plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadubjson.m | .m | Coursera_MachineLearning_Course-master/Week 5/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | saveubjson.m | .m | Coursera_MachineLearning_Course-master/Week 5/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submit.m | .m | Coursera_MachineLearning_Course-master/Week 3/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submitWithConfiguration.m | .m | Coursera_MachineLearning_Course-master/Week 3/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | savejson.m | .m | Coursera_MachineLearning_Course-master/Week 3/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadjson.m | .m | Coursera_MachineLearning_Course-master/Week 3/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadubjson.m | .m | Coursera_MachineLearning_Course-master/Week 3/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | saveubjson.m | .m | Coursera_MachineLearning_Course-master/Week 3/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submit.m | .m | Coursera_MachineLearning_Course-master/Week 4/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submitWithConfiguration.m | .m | Coursera_MachineLearning_Course-master/Week 4/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | savejson.m | .m | Coursera_MachineLearning_Course-master/Week 4/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadjson.m | .m | Coursera_MachineLearning_Course-master/Week 4/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadubjson.m | .m | Coursera_MachineLearning_Course-master/Week 4/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | saveubjson.m | .m | Coursera_MachineLearning_Course-master/Week 4/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submit.m | .m | Coursera_MachineLearning_Course-master/Week 9/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submitWithConfiguration.m | .m | Coursera_MachineLearning_Course-master/Week 9/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 5,569 | utf_8 | cc10d7a55178eb991c495a2b638947fd | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
partss = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = ... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | savejson.m | .m | Coursera_MachineLearning_Course-master/Week 9/machine-learning-ex8/ex8/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadjson.m | .m | Coursera_MachineLearning_Course-master/Week 9/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadubjson.m | .m | Coursera_MachineLearning_Course-master/Week 9/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | saveubjson.m | .m | Coursera_MachineLearning_Course-master/Week 9/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | Hannes333/Computer-Aided-Manufacturing-Programm-for-2.5D-Laser-Ablation-Version-2.0-master | F00_stlread.m | .m | Computer-Aided-Manufacturing-Programm-for-2.5D-Laser-Ablation-Version-2.0-master/F00_stlread.m | 5,235 | utf_8 | d6f780ddf573a34879cf3fe24bd1308f | function varargout = stlread(file)
% STLREAD imports geometry from an STL file into MATLAB.
% FV = STLREAD(FILENAME) imports triangular faces from the ASCII or binary
% STL file idicated by FILENAME, and returns the patch struct FV, with fields
% 'faces' and 'vertices'.
%
% [F,V] = STLREAD(FILENAME) r... |
github | Hannes333/Computer-Aided-Manufacturing-Programm-for-2.5D-Laser-Ablation-Version-2.0-master | F16_Intersections.m | .m | Computer-Aided-Manufacturing-Programm-for-2.5D-Laser-Ablation-Version-2.0-master/F16_Intersections.m | 11,787 | utf_8 | 52d726542cc35a2287fe572fc23a47bc | function [x0,y0,iout,jout] = F16_Intersections(x1,y1,x2,y2,robust)
%INTERSECTIONS Intersections of curves.
% Computes the (x,y) locations where two curves intersect. The curves
% can be broken with NaNs or have vertical segments.
%
% Example:
% [X0,Y0] = intersections(X1,Y1,X2,Y2,ROBUST);
%
% where X1 an... |
github | mohammadzainabbas/Digital-Communication-master | OFDM_AWGN.m | .m | Digital-Communication-master/Project/MIMO OFDM/OFDM_AWGN.m | 14,570 | utf_8 | 4f1deec5c4b46b12bef0fcf0a2e1bb18 | function OFDM_AWGN()
M = 2; % Modulation alphabet
k = log2(M); % Bits/symbol
numSC = 128; % Number of OFDM subcarriers
cpLen = 32; % OFDM cyclic prefix length
maxBitErrors = 100; % Maximum number of bit errors
maxNumBits = 1e7; % Maximum n... |
github | mohammadzainabbas/Digital-Communication-master | playing_with_OFDM.m | .m | Digital-Communication-master/Project/Animated OFDM/playing_with_OFDM.m | 40,142 | utf_8 | 71e051b3bd99fb915fe298f8ab87a662 | function varargout = playing_with_OFDM(varargin)
% PLAYING_WITH_OFDM MATLAB code for playing_with_OFDM.fig
% PLAYING_WITH_OFDM, by itself, creates a new PLAYING_WITH_OFDM or raises the existing
% singleton*.
%
% H = PLAYING_WITH_OFDM returns the handle to a new PLAYING_WITH_OFDM or the handle to
% t... |
github | mohammadzainabbas/Digital-Communication-master | main_polar.m | .m | Digital-Communication-master/Lab 05/main_polar.m | 1,649 | utf_8 | 08ee30612310411d7c77f65f840814b8 | function main_polar()
x = [0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 0 0];
Bit_rate = 5;
Samples_per_bit_time = 200;
Total_time = length(x)/Bit_rate;
Bit_time = 1/Bit_rate;
Tb = Bit_time;
t = Tb/Samples_per_bit_time:Tb/Samples_per_bit_time:Total_time;
pulse_NRZ = polar_NRZ(x, Samples_per_bit_time,... |
github | mohammadzainabbas/Digital-Communication-master | main_uni_polar.m | .m | Digital-Communication-master/Lab 05/main_uni_polar.m | 1,515 | utf_8 | cc85b26c7c868ea1c2711b547bbacea5 | function main_uni_polar()
x = [0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 0 0];
Bit_rate = 5;
Samples_per_bit_time = 200;
Total_time = length(x)/Bit_rate;
Bit_time = 1/Bit_rate;
Tb = Bit_time;
t = Tb/Samples_per_bit_time:Tb/Samples_per_bit_time:Total_time;
pulse_NRZ = uni_polar_NRZ(x, Samples_per_b... |
github | mohammadzainabbas/Digital-Communication-master | main.m | .m | Digital-Communication-master/Lab 05/main.m | 1,515 | utf_8 | cc85b26c7c868ea1c2711b547bbacea5 | function main_uni_polar()
x = [0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 0 0];
Bit_rate = 5;
Samples_per_bit_time = 200;
Total_time = length(x)/Bit_rate;
Bit_time = 1/Bit_rate;
Tb = Bit_time;
t = Tb/Samples_per_bit_time:Tb/Samples_per_bit_time:Total_time;
pulse_NRZ = uni_polar_NRZ(x, Samples_per_b... |
github | mohammadzainabbas/Digital-Communication-master | main.m | .m | Digital-Communication-master/Lab/Lab 6/main.m | 1,200 | utf_8 | 298580e6f6d8e6dc468b8e06d85f6c36 | function main()
Fs = 1000;
t = [0:1/Fs:1];
number_of_samples = length(t);
%freq = input('Enter your frequency: ');
freq = 5;
x = sin(2*pi*freq*t);
%x = randn(1,100);
Nyquist = 2*freq;
%At_nyquist = Fs/(Nyquist);
% Less_than_nyquist = Fs/(Nyquist/2);
More_than_nyquist = Fs/(10*Nyquist);
%x = sin(2*pi*max_freq*t);
fi... |
github | mohammadzainabbas/Digital-Communication-master | main_polar.m | .m | Digital-Communication-master/Lab/Lab 5/main_polar.m | 1,594 | utf_8 | 94cbbd8eadfbfc6992f5e41474602681 | function main_polar()
x = [0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 0 0];
Bit_rate = 5;
Samples_per_bit_time = 200;
Total_time = length(x)/Bit_rate;
Bit_time = 1/Bit_rate;
Tb = Bit_time;
t = Tb/Samples_per_bit_time:Tb/Samples_per_bit_time:Total_time;
pulse_NRZ = polar_NRZ(x, Samples_per_bit_time, Bit_rate)... |
github | mohammadzainabbas/Digital-Communication-master | main_uni_polar.m | .m | Digital-Communication-master/Lab/Lab 5/main_uni_polar.m | 1,463 | utf_8 | 68110bb38cd2bd6652f48edc0c8bbf56 | function main_uni_polar()
x = [0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 0 0];
Bit_rate = 5;
Samples_per_bit_time = 200;
Total_time = length(x)/Bit_rate;
Bit_time = 1/Bit_rate;
Tb = Bit_time;
t = Tb/Samples_per_bit_time:Tb/Samples_per_bit_time:Total_time;
pulse_NRZ = uni_polar_NRZ(x, Samples_per_bit_time, B... |
github | mohammadzainabbas/Digital-Communication-master | main.m | .m | Digital-Communication-master/Lab/Lab 5/main.m | 1,463 | utf_8 | 68110bb38cd2bd6652f48edc0c8bbf56 | function main_uni_polar()
x = [0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 0 0];
Bit_rate = 5;
Samples_per_bit_time = 200;
Total_time = length(x)/Bit_rate;
Bit_time = 1/Bit_rate;
Tb = Bit_time;
t = Tb/Samples_per_bit_time:Tb/Samples_per_bit_time:Total_time;
pulse_NRZ = uni_polar_NRZ(x, Samples_per_bit_time, B... |
github | mohammadzainabbas/Digital-Communication-master | Task_1.m | .m | Digital-Communication-master/Lab/Lab 3/Task_1.m | 851 | utf_8 | 1a46c0d098a870ec5b50b26e690d2bd7 | function Task_1()
%To generate random signal
x = random_signal();
size = length(x);
bins = input('Enter number of bins: ');
%Calculate pdf
pdf = PDF(x, bins);
%Rearranging x-axis of both so that mean appears at 0
x_axis = min(x):(max(x)-min(x))/(bins):max(x) - (max(x)-min(x))/(bins);
%x_axis = x_axis(1:size-1); ... |
github | mohammadzainabbas/Digital-Communication-master | main.m | .m | Digital-Communication-master/Lab/Lab 2/main.m | 1,420 | utf_8 | abbeb77204d625588c06f820594aa234 | function main()
%Task No. 01
%Generate 2 random signal
[x1,size1] = random_signal();
[x2,size2] = random_signal();
%Calculate pdfs of both
[pdf1, bins1] = PDF(x1);
[pdf2, bins2] = PDF(x2);
%Rearranging x-axis of both so that mean appears at 0
x1_axis = min(x1):(max(x1)-min(x1))/(bins1):max(x1);
x1_axis = x1_axis(1:s... |
github | mohammadzainabbas/Digital-Communication-master | main.m | .m | Digital-Communication-master/Lab/Lab 8/main.m | 2,810 | utf_8 | 667364a1769de5351be54047d2a1476d |
function main()
%Defining common variables
signal_length = 100000;
M = 4;
m = 2;
%Vi = [sqrt(1 + 1) sqrt(1 + 1) sqrt(1 + 1) sqrt(1 + 1)];
%Es = 1/M*sum(abs(Vi)*2)
Es = 2;
Eb = Es/m;
No = 1;
%SNR = Eb/No
SNR = [1:10];
%SNR_t = 10;
%Generate Binary Data
Binary_Data1 = round(rand(1,signal_length));
Real_BD = 2*(Binary_... |
github | mohammadzainabbas/Digital-Communication-master | LTE_channels2.m | .m | Digital-Communication-master/FBMC-master/00_FBMC/LTE_channels2.m | 1,092 | utf_8 | dfa01f96b571c56d572b9f530309cf89 |
% function [ci_imp_out] = LTE_channels (type,bandwidth)
function [delay_a pow_a] = LTE_channels2 (type,bandwidth)
%LTE channels
% % EPA = 0;
% % ETU = 1;
% % EVA = 0;
% %
bandw = bandwidth; % 5MHz
if type == 'EPA' % Low selectivity
ci_imp = zeros(1,127);
delay_a = [0 30 70 80 110 190 ... |
github | mohammadzainabbas/Digital-Communication-master | func_preamble_creation.m | .m | Digital-Communication-master/FBMC-master/00_FBMC/func_preamble_creation.m | 3,244 | utf_8 | fa32e8bcf9b434982533457708d3578c | %% func_preamble_creation: function description
function [preamble,length_preamble,est_col] = func_preamble_creation(M, preamble_sel, zero_pads, extra_zero, user_indices, eq_select, fractional)
%% func_Analysis_Filter_Bank
%
% Burak Dayi
%
% This function will return the preamble.
%
% Created: 25-02-2015
preamble = N... |
github | mohammadzainabbas/Digital-Communication-master | LTE_channels.m | .m | Digital-Communication-master/FBMC-master/00_FBMC/LTE_channels.m | 1,159 | utf_8 | 6cd1bd3a74ef171521fd74d48d312da8 |
function [ci_imp_out] = LTE_channels (type,bandwidth)
% function [delay_a pow_a] = LTE_channels (type,bandwidth)
%LTE channels
% % EPA = 0;
% % ETU = 1;
% % EVA = 0;
% %
bandw = bandwidth; % 5MHz
if type == 'EPA' % Low selectivity
% disp('epa')
ci_imp = zeros(1,127);
delay_a = [0 ... |
github | mohammadzainabbas/Digital-Communication-master | showplot.m | .m | Digital-Communication-master/FBMC-master/00_FBMC/showplot.m | 25,776 | utf_8 | 150d907ace499d22ace62fa483c60169 | function varargout = showplot(varargin)
% SHOWPLOT MATLAB code for showplot.fig
% SHOWPLOT, by itself, creates a new SHOWPLOT or raises the existing
% singleton*.
%
% H = SHOWPLOT returns the handle to a new SHOWPLOT or the handle to
% the existing singleton*.
%
% SHOWPLOT('CALLBACK',hObject,ev... |
github | mohammadzainabbas/Digital-Communication-master | LTE_channels.m | .m | Digital-Communication-master/FBMC-master/00_FBMC/tests/LTE_channels.m | 1,069 | utf_8 | 214e89d4dfc04fcfdf1e73088ca803f8 |
function [ci_imp_out] = LTE_channels (type,bandwidth)
% function [delay_a pow_a] = LTE_channels (type,bandwidth)
%LTE channels
% % EPA = 0;
% % ETU = 1;
% % EVA = 0;
% %
bandw = bandwidth; % 5MHz
if type == 'EPA' % Low selectivity
ci_imp = zeros(1,127);
delay_a = [0 30 70 80 110 190 4... |
github | mohammadzainabbas/Digital-Communication-master | simpleAM.m | .m | Digital-Communication-master/FBMC-master/00_FBMC/tests/simpleGUI/simpleAM.m | 15,132 | utf_8 | 9b0e04b34858cffdc6f36b449a8fe965 | function varargout = simpleAM(varargin)
% simpleAM MATLAB code for simpleAM.fig
% simpleAM, by itself, creates a new simpleAM or raises the existing
% singleton*.
%
% H = simpleAM returns the handle to a new simpleAM or the handle to
% the existing singleton*.
%
% simpleAM('CALLBACK',hObject,ev... |
github | mohammadzainabbas/Digital-Communication-master | LTE_channels2.m | .m | Digital-Communication-master/FBMC-master/01_OFDM/LTE_channels2.m | 1,092 | utf_8 | dfa01f96b571c56d572b9f530309cf89 |
% function [ci_imp_out] = LTE_channels (type,bandwidth)
function [delay_a pow_a] = LTE_channels2 (type,bandwidth)
%LTE channels
% % EPA = 0;
% % ETU = 1;
% % EVA = 0;
% %
bandw = bandwidth; % 5MHz
if type == 'EPA' % Low selectivity
ci_imp = zeros(1,127);
delay_a = [0 30 70 80 110 190 ... |
github | mohammadzainabbas/Digital-Communication-master | LTE_channels.m | .m | Digital-Communication-master/FBMC-master/01_OFDM/LTE_channels.m | 1,159 | utf_8 | 6cd1bd3a74ef171521fd74d48d312da8 |
function [ci_imp_out] = LTE_channels (type,bandwidth)
% function [delay_a pow_a] = LTE_channels (type,bandwidth)
%LTE channels
% % EPA = 0;
% % ETU = 1;
% % EVA = 0;
% %
bandw = bandwidth; % 5MHz
if type == 'EPA' % Low selectivity
% disp('epa')
ci_imp = zeros(1,127);
delay_a = [0 ... |
github | mohammadzainabbas/Digital-Communication-master | showplot.m | .m | Digital-Communication-master/FBMC-master/01_OFDM/showplot.m | 15,617 | utf_8 | cd7a44aa3364963c386c214a0f538843 | function varargout = showplot(varargin)
% SHOWPLOT MATLAB code for showplot.fig
% SHOWPLOT, by itself, creates a new SHOWPLOT or raises the existing
% singleton*.
%
% H = SHOWPLOT returns the handle to a new SHOWPLOT or the handle to
% the existing singleton*.
%
% SHOWPLOT('CALLBACK',hObject,ev... |
github | mohammadzainabbas/Digital-Communication-master | main.m | .m | Digital-Communication-master/Lab 02/main.m | 1,488 | utf_8 | 8489ed6eda6ec2344e9a48a58a36e4e1 | function main()
%Task No. 01
%Generate 2 random signal
[x1,size1] = random_signal();
[x2,size2] = random_signal();
%Calculate pdfs of both
[pdf1, bins1] = PDF(x1);
[pdf2, bins2] = PDF(x2);
%Rearranging x-axis of both so that mean appears at 0
x1_axis = min(x1):(max(x1)-min(x1))/(bins1):max(x1);
x1_axis ... |
github | mohammadzainabbas/Digital-Communication-master | FBMC.m | .m | Digital-Communication-master/FBMC/+Modulation/FBMC.m | 41,490 | utf_8 | 9ce6c2fc7c3c9554a06c7a9e494abba9 | classdef FBMC < handle
% =====================================================================
% This MATLAB class represents an implementation of FBMC. The
% modulation parameters are initialized by the class contructor.
% The modulation of data symbols x and the demodulation of the
% rec... |
github | mohammadzainabbas/Digital-Communication-master | BitErrorProbabilityDoublyFlatRayleigh.m | .m | Digital-Communication-master/FBMC/Theory/BitErrorProbabilityDoublyFlatRayleigh.m | 5,433 | utf_8 | dad0d52f13e8cc151062cd0b3fa6f8f4 | % Ronald Nissel, rnissel@nt.tuwien.ac.at
% (c) 2017 by Institute of Telecommunications, TU Wien
% www.tc.tuwien.ac.at
% This function calculates the bit error probability for an arbitrary
% signal constellation in a doubly flat rayleigh channel.
% It is based on "OFDM and FBMC-OQAM in doubly-selective channels:
% ... |
github | mohammadzainabbas/Digital-Communication-master | BitErrorProbabilityAWGN.m | .m | Digital-Communication-master/FBMC/Theory/BitErrorProbabilityAWGN.m | 3,640 | utf_8 | 7c47a1abaea050d1e6e70b73ddd94551 | % Ronald Nissel, rnissel@nt.tuwien.ac.at
% (c) 2017 by Institute of Telecommunications, TU Wien
% www.tc.tuwien.ac.at
% This function calculates the bit error probability for an arbitrary
% signal constellations in an AWGN channel
function BitErrorProbability = BitErrorProbabilityAWGN(...
SNR_dB, ... % T... |
github | mohammadzainabbas/Digital-Communication-master | TurboCoding.m | .m | Digital-Communication-master/FBMC/+Coding/TurboCoding.m | 5,605 | utf_8 | 0d8994721a04fe4be8e040fa698bc99d | classdef TurboCoding < handle
% =====================================================================
% This MATLAB class represents a turbo coder (LTE).
% It requires the MATLAB Communications System Toolbox!
% Usage:
% 1) CodingObject = Coding.TurboCoding(NrDataBits,NrCodedBits)
% 2) Codin... |
github | mohammadzainabbas/Digital-Communication-master | Task_1.m | .m | Digital-Communication-master/Lab 03/Task_1.m | 894 | utf_8 | b2c12a7fc937158391de0f871d09b843 | function Task_1()
%To generate random signal
x = random_signal();
size = length(x);
bins = input('Enter number of bins: ');
%Calculate pdf
pdf = PDF(x, bins);
%Rearranging x-axis of both so that mean appears at 0
x_axis = min(x):(max(x)-min(x))/(bins):max(x) - (max(x)-min(x))/(bins);
%x_axis = x_axis(1:siz... |
github | Simshang/cdc_data_prepare-master | intervaloverlapvalseconds.m | .m | cdc_data_prepare-master/THUMOS14/eval/TemporalActionLocalization/intervaloverlapvalseconds.m | 918 | utf_8 | 953715c547006494b896a8730ad7a9a9 | function ov=intervaloverlapvalseconds(i1,i2,normtype,gt,det)
%
if nargin<3 normtype=0; end
ov=zeros(size(i1,1),size(i2,1));
for i=1:size(i1,1)
for j=1:size(i2,1)
ov(i,j)=intervalsingleoverlapvalseconds(i1(i,:),i2(j,:),normtype);
if nargin==5
ov(i,j)=ov(i,j)*strcmp(gt(i).class,det(j).class);
... |
github | Simshang/cdc_data_prepare-master | TH14evalDet_Updated.m | .m | cdc_data_prepare-master/THUMOS14/eval/TemporalActionLocalization/TH14evalDet_Updated.m | 6,265 | utf_8 | 45f8f56f72274a8bf85d5373785e4762 | function [pr_all,ap_all,map]=TH14evalDet_Updated(detfilename,gtpath,subset,threshold)
% [pr_all,ap_all,map]=TH14evalDet_Updated(detfilename,gtpath,subset,[threshold])
%
% Input: detfilename: file path of the input file
% gtpath: the path of the groundtruth directory
% subset... |
github | dariodematties/Dirichlet-master | libsvm_test.m | .m | Dirichlet-master/libsvm_test.m | 2,992 | utf_8 | 2e8a59d245cc070ad3f48f3c51522537 | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | libsvm_train.m | .m | Dirichlet-master/libsvm_train.m | 7,306 | utf_8 | 726572a0c0bca0b6006f73a76ef809d3 | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | Stick_breaking_process.m | .m | Dirichlet-master/Stick_breaking_process.m | 1,873 | utf_8 | 25553562352c6bca5304da7a3ab5706f | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | Chinese_restaurant_process.m | .m | Dirichlet-master/Chinese_restaurant_process.m | 2,427 | utf_8 | 891e80fc00893ba5ed961a644685ca1c | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | Polya_urn_Dir_proc_function.m | .m | Dirichlet-master/Polya_urn_Dir_proc_function.m | 2,545 | utf_8 | a8c9415309f451b626869209d5506950 | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | Gamma_Dir_dist_function.m | .m | Dirichlet-master/Gamma_Dir_dist_function.m | 1,735 | utf_8 | 435fbd89996a9b39796206b667680fb3 | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | Stick_breaking_Dir_dist_function.m | .m | Dirichlet-master/Stick_breaking_Dir_dist_function.m | 2,183 | utf_8 | 6bda883c6e7e4e14d89f65c6f88eb79d | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | DrawLattice.m | .m | Dirichlet-master/DrawLattice.m | 1,575 | utf_8 | c759ef6c15f031920b17e6c28d9e0933 | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | ravelMultiIndex.m | .m | Dirichlet-master/ravelMultiIndex.m | 2,222 | utf_8 | bafd20eacc475ea6190e4073e143bb42 | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | Plot_Dir_proc_points.m | .m | Dirichlet-master/Plot_Dir_proc_points.m | 5,549 | utf_8 | 2b47bce1503ea4a50c4aeadcecf9566e | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | Polya_urn_Dir_dist_function.m | .m | Dirichlet-master/Polya_urn_Dir_dist_function.m | 2,518 | utf_8 | 313944ef7a5ec59c47af1a3f5ba66df3 | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | Plot_Dir_dist_points.m | .m | Dirichlet-master/Plot_Dir_dist_points.m | 4,473 | utf_8 | 1f5c93dc377fca4587a79f5970601d3c | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | dariodematties/Dirichlet-master | unravelIndex.m | .m | Dirichlet-master/unravelIndex.m | 1,840 | utf_8 | 3ef9fa450032dc8c940ea40df6acc6f9 | ##################################################################################################################
## Author: Dematties Dario Jesus ##
## Contact: dariodematties@hotmail.com.ar ##
## dariodematties@yahoo.com.ar ##
## dario.dematties@frm.utn.edu.ar ##
## Project: Engi... |
github | kpegion/SubX-master | writeNetCDFGlobalAtts.m | .m | SubX-master/Matlab/V2/lib/writeNetCDFGlobalAtts.m | 926 | utf_8 | 05fae2dd2d98259f31b338c741992208 | %================================================================================================
%================================================================================================
function []=writeNetCDFGlobalAtts(fname,title,longtitle,comments,institution,source,matlabSource)
NC_GLOBAL = netcdf.getCon... |
github | kpegion/SubX-master | setupNetCDF3D.m | .m | SubX-master/Matlab/V2/lib/setupNetCDF3D.m | 2,177 | utf_8 | 1909607bfc785f263fdd332db3645711 | %================================================================================================
% This function sets up everything for writing a netcdf file
% It assumes the following:
%
% Data to be written is dimensions (lat,lon,time)
%
% lons: standard name is 'lon'; longname is 'longitude'; units are 'degr... |
github | kpegion/SubX-master | nanfastsmooth.m | .m | SubX-master/Matlab/V2/lib/nanfastsmooth.m | 4,587 | utf_8 | f66f272406af77ed747d388ce53eaff7 | function SmoothY = nanfastsmooth(Y,w,type,tol)
% nanfastsmooth(Y,w,type,tol) smooths vector Y with moving
% average of width w ignoring NaNs in data..
%
% Y is input signal.
% w is the window width.
%
% The argument "type" determines the smooth type:
% If type=1, rectangular (sliding-average or boxcar)
% If typ... |
github | kpegion/SubX-master | writeNetCDFData3D.m | .m | SubX-master/Matlab/V2/lib/writeNetCDFData3D.m | 867 | utf_8 | b700e0acd4020b85aaf18a678c9c48cc | %================================================================================================
% This function write a 3D (lon,lat,tim) dataset to a netcdf file
%================================================================================================
function []=writeNetCDFData3D(fname,data,units,name,longn... |
github | kpegion/SubX-master | writeNetCDFGlobalAtts.m | .m | SubX-master/Matlab/V1/writeNetCDFGlobalAtts.m | 926 | utf_8 | 05fae2dd2d98259f31b338c741992208 | %================================================================================================
%================================================================================================
function []=writeNetCDFGlobalAtts(fname,title,longtitle,comments,institution,source,matlabSource)
NC_GLOBAL = netcdf.getCon... |
github | kpegion/SubX-master | setupNetCDF3D.m | .m | SubX-master/Matlab/V1/setupNetCDF3D.m | 2,185 | utf_8 | 759af610c6f245fec4dc0b39e63de670 | %================================================================================================
% This function sets up everything for writing a netcdf file
% It assumes the following:
%
% Data to be written is dimensions (lat,lon,time)
%
% lons: standard name is 'lon'; longname is 'longitude'; units are 'deg... |
github | kpegion/SubX-master | writeNetCDFData3D.m | .m | SubX-master/Matlab/V1/writeNetCDFData3D.m | 866 | utf_8 | 62f1e1f507f982a7fab6eef811d89c5a | %================================================================================================
% This function write a 3D (lon,lat,tim) dataset to a netcdf file
%================================================================================================
function []=writeNetCDFData3D(fname,data,units,name,longn... |
github | yugt/ComputerVision-master | digitOpSeparate.m | .m | ComputerVision-master/Project/digitOpSeparate.m | 1,415 | utf_8 | d3121f8a12145bdab78c3eeb27084c12 | function [ op_left,op_right,operators,answers ] = digitOpSeparate( eqns,add,minus,times,divide,answers )
operators=zeros(size(eqns,1),1);
op_left=zeros(size(eqns));
op_right=zeros(size(eqns));
for i=1:size(eqns,1)
right=0;
for j=1:size(eqns,2)
if eqns(i,j)==0
break
elseif any((add... |
github | yugt/ComputerVision-master | horizon.m | .m | ComputerVision-master/Project/horizon.m | 4,632 | utf_8 | 43889a5295a61f0b02825266b3cfb251 | function [angle] = horizon(image, varargin)
% HORIZON estimates the horizon rotation in the image.
% ANGLE=HORIZON(I) returns rotation of an estimated horizon
% in the image I. The returned value ANGLE is in the
% range <-45,45> degrees.
%
% ANGLE=HORIZON(I, PRECISION) aligns the image I with
% the pre... |
github | yugt/ComputerVision-master | ginput2.m | .m | ComputerVision-master/Lectures/0907_math_background/ginput2.m | 6,307 | utf_8 | 9a3aa4e541096e823c927053daa3bc42 | function [out1,out2,out3] = ginput2(arg1)
%GINPUT Graphical input from mouse.
% [X,Y] = GINPUT(N) gets N points from the current axes and returns
% the X- and Y-coordinates in length N vectors X and Y. The cursor
% can be positioned using a mouse (or by using the Arrow Keys on some
% systems). Data poi... |
github | yugt/ComputerVision-master | graphcut.m | .m | ComputerVision-master/Homework/hw5/graphcut.m | 4,766 | utf_8 | e049b47f50637ed996a9eeece3177528 | function [B] = graphcut(segmentimage,segments,keyindex)
% function [B] = graphcut(segmentimage,segments,keyindex
%
% EECS 442 Computer Vision;
% Jason Corso
%
% Function to take a superpixel set and a keyindex and convert to a
% foreground/background segmentation.
%
% keyindex is the index to the superpixel ... |
github | yugt/ComputerVision-master | bfs_augmentpath.m | .m | ComputerVision-master/Homework/hw5/bfs_augmentpath.m | 1,195 | utf_8 | 9c16f5bdd848adf28ab0a2ef4a9d6878 | %WHITE =0;
%GRAY=1;
%BLACK=2
function augmentpath=bfs_augmentpath(start,target,current_flow,capacity,n)
WHITE =0;
GRAY=1;
BLACK=2;
color(1:n)=WHITE;
head=1;
tail=1;
q=[];
augmentpath=[];
%ENQUEUE
q=[start q];
color(start)=GRAY;
pred(start) = -1;
pred=z... |
github | yugt/ComputerVision-master | stitchImages.m | .m | ComputerVision-master/Homework/hw2/problem3/stitchImages.m | 9,979 | utf_8 | 1a0fad27cea6fbdb8bebd0fdbd42cceb | function [Is, alpha] = stitchImages(It,varargin)
%
% Syntax: [Is, alpha] = stitchImages(It);
% [Is, alpha] = stitchImages(...,'dim',dim,...);
% [Is, alpha] = stitchImages(...,'b0',b0,...);
% [Is, alpha] = stitchImages(...,'view',view,...);
% [Is, alpha] = st... |
github | yugt/ComputerVision-master | nonmaxsuppts.m | .m | ComputerVision-master/Homework/hw2/problem3/nonmaxsuppts.m | 5,085 | utf_8 | b2a36d9b59c2f7914f7a5c33e132e7a2 | % NONMAXSUPPTS - Non-maximal suppression for features/corners
%
% Non maxima suppression and thresholding for points generated by a feature
% or corner detector.
%
% Usage: [r,c] = nonmaxsuppts(cim, radius, thresh, im)
% /
% ... |
github | yugt/ComputerVision-master | match.m | .m | ComputerVision-master/Homework/hw2/problem3/match.m | 1,432 | utf_8 | a03ee15e52b0715f903e75b7fde00f78 | function M = match(F1,F2,k)
% function M = match(F1,F2,k)
%
% EECS 442;
% Jason Corso
%
% Wrapper for function to matching extracted feature vectors from a pair
% of images
%
% F1 is the feature matrix (rows -dimensions and cols number of points)
% from image 1
% F2 feature matrix from image 2
% k is... |
github | yugt/ComputerVision-master | colorcircle.m | .m | ComputerVision-master/Homework/hw3/problem1/colorcircle.m | 661 | utf_8 | c257dd26bb66c52d14de02d1bd93a1f3 | % Color CIRCLE - Draws a circle.
%
% Usage: colorcircle(c, r, s, n)
%
% Arguments: c - A 2-vector [x y] specifying the centre.
% r - The radius.
% n - Optional number of sides in the polygonal approximation.
% (defualt is 16 sides)
% s - color of the line segmen... |
github | yugt/ComputerVision-master | colorcircle.m | .m | ComputerVision-master/Homework/hw3/problem2/colorcircle.m | 677 | utf_8 | fc8960bac7b8d86d43fcb447ebcf1370 | % Color CIRCLE - Draws a circle.
%
% Usage: colorcircle(c, r, s, n)
%
% Arguments: c - A 2-vector [x y] specifying the centre.
% r - The radius.
% n - Optional number of sides in the polygonal approximation.
% (defualt is 16 sides)
% s - color of the line segmen... |
github | yugt/ComputerVision-master | match.m | .m | ComputerVision-master/Homework/hw3/problem2/match.m | 1,432 | utf_8 | a03ee15e52b0715f903e75b7fde00f78 | function M = match(F1,F2,k)
% function M = match(F1,F2,k)
%
% EECS 442;
% Jason Corso
%
% Wrapper for function to matching extracted feature vectors from a pair
% of images
%
% F1 is the feature matrix (rows -dimensions and cols number of points)
% from image 1
% F2 feature matrix from image 2
% k is... |
github | yugt/ComputerVision-master | hog.m | .m | ComputerVision-master/Homework/hw3/problem2/hog.m | 6,966 | utf_8 | 094b0c972d07da4c8fa2b4fbe25c1170 | function v = hog(im,x,y,Wfull)
% function v = hog(im,x,y,Wfull)
%
% EECS Foundation of Computer Vision;
% Chenliang Xu and Jason Corso
%
% Compute the histogram of oriented gradidents on image (im)
% for a given location (x,y) and scale (Wfull)
%
% v is the output column vector of the hog.
%
% Use Lo... |
github | yugt/ComputerVision-master | potts.m | .m | ComputerVision-master/Homework/hw1/problem4/potts.m | 306 | utf_8 | 41094b0510a36731bafe61ad02847dde | % potts.m
% to be completed by students
function E = potts(I,beta)
if nargin==1
beta=1;
end
%%% FILL IN HERE
L=int32(I); % convert to signed long to avoid overflow
X=L(:,:,1)+L(:,:,2)*256+L(:,:,3)*65536;
[m,n]=size(X);
E=beta*(nnz(X(1:m-1,:)-X(2:m,:))+nnz(X(:,1:n-1)-X(:,2:n)));
%%% FILL IN HERE |
github | garrickbrazil/SDS-RCNN-master | roidb_generate.m | .m | SDS-RCNN-master/functions/utils/roidb_generate.m | 4,496 | utf_8 | 67b84b2e5a6f48060c1022d41f9d78b8 | function roidb = roidb_generate(imdb, flip, cache_dir, dataset, min_gt_height)
% roidb = roidb_generate(imdb, flip)
% Package the roi annotations into the imdb.
%
% Inspired by Ross Girshick's imdb and roidb code.
% AUTORIGHTS
% ---------------------------------------------------------
% Copyright (c) 2014, Ross ... |
github | garrickbrazil/SDS-RCNN-master | evaluate_result_dir.m | .m | SDS-RCNN-master/functions/utils/evaluate_result_dir.m | 22,081 | utf_8 | ca54816fbdcadc7110cf390fb95d574b | function [scores, thres, recall, dts, gts, res, occls, ols] = dbEval(aDirs, db, minh)
% Evaluate and plot all pedestrian detection results.
%
% Set parameters by altering this function directly.
%
% USAGE
% dbEval
%
% INPUTS
%
% OUTPUTS
%
% EXAMPLE
% dbEval
%
% See also bbGt, dbInfo
%
% Caltech Pedestrian Dataset ... |
github | garrickbrazil/SDS-RCNN-master | fast_rcnn_generate_sliding_windows.m | .m | SDS-RCNN-master/functions/utils/fast_rcnn_generate_sliding_windows.m | 1,729 | utf_8 | a788da565d8e7d1810407473c3135094 | function roidb = fast_rcnn_generate_sliding_windows(conf, imdb, roidb, roipool_in_size)
% [pred_boxes, scores] = fast_rcnn_conv_feat_detect(conf, im, conv_feat, boxes, max_rois_num_in_gpu, net_idx)
% --------------------------------------------------------
% Fast R-CNN
% Reimplementation based on Python Fast R-CNN (htt... |
github | garrickbrazil/SDS-RCNN-master | proposal_generate_anchors.m | .m | SDS-RCNN-master/functions/rpn/proposal_generate_anchors.m | 1,760 | utf_8 | a7edd291c6d30be7bd615061b1d5e8be | function anchors = proposal_generate_anchors(conf)
% anchors = proposal_generate_anchors(conf)
% --------------------------------------------------------
% RPN_BF
% Copyright (c) 2015, Liliang Zhang
% Licensed under The MIT License [see LICENSE for details]
% --------------------------------------------------------
... |
github | garrickbrazil/SDS-RCNN-master | proposal_compute_targets.m | .m | SDS-RCNN-master/functions/rpn/proposal_compute_targets.m | 4,252 | utf_8 | cd447531a971eac63c9f754dbbd27dd4 | function [bbox_targets, overlaps, targets ] = proposal_compute_targets(conf, gt_rois, gt_ignores, gt_labels, ex_rois, image_roidb, im_scale)
% output: bbox_targets
% positive: [class_label, regression_label]
% ingore: [0, zero(regression_label)]
% negative: [-1, zero(regression_label)]
g... |
github | garrickbrazil/SDS-RCNN-master | proposal_locate_anchors.m | .m | SDS-RCNN-master/functions/rpn/proposal_locate_anchors.m | 2,065 | utf_8 | 92ae934220e4d73044787702a7ee66b5 | function [anchors, im_scales] = proposal_locate_anchors(conf, im_size, target_scale, feature_map_size)
% [anchors, im_scales] = proposal_locate_anchors(conf, im_size, target_scale, feature_map_size)
% --------------------------------------------------------
% Faster R-CNN
% Copyright (c) 2015, Shaoqing Ren
% Licensed u... |
github | garrickbrazil/SDS-RCNN-master | proposal_prepare_image_roidb.m | .m | SDS-RCNN-master/functions/rpn/proposal_prepare_image_roidb.m | 3,295 | utf_8 | 3dc89509d3e21ef9b4675e9c54593241 | function [image_roidb, bbox_means, bbox_stds] = proposal_prepare_image_roidb_caltech(conf, imdbs, roidbs)
% --------------------------------------------------------
% RPN_BF
% Copyright (c) 2016, Liliang Zhang
% Licensed under The MIT License [see LICENSE for details]
% -------------------------------------------------... |
github | garrickbrazil/SDS-RCNN-master | proposal_im_detect.m | .m | SDS-RCNN-master/functions/rpn/proposal_im_detect.m | 4,354 | utf_8 | bddedd391689c4b62b1982733ab32434 | function [pred_boxes, scores, feat_scores_bg, feat_scores_fg] = proposal_im_detect(conf, caffe_net, im)
% [pred_boxes, scores, feat_scores_bg, feat_scores_fg] = proposal_im_detect(conf, caffe_net, im)
% --------------------------------------------------------
% RPN_BF
% Copyright (c) 2016, Liliang Zhang
% Licensed unde... |
github | garrickbrazil/SDS-RCNN-master | proposal_generate_minibatch.m | .m | SDS-RCNN-master/functions/rpn/proposal_generate_minibatch.m | 8,106 | utf_8 | 60b7b8f202ab7fb43a0f00bf441f87cf | function [input_blobs, random_scale_inds, im_rgb] = proposal_generate_minibatch(conf, image_roidb)
% [input_blobs, random_scale_inds, im_rgb] = proposal_generate_minibatch(conf, image_roidb)
% --------------------------------------------------------
% RPN_BF
% Copyright (c) 2016, Liliang Zhang
% Licensed under The MIT ... |
github | garrickbrazil/SDS-RCNN-master | classification_demo.m | .m | SDS-RCNN-master/external/caffe/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 | garrickbrazil/SDS-RCNN-master | vbb.m | .m | SDS-RCNN-master/external/caltech_toolbox/vbb.m | 26,999 | utf_8 | 49eea1941e375a3293a6f9aa9ee21726 | function varargout = vbb( action, varargin )
% Data structure for video bounding box (vbb) annotations.
%
% A video bounding box (vbb) annotation stores bounding boxes (bbs) for
% objects of interest. The primary difference from a static annotation is
% that each object can exist for multiple frames, ie, a vbb annotati... |
github | garrickbrazil/SDS-RCNN-master | vbbLabeler.m | .m | SDS-RCNN-master/external/caltech_toolbox/vbbLabeler.m | 38,968 | utf_8 | 03ea75bed8df14e50d44027476666f52 | function vbbLabeler( objTypes, vidNm, annNm )
% Video bound box (vbb) Labeler.
%
% Used to annotated a video (seq file) with (tracked) bounding boxes. An
% online demo describing usage is available. The code below is fairly
% complex and poorly documented. Please do not email me with question about
% how it works (unle... |
github | garrickbrazil/SDS-RCNN-master | dbEval.m | .m | SDS-RCNN-master/external/caltech_toolbox/dbEval.m | 20,334 | utf_8 | ed18d84be7b0e43ce99c1ccd2fb9bc08 | function dbEval
% Evaluate and plot all pedestrian detection results.
%
% Set parameters by altering this function directly.
%
% USAGE
% dbEval
%
% INPUTS
%
% OUTPUTS
%
% EXAMPLE
% dbEval
%
% See also bbGt, dbInfo
%
% Caltech Pedestrian Dataset Version 3.2.1
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
... |
github | garrickbrazil/SDS-RCNN-master | imagesAlign.m | .m | SDS-RCNN-master/external/pdollar_toolbox/videos/imagesAlign.m | 8,167 | utf_8 | d125eb5beb502d940be5bd145521f34b | function [H,Ip] = imagesAlign( I, Iref, varargin )
% Fast and robust estimation of homography relating two images.
%
% The algorithm for image alignment is a simple but effective variant of
% the inverse compositional algorithm. For a thorough overview, see:
% "Lucas-kanade 20 years on A unifying framework,"
% S. B... |
github | garrickbrazil/SDS-RCNN-master | opticalFlow.m | .m | SDS-RCNN-master/external/pdollar_toolbox/videos/opticalFlow.m | 7,386 | utf_8 | bf636ebdd9a6e87b4705c8e9f4ffda81 | function [Vx,Vy,reliab] = opticalFlow( I1, I2, varargin )
% Coarse-to-fine optical flow using Lucas&Kanade or Horn&Schunck.
%
% Implemented 'type' of optical flow estimation:
% LK: http://en.wikipedia.org/wiki/Lucas-Kanade_method
% HS: http://en.wikipedia.org/wiki/Horn-Schunck_method
% SD: Simple block-based sum of ... |
github | garrickbrazil/SDS-RCNN-master | seqWriterPlugin.m | .m | SDS-RCNN-master/external/pdollar_toolbox/videos/seqWriterPlugin.m | 8,280 | utf_8 | 597792f79fff08b8bb709313267c3860 | function varargout = seqWriterPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow writing of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (swp=seqWriterPlugin):
% h=swp('open',h,fName,info) % Open a seq file for writing ... |
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