .m | Matlab_functions-master/colorConversion/CMYK2RGB.m | 464 | utf_8 | 13a7303e75a3fb58a729fdd91936a23e | %% Copyright 2014 MERCIER David
function RGB = CMYK2RGB(CMYK, varargin)
%% Function to convert CMYK color to RGB color
% RGB for red, green and blue color levels (0..255)
% CMYK for cyan, magenta, yellow, black
% author: david9684@gmail.com
if nargin < 1
CMYK = [rand rand rand rand];
end
R = 255 * (1-CMYK(1)) * ... |
github | DavidMercier/Matlab_functions-master | Hex2RGB.m | .m | Matlab_functions-master/colorConversion/Hex2RGB.m | 399 | utf_8 | b7551fde59ef8e154d4ded5f853cb812 | %% Copyright 2014 MERCIER David
function RGB = Hex2RGB(Hex, varargin)
%% Function to convert Hex color to RGB color
% RGB for red, green and blue color levels (0..255)
% Hex for 6 digits hex color
% author: david9684@gmail.com
if nargin < 1
Hex = RGB2Hex;
end
RGB(1) = base2dec(Hex(2:3), 16);
RGB(2) = base2dec(He... |
github | DavidMercier/Matlab_functions-master | RGB2CMYK.m | .m | Matlab_functions-master/colorConversion/RGB2CMYK.m | 497 | utf_8 | 17bd60fe436f1d75c8f1c8c1b36cd741 | %% Copyright 2014 MERCIER David
function CMYK = RGB2CMYK(RGB, varargin)
%% Function to convert RGB color to CMYK color
% RGB for red, green and blue color levels (0..255)
% CMYK for cyan, magenta, yellow, black
% author: david9684@gmail.com
if nargin < 1
RGB = [randi(255) randi(255) randi(255)];
end
R = RGB(1)/2... |
github | DavidMercier/Matlab_functions-master | RGB2Hex.m | .m | Matlab_functions-master/colorConversion/RGB2Hex.m | 458 | utf_8 | b9b0bf70a6cd654389b00d2f0c0e4057 | %% Copyright 2014 MERCIER David
function Hex = RGB2Hex(RGB, varargin)
%% Function to convert RGB color to Hex color
% RGB for red, green and blue color levels (0..255)
% Hex for 6 digits hex color
% author: david9684@gmail.com
if nargin < 1
RGB = [randi(255) randi(255) randi(255)];
end
Hex_R = dec2base(RGB(1), 1... |
github | DavidMercier/Matlab_functions-master | HV2GPa.m | .m | Matlab_functions-master/mechanicalContact/indentation/HV2GPa.m | 367 | utf_8 | cde8991448d1b1a20a5928b23f3799fd | %% Copyright 2014 MERCIER David
function nanoHardnessVal = HV2GPa(HV, varargin)
%% Stress distributions at the surface and along the axis of symmetry
%% Function to convert Vickers hardness (HV) in nanohardness (GPa)
% HV: Vickers hardness (HV)
% author: david9684@gmail.com
close all;
if nargin == 0
HV = 100;
e... |
github | DavidMercier/Matlab_functions-master | GPa2HV.m | .m | Matlab_functions-master/mechanicalContact/indentation/GPa2HV.m | 318 | utf_8 | 5c11eeee1753f5f4fbc4dba2c2e7ddae | %% Copyright 2014 MERCIER David
function HV = GPa2HV(nanoHardnessVal, varargin)
%% Function to convert nanohardness (GPa) in Vickers hardness (HV)
% nanoHardnessVal: nanohardness in GPa
% author: david9684@gmail.com
close all;
if nargin == 0
nanoHardnessVal = 1;
end
HV = 1e3 * nanoHardnessVal / 9.80665;
end |
github | DavidMercier/Matlab_functions-master | VickersHardness.m | .m | Matlab_functions-master/mechanicalContact/indentation/VickersHardness.m | 283 | utf_8 | f99af1ff1517547983425c6aac122102 | %% Copyright 2014 MERCIER David
function HV = VickersHardness(load, diam)
%% Function to calculate Vickers hardness (HV)
% load: applied load in kgf (1kgf = 9.80665 N)
% diam: mean diameter in mm
% author: david9684@gmail.com
close all;
HV = 2*sind(136/2) * load / (diam^2);
end |
github | DavidMercier/Matlab_functions-master | Hertz_equations.m | .m | Matlab_functions-master/mechanicalContact/hertzEquations/Hertz_equations.m | 3,976 | utf_8 | de488861f41ecef57988dc54c8e91fe9 | %% Copyright 2014 MERCIER David
function s = Hertz_equations(nu, varargin)
%% Stress distributions at the surface and along the axis of symmetry
% causes by Hertz pressure acting on a circular area radius a.
% From "Contact Mechanics" - K. L. Johnson (1987) (ISBN:9780521347969)
% Equations are p.62 and graphics p.94.
... |
github | DavidMercier/Matlab_functions-master | radiusPlate.m | .m | Matlab_functions-master/platesShells/circularPlate_loadedCenter/radiusPlate.m | 1,073 | utf_8 | 0b9a23049f4ced5793f0ed41a07a6327 | %% Copyright 2014 MERCIER David
function radius = radiusPlate(E_plate, t_plate, nu_plate, load_Plate, disp, varargin)
%% Function giving the expression of the radius of a circular plate,
% loaded at the center in function of the applied load.
% author: david9684@gmail.com
% See Timoshenko S.P. and Woinowsky-Krieger S... |
github | DavidMercier/Matlab_functions-master | loadPlate.m | .m | Matlab_functions-master/platesShells/circularPlate_loadedCenter/loadPlate.m | 1,033 | utf_8 | d949141fa7f53e7cfa4a540d7aab2db3 | %% Copyright 2014 MERCIER David
function load = loadPlate(E_plate, t_plate, nu_plate, r_Plate, disp, varargin)
%% Function giving the load function for large deflections of circular plates,
% loaded at the center
% author: david9684@gmail.com
% See Timoshenko S.P. and Woinowsky-Krieger S., "Theory of plates and
% she... |
github | DavidMercier/Matlab_functions-master | radialStressPlate.m | .m | Matlab_functions-master/platesShells/circularPlate_loadedCenter/radialStressPlate.m | 1,112 | utf_8 | 0010dc9df8862d5cb8fc0b7dd1f75f5d | %% Copyright 2014 MERCIER David
function radialStress = radialStressPlate(E_plate, t_plate, r_Plate, disp, varargin)
%% Function giving the expression of the radial tensil stress in the plate
% loaded at the center.
% author: david9684@gmail.com
% See Timoshenko S.P. and Woinowsky-Krieger S., "Theory of plates and
% ... |
github | DavidMercier/Matlab_functions-master | primeNumber.m | .m | Matlab_functions-master/mathematics/primeNumber.m | 1,548 | utf_8 | 6f329f66a23ca2a33be162086a3356c5 | %This code find all prime numbers
%upto the entered number
clear all;
close all;
%N=input('Prime Numbers until:');
N=200;
if N<2
return;
elseif N==2
disp(2);
return;
end
Pr(1)=2;Pr(2)=3;Count=3;
for i=4:N
C=Check(i);
if C==1
Pr(Count)=i;
Count = Count +1;
end
end
%disp(Pr);
PrSu... |
github | DavidMercier/Matlab_functions-master | ODF_W_function.m | .m | Matlab_functions-master/Gauss_distribution/ODF_W_function.m | 1,258 | utf_8 | 8580b96add6515acadc6b48e8b517148 | %% Copyright 2014 MERCIER David
function W = ODF_W_function(phi2, alpha, varargin)
% Gaussian distribution function to describe a texture
% See Li J.Y. / J. Mech. Phys. Solids 48 (2000) 529-552
% DOI : 10.1016/S0022-5096(99)00042-3
% author: david9684@gmail.com
% phi2 : Euler angles in rad
% alpha : Parameter in Gau... |
github | DavidMercier/Matlab_functions-master | HallPetch.m | .m | Matlab_functions-master/crystalProperties/HallPetch.m | 1,530 | utf_8 | bc70146ba7242b797717f01fb923f4ad | %% Copyright 2014 MERCIER David
function HP_parameters = HallPetch(S, d, varargin)
% S: stress = yield stress or hardness in GPa
% d: grain size in micron
% author: david9684@gmail.com
clear all;
clc;
close all;
if nargin < 2
S = [2.8 1.7]*1e3;
d = [8 20];
end
xdata_1 = d.^(-0.5);
ydata_1 = S;
xdata_2 = d;
... |
github | DavidMercier/Matlab_functions-master | coeff_poisson.m | .m | Matlab_functions-master/crystalProperties/coeff_poisson.m | 638 | utf_8 | 2b4f56d9958bb22702816f9c862a6e27 | %% Copyright 2014 MERCIER David
function nu = coeff_poisson(C12, C44, varargin)
% C12 and C14 : compliances in TPa
% nu: Poisson's coefficient
% author: david9684@gmail.com
% [LEVY 2001] : "Handbook of elastic properties of solids, liquids and
% gases" by M. Levy, H.E. Bass and R.S. Stern (Vol. 2 - Elastic
% Properti... |
github | DavidMercier/Matlab_functions-master | shear_modulus.m | .m | Matlab_functions-master/crystalProperties/shear_modulus.m | 387 | utf_8 | 2dcb607f7a9c51cf099af1f7b57064b3 | %% Copyright 2014 MERCIER David
function G = shear_modulus(C12, C44, varargin)
% E : Young's modulus in TPa
% nu: Poisson's coefficient
% G : Shear modulus in TPa
% author: david9684@gmail.com
if nargin < 2
% Elastic constants of the Titanium (hcp)
C12 = 0.0903;
C44 = 0.0465;
end
E = C44*(3*C12 + 2*C44)/... |
github | DavidMercier/Matlab_functions-master | bulk_modulus.m | .m | Matlab_functions-master/crystalProperties/bulk_modulus.m | 390 | utf_8 | 7e7433b1d9d1539e7fa2dfabcb42a07d | %% Copyright 2014 MERCIER David
function K = bulk_modulus(C12, C44, varargin)
% E : Young's modulus in TPa
% nu: Poisson's coefficient
% K : Shear modulus in TPa
% author: david9684@gmail.com
if nargin < 2
% Elastic constants of the Titanium (hcp)
C12 = 0.0903;
C44 = 0.0465;
end
E = C44*(3*C12 + 2*C44)/(... |
github | DavidMercier/Matlab_functions-master | compliance_to_stiffnesse.m | .m | Matlab_functions-master/crystalProperties/compliance_to_stiffnesse.m | 1,439 | utf_8 | 23eeab643200bc416c91d29b33f95df8 | %% Copyright 2014 MERCIER David
function compliances = compliance_to_stiffnesse(structure, elast_const, varargin)
%% Function used to give the elastic stiffness constants in 1/GPa
% structure : structure of the given material (hcp, bcc, fcc, dia, tet or bct)
% elast_const: [S11 S12 S13 S33 S44 S66] in 1/TPa;
% complian... |
github | aravindhm/deep-goggle-master | invert_nn.m | .m | deep-goggle-master/core/invert_nn.m | 11,582 | utf_8 | 7580dc3a47a32ad24a3437c71e0b921c | function res = invert_nn(net, ref, varargin)
% INVERT Invert a CNN representation
opts.learningRate = 0.001*[...
ones(1,800), ...
0.1 * ones(1,500), ...
0.01 * ones(1,500), ...
0.001 * ones(1,200), ...
0.0001 * ones(1,100) ] ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.maxNumIterations = numel(o... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.