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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | fuenwang/BiomedicalSound-master | xdc_apodization.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_apodization.m | 1,388 | utf_8 | 25117e1e732a9ce4b90082b7438415be | % Procedure for creating an apodization time line for an aperture
%
% Calling: xdc_apodization (Th, times, values);
%
% Parameters: Th - Pointer to the transducer aperture.
% times - Time after which the associated apodization is valid.
% values - Apodization values. Matrix wit... |
github | fuenwang/BiomedicalSound-master | xdc_rectangles.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_rectangles.m | 2,000 | utf_8 | 3c16ff913ea2c63dceb2fc0ab4a15e4a | % Procedure for creating an aperture consisting of rectangles
%
% Calling: Th = xdc_rectangles (rect, center, focus);
%
% Parameters:
%
% rect - Information about the rectangles. One row
% for each rectangle. The contents is:
%
% Index Variable Value
% --------------------------... |
github | fuenwang/BiomedicalSound-master | xdc_lines.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_lines.m | 1,855 | utf_8 | 29ba579e4f1931484afb9a06b2dd7a9c | % Procedure for creating an aperture bounded by a set of lines
%
% Calling: Th = xdc_lines (lines, center, focus);
%
% Parameters:
%
% lines - Information about the lines. One row
% for each line. The contents is:
%
% Index Variable Value
% ---------------------------------------... |
github | fuenwang/BiomedicalSound-master | field_init.m | .m | BiomedicalSound-master/hw02/code/Field2/field_init.m | 826 | utf_8 | 90e0eb5b1deed3de2124f6a97f35b1ca | % Procedure for initializing the Field II program system. Must be
% the first routine that is called before using the system.
%
% Calling: field_init (suppress);
%
% Return: nothing.
%
% Input: suppress: An optional argument suppress with a value
% of zero can be given to suppress the
% ... |
github | fuenwang/BiomedicalSound-master | xdc_piston.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_piston.m | 801 | utf_8 | 2d4e9da5ba5b915ff42817cb169f086f | % Procedure for creating a flat, round piston transducer
%
% Calling: Th = xdc_piston (radius, ele_size);
%
% Parameters: radius - Radius of aperture.
% ele_size - Size of elements for modeling transducer.
%
% All dimensions are in meters.
%
% Return: A handle Th as a pointer to thi... |
github | fuenwang/BiomedicalSound-master | calc_scat_multi.m | .m | BiomedicalSound-master/hw02/code/Field2/calc_scat_multi.m | 1,386 | utf_8 | 3609fe45f60deeb44851e90d70462686 | % Procedure for calculating the received signal from a collection of scatterers
% and for each of the elements in the receiving aperture.
%
% Calling: [scat, start_time] = calc_scat_multi (Th1, Th2, points, amplitudes);
%
% Parameters: Th1 - Pointer to the transmit aperture.
% Th2 -... |
github | fuenwang/BiomedicalSound-master | xdc_convex_focused_multirow.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_convex_focused_multirow.m | 3,091 | utf_8 | 6e7bc611cdc55a3d5ee573deaaa4331e | % Procedure for creating a convex, elevation focused array transducer
% with an number of rows (1.5D array)
%
% Calling: Th = xdc_convex_focused_multirow (no_elem_x, width, no_ele_y, heights, kerf_x, kerf_y,
% Rconvex, Rfocus, no_sub_x, no_sub_y, focus);
%
% Parameter... |
github | fuenwang/BiomedicalSound-master | xdc_excitation.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_excitation.m | 574 | utf_8 | bac08439495579ba8898d8f45b808c19 | % Procedure for setting the excitation pulse of an aperture
%
% Calling: xdc_excitation (Th,pulse);
%
% Parameters: Th - Pointer to the transducer aperture.
% pulse - Excitation pulse of aperture as row vector
%
% Return: None
%
% Version 1.0, November 27, 1995 by Joergen Arendt Jensen
... |
github | fuenwang/BiomedicalSound-master | set_sampling.m | .m | BiomedicalSound-master/hw02/code/Field2/set_sampling.m | 443 | utf_8 | 19a9a87e44caf059edbabc33278a0f25 | % Set the sampling frequency the system uses.
%
% Remember that the pulses used in all apertures must
% be reset for the new sampling frequency to take effect.
%
% Calling: set_sampling (fs);
%
% Parameters: fs - The new sampling frequency.
%
% Return: nothing.
%
% Version 1.0, December 7, 1995 by Joergen Ar... |
github | fuenwang/BiomedicalSound-master | calc_hp.m | .m | BiomedicalSound-master/hw02/code/Field2/calc_hp.m | 758 | utf_8 | d03bb55bfe2471bfa8b0101d500593b0 | % Procedure for calculating the emitted field.
%
% Calling: [hp, start_time] = calc_hp(Th, points);
%
% Parameters: Th - Pointer to the transmit aperture.
% points - Field points. Matrix with three columns (x,y,z)
% and one row for each field point.
%
% Return: h... |
github | fuenwang/BiomedicalSound-master | xdc_line_convert.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_line_convert.m | 547 | utf_8 | 4414b945fe2cbc13211cdc60424674b5 | % Procedure for converting an aperture from consisting of rectangles
% to consist of triangles
%
% Calling: xdc_line_convert (Th);
%
% Parameters: A handle Th as a pointer to this transducer aperture. The
% pointer value will be the same as for the rectangular aperture.
% The recta... |
github | fuenwang/BiomedicalSound-master | xdc_show.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_show.m | 1,111 | utf_8 | ed012ebd65bba199cd8df0621c755e74 | % Procedure for showing an aperture
%
% Calling: xdc_show(Th, info_type);
%
% Parameters: Th - Pointer to the transducer aperture.
% info_type - Which information to show (text string).
% The possibilities are:
% elements - information about eleme... |
github | fuenwang/BiomedicalSound-master | ele_apodization.m | .m | BiomedicalSound-master/hw02/code/Field2/ele_apodization.m | 1,152 | utf_8 | c6a4555ea1cc728637efcd91ab5233ba | % Procedure for setting the apodization of individual
% mathematical elements making up the transducer
%
% Calling: ele_apodization (Th, element_no, apo);
%
% Parameters: Th - Pointer to the transducer aperture.
% element_no - Column vector with one integer for each physical
% ... |
github | fuenwang/BiomedicalSound-master | xdc_linear_array.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_linear_array.m | 1,611 | utf_8 | d27a9a60bef0ec89fadd7bf249490007 | % Procedure for creating a linear array transducer
%
% Calling: Th = xdc_linear_array (no_elements, width, height, kerf, no_sub_x, no_sub_y, focus);
%
% Parameters: no_elements - Number of physical elements.
% width - Width in x-direction of elements.
% height - Width ... |
github | fuenwang/BiomedicalSound-master | calc_h.m | .m | BiomedicalSound-master/hw02/code/Field2/calc_h.m | 792 | utf_8 | 7ba23939c948eef024e70681a1de5ff1 | % Procedure for calculating the spatial impulse response
% for an aperture.
%
% Calling: [h, start_time] = calc_h(Th,points);
%
% Parameters: Th - Pointer to the transducer aperture.
% points - Field points. Vector with three columns (x,y,z)
% and one row for each fiel... |
github | fuenwang/BiomedicalSound-master | xdc_focused_multirow.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_focused_multirow.m | 2,568 | utf_8 | 29a55e195336462ae8b6e9faef339f63 | % Procedure for creating a linear, elevation focused array transducer
% with an number of rows (1.5D array)
%
% Calling: Th = xdc_focused_multirow (no_elem_x, width, no_ele_y, heights, kerf_x, kerf_y,
% Rfocus, no_sub_x, no_sub_y, focus);
%
% Parameters: no_elem_x - Num... |
github | fuenwang/BiomedicalSound-master | xdc_focused_array.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_focused_array.m | 1,961 | utf_8 | a03c5389415f577298a129c19698db4c | % Procedure for creating an elevation focused linear array transducer
%
% Calling: Th = xdc_focused_array (no_elements, width, height, kerf, Rfocus,
% no_sub_x, no_sub_y, focus);
%
% Parameters: no_elements - Number of physical elements.
% width - Width ... |
github | fuenwang/BiomedicalSound-master | xdc_convex_array.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_convex_array.m | 1,797 | utf_8 | 07ab286d9a5610c29e85a4ba04211814 | % Procedure for creating a convex array transducer
%
% Calling: Th = xdc_convex_array (no_elements, width, height, kerf, Rconvex,
% no_sub_x, no_sub_y, focus);
%
% Parameters: no_elements - Number of physical elements.
% width - Width in x-direction of el... |
github | fuenwang/BiomedicalSound-master | xdc_convert.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_convert.m | 539 | utf_8 | 8604fda4c1a15e278645bf2cd1753e04 | % Procedure for converting an aperture from consisting of rectangles
% to consist of triangles
%
% Calling: xdc_convert (Th);
%
% Parameters: A handle Th as a pointer to this transducer aperture. The
% pointer value will be the same as for the rectangular aperture.
% The rectangles... |
github | fuenwang/BiomedicalSound-master | xdc_impulse.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_impulse.m | 561 | utf_8 | 47508acfd92d9a56a768a033e6c1db17 | % Procedure for setting the impulse response of an aperture
%
% Calling: xdc_impulse (Th,pulse);
%
% Parameters: Th - Pointer to the transducer aperture.
% pulse - Impulse response of aperture as row vector
%
% Return: None
%
% Version 1.01, May 20, 1997 by Joergen Arendt Jensen
function r... |
github | fuenwang/BiomedicalSound-master | ele_delay.m | .m | BiomedicalSound-master/hw02/code/Field2/ele_delay.m | 1,137 | utf_8 | 4796ca443b2775bc4744303e083f022c | % Procedure for setting the delay of individual
% mathematical elements making up the transducer
%
% Calling: ele_delay (Th, element_no, delays);
%
% Parameters: Th - Pointer to the transducer aperture.
% element_no - Column vector with one integer for each physical
% ... |
github | fuenwang/BiomedicalSound-master | calc_scat_all.m | .m | BiomedicalSound-master/hw02/code/Field2/calc_scat_all.m | 2,504 | utf_8 | a7501310b5fc586ce5873f611312544a | % Procedure for calculating the received signal from a collection
% of scatterers, when transmitting with each individual element
% and receiving with each of the elements in the receiving aperture.
%
% Calling: [scat, start_time] = calc_scat_all (Th1, Th2, points, amplitudes, dec_factor);
%
% Parameters: Th1 ... |
github | fuenwang/BiomedicalSound-master | field_end.m | .m | BiomedicalSound-master/hw02/code/Field2/field_end.m | 295 | utf_8 | 97a52cdc6ae50d808867fc40cca696a4 | % Procedure for ending the Field II program system and releasing the storage.
%
% Calling: field_end ;
%
% Return: nothing.
%
% Version 1.0, November 28, 1995 by Joergen Arendt Jensen
function res = field_end ()
% Call the C-part of the program to initialize it
Mat_field (5002);
|
github | fuenwang/BiomedicalSound-master | xdc_center_focus.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_center_focus.m | 785 | utf_8 | c624b22a08dca4ff6085f3792409af80 | % Procedure for setting the center point for the focusing.
% This point is used as a reference for calculating the
% focusing delay times and as a starting point for dynamic
% focusing.
%
% Calling: xdc_center_focus (Th, point);
%
% Parameters: Th - Pointer to the transducer aperture.
% poi... |
github | fuenwang/BiomedicalSound-master | xdc_convex_focused_array.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_convex_focused_array.m | 2,559 | utf_8 | 0dd7c8488c9893e0bfccae8324bdc81d | % Procedure for creating a convex array transducer
%
% Calling: Th = xdc_convex_focused_array (no_elements, width, height, kerf, Rconvex, Rfocus
% no_sub_x, no_sub_y, focus);
%
% Parameters: no_elements - Number of physical elements.
% width - Wid... |
github | fuenwang/BiomedicalSound-master | xdc_focus_times.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_focus_times.m | 1,021 | utf_8 | 61d328e14b1fbffc6e758e5186c570e8 | % Procedure for creating a focus time line for an aperture
% The user here supplies the delay times for each element
%
% Calling: xdc_times_focus (Th, times, delays);
%
% Parameters: Th - Pointer to the transducer aperture.
% times - Time after which the associated apodization is valid.
% ... |
github | fuenwang/BiomedicalSound-master | xdc_concave.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_concave.m | 970 | utf_8 | 8c824a44235f0c6a21b54ef24e5ac723 | % Procedure for creating a concave transducer
%
% Calling: Th = xdc_concave (radius, focal_radius, ele_size);
%
% Parameters: radius - Radius of aperture.
% focal_radius - Focal radius of aperture.
% ele_size - Size of elements for modeling transducer.
%
% All dimensions ... |
github | fuenwang/BiomedicalSound-master | set_field.m | .m | BiomedicalSound-master/hw02/code/Field2/set_field.m | 2,536 | utf_8 | 06a180d9718d2ab368f3edc7017661f1 | % Set options for the program.
%
% Calling: set_field (option_name, value);
%
% Possible options Value
%
% use_att Whether to use attenuation (<> 0 for attenuation)
% att Frequency independent attenuation in dB/m.
% freq_att Frequency dependent attenuation... |
github | fuenwang/BiomedicalSound-master | calc_scat.m | .m | BiomedicalSound-master/hw02/code/Field2/calc_scat.m | 1,197 | utf_8 | ec67e811c62195f4335010a15391b23c | % Procedure for calculating the received signal from a collection of scatterers.
%
% Calling: [scat, start_time] = calc_scat(Th1, Th2, points, amplitudes);
%
% Parameters: Th1 - Pointer to the transmit aperture.
% Th2 - Pointer to the receive aperture.
% points - ... |
github | fuenwang/BiomedicalSound-master | xdc_free.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_free.m | 347 | utf_8 | f38fbe69e287e3b483b560a3c499c8b9 | % Procedure for freeing the storage occupied by an aperture
%
% Calling: xdc_free(Th);
%
% Parameters: Th - Pointer to the transducer aperture.
%
% Return: None
%
% Version 1.0, November 28, 1995 by Joergen Arendt Jensen
function res = xdc_free (Th)
% Call the C-part of the program to show aperture... |
github | fuenwang/BiomedicalSound-master | xdc_quantization.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_quantization.m | 803 | utf_8 | 13b54f5af11362935130d247c9458559 | % Procedure for setting the minimum quantization interval that
% can be used when phasing the transducer.
%
% Remember that the focus time lines must be set again for the
% quantization to take effect. This setting does not affect the
% user calculated delays.
%
% Calling: xdc_quantization (Th, min_delay);
%
% ... |
github | fuenwang/BiomedicalSound-master | xdc_2d_array.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_2d_array.m | 2,443 | utf_8 | 97821d42dd3f5244d52eea314110b8e8 | % Procedure for creating a 2d (sparse) array transducer
%
% Calling: Th = xdc_2d_array (no_ele_x, no_ele_y, width, height, kerf_x, kerf_y,
% enabled, no_sub_x, no_sub_y, focus);
%
% Parameters: no_ele_x - Number of physical elements in x-direction.
% no_ele_y ... |
github | fuenwang/BiomedicalSound-master | xdc_get.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_get.m | 1,373 | utf_8 | 16e2be486c7a1780a5b0e0989f3631eb | % Procedure for getting data for an aperture
%
% Calling: data = xdc_get(Th, info_type);
%
% Parameters: Th - Pointer to the transducer aperture.
% info_type - Which information to get (text string).
% The possibilities are:
% rect - informati... |
github | fuenwang/BiomedicalSound-master | xdc_times_focus.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_times_focus.m | 1,025 | utf_8 | 63ceefb4f93d3dcc0ceb1adf0a0dc6dc | % Procedure for creating a focus time line for an aperture
% The user here supplies the delay times for each element
%
% Calling: xdc_times_focus (Th, times, delays);
%
% Parameters: Th - Pointer to the transducer aperture.
% times - Time after which the associated apodization is valid.
% ... |
github | fuenwang/BiomedicalSound-master | xdc_baffle.m | .m | BiomedicalSound-master/hw02/code/Field2/xdc_baffle.m | 611 | utf_8 | 3cd630e65c95f443e69b285292f0eebf | % Procedure for setting the baffle condition for the aperture.
%
% Calling: xdc_baffle (Th, soft_baffle);
%
% Parameters: Th - Pointer to the transducer aperture.
% soft_baffle - Whether to use the soft-baffle condition:
% 1 - using soft baffle
% ... |
github | fuenwang/BiomedicalSound-master | saveFig.m | .m | BiomedicalSound-master/hw04-1/code/saveFig.m | 225 | utf_8 | 1e79a8c1f6d13a39941aa0d64550e925 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function saveFig(fig, path)
fig.PaperPositionMode = 'auto';
fig_pos = fig.PaperPosition;
fig.PaperSize = [fig_pos(3) fig_pos(4)];
print(fig, path, '-dpdf')
end |
github | fuenwang/BiomedicalSound-master | saveFig.m | .m | BiomedicalSound-master/hw03/submit/saveFig.m | 225 | utf_8 | 1e79a8c1f6d13a39941aa0d64550e925 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function saveFig(fig, path)
fig.PaperPositionMode = 'auto';
fig_pos = fig.PaperPosition;
fig.PaperSize = [fig_pos(3) fig_pos(4)];
print(fig, path, '-dpdf')
end |
github | fuenwang/BiomedicalSound-master | saveFig.m | .m | BiomedicalSound-master/hw03/code/saveFig.m | 225 | utf_8 | 1e79a8c1f6d13a39941aa0d64550e925 | %
% EE6265 Fu-En Wang 106061531 HW2 11/14/2017
%
function saveFig(fig, path)
fig.PaperPositionMode = 'auto';
fig_pos = fig.PaperPosition;
fig.PaperSize = [fig_pos(3) fig_pos(4)];
print(fig, path, '-dpdf')
end |
github | maxkferg/casting-defect-detection-master | wacv_demo.m | .m | casting-defect-detection-master/sliding_window/wacv/wacv_demo.m | 7,387 | utf_8 | 17fb9188c96b8fb70d8a3822ae2e1804 | % [T,p] = wacv_demo(fxname,clname,clparameter)
%
% Mery, D.; Arteta, C.: Automatic Defect Recognition in X-ray Testing
% using Computer Vision. In 2017 IEEE Winter Conference on Applications of
% Computer Vision, WACV2017.
%
% Paper: http://dmery.sitios.ing.uc.cl/Prints/Conferences/International/2017-WACV.pdf
%
% (c) 2... |
github | maxkferg/casting-defect-detection-master | xnet_cnn.m | .m | casting-defect-detection-master/sliding_window/wacv/xnet/xnet_cnn.m | 7,974 | utf_8 | aeb905b10910c4d48dddea394f5a9aa7 | % function [net, info] = xnet_cnn(param,epochs)
function [net, info] = xnet_cnn(var1,var2,cnnmode)
if strcmp(cnnmode,'train')==1
param = var1;
epochs = var2;
train = true;
else
info = var2;
train = false;
epochs = info.opts.train.numEpochs;
param = info.param;
end
basepath = '';
opt... |
github | maxkferg/casting-defect-detection-master | test_examples.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/utils/test_examples.m | 1,591 | utf_8 | 16831be7382a9343beff5cc3fe301e51 | function test_examples()
%TEST_EXAMPLES Test some of the examples in the `examples/` directory
addpath examples/mnist ;
addpath examples/cifar ;
trainOpts.gpus = [] ;
trainOpts.continue = true ;
num = 1 ;
exps = {} ;
for networkType = {'dagnn', 'simplenn'}
for index = 1:4
clear ex ;
ex.trainOpts = trainOp... |
github | maxkferg/casting-defect-detection-master | simplenn_caffe_compare.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/utils/simplenn_caffe_compare.m | 5,638 | utf_8 | 8e9862ffbf247836e6ff7579d1e6dc85 | function diffStats = simplenn_caffe_compare( net, caffeModelBaseName, testData, varargin)
% SIMPLENN_CAFFE_COMPARE compare the simplenn network and caffe models
% SIMPLENN_CAFFE_COMPARE(NET, CAFFE_BASE_MODELNAME) Evaluates a forward
% pass of a simplenn network NET and caffe models stored in
% CAFFE_BASE_MODELNAM... |
github | maxkferg/casting-defect-detection-master | cnn_train_dag.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/cnn_train_dag.m | 15,440 | utf_8 | 78d69d39fb6f236ce9efd43f995dbdae | function [net,stats] = cnn_train_dag(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
... |
github | maxkferg/casting-defect-detection-master | cnn_train.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/cnn_train.m | 21,052 | utf_8 | 355e8041424653a50b61ea9730fb9d11 | function [net, stats] = cnn_train(net, imdb, getBatch, varargin)
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable... |
github | maxkferg/casting-defect-detection-master | cnn_stn_cluttered_mnist.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/spatial_transformer/cnn_stn_cluttered_mnist.m | 3,872 | utf_8 | 3235801f70028cc27d54d15ec2964808 | function [net, info] = cnn_stn_cluttered_mnist(varargin)
%CNN_STN_CLUTTERED_MNIST Demonstrates training a spatial transformer
% The spatial transformer network (STN) is trained on the
% cluttered MNIST dataset.
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.data... |
github | maxkferg/casting-defect-detection-master | fast_rcnn_train.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/fast_rcnn/fast_rcnn_train.m | 6,399 | utf_8 | 54b0bc7fa26d672ed6673d3f1832944e | function [net, info] = fast_rcnn_train(varargin)
%FAST_RCNN_TRAIN Demonstrates training a Fast-RCNN detector
% Copyright (C) 2016 Hakan Bilen.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
run(fullfile(fileparts(m... |
github | maxkferg/casting-defect-detection-master | fast_rcnn_evaluate.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/fast_rcnn/fast_rcnn_evaluate.m | 6,941 | utf_8 | a54a3f8c3c8e5a8ff7ebe4e2b12ede30 | function [aps, speed] = fast_rcnn_evaluate(varargin)
%FAST_RCNN_EVALUATE Evaluate a trained Fast-RCNN model on PASCAL VOC 2007
% Copyright (C) 2016 Hakan Bilen.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
run(fu... |
github | maxkferg/casting-defect-detection-master | cnn_cifar.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/cifar/cnn_cifar.m | 5,334 | utf_8 | eb9aa887d804ee635c4295a7a397206f | function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR-10
% The demo includes two standard model: LeNet and Network in
% Network (NIN). Use the 'modelType' option to choose one.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.... |
github | maxkferg/casting-defect-detection-master | cnn_cifar_init_nin.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/cifar/cnn_cifar_init_nin.m | 5,561 | utf_8 | aca711e04a8cd82821f658922218368c | function net = cnn_cifar_init_nin(varargin)
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;
% CIFAR-10 model from
% M. Lin, Q. Chen, and S. Yan. Network in network. CoRR,
% abs/1312.4400, 2013.
%
% It reproduces the NIN + Dropout result of Table 1 (<= 10.41% top1 error).
net.layers = {} ;
lr = [... |
github | maxkferg/casting-defect-detection-master | cnn_imagenet_init_resnet.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/imagenet/cnn_imagenet_init_resnet.m | 6,717 | utf_8 | aa905a97830e90dc7d33f75ad078301e | function net = cnn_imagenet_init_resnet(varargin)
%CNN_IMAGENET_INIT_RESNET Initialize the ResNet-50 model for ImageNet classification
opts.classNames = {} ;
opts.classDescriptions = {} ;
opts.averageImage = zeros(3,1) ;
opts.colorDeviation = zeros(3) ;
opts.cudnnWorkspaceLimit = 1024*1024*1204 ; % 1GB
opts = vl_argp... |
github | maxkferg/casting-defect-detection-master | cnn_imagenet_init.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/imagenet/cnn_imagenet_init.m | 15,279 | utf_8 | 43bffc7ab4042d49c4f17c0e44c36bf9 | function net = cnn_imagenet_init(varargin)
% CNN_IMAGENET_INIT Initialize a standard CNN for ImageNet
opts.scale = 1 ;
opts.initBias = 0 ;
opts.weightDecay = 1 ;
%opts.weightInitMethod = 'xavierimproved' ;
opts.weightInitMethod = 'gaussian' ;
opts.model = 'alexnet' ;
opts.batchNormalization = false ;
opts.networkType... |
github | maxkferg/casting-defect-detection-master | cnn_imagenet.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/imagenet/cnn_imagenet.m | 6,211 | utf_8 | f11556c91bb9796f533c8f624ad8adbd | function [net, info] = cnn_imagenet(varargin)
%CNN_IMAGENET Demonstrates training a CNN on ImageNet
% This demo demonstrates training the AlexNet, VGG-F, VGG-S, VGG-M,
% VGG-VD-16, and VGG-VD-19 architectures on ImageNet data.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m... |
github | maxkferg/casting-defect-detection-master | cnn_imagenet_deploy.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/imagenet/cnn_imagenet_deploy.m | 6,585 | utf_8 | 2f3e6d216fa697ff9adfce33e75d44d8 | function net = cnn_imagenet_deploy(net)
%CNN_IMAGENET_DEPLOY Deploy a CNN
isDag = isa(net, 'dagnn.DagNN') ;
if isDag
dagRemoveLayersOfType(net, 'dagnn.Loss') ;
dagRemoveLayersOfType(net, 'dagnn.DropOut') ;
else
net = simpleRemoveLayersOfType(net, 'softmaxloss') ;
net = simpleRemoveLayersOfType(net, 'dropout')... |
github | maxkferg/casting-defect-detection-master | cnn_imagenet_evaluate.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/imagenet/cnn_imagenet_evaluate.m | 5,089 | utf_8 | f22247bd3614223cad4301daa91f6bd7 | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'ILSVRC2012') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.m... |
github | maxkferg/casting-defect-detection-master | cnn_mnist_init.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/mnist/cnn_mnist_init.m | 3,156 | utf_8 | 6e6819c9281561e385955ece4ec7a1a4 | function net = cnn_mnist_init(varargin)
% CNN_MNIST_LENET Initialize a CNN similar for MNIST
opts.batchNormalization = true ;
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;
rng('default');
rng(0) ;
f=1/100 ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
... |
github | maxkferg/casting-defect-detection-master | cnn_mnist.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/mnist/cnn_mnist.m | 4,613 | utf_8 | d23586e79502282a6f6d632c3cf8a47e | function [net, info] = cnn_mnist(varargin)
%CNN_MNIST Demonstrates MatConvNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.batchNormalization = false ;
opts.network = [] ;
opts.networkType = 'simplenn' ;
[opts, varargin] = vl_argparse(opts, varargin) ;
s... |
github | maxkferg/casting-defect-detection-master | cnn_toy_data.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/examples/custom_imdb/cnn_toy_data.m | 5,535 | utf_8 | eb12be3c467c548d0480c46c818e05cd | function [net, stats] = cnn_toy_data(varargin)
% CNN_TOY_DATA
% Minimal demonstration of MatConNet training of a CNN on toy data.
%
% It also serves as a short tutorial on creating and using a custom imdb
% (image database).
%
% The task is to distinguish between images of triangles, squares and
% circles.
% Copyright... |
github | maxkferg/casting-defect-detection-master | vl_nnloss.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/matlab/vl_nnloss.m | 11,336 | utf_8 | e33da54333122fdd2f1a017a29e4f586 | function y = vl_nnloss(x,c,varargin)
%VL_NNLOSS CNN categorical or attribute loss.
% Y = VL_NNLOSS(X, C) computes the loss incurred by the prediction
% scores X given the categorical labels C.
%
% The prediction scores X are organised as a field of prediction
% vectors, represented by a H x W x D x N array. The... |
github | maxkferg/casting-defect-detection-master | vl_compilenn.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/matlab/vl_compilenn.m | 30,050 | utf_8 | 6339b625106e6c7b479e57c2b9aa578e | function vl_compilenn(varargin)
%VL_COMPILENN Compile the MatConvNet toolbox.
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following options:
%... |
github | maxkferg/casting-defect-detection-master | getVarReceptiveFields.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/matlab/+dagnn/@DagNN/getVarReceptiveFields.m | 3,633 | utf_8 | d0bd8171e7f72fe003abbc2f859b0678 | function rfs = getVarReceptiveFields(obj, var)
%GETVARRECEPTIVEFIELDS Get the receptive field of a variable
% RFS = GETVARRECEPTIVEFIELDS(OBJ, VAR) gets the receptivie fields RFS of
% all the variables of the DagNN OBJ into variable VAR. VAR is a variable
% name or index.
%
% RFS has one entry for each variable... |
github | maxkferg/casting-defect-detection-master | rebuild.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/matlab/+dagnn/@DagNN/rebuild.m | 3,243 | utf_8 | e368536d9e70c805d8424cdd6b593960 | function rebuild(obj)
%REBUILD Rebuild the internal data structures of a DagNN object
% REBUILD(obj) rebuilds the internal data structures
% of the DagNN obj. It is an helper function used internally
% to update the network when layers are added or removed.
varFanIn = zeros(1, numel(obj.vars)) ;
varFanOut = zero... |
github | maxkferg/casting-defect-detection-master | print.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/matlab/+dagnn/@DagNN/print.m | 15,032 | utf_8 | 7da4e68e624f559f815ee3076d9dd966 | function str = print(obj, inputSizes, varargin)
%PRINT Print information about the DagNN object
% PRINT(OBJ) displays a summary of the functions and parameters in the network.
% STR = PRINT(OBJ) returns the summary as a string instead of printing it.
%
% PRINT(OBJ, INPUTSIZES) where INPUTSIZES is a cell array of ... |
github | maxkferg/casting-defect-detection-master | fromSimpleNN.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/matlab/+dagnn/@DagNN/fromSimpleNN.m | 7,258 | utf_8 | 83f914aec610125592263d74249f54a7 | function obj = fromSimpleNN(net, varargin)
% FROMSIMPLENN Initialize a DagNN object from a SimpleNN network
% FROMSIMPLENN(NET) initializes the DagNN object from the
% specified CNN using the SimpleNN format.
%
% SimpleNN objects are linear chains of computational layers. These
% layers exchange information th... |
github | maxkferg/casting-defect-detection-master | vl_simplenn_display.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/matlab/simplenn/vl_simplenn_display.m | 12,455 | utf_8 | 65bb29cd7c27b68c75fdd27acbd63e2b | function [info, str] = vl_simplenn_display(net, varargin)
%VL_SIMPLENN_DISPLAY Display the structure of a SimpleNN network.
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO = VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the netw... |
github | maxkferg/casting-defect-detection-master | vl_test_economic_relu.m | .m | casting-defect-detection-master/sliding_window/wacv/matconvnet/matlab/xtest/vl_test_economic_relu.m | 790 | utf_8 | 35a3dbe98b9a2f080ee5f911630ab6f3 | % VL_TEST_ECONOMIC_RELU
function vl_test_economic_relu()
x = randn(11,12,8,'single');
w = randn(5,6,8,9,'single');
b = randn(1,9,'single') ;
net.layers{1} = struct('type', 'conv', ...
'filters', w, ...
'biases', b, ...
'stride', 1, ...
... |
github | sg-s/puppeteer-master | resetSliderBounds.m | .m | puppeteer-master/@puppeteer/resetSliderBounds.m | 1,039 | utf_8 | 82baa1ea06599b50bf5afcd9a8054c78 |
function resetSliderBounds(self,src,event)
if any(self.handles.lbcontrol == src)
% some lower bound being changed
this_param = find(self.handles.lbcontrol == src);
new_bound = event.Value;
if self.handles.sliders(this_param).Value < new_bound
self.handles.sliders(this_param).Value = new... |
github | sg-s/puppeteer-master | reset.m | .m | puppeteer-master/@puppeteer/reset.m | 1,261 | utf_8 | bff70e269444a80480f55ab563c75b2f | % callback for the reset button
function reset(self,~,~)
% first copy the original values from the cache to the Pstrings array
for i = 1:length(self.Pstrings)
self.Pstrings(i).Value = self.original_values(i).Value;
end
% now update all the sliders
for i = 1:length(self.handles.sliders)
if self.Pstrings(i).Toggl... |
github | sg-s/puppeteer-master | makeUI.m | .m | puppeteer-master/@puppeteer/makeUI.m | 4,237 | utf_8 | 7eccae14684f6674f4bc04a3b561f747 |
function handles = makeUI(self)
warning('off','MATLAB:hg:uicontrol:MinMustBeLessThanMax')
% need to compute the maximum # of controls in each group
group_names = categories([self.Pstrings.Group]);
n_controls = zeros(length(group_names),1);
for i = 1:length(group_names)
this = [self.Pstrings.Group] == group_name... |
github | stephenslab/mixsqp-paper-master | minConf_SPG.m | .m | mixsqp-paper-master/code/minConf_SPG.m | 12,576 | utf_8 | a320eb6e57068a94152968d150260851 | function [x, obj, funEvals, projects, timings] = ...
minConf_SPG(funObj, x, funProj, options)
% function [x,f] = minConF_SPG(funObj,x,funProj,options)
%
% Function for using Spectral Projected Gradient to solve problems of the form
% min funObj(x) s.t. x in C
%
% @funObj(x): function to minimize (returns gradient... |
github | stephenslab/mixsqp-paper-master | mixobj.m | .m | mixsqp-paper-master/code/mixobj.m | 291 | utf_8 | 7b74c7f79fcc58d369c351aca993583a | % Compute the objective, and gradient of this objective, optimized by
% mix-SQP.
function [f, g] = mixobj (L, x, e)
m = numel(x);
y = L*x + e;
if any(y <= 0)
f = Inf;
g = zeros(m,1);
else
n = size(L,1);
f = -sum(log(y));
d = 1./(y + e);
g = -(d'*L)';
end
|
github | kuhu12/BreastCancerDetection-master | Binary_Genetic_Algorithm_original.m | .m | BreastCancerDetection-master/Neural Networks/Binary_Genetic_Algorithm_original.m | 3,000 | utf_8 | 6dd871e2d5c9bb857a256490e67e7e66 |
function Feat_Index = Binary_Genetic_Algorithm_original(X1,Y1)
% Written by BABATUNDE Oluleye H, PhD Student
% Address: eAgriculture Research Group, School of Computer and Security
% Science, Edith Cowan University, Mt Lawley, 6050, WA, Australia
% Date: 2013
% Please cite any of the article below (if you us... |
github | burakbayramli/dersblog-master | rcs2.m | .m | dersblog-master/compscieng/compscieng_app20cfit2/rcspline/code/rcs2.m | 802 | utf_8 | 84d15ceabde2f057b078f701a961d605 |
function [bhat X]=rcs2(x,y,knots,plots)
n=length(y);
k=knots;
X1=x;
q=length(k);
myX=zeros(n,length(knots)-2);
for j=1:(q-2)
XX=(x-k(j)).^3.*(x>k(j))-(x-k(q-1)).^3.*(x>k(q-1)).*(k(q)-k(j))./(k(q)-k(q-1));
XX=XX+(x-k(q)).^3.*(x>k(q)).*(k(q-1)-k(j))./(k(q)-k(q-1));
myX(:,j)=XX;
end
X=[ones(n,... |
github | burakbayramli/dersblog-master | rcs.m | .m | dersblog-master/compscieng/compscieng_app20cfit2/rcspline/code/rcs.m | 3,123 | utf_8 | 9fadf94545203f04e3c43efa7a4c69fa |
function [bhat ff sse X]=rcs(x,y,knots,plots)
%INTERIOR FUNCTION FOR THE rcspline function:
%Fits a restricted cubic spline via least squares.
%The obtained spline is linear beyond the first and the last knot. The
%power basis representation is used. That is, the fitted spline is of the
%form: f(x)=b0+b1*x+b... |
github | burakbayramli/dersblog-master | rcs3.m | .m | dersblog-master/compscieng/compscieng_app20cfit2/rcspline/code/rcs3.m | 878 | utf_8 | 7b83b1f01951e48c1d9e2d0b281c3abd |
function [bhat X]=rcs3(x,y,knots)
n=length(y);
k=knots;
X1=x;
q=length(k);
myX=zeros(n,length(knots)-2);
for j=1:(q-2)
tmp1 = (x-k(j)).^3.*(x>k(j));
tmp2 = (x-k(q-1)).^3.*(x>k(q-1)).*(k(q)-k(j));
XX= tmp1-tmp2./(k(q)-k(q-1));
tmp1 = (x-k(q)).^3.*(x>k(q));
tmp2 = (k(q-1)-k(j));
XX=XX+tmp1... |
github | burakbayramli/dersblog-master | rcspline.m | .m | dersblog-master/compscieng/compscieng_app20cfit2/rcspline/code/rcspline.m | 7,637 | utf_8 | e6f8dd883bbf019f75d9df641bd87bb5 |
function [bhat f sse knots CI]=rcspline(x,y,knots,bootsams,atwhich,plots)
%Fits the so called restricted cubic spline via least squares (see Harrell
%(2001)). The obtained spline is linear beyond the first and the last
%knot. The truncated power basis representation is used. That is, the
%fitted spline is of... |
github | burakbayramli/dersblog-master | minsky_III_dx.m | .m | dersblog-master/chaos/chaos_app02/minsky_III_dx.m | 1,328 | utf_8 | f7e326cfb498912ac92b72172a5e9633 | % This code was written as a part of Reseacrh Methods MSc course
% Coded by: Piotr Z. Jelonek, e-mail: p.z.jelonek@warwick.ac.uk,
% 22nd February 2016
%
% Disclaimer:
% 1. This script is intended for a non-commercial use.
% 2. You can use, amend and edit it to fit to your purposes for your own use only,
% b... |
github | burakbayramli/dersblog-master | minsky_II_dx.m | .m | dersblog-master/chaos/chaos_app02/minsky_II_dx.m | 1,510 | utf_8 | f13e0e8b8f3c81bef08fcfd6be545a7e | % This code was written as a part of Reseacrh Methods MSc course
% Coded by: Piotr Z. Jelonek, e-mail: p.z.jelonek@warwick.ac.uk,
% 20th February 2016
%
% Disclaimer:
% 1. This script is intended for a non-commercial use.
% 2. You can use, amend and edit it to fit to your purposes for your own use only,
% b... |
github | burakbayramli/dersblog-master | Arenstorf.m | .m | dersblog-master/chaos/chaos_app01/Arenstorf.m | 323 | utf_8 | 5c28f737e58b0fe1a1b313e027acaaf7 | % Gander, {\em Scientific Computing An Introduction using Maple and MATLAB}
% pg 618
function yp=Arenstorf(t,y);
a=0.012277471; b=1-a;
D1=((y(1)+a)^2+y(2)^2)^(3/2);
D2=((y(1)-b)^2+y(2)^2)^(3/2);
yp(1,1)=y(3);
yp(2,1)=y(4);
yp(3,1)=y(1)+2*y(4)-b*(y(1)+a)/D1-a*(y(1)-b)/D2;
yp(4,1)=y(2)-2*y(3)-b*y(2)/D1-a*y(2)/D2;
yp=yp(:... |
github | burakbayramli/dersblog-master | subgrad_func.m | .m | dersblog-master/func_analysis/func_42_subgrad/octave/subgrad_func.m | 597 | utf_8 | 8d6a2d708538f2955ccfcc6728af2add | % https://raw.githubusercontent.com/fengcls/Lasso/master/lasso_main.m
% 0.5*||Ax - b||_2 + lambda*||x||_1
% subgradient method
function subgrad_func(A,b,lambda)
[~,n2] = size(A);
x = zeros(n2,1);
k=1;
g = ones(n2,1);
t = 0.01;
while k<3 || abs(f(k-1)-f(k-2))/f(k-1)>1e-5
% f(round(k/10)+1)=0.5*norm(A*x-... |
github | burakbayramli/dersblog-master | addblock_svd_update2.m | .m | dersblog-master/linear/linear_29/matlab/addblock_svd_update2.m | 754 | utf_8 | ade811810150881725a00f947bf13b82 | % kolon ekini satir ekine cevir
function [Up1,Sp,Vp1] = addblock_svd_update2( Uarg, Sarg, Varg, Aarg, force_orth )
U = Varg;
V = Uarg;
S = Sarg;
A = Aarg';
current_rank = size( U, 2 );
m = U' * A;
p = A - U*m;
P = orth( p );
P = [ P zeros(size(P,1), size(p,2)-size(P,2)) ];
Ra = P' * p;
z = zer... |
github | b-xiang/webrtc-master | readDetection.m | .m | webrtc-master/modules/audio_processing/transient/test/readDetection.m | 927 | utf_8 | f6af5020971d028a50a4d19a31b33bcb | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | b-xiang/webrtc-master | readPCM.m | .m | webrtc-master/modules/audio_processing/transient/test/readPCM.m | 821 | utf_8 | 76b2955e65258ada1c1e549a4fc9bf79 | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | b-xiang/webrtc-master | plotDetection.m | .m | webrtc-master/modules/audio_processing/transient/test/plotDetection.m | 923 | utf_8 | e8113bdaf5dcfe4f50200a3ca29c3846 | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | b-xiang/webrtc-master | apmtest.m | .m | webrtc-master/modules/audio_processing/test/apmtest.m | 9,874 | utf_8 | 17ad6af59f6daa758d983dd419e46ff0 | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | b-xiang/webrtc-master | parse_delay_file.m | .m | webrtc-master/modules/audio_coding/neteq/test/delay_tool/parse_delay_file.m | 6,405 | utf_8 | 4cc70d6f90e1ca5901104f77a7e7c0b3 | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | b-xiang/webrtc-master | plot_neteq_delay.m | .m | webrtc-master/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,967 | utf_8 | cce342fed6406ef0f12d567fe3ab6eef | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | b-xiang/webrtc-master | rtpAnalyze.m | .m | webrtc-master/tools_webrtc/matlab/rtpAnalyze.m | 7,892 | utf_8 | 46e63db0fa96270c14a0c205bbab42e4 | function rtpAnalyze( input_file )
%RTP_ANALYZE Analyze RTP stream(s) from a txt file
% The function takes the output from the command line tool rtp_analyze
% and analyzes the stream(s) therein. First, process your rtpdump file
% through rtp_analyze (from command line):
% $ out/Debug/rtp_analyze my_file.rtp my_f... |
github | markcannon/markcannon.github.io-master | sim_qpmin_d.m | .m | markcannon.github.io-master/assets/downloads/teaching/C21_Model_Predictive_Control/mcode/sim_qpmin_d.m | 4,036 | utf_8 | ac2c23994b018bcb45e4309d2060e82b | function [t,z,u,y,J,Jrun,info] = ...
sim_qpmin_d(x0,Bd,d,dbnd,N,s,p,w,c,opt_flag,options)
%sim_qpmin Simulate closed-loop response for QP-based control law.
% [t,z,u,y,J,info] = sim_qpmin(x0,s,p,w,c,options)
% Input arguments:
% x0 -- initial plant state
% s -- plant state space model
% p -- structure ... |
github | CankayaUniversity/ceng-407-408-2017-2018-project-blood-vessel-segmentation-master | segmentation.m | .m | ceng-407-408-2017-2018-project-blood-vessel-segmentation-master/Project/segmentation.m | 695 | utf_8 | 8468fbee34ea6573785311ec6c4a67c8 | % Segmentation function.
function [ves] = segmentation(path)
% Read image.
im=imread(path);
% Image enhancement & gray scale of a green channel image.
image = imageEnhancement(im);
% Load network.
load net;
[m,n] = size(image);
ves=uint8(zeros(size(image)));
% Classification.
for i = 1:1:m-(9-1)
for j =... |
github | UGM-Geofisika/Dispersion_Inversion-master | main.m | .m | Dispersion_Inversion-master/main.m | 35,750 | utf_8 | d9dada994aca8aadc9c712bc8898076f | function varargout = main(varargin)
% MAIN MATLAB code for main.fig
% MAIN, by itself, creates a new MAIN or raises the existing
% singleton*.
%
% H = MAIN returns the handle to a new MAIN or the handle to
% the existing singleton*.
%
% MAIN('CALLBACK',hObject,eventData,handles,...) calls the l... |
github | UGM-Geofisika/Dispersion_Inversion-master | cfg_sol.m | .m | Dispersion_Inversion-master/gui/cfg_sol.m | 9,758 | utf_8 | c33866390ebc4a79a24b23945faa1e47 | function varargout = cfg_sol(varargin)
% ROOT MATLAB code for root.fig
% ROOT, by itself, creates a new ROOT or raises the existing
% singleton*.
%
% H = ROOT returns the handle to a new ROOT or the handle to
% the existing singleton*.
%
% ROOT('CALLBACK',hObject,eventData,handles,...) calls th... |
github | UGM-Geofisika/Dispersion_Inversion-master | view_disp_table.m | .m | Dispersion_Inversion-master/gui/view_disp_table.m | 3,934 | utf_8 | d795f65e03e1977c37b975f14f367207 | function varargout = view_disp_table(varargin)
% VIEW_DISPERSION_TABLE MATLAB code for view_disp_table.fig
% VIEW_DISPERSION_TABLE, by itself, creates a new VIEW_DISPERSION_TABLE or raises the existing
% singleton*.
%
% H = VIEW_DISPERSION_TABLE returns the handle to a new VIEW_DISPERSION_TABLE or the ha... |
github | UGM-Geofisika/Dispersion_Inversion-master | manual.m | .m | Dispersion_Inversion-master/gui/manual.m | 2,812 | utf_8 | 547b164df037a6ccb4ebc0699cc92fd3 | function varargout = manual(varargin)
% MANUAL MATLAB code for manual.fig
% MANUAL, by itself, creates a new MANUAL or raises the existing
% singleton*.
%
% H = MANUAL returns the handle to a new MANUAL or the handle to
% the existing singleton*.
%
% MANUAL('CALLBACK',hObject,eventData,handles,... |
github | UGM-Geofisika/Dispersion_Inversion-master | cfg_inv.m | .m | Dispersion_Inversion-master/gui/cfg_inv.m | 12,746 | utf_8 | 5d47aac8bf783746377d2552555b7bec | function varargout = cfg_inv(varargin)
% ROOT MATLAB code for root.fig
% ROOT, by itself, creates a new ROOT or raises the existing
% singleton*.
%
% H = ROOT returns the handle to a new ROOT or the handle to
% the existing singleton*.
%
% ROOT('CALLBACK',hObject,eventData,handles,...) calls th... |
github | UGM-Geofisika/Dispersion_Inversion-master | mat_inverse.m | .m | Dispersion_Inversion-master/bin/mat_inverse.m | 2,755 | utf_8 | ecba5e9d7c128ae9a9c8c0df5527cdcd | function [niter, vr_iter, vp_iter, vs_iter, dns_iter] = mat_inverse(freq, vr_exp, ...
sigma, thk, vp, vs, dns, maxiter, mu, tol_vs, gui, object, msg)
% input:
% 1. dispersion curve
% freq, vr_exp, sigma
% 2. initial model
% thk, vp, vs, dns
% 3. parameters control the inversion
% maxiter, mu... |
github | foss-for-synopsys-dwc-arc-processors/synopsys-caffe-main | classification_demo.m | .m | synopsys-caffe-main/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | foss-for-synopsys-dwc-arc-processors/synopsys-caffe-main | MyVOCevalseg.m | .m | synopsys-caffe-main/matlab/my_script/MyVOCevalseg.m | 4,471 | utf_8 | f0b406d4e609f1cc3d3694948aeceb67 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEVALSEG(... |
github | foss-for-synopsys-dwc-arc-processors/synopsys-caffe-main | MyVOCevalsegBoundary.m | .m | synopsys-caffe-main/matlab/my_script/MyVOCevalsegBoundary.m | 4,279 | utf_8 | 704c57ab30eda1a0f001187608d3c786 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEVALSEG(... |
github | raalf/VAP3-master | fcnXML2STRUCT.m | .m | VAP3-master/fcnXML2STRUCT.m | 6,958 | utf_8 | f865267aab457943222a8412bb26b6a7 | function [ s ] = fcnXML2STRUCT( file )
%Convert xml file into a MATLAB structure
% [ s ] = xml2struct( file )
%
% A file containing:
% <XMLname attrib1="Some value">
% <Element>Some text</Element>
% <DifferentElement attrib2="2">Some more text</Element>
% <DifferentElement attrib3="2" attrib4="1">Even mor... |
github | raalf/VAP3-master | fcnPLOTCIRC.m | .m | VAP3-master/fcnPLOTCIRC.m | 2,440 | utf_8 | e988613a8ca8bc1d1f6be5ec87a03277 | function [] = fcnPLOTCIRC(valNELE, matDVE, matVLST, matCENTER, vecDVEROLL, vecDVEPITCH, vecDVEYAW, matCOEFF, ppa)
for i = 1:valNELE
corners = fcnGLOBSTAR(matVLST(matDVE(i,:),:) - matCENTER(i,:), repmat(vecDVEROLL(i),4,1), repmat(vecDVEPITCH(i),4,1), repmat(vecDVEYAW(i),4,1));
points = polygrid(corners(:,1)... |
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