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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 | ZHANGXinxinPKU/defocus-deblurring-master | vl_click.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | ZHANGXinxinPKU/defocus-deblurring-master | vl_pr.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/plotop/vl_pr.m | 9,138 | utf_8 | c7fe6832d2b6b9917896810c52a05479 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | ZHANGXinxinPKU/defocus-deblurring-master | vl_ubcread.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | ZHANGXinxinPKU/defocus-deblurring-master | vl_frame2oell.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/sift/vl_frame2oell.m | 2,806 | utf_8 | c93792632f630743485fa4c2cf12d647 | function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an orien... |
github | ZHANGXinxinPKU/defocus-deblurring-master | vl_plotsiftdescriptor.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/sift/vl_plotsiftdescriptor.m | 5,114 | utf_8 | a4e125a8916653f00143b61cceda2f23 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warpe... |
github | ZHANGXinxinPKU/defocus-deblurring-master | phow_caltech101.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/apps/phow_caltech101.m | 11,594 | utf_8 | 7f4890a2e6844ca56debbfe23cca64f3 | function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedu... |
github | ZHANGXinxinPKU/defocus-deblurring-master | sift_mosaic.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
github | ZHANGXinxinPKU/defocus-deblurring-master | encodeImage.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/apps/recognition/encodeImage.m | 5,278 | utf_8 | 5d9dc6161995b8e10366b5649bf4fda4 | function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple ... |
github | ZHANGXinxinPKU/defocus-deblurring-master | experiments.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/apps/recognition/experiments.m | 6,905 | utf_8 | 1e4a4911eed4a451b9488b9e6cc9b39c | function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all... |
github | ZHANGXinxinPKU/defocus-deblurring-master | getDenseSIFT.m | .m | defocus-deblurring-master/Defocus_code_xxzhang/vlfeat-0.9.20-bin/vlfeat-0.9.20/apps/recognition/getDenseSIFT.m | 1,679 | utf_8 | 2059c0a2a4e762226d89121408c6e51c | function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made availab... |
github | taylordr/SAF_optimization-master | add_edge.m | .m | SAF_optimization-master/SAF-CODE-release/add_edge.m | 1,038 | utf_8 | 11f073d7be96141cf17b6f71ec307049 | %% net = add_edge(net,edges)
%
% Use this script to add an edge or edges to a network, and then update its
% properties
%
% INPUT:
% net - the struct that has the network properties
% edges - a num_edges x 2 matrix in which a row denotes the edge to add
%
% OUTPUT:
% net - the struct that has the... |
github | taylordr/SAF_optimization-master | compute_Q_matrix.m | .m | SAF_optimization-master/SAF-CODE-release/compute_Q_matrix.m | 1,103 | utf_8 | fba3c9a074d28bb5d40752e58af9b2de | %% function [Q_approx] = compute_Q_matrix(net,w,potential_edges)
%
% Create the matrix Q, wherein entry Q_{pq} approximates the change to the
% SAF after adding a new edge (p,q). Note that removing edge (p,q)
% corresponds to -Q_{pq}.
%
% INPUT:
% net = struct containing the network properties
% ... |
github | taylordr/SAF_optimization-master | create_chain.m | .m | SAF_optimization-master/SAF-CODE-release/create_chain.m | 2,301 | utf_8 | 97bf5c52d7328411148c20a688bee80c | %function net = create_chain(size,visualization)
%
% Create an undirected chain containing N nodes
%
% Input:
% size = number of nodes
% visualization = 1/0 indicates whether or not to make figures
%
% Output:
% net = a struct that contains all the info about the chain
%
% Dane R. Taylor - July 27,... |
github | taylordr/SAF_optimization-master | change_in_SAF_under_edge_addition.m | .m | SAF_optimization-master/SAF-CODE-release/change_in_SAF_under_edge_addition.m | 1,437 | utf_8 | 233658778104943017dde39679d98374 | %% function [deltaSAF] = change_in_SAF_under_edge_addition(w,net,p,q)
%
% Create the matrix Q, wherein entry Q_{pq} approximates the change to the
% SAF after adding a new edge (p,q). Note that removing edge (p,q)
% corresponds to -Q_{pq}.
%
% INPUT:
% net = struct containing the network properties
% ... |
github | taylordr/SAF_optimization-master | rank_edges.m | .m | SAF_optimization-master/SAF-CODE-release/rank_edges.m | 1,689 | utf_8 | e1402f254b3367b7a0151fb45f6f3da1 | %% function [rank,Q] = rank_edges(net,w,potential_edges,visualization)
%
% Rank potential edges so that the SAF decreases the most. This maximizes the
% order parameter R
%
% Input:
% net = struct containing the network properties
% w = the natural frequencies
% edges = a matrix in which t... |
github | taylordr/SAF_optimization-master | create_SF.m | .m | SAF_optimization-master/SAF-CODE-release/create_SF.m | 1,167 | utf_8 | 7f800393c797646bf2296a073e5dc5f5 | %function net = create_SF(type,size,visualization)
%
% Create a a scale free network using Chung Lu model
%
% Input:
% size = number of nodes
% visualization = 1/0 indicates whether or not to make figures
%
% Output:
% net = a struct that contains all the info about the chain
%
% Dane R. Taylor - J... |
github | taylordr/SAF_optimization-master | compute_SAF.m | .m | SAF_optimization-master/SAF-CODE-release/compute_SAF.m | 546 | utf_8 | 0ca3bb11e72ff45182bf2f9b80c45de3 | %function SAF = compute_SAF(w,L)
%
% Compute the synchrony alignment function (SAF) for given w and L
%
% Input:
% w = frequency vector
% L = unnormalized Laplacian matrix
%
% Output:
% SAF = synchrony alignment function
%
% Dane R. Taylor - July 27, 2016
function SAF = compute_SAF(w,L)
%ca... |
github | taylordr/SAF_optimization-master | algorithm_6_2.m | .m | SAF_optimization-master/SAF-CODE-release/algorithm_6_2.m | 1,380 | utf_8 | 04aa273e19df34b217bbe3e0a035c18a | %function [SAF_approx,SAF_actual] = algorithm_6_2(net,w,max_iter,SAF_0)
%
% Create an undirected chain containing N nodes
%
% Input:
% net = struct containing the network properties
% w = the natural frequencies
% max_iter = number of edges to add
% SAF_0 = original synchrony ali... |
github | taylordr/SAF_optimization-master | algorithm_6_1.m | .m | SAF_optimization-master/SAF-CODE-release/algorithm_6_1.m | 1,371 | utf_8 | 4cc9cc305b82015e4bbf3f86b34e1c92 | %function [SAF_approx,SAF_actual] = algorithm_6_2(net,w,max_iter,SAF_0)
%
% Create an undirected chain containing N nodes
%
% Input:
% net = struct containing the network properties
% w = the natural frequencies
% max_iter = number of edges to add
% SAF_0 = original synchrony ali... |
github | nwayt001/SSVEP_Speller-master | start_speller_task.m | .m | SSVEP_Speller-master/scripts/start_speller_task.m | 1,130 | utf_8 | a3836c523576761b2f6f537fcf7d431f | %% SSVEP Visual Stimulator / Speller Application
function start_speller_task
clc
%SSVEP Speller Options
options = [];
% Set Speller Modes
options.debugMode = true;
options.spellerMode = 'copyspell';
options.showFeedback = true;
options.wordPredictionMode = false;
options.twitterMode = false;
options.TTS_Mode = false;... |
github | nwayt001/SSVEP_Speller-master | start_fmri_speller.m | .m | SSVEP_Speller-master/scripts/start_fmri_speller.m | 934 | utf_8 | 692d6924d74b1669202cee35c3ba6c61 | %% SSVEP Visual Stimulator / Speller Application
function start_fmri_speller
%SSVEP Speller Options
options = [];
% Set Speller Modes
options.debugMode = true;
options.spellerMode = 'copypell';
options.offlineMode = true;
options.wordPredictionMode = false;
options.twitterMode = false;
options.TTS_Mode = false;
opt... |
github | nwayt001/SSVEP_Speller-master | Combined_CCA_Classify.m | .m | SSVEP_Speller-master/classifiers/Combined_CCA_Classify.m | 1,447 | utf_8 | 25978914f3e0a0392fb250c034191e13 | %% Modified CCA based on PNAS paper
function [y] = Combined_CCA_Classify(X,yRef,Xtrain)
% INPUT:
% X is [ timepoints x channels ]
% Xtrain ix [timepoints x channels x class]
% yRef is [class x timepoints x harmonics]
% OUTPUT:
% Class y
% Wx1 = CCA between X and Y
% Wx2 = CCA between X and X^
% Wx3 = CC... |
github | nwayt001/SSVEP_Speller-master | CCA_Classify.m | .m | SSVEP_Speller-master/classifiers/CCA_Classify.m | 450 | utf_8 | 5bfca6c02031dc3eea73543ca775f88c | %% CCA classification function
function [y] = CCA_Classify(X,yRef)
% X is a single SSVEP trial to be classified
% [ timepoints x channels ]
% yRef are the cca templates
% compute CCA for each frequency class
for ii = 1:size(yRef,1)
% display(size(X));
% tmp = squeeze(yRef(ii,1:size(X,1),:));
% ... |
github | nwayt001/SSVEP_Speller-master | PredictWords.m | .m | SSVEP_Speller-master/addons/PredictWords.m | 693 | utf_8 | a7b06c4a221eae64e0d808ad7b95e027 | %% Predictive Speller (requires word corpus)
function [likelyWords]=PredictWords(letters,Corpus,Words)
% Predictive Speller
if(isempty(letters))
likelyWords = Corpus.words;
else
try
for i=1:length(letters)
Corpus = Corpus.(letters(i));
if(i==length(letters))
likelyWor... |
github | abearman/whats-the-point1-master | learnTheta.m | .m | whats-the-point1-master/caffe/objectness-release-v2.2/learnTheta.m | 3,167 | utf_8 | 5a1ddfd594ba3880072613f50039cc87 | function [thetaOpt likelihoodOpt pobj] = learnTheta(cue,params)
fprintf('Learning theta for %s\n',cue);
if nargin < 2
params = defaultParams;
end
try
struct = load([params.trainingExamples '/posneg.mat'] );
posneg = struct.posneg;
clear struct;
catch
posneg = generatePosNeg(params);
... |
github | abearman/whats-the-point1-master | learnParameters.m | .m | whats-the-point1-master/caffe/objectness-release-v2.2/learnParameters.m | 3,264 | utf_8 | 44d62180e6b190c232ba5991567a5050 | function params = learnParameters(pathNewTrainingFolder,dir_root)
%learns the parameters of the objectness function: theta_MS (for 5 scales),
%theta_CC, theta_ED, theta_SS and also the likelihoods corresp to each cue
%dir_root - path where the software is installed - see README Setting things up
if nargin < 2
dir_... |
github | abearman/whats-the-point1-master | runObjectness.m | .m | whats-the-point1-master/caffe/objectness-release-v2.2/runObjectness.m | 4,036 | utf_8 | fa1177ca617ed91c41ca5b235a639833 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Objectness measure
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Top-level routine implementation
% of the system described in the paper:
%
% B. Alexe, T. Deselaers, V. Ferrari
% What is an object?
% CVPR 2010
%
% The author and copyright holder is Bogdan Alexe.
% You might redistribute,... |
github | abearman/whats-the-point1-master | computeScores.m | .m | whats-the-point1-master/caffe/objectness-release-v2.2/computeScores.m | 8,601 | utf_8 | 8dbf6f5c9e919fa57435db604ce6164f | function boxes = computeScores(img,cue,params,windows)
addpath('pff_segment');
if nargin<4
%no windows provided - so generate them -> single cues
switch cue
case 'MS' %Multi-scale Saliency
xmin = [];
ymin = [];
xmax = [];
ymax = [];... |
github | abearman/whats-the-point1-master | learnThetaMS.m | .m | whats-the-point1-master/caffe/objectness-release-v2.2/learnThetaMS.m | 3,169 | utf_8 | 0f7633e211294ad6e3d7320542a14fc5 | function thresholdOpt = learnThetaMS(params,scale)
fprintf('Learning theta_MS for scale = %d \n',scale);
bestScoreThreshold = -inf;
level = find(params.MS.scale == scale);
for idxThr = 1:length(params.MS.domain(level,:)) %loop over the possible threshold values
threshold = params.MS.domain(level,idxThr); ... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFL0.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodMono/FDAFL0.m | 4,259 | utf_8 | 46f4059170217166c762213f45c19b7f | function [ error, MSerror, timeOfConvergence,timeOfComputation, w, filters ] = FDAFL0( referenceSignal, filtredSignal, filterSize, convergenceThreshold )
% FDADOVM is the implementation of the L0 norm of the FDAF algorithm based on the overlap
% save sectionning method.
% Returns:
% 1) The error computed at eac... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFOSM.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodMono/FDAFOSM.m | 3,608 | utf_8 | d3474f593bef6feb76c169871f9ed55d | function [ error, MSerror, timeOfConvergence,timeOfComputation, w, filters ] = FDAFOSM( referenceSignal, filtredSignal, filterSize, convergenceThreshold )
% FDADOVM is the implementation of the FDAF algorithm based on the overlap
% save sectionning method.
% Returns:
% 1) The error computed at each sample
% 2) The m... |
github | 3arbouch/ActiveNoiseCancelling-master | BLMS.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodMono/BLMS.m | 2,470 | utf_8 | 8a6df3af6b9c930a1f5986b74378c2eb | function [ error , MSerror, timeOfConvergence , timeOfComputation, w] = BLMS( referenceSignal, filtredSignal, filterSize,blockSize, numberOfIterations, convergenceThreshold )
% This function implements The Block LMS algorithm
% Estimate the filter of size filterSize between the reference signal and the filtred signal.... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFL1.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodMono/FDAFL1.m | 3,956 | utf_8 | 08e03b07621240ac233171b29884e31a | function [ error, MSerror, timeOfConvergence,timeOfComputation, w, filters ] = FDAFL1( referenceSignal, filtredSignal, filterSize, convergenceThreshold )
% FDADOVM is the implementation of the L1 norm FDAF algorithm based on the overlap
% save sectionning method.
% Returns:
% 1) The error computed at each sampl... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFSterioRealTime.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodsStereo/FDAFSterioRealTime.m | 3,438 | utf_8 | 25391c94b8f31b7ea7c7b232690b6fe9 | function [ distances, W1, W2, P_k1, P_k2] = FDAFSterioRealTime( signals, W1Previous, W2Previous, P_k1_previous, P_k2_previous,g,K)
% FDADOVM is the implementation of the sterio FDAF algorithm based on the overlap
% save sectionning method.
% This function estimates two filters that represent the different paths
% b... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFSterioL1.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodsStereo/FDAFSterioL1.m | 4,774 | utf_8 | 51eb87c2ac3305fe2a224fdc5634f249 | function [ error, MSerror, timeOfConvergence,timeOfComputation, w1, w2, filters1, filters2] = FDAFSterioL0( referenceSignal1, referenceSignal2, filtredSignal, filterSize, convergenceThreshold, lambda )
% FDADOVM is the implementation of the sterio L1 norm of the FDAF algorithm based on the overlap
% save sectionnin... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFSterio.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodsStereo/FDAFSterio.m | 4,708 | utf_8 | 6dd456ddd954a46cfdabd382d9ff9e9c | function [ error, MSerror, timeOfConvergence,timeOfComputation, w1, w2, filters1, filters2] = FDAFSterio( referenceSignal1, referenceSignal2, filtredSignal, filterSize, convergenceThreshold )
% FDADOVM is the implementation of the sterio FDAF algorithm based on the overlap
% save sectionning method.
% This functio... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFSterioL0.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodsStereo/FDAFSterioL0.m | 4,796 | utf_8 | 0baa80fb42e930e926084bf14821a824 | function [ error, MSerror, timeOfConvergence,timeOfComputation, w1, w2, filters1, filters2] = FDAFSterioL0( referenceSignal1, referenceSignal2, filtredSignal, filterSize, convergenceThreshold, lambda )
% FDADOVM is the implementation of the L0 norm of the sterio FDAF algorithm based on the overlap
% save sectionnin... |
github | 3arbouch/ActiveNoiseCancelling-master | FilteringOverlapSaveMethod.m | .m | ActiveNoiseCancelling-master/ANC/STFT/FilteringOverlapSaveMethod.m | 1,348 | utf_8 | 09ebf0ba13ff1ccf840d8249f6ff76f1 | function [out] = FilteringOverlapSaveMethod
%% This script shows how filtering by blocks operates using the overlap save method
close all
f0= 800 ;
fs = 44100;
sineTime = 2 ;
filterSize = 512 ;
time = 0:(1/fs):sineTime - (1/fs) ;
% Define the signal
x= sin(2*pi*f0.*time);
% x = 0.25*ones(1,400) ;
x = randn(... |
github | 3arbouch/ActiveNoiseCancelling-master | OFDMmod.m | .m | ActiveNoiseCancelling-master/ANC/BookExamples/MATLAB/OFDMmod.m | 540 | utf_8 | 403018e85ec611c70580c6e297aebe8d | %
% Modulator part of an OFDM transceiver
%
function stime=OFDMmod(sfreq,N,Nactive,Ncp)
stime=[];
for k=1:Nactive:length(sfreq)
A1=sfreq(k:k+Nactive-1); % Take a block of QAM symbols
A=zeros(N,1); % and put them at right places in
A(2:Nactive/2+1)=A1(1:Nactive/2); % the vector A, before ... |
github | 3arbouch/ActiveNoiseCancelling-master | sr_Nyquist_p.m | .m | ActiveNoiseCancelling-master/ANC/BookExamples/MATLAB/sr_Nyquist_p.m | 1,370 | utf_8 | fd85fea549cb23cd3ec63277c01142e7 | %
% Square-root raised-cosine pulse-shape design
%
% h=srNyquist(N,M,alpha);
%
% It designs a square-root raised-cosine pulse-shape with the following
% parameters:
% N: filter order (filter length = N+1)
% M: number of samples per symbol period Tb
% alpha: roll-off factor (between 0 and 1)
%
function h=sr_Nyquis... |
github | 3arbouch/ActiveNoiseCancelling-master | CycPilot.m | .m | ActiveNoiseCancelling-master/ANC/BookExamples/MATLAB/CycPilot.m | 239 | utf_8 | b097ae27bf4f24bdb87eadbce7424285 | %
% This function designs a cyclic pilot sequence of length N+1.
% It follows the construction formula (11.55).
%
function s=CycPilot(N)
if rem(N+1,2)==0
s=exp(j*pi*([0:N]').^2/(N+1));
else
s=exp(j*pi*[0:N]'.*[1:N+1]'/(N+1));
end
|
github | 3arbouch/ActiveNoiseCancelling-master | Eigenfir.m | .m | ActiveNoiseCancelling-master/ANC/BookExamples/MATLAB/Eigenfir.m | 1,389 | utf_8 | a3863efe776f436c7ba3706d6811e023 | %*******************************************************
%** This function can be added to MATLAB's vocabulary **
%** for designing the propotype filter of an M-band **
%** complementary filter bank with the controlable **
%** delay. The input parameters are: **
%** M --- No. of bands of t... |
github | 3arbouch/ActiveNoiseCancelling-master | lsfit.m | .m | ActiveNoiseCancelling-master/ANC/BookExamples/MATLAB/lsfit.m | 515 | utf_8 | 734b66d647d364de06127d32b48017b5 | %
%This function finds the coefficients of an ARMA model which
%for a given input sequence x(n) results in an output which
%matches best the sequence d(n) in the least-squares sence.
%
% This program is called by 'iirdsgn.m'.
%
%
% Last updated on April 28, 1998
%
function [A,B]=lsfit(x,d,N_d,N_n);
N=leng... |
github | 3arbouch/ActiveNoiseCancelling-master | expander.m | .m | ActiveNoiseCancelling-master/ANC/BookExamples/MATLAB/expander.m | 409 | utf_8 | 67a8113596c236a626e2362a3ef06ad0 | %
% EXPANDER: y=expander(x,L)
% When x is a vector, this function adds L-1 after each element of x.
% When x is a matrix, each column of it treated as vector and expanded L
% fold.
function y=expander(x,L)
[M,N]=size(x);
if (N==1)|(M==1)
if M<N
y=zeros(1,N*L);
y(1:L:end)=x;
else
y=zero... |
github | 3arbouch/ActiveNoiseCancelling-master | sr_cos_p.m | .m | ActiveNoiseCancelling-master/ANC/BookExamples/MATLAB/sr_cos_p.m | 621 | utf_8 | 8aa100574f037a1ea24e5e1f88879da0 | %
% SQUARE-ROOT RAISED-COSINE PULSE: h=sr_cos_p(N,L,alpha)
% This function generates a square-root raised-cosine pulse of length N+1.
% There are L samples per symbol period.
% alpha is the roll-off factor.
%
function h=sr_cos_p(N,L,alpha)
t=[-N/2:1:N/2]/L;
h=zeros(size(t));
for k=1:length(t)
if t(k)==0
h(... |
github | 3arbouch/ActiveNoiseCancelling-master | MCMCEq.m | .m | ActiveNoiseCancelling-master/ANC/BookExamples/MATLAB/MCMCEq.m | 6,124 | utf_8 | 35945f22a961fec6a60134e3a7859513 |
function llr = mcmc_eq2(x, h, lambda2e, mc_iter, nvar)
%Generate init sample bit sequence randomly
L = length(h);
N = length(lambda2e);
p1 = 1./(1+exp(lambda2e));
p0 = 1-p1;
candidate = (rand(1, N)>p0);
sa = 1-2*candidate;
sa = [zeros(1,L-1), sa, zeros(1,L-1)];
%Run Gibbs sampler to generate bit se... |
github | 3arbouch/ActiveNoiseCancelling-master | tfplot.m | .m | ActiveNoiseCancelling-master/ANC/TFPlots/tfplot.m | 1,283 | utf_8 | 7e7ccb59bba6c28d108b12996babf4bf | % TFPLOT Time and frequency plot
% TFPLOT(S, FS, NAME, TITLE) displays a figure window with two subplots.
% Above, the signal S is plotted in time domain; below, the signal is plotted
% in frequency domain. NAME is the "name" of the signal, e.g., if NAME is
% 's', then the labels on the y-axes will be 's(t)... |
github | 3arbouch/ActiveNoiseCancelling-master | LMSRealTimeOverlapSaveMethod.m | .m | ActiveNoiseCancelling-master/ANC/LMSRealTime/LMSRealTimeOverlapSaveMethod.m | 3,328 | utf_8 | 5d75f7b59dcb53abfef15f3e5b16cffb | function [] = LMSRealTimeOverlapSaveMethod
%% This script shows how filtering by blocks operates using the overlap save method
close all
data = load ('experiment90Filters(2048, 32khz)');
f0= 200 ;
fs = 32000;
sineTime = 1 ;
filterSize = 2048 ;
time = 0:(1/fs):sineTime - (1/fs) ;
% Define the signal
%x= sin(2*p... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFCC.m | .m | ActiveNoiseCancelling-master/ANC/LMSExamples/FDAFCC.m | 2,490 | utf_8 | 3fea4d758f69dccacacc8c329636b44b | function [ error, MSerror, timeOfConvergence,timeOfComputation, w ] = FDAFCC( referenceSignal, filtredSignal, filterSize, numberOfIterations, convergenceThreshold )
% This function implements the FDAF algorithm based on circular convolution
% Returns:
% 1) The error computed at each sample
% 2) The mean sqaure er... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFOSMBLOCK.m | .m | ActiveNoiseCancelling-master/ANC/LMSExamples/FDAFOSMBLOCK.m | 1,990 | utf_8 | 47c0f81eca5631e25fb0cf119f30e960 | function [ error, MSerror, timeOfConvergence, w ] = FDAFOSMBLOCK( referenceSignal, filtredSignal, filterSize,blockSize, numberOfIterations )
% FDADOVM is the implementation of the FDAF algorithm based on the overlap
% save sectionning
x = referenceSignal ;
d = filtredSignal ;
W = zeros(filterSize + blockSize, ... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFOSM.m | .m | ActiveNoiseCancelling-master/ANC/LMSExamples/FDAFOSM.m | 3,200 | utf_8 | 91b8c5cf42f31b08a4732d5cb75da5da | function [ error, MSerror, timeOfConvergence,timeOfComputation, w ] = FDAFOSM( referenceSignal, filtredSignal, filterSize, numberOfIterations, convergenceThreshold )
% FDADOVM is the implementation of the FDAF algorithm based on the overlap
% save sectionning method.
% Returns:
% 1) The error computed at each sampl... |
github | 3arbouch/ActiveNoiseCancelling-master | BLMS.m | .m | ActiveNoiseCancelling-master/ANC/LMSExamples/BLMS.m | 2,470 | utf_8 | 8a6df3af6b9c930a1f5986b74378c2eb | function [ error , MSerror, timeOfConvergence , timeOfComputation, w] = BLMS( referenceSignal, filtredSignal, filterSize,blockSize, numberOfIterations, convergenceThreshold )
% This function implements The Block LMS algorithm
% Estimate the filter of size filterSize between the reference signal and the filtred signal.... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFSterioL0Weighted.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodsWeighted/FDAFSterioL0Weighted.m | 7,394 | utf_8 | 154133b78bb11435e0768f6427b33bb9 | function [ error, MSerror, timeOfConvergence,timeOfComputation, w1, w2, filters1, filters2,weights1, weights2,variances1, variances2] = FDAFSterioL0Weighted( referenceSignal1, referenceSignal2, filtredSignal, filterSize, convergenceThreshold, mu_k1, mu_k2, interval1, interval2, upperLimit, memoryInSeconds )
% This f... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFSterioL1Weighted.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveFilteringMethodsWeighted/FDAFSterioL1Weighted.m | 7,590 | utf_8 | 93e83fbaa98cc9394e3d3cd508968765 | function [ error, MSerror, timeOfConvergence,timeOfComputation, w1, w2, filters1, filters2,weights1, weights2, variances1,variances2] = FDAFSterioL1Weighted( referenceSignal1, referenceSignal2, filtredSignal, filterSize, convergenceThreshold, mu_k1, mu_k2, interval1, interval2, upperLimit, memoryInSeconds )
% This f... |
github | 3arbouch/ActiveNoiseCancelling-master | CancellingAdaptationRealTimeOverlapSaveMethod.m | .m | ActiveNoiseCancelling-master/ANC/ReaTimeCancelling/CancellingAdaptationRealTimeOverlapSaveMethod.m | 8,640 | utf_8 | 50b7a8bedfb1eff1be80b6a79ccefd9d | function []=CancellingAdaptationRealTimeOverlapSaveMethod
% This script performs the adaptation of the filters and the cancelling
% process in real Time:
close all
clear all
%% Initialization
InitializePsychSound
% Define the recording time in seconds
recordingTime = 60 ;
fs = 8000 ;
... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFCC.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveAlgorithms/FDAFCC.m | 2,490 | utf_8 | 3fea4d758f69dccacacc8c329636b44b | function [ error, MSerror, timeOfConvergence,timeOfComputation, w ] = FDAFCC( referenceSignal, filtredSignal, filterSize, numberOfIterations, convergenceThreshold )
% This function implements the FDAF algorithm based on circular convolution
% Returns:
% 1) The error computed at each sample
% 2) The mean sqaure er... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFOSMBLOCK.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveAlgorithms/FDAFOSMBLOCK.m | 1,990 | utf_8 | 47c0f81eca5631e25fb0cf119f30e960 | function [ error, MSerror, timeOfConvergence, w ] = FDAFOSMBLOCK( referenceSignal, filtredSignal, filterSize,blockSize, numberOfIterations )
% FDADOVM is the implementation of the FDAF algorithm based on the overlap
% save sectionning
x = referenceSignal ;
d = filtredSignal ;
W = zeros(filterSize + blockSize, ... |
github | 3arbouch/ActiveNoiseCancelling-master | FDAFOSM.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveAlgorithms/FDAFOSM.m | 3,200 | utf_8 | 91b8c5cf42f31b08a4732d5cb75da5da | function [ error, MSerror, timeOfConvergence,timeOfComputation, w ] = FDAFOSM( referenceSignal, filtredSignal, filterSize, numberOfIterations, convergenceThreshold )
% FDADOVM is the implementation of the FDAF algorithm based on the overlap
% save sectionning method.
% Returns:
% 1) The error computed at each sampl... |
github | 3arbouch/ActiveNoiseCancelling-master | BLMS.m | .m | ActiveNoiseCancelling-master/ANC/AdaptiveAlgorithms/BLMS.m | 2,470 | utf_8 | 8a6df3af6b9c930a1f5986b74378c2eb | function [ error , MSerror, timeOfConvergence , timeOfComputation, w] = BLMS( referenceSignal, filtredSignal, filterSize,blockSize, numberOfIterations, convergenceThreshold )
% This function implements The Block LMS algorithm
% Estimate the filter of size filterSize between the reference signal and the filtred signal.... |
github | 3arbouch/ActiveNoiseCancelling-master | CancellationAdpatationOffline.m | .m | ActiveNoiseCancelling-master/ANC/Experiments/CancellationAdpatationOffline.m | 6,259 | utf_8 | 7b578331286a730ccf17572d6ea28aeb | function []=CancellationAdpatationOffline
% This script performs the adaptation of the filters and the cancelling
% process in real Time:
%%
% close all
% clear all
%
% load('RealTimeCancellingExperiment')
% data = load('experiment80Filters(4096, 16khz).mat');
%
%
%
% referenceSignal = completeReferenceMic... |
github | 3arbouch/ActiveNoiseCancelling-master | RemoveCarriageReturns.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/managementtools/RemoveCarriageReturns.m | 1,539 | utf_8 | 3a37e4cabdc9eeb0af057010f9eae55c | function RemoveCarriageReturns(folder,qRecursive,qDryRun, exts_filter)
% finds all 13s and 10s (decimal char number)
% throws out all 13s, but if not followed by 10 it replaces it by 10
%
% make sure to do a dryrun first and set the fourth argument such that no
% files you dont want to touch get touched. e.g.: {'m','c'... |
github | 3arbouch/ActiveNoiseCancelling-master | moglFDF.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychOpenGL/moglFDF.m | 66,051 | utf_8 | ce67385acfd1293324b3ba69f9d237d3 | function varargout = moglFDF(cmd, varargin)
% moglFDF(cmd [, arg1][, arg2][, ...]) - "MOGL FormlessDotFields"
%
% Implementation of Sheinberg et al. inspired random dot structure from motion
% rendering. This routine is a fast implementation of "Formless dot field
% structure-from-motion stimuli". It is based on - and ... |
github | 3arbouch/ActiveNoiseCancelling-master | moglmorpher.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychOpenGL/moglmorpher.m | 85,818 | utf_8 | 0fae12070fc6521641e484f225b1474d | function [rc, varargout] = moglmorpher(cmd, arg1, arg2, arg3, arg4, arg5)
%
% Matlab OpenGL Morpher - Performs linear morphs between different 3D shapes and
% renders the resulting shape via OpenGL. Supports high-performance GPU
% based morphing on recent graphics hardware. Also performs linear morphing
% (linear combi... |
github | 3arbouch/ActiveNoiseCancelling-master | glm_mogldemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychOpenGL/MOGL/glm_mogldemo.m | 3,677 | utf_8 | 7c3947ddb14cb9d0dbd623395c6e4341 | function glm_mogldemo
% GLM_MOGLDEMO Demonstrate use of MATLAB-OpenGL toolbox independent of Psychtoolbox.
%
% Instead of using Psychtoolbox as host environment, one can also use the built-in
% glm - toolbox. It provides basic functions to open windows, swap buffers, collect
% keyboard responses and such -- a minimal... |
github | 3arbouch/ActiveNoiseCancelling-master | oglconst.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychOpenGL/MOGL/source/oglconst.m | 6,279 | utf_8 | 87227ca3bcece69b14766a14c4dcfc19 | function oglconst(glheaderpath, aglheaderpath)
% OGLCONST Collect GL, GLU, and AGL constants from C header files, and
% store them in oglconst.mat
%
% usage: oglconst
% 09-Dec-2005 -- created (RFM)
% 23-Jan-2005 -- constants saved in both struct and OpenGL style (RFM)
% 05-Mar-2006 -- ability to spec. sys... |
github | 3arbouch/ActiveNoiseCancelling-master | gluLookAt.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychOpenGL/MOGL/wrap/gluLookAt.m | 1,546 | utf_8 | c5f9d157c01d241c1d1d6f1b4bf020cb | function gluLookAt( eyeX, eyeY, eyeZ, centerX, centerY, centerZ, upX, upY, upZ )
% gluLookAt Interface to OpenGL function gluLookAt
%
% usage: gluLookAt( eyeX, eyeY, eyeZ, centerX, centerY, centerZ, upX, upY, upZ )
%
% C function: void gluLookAt(GLdouble eyeX, GLdouble eyeY, GLdouble eyeZ, GLdouble centerX, GLdoubl... |
github | 3arbouch/ActiveNoiseCancelling-master | OldEyetrackertest.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychOpenGL/PsychGLEyetracking/OldEyetrackertest.m | 9,039 | utf_8 | 5b5cdd1323f07a124fcc91df2da7fc46 | function Eyetrackertest(imfilename)
% Eyetrackertest -- Testscript to test out a few weird
% ideas about Computervision + GLSL based eye tracking
% Not seriously useful for anything in the near future.
%
% Written by Mario Kleiner.
global rayvisdisplaylist;
try
% Assign default name for test-image or test-mo... |
github | 3arbouch/ActiveNoiseCancelling-master | PsychCamSettings.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychVideoCapture/PsychCamSettings.m | 26,930 | utf_8 | 41963f3d84734e2a52f00a3c3239710f | function rc = PsychCamSettings(cmd, grabber, varargin)
% rc = PsychCamSettings(cmd, grabber [, arg0, arg1, ...])
%
% Setup tool for video sources for use with Psychtoolbox
% video capture functions. This function can mostly only operate
% on IIDC/DCAM machine vision standard compliant camera connected
% via IEEE1394-Fi... |
github | 3arbouch/ActiveNoiseCancelling-master | PsychVideoDelayLoop.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychVideoCapture/PsychVideoDelayLoop.m | 25,820 | utf_8 | 7b7a23abb7275984b3e8d429f4b1adc1 | function rc = PsychVideoDelayLoop(cmd, varargin)
% PsychVideoDelayLoop(subcommand, arg1, arg2, ...)
%
% This implements a realtime video feedback loop with adjustable
% delay, e.g., for action-perception studies.
%
% Arguments:
% subcommand - Is a string containing the subcommand to call.
% arg1, ... argn - Are the arg... |
github | 3arbouch/ActiveNoiseCancelling-master | PanelFitterDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/PanelFitterDemo.m | 10,745 | utf_8 | 9eec29b529dad010dbfdc21b673e4bff | function PanelFitterDemo
% PanelFitterDemo - Demonstrate use of the panel fitter.
%
% This demo shows how to use Screen()'s panel fitter function by using the
% high-level setup code in PsychImaging().
%
% The panelfitter allows to implement "rotated framebuffers", which have a
% shape (width and height in pixels) that... |
github | 3arbouch/ActiveNoiseCancelling-master | VideoTextureExtractionDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/VideoTextureExtractionDemo.m | 14,229 | utf_8 | 4fb58ca6aebf8865c08a77e5601d51ce | function VideoTextureExtractionDemo(lightson, objtype, multiMarker)
% Use ARToolkit to track and visualize 3D objects in live-video.
%
% Usage: ARToolkitDemo([lightson = 0][, objtype = 3][, multiMarker = 2])
%
% Minimalistic demo on how to capture video data and use ARToolkit to
% detect and track the rigid position an... |
github | 3arbouch/ActiveNoiseCancelling-master | GarboriumDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/GarboriumDemo.m | 10,956 | utf_8 | d3dd52045f6d90267f07fab2ff9a1d3f | function GarboriumDemo(ngabors, internalRotation)
% GarboriumDemo([ngabors=200] [, internalRotation=0]) -- An aquarium full of cute little gabors!
%
% This demo shows how to use the Screen('DrawTextures') command to draw a
% large number of similar images quickly - in this case, Gabor patches of
% different position, s... |
github | 3arbouch/ActiveNoiseCancelling-master | KbDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/KbDemo.m | 9,030 | utf_8 | 3bddfb61feb96aa6e64931feeab2cdde | function KbDemo
%% KbDemo
% Shows how to detect when the user has pressed a key.
% See KbCheck, KbName, KbWait, GetChar, CharAvail.
%
% The KbXXX functions are low-level and go after the state of the keyboard.
% The GetChar/CharAvail interface pulls characters out of the event queue.
% The advantage of the KbXXX functi... |
github | 3arbouch/ActiveNoiseCancelling-master | ProceduralGaborDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/ProceduralGaborDemo.m | 9,140 | utf_8 | dfe74886222b5210ba59821bec57c3db | function ProceduralGaborDemo(benchmark, nonsymmetric)
% ProceduralGaborDemo([benchmark=0][, nonsymmetric=0])
%
% This demo demonstrates fast drawing of Gabor patches via use procedural
% texture mapping. It only works on hardware with support for the GLSL
% shading language, vertex- and fragment-shaders.
%
% Gabors are... |
github | 3arbouch/ActiveNoiseCancelling-master | KbQueueDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/KbQueueDemo.m | 9,998 | utf_8 | 67a9b319c33581e3837088b940e00c35 | function KbQueueDemo(deviceIndex)
%% KbQueueDemo([deviceIndex])
% Shows how to detect when the user has pressed a key.
% See KbQueueCheck, KbQueueWait, KbName, KbCheck, KbWait, GetChar, CharAvail.
%
% The KbQueueXXX functions are low-level like KbCheck and KbWait, but, like
% GetChar/CharAvail, they use a queue so that... |
github | 3arbouch/ActiveNoiseCancelling-master | LinesDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/LinesDemo.m | 5,728 | utf_8 | df6c2e62eeba3c8287df493393bbca15 |
function LinesDemo
% Line motion demo using SCREEN('DrawLines') subfunction
% Derived from DotDemo, whose original author was Keith Schneider, 12/13/04
%
% Caution: This demo is a quick & dirty hack to get you started. It
% demonstrates a lot of inefficient techniques of how not to do this!!!
%HISTORY
%
% mm/dd/yy
% ... |
github | 3arbouch/ActiveNoiseCancelling-master | Kinect3DDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/Kinect3DDemo.m | 11,757 | utf_8 | a4c3f4c39612d3c588bbb121b61c0f33 | function Kinect3DDemo(stereomode)
% Kinect3DDemo - Capture and display video and depths data from a Kinect box.
%
% Usage:
%
% Kinect3DDemo([stereomode=0])
%
% This connects to a Microsoft Kinect device on the USB bus, then captures
% and displays video and depths data delivered by the Kinect.
%
% This is an early prot... |
github | 3arbouch/ActiveNoiseCancelling-master | ProceduralGarboriumDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/ProceduralGarboriumDemo.m | 12,112 | utf_8 | 3d8089b7d9e8a20988ad4b2ade134ff0 | function ProceduralGarboriumDemo(ngabors)
% ProceduralGarboriumDemo([ngabors=200]) -- An aquarium full of cute little procedural gabors!
%
% This demo shows how to use the Screen('DrawTextures') command to draw a
% large number of similar images quickly - in this case, Gabor patches of
% different position, size and or... |
github | 3arbouch/ActiveNoiseCancelling-master | SuperShapeDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/OpenGL4MatlabDemos/SuperShapeDemo.m | 16,334 | UNKNOWN | 79341ad2be2f702abbe52b5804b6ec48 | function SuperShapeDemo
% Draw SuperShapes with OpenGL
%
% This demo computes and displays a 3D supershape using the formula
% found by Gielis (A generic geometric transformation that unifies
% a wide range of natural and abstract shapes. American Journal of
% Botany 90(3):333�338, 2003)
%
% See also http://en.wikiped... |
github | 3arbouch/ActiveNoiseCancelling-master | SpinningMovieCube.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/OpenGL4MatlabDemos/SpinningMovieCube.m | 8,007 | utf_8 | 9b83f101e854e789a0c55c0cfb311312 | function SpinningMovieCube(moviename)
% SpinningMovieCubeDemo - Demonstrate use of MATLAB-OpenGL toolbox
%
% This demo demonstrates use of OpenGL commands in a Matlab script together
% with the movie playback functions of PTB. It shows a randomly
% spinning, three dimensional textured cube. The six sides of the cube
% ... |
github | 3arbouch/ActiveNoiseCancelling-master | SpinningCubeDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/OpenGL4MatlabDemos/SpinningCubeDemo.m | 10,263 | utf_8 | 93ea17f87149faa4e2cf0d1548e85e6c | function SpinningCubeDemo
% SpinningCubeDemo - Demonstrate use of MATLAB-OpenGL toolbox
%
% This demo demonstrates use of OpenGL commands in a Matlab script to
% perform some funky 3D animation in Psychtoolbox.
% It shows a randomly spinning, textured cube. The six sides of the cube
% are textured via binary texture da... |
github | 3arbouch/ActiveNoiseCancelling-master | MorphDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/OpenGL4MatlabDemos/MorphDemo/MorphDemo.m | 18,020 | utf_8 | 83756d54675fd95a9630cd3dc5adf42a | function MorphDemo(textureon, dotson, normalson, stereomode)
% function MorphDemo([textureon][, dotson][, normalson][, stereomode])
% MorphDemo -- Demonstrates use of "moglmorpher" for fast morphing
% and rendering of 3D shapes. See "help moglmorpher" for info on
% moglmorphers purpose and capabilities.
%
% This demo w... |
github | 3arbouch/ActiveNoiseCancelling-master | MorphTextureDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychDemos/OpenGL4MatlabDemos/MorphDemo/MorphTextureDemo.m | 16,564 | utf_8 | ce9dc4a6962c3dd4f32ba3add6daf8fa | function MorphTextureDemo(dotson, normalson, stereomode, usefastoffscreenwindows)
% function MorphTextureDemo([dotson][, normalson][,stereomode][, usefastoffscreenwindows])
%
% MorphTextureDemo -- Demonstrates use of "moglmorpher" for fast morphing
% and rendering of 3D shapes *and* textures. See "help moglmorpher" for... |
github | 3arbouch/ActiveNoiseCancelling-master | OptiCAL.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/OptiCAL.m | 6,586 | utf_8 | 52c0afbb0e90e6d29ed54068d0fe566f | % OptiCAL - Psychtoolbox IOPort interface to the CRS OptiCAL luminance
% meter device
%
% Usage:
% >> handle = OptiCAL('Open', port)
% >> lum = OptiCAL('Read', handle)
% >> OptiCAL('Close', handle)
% >> OptiCAL('CloseAll')
%
% Inputs:
% command - string 'Open', 'Read', 'Close', or 'CloseAll'
% p... |
github | 3arbouch/ActiveNoiseCancelling-master | NetStation.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/NetStation.m | 13,226 | utf_8 | 5dd9b945ede3c7590f9ed25688fbf40e | function [status, error] = NetStation(varargin)
%
% NetStation - Basic control of the EGI/NetStation EEG recording system via
% TCP/IP network connection. (See http://www.egi.com)
%
% This function was developed and contributed to Psychtoolbox by Gergely Csibra, 2006-2008.
% Code is based on Rick Gilmore's routines, 20... |
github | 3arbouch/ActiveNoiseCancelling-master | CedrusResponseBox.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/CedrusResponseBox.m | 78,491 | UNKNOWN | 8f7aef325a343cb3a5ba7f10997865ee | function varargout = CedrusResponseBox(cmd, varargin)
% CedrusResponseBox - Interface to Cedrus Response Boxes.
%
% This function provides an interface to response button boxes from Cedrus,
% specifically model RB 530,...,830 and compatible models supporting the
% XID protocol (see http://www.cedrus.com).
%
% Th... |
github | 3arbouch/ActiveNoiseCancelling-master | CMUBox.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/CMUBox.m | 38,138 | utf_8 | 42c4b7f017b331279d18553a190a9ec4 | function varargout = CMUBox(cmd, handle, varargin)
% CMUBox - Access CMU response button box or PST serial response button box as well as fORP and Bitwhacker devices.
%
% This allows to query button response boxes of type CMU (Carnegie Mellon
% University box) and PST (E-Prime response box). It also allows to use a
% ... |
github | 3arbouch/ActiveNoiseCancelling-master | PsychRTBox.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/PsychRTBox.m | 136,397 | utf_8 | 6d0862058bbb8abe74e7272f5d138aca | function varargout = PsychRTBox(varargin)
% Driver for the USTC reaction time button box (RTBox) by Xiangrui Li et al.
% varargout = PsychRTBox(cmd, varargin);
%
% This driver allows to control most functions of the USTC RTBox response
% button box. In theory this driver should support boxes up to Box/Firmware
% versio... |
github | 3arbouch/ActiveNoiseCancelling-master | FindSerialPort.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/FindSerialPort.m | 10,310 | utf_8 | dae71db816317da1bee98f5bbef710a7 | function PortNumber = FindSerialPort(PortString, forIOPort, dontFail)
% Syntax: PortNumber = FindSerialPort([PortString][, forIOPort=0][, dontFail=0])
%
% Purpose: Find serial port number/name associated with a particular input string.
%
% If the optional parameter 'PortString' is omitted, the routine will use a
% buil... |
github | 3arbouch/ActiveNoiseCancelling-master | bitsGoggles.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/BitsPlusToolbox/bitsGoggles.m | 720 | utf_8 | f83c1e0f1f9cceafa1ba9b907bbf137a | % usage bitsGoggles(left,right,window)
%
% where 0 = goggle open
% and 1 = goggle closed
%
% drives the FE1 goggles connected to bits 4 and 5 of the digital output.
function bitsGoggles(left,right,window)
% Bits Shutter
% 5 4 L R
% --------------------
% 1 1 O O
% 0 0 ... |
github | 3arbouch/ActiveNoiseCancelling-master | bitsEncodeDIO.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/BitsPlusToolbox/bitsEncodeDIO.m | 2,299 | utf_8 | 9d2be8ceba0be3e788019c4e488de517 | % encodedDIOdata = bitsEncodeDIO(Mask, Data, Command, windowPtr, [setGammaTable])
% Use it when you want to write DIO synchronised with screen frame.
% It will sync timer interrupt with each frame.
%
% 'Mask' is DIO mask that must be an integer.
%
% 'Data' is a 248 element array of integers.
%
% 'Command' is the ... |
github | 3arbouch/ActiveNoiseCancelling-master | BitsPlusCSFDemo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/BitsPlusToolbox/BitsPlusDemos/BitsPlusCSFDemo.m | 17,176 | utf_8 | 7e26b049478f62563b07b36adad3958b | function BitsPlusCSFDemo(screenid, gamma, method, charttype)
% BitsPlusCSFDemo([screenid=max] [, gamma = 2.2][, method=0][, charttype=0])
%
% This demo utilizes the Psychtoolbox imaging pipeline. Therefore it won't
% work on gfx-hardware older than ATI Radeon X1000 or NVidia Geforce 6000.
%
% Demonstrates advantage of ... |
github | 3arbouch/ActiveNoiseCancelling-master | ResponsePixx.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/DatapixxToolbox/DatapixxBasic/ResponsePixx.m | 22,738 | utf_8 | 61bfc27d35338f9b72631c4b18073dc1 | function varargout = ResponsePixx(cmd, varargin)
% ResponsePixx - Control and use the ResponsePixx response button box.
%
% This allows to record button events from the ResponsePixx response box
% device. Start and stop of logging of button responses and control of the
% button lights (on/off and intensity) can be done... |
github | 3arbouch/ActiveNoiseCancelling-master | PsychDataPixx.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/DatapixxToolbox/DatapixxBasic/PsychDataPixx.m | 66,787 | utf_8 | 1265fb5f733bba13d82eaeec201b72fa | function varargout = PsychDataPixx(cmd, varargin)
% PsychDataPixx - High level control driver for the VPixx - DataPixx device.
%
% This driver provides common high-level functionality for interaction with
% the VPixx Technologies DataPixx device. The driver provides high-level
% functions for basic device operations an... |
github | 3arbouch/ActiveNoiseCancelling-master | psychsendmail.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/iViewXToolbox/cbase64/psychsendmail.m | 7,893 | utf_8 | 36627d8d5985dcfaea497b28b3cc2624 | function [retcode] = sendmail(from, to, subj, mesg, varargin)
% SENDMAIL Send Internet e-mail
% Using SENDMAIL (which needs the TCP/UDP/IP toolbox, freely available from
% http://petrydpc.ite.mh.se/tools/) it is possible to send e-mail messages
% from the Matlab prompt. This can be useful when you want to be ... |
github | 3arbouch/ActiveNoiseCancelling-master | popmail_demo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/iViewXToolbox/tcp_udp_ip/popmail_demo.m | 2,618 | utf_8 | e7b2af709497224ea1e5d6c7ace0044a | function popmail_demo(site,user,pass)
% popmail_demo - Demo that read mail from pop mail server (not delete).
% The first lines of each mail will be printed out.
%
% Syntax:
% popmail_demo(site,user,password)
% or
% popmail_demo site user password
% or
% popmail_demo
%
% In the last case you ... |
github | 3arbouch/ActiveNoiseCancelling-master | pnet_remote.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/iViewXToolbox/tcp_udp_ip/pnet_remote.m | 12,575 | utf_8 | 311748bd33a589c8b0344cf264c0a069 | function varargout=pnet_remote(varargin)
% PNET_REMOTE - Evaluation of matlab expression in remote host PNET
%
% Version: First includes in the tcp/udp/ip toolbox 2002-02-13
% (C) 2002 Peter Rydesaeter, GNU Public License
%
% This function uses PNET for nonblocking remote controll of oth... |
github | 3arbouch/ActiveNoiseCancelling-master | webserver_demo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/iViewXToolbox/tcp_udp_ip/tcpip/webserver_demo.m | 1,193 | utf_8 | cb496143457b3892ad24ee80cfa8d5b4 | function www_sql()
%
% WWW-server as interface to ALIS data in MySQL
%
tcpip_close all;
while 1,
sock=-1;
while sock<0,
disp 'Open socket 8080...'
sock=tcpip_servsocket(8080);
if(con<0), pause(0.1); end
end
while tcpip_status(sock),
disp 'Listen for connections...'
... |
github | 3arbouch/ActiveNoiseCancelling-master | tcpip_test.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/iViewXToolbox/tcp_udp_ip/tcpip/tcpip_test.m | 1,514 | utf_8 | 6c1c02d14af3da7addb7751f7b394e00 | function tcpip_test()
disp ' '
disp '=========== START TCPIP TEST ========================='
disp 'If this test stops or gives any other error it failed!!'
tcpip_close all;
local_talk([999 1025 2000:2010 2777 21777 55123]);
disp '=========== END ========================='
return
function l... |
github | 3arbouch/ActiveNoiseCancelling-master | popmail_demo.m | .m | ActiveNoiseCancelling-master/Psychtoolbox/Psychtoolbox/PsychHardware/iViewXToolbox/tcp_udp_ip/tcpip/popmail_demo.m | 2,868 | utf_8 | d91c5d8c26bdfb266cb3c82f0471761d | function popmail_demo(site,user,pass)
% popmail_demo - Demo that read mail from pop mail server (not delete).
% The first lines of each mail will be printed out.
%
% Syntax:
% popmail_demo(site,user,password)
% or
% popmail_demo site user password
% or
% popmail_demo
%
% In the last case y... |
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