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 | Sbte/RAILS-master | RAILSsolver.m | .m | RAILS-master/matlab/RAILSsolver.m | 20,341 | utf_8 | 31b3890dc232722f74a3de00509c8c0c | function [V,T,res,iter,resvec,timevec,restart_data] = RAILSsolver(A, M, B, varargin)
% Solver for A*V*T*V'*M+M*V*T*V'*A'+B*B'=0
% [V,T,res,iter,resvec] = RAILSsolver(A, M, B, maxit, tol, opts);
%
% Input should be clear. M can be left empty for standard Lyapunov.
%
% opts.projection_method:
% Projected equations that... |
github | Sbte/RAILS-master | test_random.m | .m | RAILS-master/matlab/test/test_random.m | 1,058 | utf_8 | 91077ecde07f2915b1b3356db39952c0 | function test_suite = test_random
try
test_functions = localfunctions();
test_suite = functiontests(test_functions);
catch
end
try
initTestSuite;
catch
end
end
function seed()
if ~exist('rng')
rand('state', 4634);
else
rng(4634)
end
end
func... |
github | Sbte/RAILS-master | test_Laplace.m | .m | RAILS-master/matlab/test/test_Laplace.m | 2,469 | utf_8 | 07e753f63eca25b1bf772d066ffe2f10 | function test_suite = test_Laplace
try
test_functions = localfunctions();
test_suite = functiontests(test_functions);
catch
end
try
initTestSuite;
catch
end
end
function A = laplacian2(n)
m = sqrt(n);
I = speye(m);
e = ones(m,1);
T = spdiags([e -4*e e], ... |
github | Sbte/RAILS-master | test_MOC.m | .m | RAILS-master/matlab/test/test_MOC.m | 3,671 | utf_8 | 0321d28c9adbeba96274c4647a162ff9 | function test_suite = test_MOC
try
test_functions = localfunctions();
test_suite = functiontests(test_functions);
catch
end
try
initTestSuite;
catch
end
end
function test_MOC_Erik(t)
[A,M,B] = get_MOC_data();
n = size(A, 1);
t.assertEqual(n, 8*8*4*6);
... |
github | Sbte/RAILS-master | test_opts.m | .m | RAILS-master/matlab/test/test_opts.m | 5,219 | utf_8 | 978879b3a34be12cc2cbaadd5018327f | function test_suite = test_opts
try
test_functions = localfunctions();
test_suite = functiontests(test_functions);
catch
end
try
initTestSuite;
catch
end
end
function A = laplacian2(n)
m = sqrt(n);
I = speye(m);
e = ones(m,1);
T = spdiags([e -4*e e], -1:... |
github | nlbucki/E231A_QuadcopterMPC-master | even_sample.m | .m | E231A_QuadcopterMPC-master/gen/even_sample.m | 2,009 | utf_8 | 1d30ba0d23065fd6540ed2803c83ed56 | %***********************************************************************
%
% CONVERTS A RANDOMLY SAMPLED SIGNAL SET INTO AN EVENLY SAMPLED
% SIGNAL SET (by interpolation)
%
% By : Haldun KOMSUOGLU
% Start : 07/23/1999
% Last : 07/23/1999
% Statue : Neural Model Research Material
% ... |
github | nlbucki/E231A_QuadcopterMPC-master | generate_ref_trajectory.m | .m | E231A_QuadcopterMPC-master/gen/generate_ref_trajectory.m | 1,320 | utf_8 | 9b963154ee1991952c8362024c02885b | function[xref,uref] = generate_ref_trajectory(t, sys)
%%
% function to generate reference trajectory
%%
% time = 0:params.Ts:(params.Tf+params.N*params.Ts);
traj = @(t) sin_traj(t);
% traj = @(t) circ_traj(t);
% xref = [];
% uref = [];
% for it = time
[ref] = sys.flat2state(traj(t));
xref = [ref.y; ref.z; r... |
github | nlbucki/E231A_QuadcopterMPC-master | struct_overlay.m | .m | E231A_QuadcopterMPC-master/gen/struct_overlay.m | 2,259 | utf_8 | 658e5adeaf0be01c43bb8ef6922d5f7f | %> @brief struct_overlay Overlay default fields with input fields
%> @author Eric Cousineau <eacousineau@gmail.com>
%> @note From optOverlay() on Mathworks, modified
function [opts] = struct_overlay(opts_default, opts_in, options)
% Simple cases
if iscell(opts_default)
opts_default = struct(opts_defaul... |
github | nlbucki/E231A_QuadcopterMPC-master | animateQuadrotorload.m | .m | E231A_QuadcopterMPC-master/@Quadrotorload/animateQuadrotorload.m | 4,679 | utf_8 | 71d2d272f431653bc24b013a34dc03b5 | function animateQuadrotorload(obj,opts_in)
% function to animate the quadrotor
% default options
opts_default.RATE = 25 * 2;
opts_default.t = [];
opts_default.x = [];
opts_default.xd = [0;0];
opts_default.td = [];
opts_default.interp_type = 'spline';
opts_default.vid.MAKE_MOVIE = 0;
opts_default.vid.filename = 'resul... |
github | nlbucki/E231A_QuadcopterMPC-master | bSpline2.m | .m | E231A_QuadcopterMPC-master/mpc_ayush/bSpline2.m | 2,059 | utf_8 | b45ad2816bc9e52ce44275b49f0c1de6 | function x = bSpline2(tGrid,xGrid,fGrid,t)
% x = bSpline2(tGrid,xGrid,fGrid,t)
%
% This function does piece-wise quadratic interpolation of a set of data.
% The quadratic interpolant is constructed such that the slope matches on
% both sides of each interval, and the function value matches on the lower
% side of the in... |
github | nlbucki/E231A_QuadcopterMPC-master | pwPoly2.m | .m | E231A_QuadcopterMPC-master/traj_opt_ayush/pwPoly2.m | 2,091 | utf_8 | 711acbf5b78fa5ee04eaca84e6de2f0c | function x = pwPoly2(tGrid,xGrid,t)
% x = pwPoly2(tGrid,xGrid,t)
%
% This function does piece-wise quadratic interpolation of a set of data,
% given the function value at the edges and midpoint of the interval of
% interest.
%
% INPUTS:
% tGrid = [1, 2*n-1] = time grid, knot idx = 1:2:end
% xGrid = [m, 2*n-1] = fun... |
github | nlbucki/E231A_QuadcopterMPC-master | pwPoly3.m | .m | E231A_QuadcopterMPC-master/traj_opt_ayush/pwPoly3.m | 2,445 | utf_8 | f6e1d4ad154e13881760efe5bc8ffe9b | function x = pwPoly3(tGrid,xGrid,fGrid,t)
% x = pwPoly3(tGrid,xGrid,fGrid,t)
%
% This function does piece-wise quadratic interpolation of a set of data,
% given the function value at the edges and midpoint of the interval of
% interest.
%
% INPUTS:
% tGrid = [1, 2*n-1] = time grid, knot idx = 1:2:end
% xGrid = [m, ... |
github | nlbucki/E231A_QuadcopterMPC-master | traj_gen_QR_pointmass.m | .m | E231A_QuadcopterMPC-master/traj_gen/traj_gen_QR_pointmass.m | 2,983 | utf_8 | 98d5b8d09bae71769f0a9205fae541d3 | function traj = traj_gen_QR_pointmass(obj)
% FUNCTION INPUTS:
x0 = [-10.5;-10.5;0;0;0;0];
xF = [0;0;0;0;0;0];
xU = inf(6,1);
xL = -xU;
uU = 15*ones(2,1); % at least 10 to be able to compensate gravity
uL = zeros(2,1);
N = 80;
% Generate obstacle
% LATER: FUNCTION INPUT
O = {Polyhedron('V',[-1 1; -20 1; -1 -2; -20 -2]... |
github | nlbucki/E231A_QuadcopterMPC-master | traj_gen_QR_polyhedron.m | .m | E231A_QuadcopterMPC-master/traj_gen/traj_gen_QR_polyhedron.m | 3,925 | utf_8 | 6bc3e92343784f6bb380b88cb9079e27 | function traj = traj_gen_QR_polyhedron(obj)
% FUNCTION INPUTS:
x0 = [-10.5;-10.5;0;0;0;0];
xF = [0;0;0;0;0;0];
xU = inf(6,1);
xL = -xU;
uU = 15*ones(2,1); % at least 10 to be able to compensate gravity
uL = zeros(2,1);
N = 80;
QR_width = 2*0.02;
QR_height = 0.015;
% Generate obstacle
% LATER: FUNCTION INPUT
O ={Poly... |
github | nlbucki/E231A_QuadcopterMPC-master | animateQuadrotor.m | .m | E231A_QuadcopterMPC-master/@Quadrotor/animateQuadrotor.m | 4,375 | utf_8 | 419f861ed023b2edf8e60017902ac05d | function animateQuadrotor(obj,opts_in)
% function to animate the quadrotor
% default options
opts_default.RATE = 25 * 2;
opts_default.t = [];
opts_default.x = [];
opts_default.xd = [0;0];
opts_default.td = [];
opts_default.interp_type = 'spline';
opts_default.vid.MAKE_MOVIE = 0;
opts_default.vid.filename = 'results/v... |
github | alexisgoar/MA_RADAR-master | animate.m | .m | MA_RADAR-master/MDoppler1.1.3/Functions/target/animate.m | 617 | utf_8 | f74360fa02b1140e4652aa675a785d48 | % function to plot the target per time
function animate(obj,tstep,tstart,tend,rx,tx)
if nargin == 4
obj.move(tstart);
time = tstart;
while time < tend
plot(obj);
pause(0.05);
time = time + tstep;
obj.move(tstep);
end
% Animate target with antennas
elseif nargin == 6
... |
github | alexisgoar/MA_RADAR-master | plot_setup.m | .m | MA_RADAR-master/MDoppler1.1.3/Functions/signal/plot_setup.m | 376 | utf_8 | 02bb14f2bcd9385a6f19ee7a9865f454 | % Plots the whole setup given a signal class
% the obj variable must of type signal (class)
function plot_setup(obj,ax)
if nargin == 1
plot(obj.tx);
plot(obj.rx);
size(obj.target,2);
for i = 1:size(obj.target,2)
plot(obj.target(i));
end
else
plot(obj.tx,ax);
plot(obj.rx,ax);
size(obj.target,2);
for i = 1:si... |
github | alexisgoar/MA_RADAR-master | animate.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/target/animate.m | 617 | utf_8 | f74360fa02b1140e4652aa675a785d48 | % function to plot the target per time
function animate(obj,tstep,tstart,tend,rx,tx)
if nargin == 4
obj.move(tstart);
time = tstart;
while time < tend
plot(obj);
pause(0.05);
time = time + tstep;
obj.move(tstep);
end
% Animate target with antennas
elseif nargin == 6
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_ranges2.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_estimated_ranges2.m | 637 | utf_8 | cdbb94a28b4b47ca4a4b659282732880 | %plots the estimated range for all possible paths
% for a single target
function plot_estimated_ranges2(obj,NPulses)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
% subplot(obj.tx.numberofElements*obj.rx.numberofElements,1,1);
txn = obj.tx.numberofElements;
rxn = obj.rx.numberofElements;
for i = 1:txn
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_rangerate4.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_estimated_rangerate4.m | 1,530 | utf_8 | c613c13cd1546484cb4c76f4a1386129 | %Plots the received signal in time domain for a single path
function s = plot_estimated_rangerate4(obj)
NPulses = 100;
NObj = size(obj.target,2);
NSamples = 54*2;
sampleRate = NSamples/obj.tx.tchirp;
sampleFreq = 1/sampleRate;
Nout = 2;
signal_freq = zeros(NPulses,NSamples);
signal_rr = zeros(NPulses,1);
fig... |
github | alexisgoar/MA_RADAR-master | plot_setup.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_setup.m | 229 | utf_8 | 63b8be9863ae28ac20b890400848e6b9 | % Plots the whole setup given a signal class
% the obj variable must of type signal (class)
function plot_setup(obj)
plot(obj.tx);
plot(obj.rx);
size(obj.target,2);
for i = 1:size(obj.target,2)
plot(obj.target(i));
end
end |
github | alexisgoar/MA_RADAR-master | plot_rxSignal.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_rxSignal.m | 452 | utf_8 | dc6475688db4a256bfd803e0f1e3fa84 | %Plots the frequency of the received signal after considering all delays
%for one transmitter, one receiver and one target
function s = plot_rxSignal(obj,txi,rxi,NPulses)
i = 1;
while obj.tx.chirpi < NPulses
for j = 1:size(obj.target,2)
freq(i,j) = rxSignal(obj,obj.tx.time,txi,rxi,j);
end
timeStamp... |
github | alexisgoar/MA_RADAR-master | plot_rxSignals.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_rxSignals.m | 819 | utf_8 | d05ac00f37d2221db46426069088e7ef | %Plots the frequency of the received signal after considering all delays
%for one transmitter, one receiver and one target
function s = plot_rxSignals(obj,NPulses)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
txn = obj.tx.numberofElements;
rxn = obj.rx.numberofElements;
for i = 1:txn
for j = 1:rxn
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_range.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_estimated_range.m | 567 | utf_8 | 0a86ee0e977e8a645237ec1a33094865 | % plots the estimated range based on the received signal after
% applying an FFT for transimeter txi and receiver rxi
% for a single target
function h = plot_estimated_range(obj,txi,rxi,targeti)
numberofChirps = 1;
samplesperChirp = obj.tx.tchirp/obj.tx.samplingRate;
time = 0:obj.tx.tchirp/samplesperChirp:obj.tx.tchirp... |
github | alexisgoar/MA_RADAR-master | plot_estimated_range3.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_estimated_range3.m | 528 | utf_8 | 902b6ff1ee462a7b5c2eaa786ad93ad2 | %Plots the received signal in time domain for a single path
function s = plot_estimated_range3(obj,txi,rxi,NPulses)
i = 1;
while obj.tx.chirpi < NPulses
for j = 1:size(obj.target,2)
s(i,j) =(rxSignal3(obj,obj.tx.time,txi,rxi,j));
end
timeStamp(i) = obj.tx.time;
obj.tx.nextStep_sampling();
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_ranges.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_estimated_ranges.m | 606 | utf_8 | 417473a6bd71ed69b06b59f4c81aed8b | %plots the estimated range for all possible paths
% for a single target
function plot_estimated_ranges(obj,targeti)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
% subplot(obj.tx.numberofElements*obj.rx.numberofElements,1,1);
txn = obj.tx.numberofElements;
rxn = obj.rx.numberofElements;
for i = 1:txn
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_rangerate3.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_estimated_rangerate3.m | 1,990 | utf_8 | 7623b9ef50bdfc4f65a62a76448d71e7 | %Plots the received signal in time domain for a single path
function s = plot_estimated_rangerate3(obj)
i = 1;
txn = obj.tx.numberofElements;
rxn = obj.rx.numberofElements;
range_rate = [0:0.2:5];
txi = 1;
rxi = 1;
NPulses = 100;
% Get the time domain signal for all targets
while obj.tx.chirpi < NPulses
for ... |
github | alexisgoar/MA_RADAR-master | plot_estimated_range4.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_estimated_range4.m | 856 | utf_8 | a5ccee64f14a4313e9fb9f2473713737 | %Used optimized received signal
function s = plot_estimated_range4(obj)
[signal,timeStamp] = obj.rxSignal4();
N=size(timeStamp,2);
freq =0:1/(obj.tx.samplingRate*N):(N-1)/(N*obj.tx.samplingRate);
R = obj.tx.c*freq/(obj.tx.k*2);
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
% subplot(obj.tx.numberofEl... |
github | alexisgoar/MA_RADAR-master | plot_estimated_range2.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/signal/plot_estimated_range2.m | 528 | utf_8 | ad8d65f05db9e320d854467e40bc28a4 | %Plots the received signal in time domain for a single path
function s = plot_estimated_range2(obj,txi,rxi,NPulses)
i = 1;
while obj.tx.chirpi < NPulses
for j = 1:size(obj.target,2)
s(i,j) =(rxSignal2(obj,obj.tx.time,txi,rxi,j));
end
timeStamp(i) = obj.tx.time;
obj.tx.nextStep_sampling();
... |
github | alexisgoar/MA_RADAR-master | plot_txSignals.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/txarray/plot_txSignals.m | 539 | utf_8 | ab7ea48f68445ea24241a87408e1cdf1 | % Function used by class txarray to plot a sent signal
% Plots all transmitters
%obj must me a txarray class
function h = plot_txSignals(obj,NPulses)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
txn = obj.numberofElements;
for i = 1:txn
h = subplot(txn,1,i);
plot_txSignal(obj,i,NPulses);
Txn... |
github | alexisgoar/MA_RADAR-master | plot_txSignal.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.1/Functions/txarray/plot_txSignal.m | 324 | utf_8 | 3a69862f61a7f0651a3fac09348bf0ba | % Function used by class txarray to plot a sent signal
% For one receiver only
%obj must me a txarray class
function h = plot_txSignal(obj,txi,NPulses)
i = 1;
while obj.chirpi < NPulses
freq(i) = txSignal(obj,txi,obj.time);
timeStamp(i) = obj.time;
obj.nextStep();
i = i+1;
end
h = plot(timeStamp,freq);... |
github | alexisgoar/MA_RADAR-master | animate.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.3/Functions/target/animate.m | 617 | utf_8 | f74360fa02b1140e4652aa675a785d48 | % function to plot the target per time
function animate(obj,tstep,tstart,tend,rx,tx)
if nargin == 4
obj.move(tstart);
time = tstart;
while time < tend
plot(obj);
pause(0.05);
time = time + tstep;
obj.move(tstep);
end
% Animate target with antennas
elseif nargin == 6
... |
github | alexisgoar/MA_RADAR-master | plot_setup.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.3/Functions/signal/plot_setup.m | 376 | utf_8 | 02bb14f2bcd9385a6f19ee7a9865f454 | % Plots the whole setup given a signal class
% the obj variable must of type signal (class)
function plot_setup(obj,ax)
if nargin == 1
plot(obj.tx);
plot(obj.rx);
size(obj.target,2);
for i = 1:size(obj.target,2)
plot(obj.target(i));
end
else
plot(obj.tx,ax);
plot(obj.rx,ax);
size(obj.target,2);
for i = 1:si... |
github | alexisgoar/MA_RADAR-master | animate.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.2/Functions/target/animate.m | 617 | utf_8 | f74360fa02b1140e4652aa675a785d48 | % function to plot the target per time
function animate(obj,tstep,tstart,tend,rx,tx)
if nargin == 4
obj.move(tstart);
time = tstart;
while time < tend
plot(obj);
pause(0.05);
time = time + tstep;
obj.move(tstep);
end
% Animate target with antennas
elseif nargin == 6
... |
github | alexisgoar/MA_RADAR-master | plot_setup.m | .m | MA_RADAR-master/MA_RADAR/MDoppler1.1.2/Functions/signal/plot_setup.m | 376 | utf_8 | 02bb14f2bcd9385a6f19ee7a9865f454 | % Plots the whole setup given a signal class
% the obj variable must of type signal (class)
function plot_setup(obj,ax)
if nargin == 1
plot(obj.tx);
plot(obj.rx);
size(obj.target,2);
for i = 1:size(obj.target,2)
plot(obj.target(i));
end
else
plot(obj.tx,ax);
plot(obj.rx,ax);
size(obj.target,2);
for i = 1:si... |
github | alexisgoar/MA_RADAR-master | animate.m | .m | MA_RADAR-master/MDoppler/Functions/target/animate.m | 617 | utf_8 | f74360fa02b1140e4652aa675a785d48 | % function to plot the target per time
function animate(obj,tstep,tstart,tend,rx,tx)
if nargin == 4
obj.move(tstart);
time = tstart;
while time < tend
plot(obj);
pause(0.05);
time = time + tstep;
obj.move(tstep);
end
% Animate target with antennas
elseif nargin == 6
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_ranges2.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_estimated_ranges2.m | 637 | utf_8 | cdbb94a28b4b47ca4a4b659282732880 | %plots the estimated range for all possible paths
% for a single target
function plot_estimated_ranges2(obj,NPulses)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
% subplot(obj.tx.numberofElements*obj.rx.numberofElements,1,1);
txn = obj.tx.numberofElements;
rxn = obj.rx.numberofElements;
for i = 1:txn
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_rangerate4.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_estimated_rangerate4.m | 1,599 | utf_8 | 9057be9c1bb2fa5abf70d1ce5ccb0226 | %Plots the received signal in time domain for a single path
function s = plot_estimated_rangerate4(obj)
NPulses = 100;
NObj = size(obj.target,2);
NSamples = 54;
sampleRate = NSamples/obj.tx.tchirp;
sampleFreq = 1/sampleRate;
Nout = 2;
signal_freq = zeros(NPulses,NSamples);
signal_rr = zeros(NPulses,1);
for c... |
github | alexisgoar/MA_RADAR-master | plot_setup.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_setup.m | 288 | utf_8 | 3fa316db0ca5a0f2da80b975d223b62f | % Plots the whole setup given a signal class
% the obj variable must of type signal (class)
function plot_setup(obj)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
plot(obj.tx);
plot(obj.rx);
size(obj.target,2);
for i = 1:size(obj.target,2)
plot(obj.target(i));
end
end |
github | alexisgoar/MA_RADAR-master | plot_rxSignal.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_rxSignal.m | 452 | utf_8 | dc6475688db4a256bfd803e0f1e3fa84 | %Plots the frequency of the received signal after considering all delays
%for one transmitter, one receiver and one target
function s = plot_rxSignal(obj,txi,rxi,NPulses)
i = 1;
while obj.tx.chirpi < NPulses
for j = 1:size(obj.target,2)
freq(i,j) = rxSignal(obj,obj.tx.time,txi,rxi,j);
end
timeStamp... |
github | alexisgoar/MA_RADAR-master | plot_rxSignals.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_rxSignals.m | 819 | utf_8 | d05ac00f37d2221db46426069088e7ef | %Plots the frequency of the received signal after considering all delays
%for one transmitter, one receiver and one target
function s = plot_rxSignals(obj,NPulses)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
txn = obj.tx.numberofElements;
rxn = obj.rx.numberofElements;
for i = 1:txn
for j = 1:rxn
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_range.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_estimated_range.m | 567 | utf_8 | 0a86ee0e977e8a645237ec1a33094865 | % plots the estimated range based on the received signal after
% applying an FFT for transimeter txi and receiver rxi
% for a single target
function h = plot_estimated_range(obj,txi,rxi,targeti)
numberofChirps = 1;
samplesperChirp = obj.tx.tchirp/obj.tx.samplingRate;
time = 0:obj.tx.tchirp/samplesperChirp:obj.tx.tchirp... |
github | alexisgoar/MA_RADAR-master | plot_estimated_range3.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_estimated_range3.m | 528 | utf_8 | 902b6ff1ee462a7b5c2eaa786ad93ad2 | %Plots the received signal in time domain for a single path
function s = plot_estimated_range3(obj,txi,rxi,NPulses)
i = 1;
while obj.tx.chirpi < NPulses
for j = 1:size(obj.target,2)
s(i,j) =(rxSignal3(obj,obj.tx.time,txi,rxi,j));
end
timeStamp(i) = obj.tx.time;
obj.tx.nextStep_sampling();
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_ranges.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_estimated_ranges.m | 606 | utf_8 | 417473a6bd71ed69b06b59f4c81aed8b | %plots the estimated range for all possible paths
% for a single target
function plot_estimated_ranges(obj,targeti)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
% subplot(obj.tx.numberofElements*obj.rx.numberofElements,1,1);
txn = obj.tx.numberofElements;
rxn = obj.rx.numberofElements;
for i = 1:txn
... |
github | alexisgoar/MA_RADAR-master | plot_estimated_rangerate3.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_estimated_rangerate3.m | 1,990 | utf_8 | 7623b9ef50bdfc4f65a62a76448d71e7 | %Plots the received signal in time domain for a single path
function s = plot_estimated_rangerate3(obj)
i = 1;
txn = obj.tx.numberofElements;
rxn = obj.rx.numberofElements;
range_rate = [0:0.2:5];
txi = 1;
rxi = 1;
NPulses = 100;
% Get the time domain signal for all targets
while obj.tx.chirpi < NPulses
for ... |
github | alexisgoar/MA_RADAR-master | plot_estimated_range4.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_estimated_range4.m | 856 | utf_8 | a5ccee64f14a4313e9fb9f2473713737 | %Used optimized received signal
function s = plot_estimated_range4(obj)
[signal,timeStamp] = obj.rxSignal4();
N=size(timeStamp,2);
freq =0:1/(obj.tx.samplingRate*N):(N-1)/(N*obj.tx.samplingRate);
R = obj.tx.c*freq/(obj.tx.k*2);
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
% subplot(obj.tx.numberofEl... |
github | alexisgoar/MA_RADAR-master | plot_estimated_range2.m | .m | MA_RADAR-master/MDoppler/Functions/signal/plot_estimated_range2.m | 528 | utf_8 | ad8d65f05db9e320d854467e40bc28a4 | %Plots the received signal in time domain for a single path
function s = plot_estimated_range2(obj,txi,rxi,NPulses)
i = 1;
while obj.tx.chirpi < NPulses
for j = 1:size(obj.target,2)
s(i,j) =(rxSignal2(obj,obj.tx.time,txi,rxi,j));
end
timeStamp(i) = obj.tx.time;
obj.tx.nextStep_sampling();
... |
github | alexisgoar/MA_RADAR-master | plot_txSignals.m | .m | MA_RADAR-master/MDoppler/Functions/txarray/plot_txSignals.m | 539 | utf_8 | ab7ea48f68445ea24241a87408e1cdf1 | % Function used by class txarray to plot a sent signal
% Plots all transmitters
%obj must me a txarray class
function h = plot_txSignals(obj,NPulses)
figure;
set(0,'DefaultFigureWindowStyle','docked');
hold on;
txn = obj.numberofElements;
for i = 1:txn
h = subplot(txn,1,i);
plot_txSignal(obj,i,NPulses);
Txn... |
github | alexisgoar/MA_RADAR-master | plot_txSignal.m | .m | MA_RADAR-master/MDoppler/Functions/txarray/plot_txSignal.m | 324 | utf_8 | 3a69862f61a7f0651a3fac09348bf0ba | % Function used by class txarray to plot a sent signal
% For one receiver only
%obj must me a txarray class
function h = plot_txSignal(obj,txi,NPulses)
i = 1;
while obj.chirpi < NPulses
freq(i) = txSignal(obj,txi,obj.time);
timeStamp(i) = obj.time;
obj.nextStep();
i = i+1;
end
h = plot(timeStamp,freq);... |
github | daniellerch/aletheia-master | GFR.m | .m | aletheia-master/aletheia-octave/octave/GFR.m | 6,318 | utf_8 | 5907e54c33bb3ff14818fcfa87056123 | function F=GFR(IMAGE,NR,QF,channel)
% -------------------------------------------------------------------------
% Copyright (c) 2015 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy, modify, and distribu... |
github | daniellerch/aletheia-master | HUGO.m | .m | aletheia-master/aletheia-octave/octave/HUGO.m | 7,480 | utf_8 | f1d2eb26eef610cf221886e0ceff7633 | function [stego, distortion] = HUGO(cover, payload)
params.gamma = 1;
params.sigma = 1;
cover = double(imread(cover));
wetCost = 10^8;
responseP1 = [0; 0; -1; +1; 0; 0];
% create mirror padded cover image
padSize = 3;
coverPadded = padarray(cover, [padSize padSize], 'symmetric');
% create residuals
C_... |
github | daniellerch/aletheia-master | HILL_MAXSRM.m | .m | aletheia-master/aletheia-octave/octave/HILL_MAXSRM.m | 2,109 | utf_8 | ea3a3722b129eca8a5940ed6b496b13c | function f = HILL_MAXSRM(IMAGE)
if ischar(IMAGE)
X = imread(IMAGE);
else
X = IMAGE;
end
P = f_cal_cost(X);
M = 1./P;
f = MAXSRM(X, M);
end
function cost = f_cal_cost(cover)
% Copyright (c) 2014 Shenzhen University,
% All Rights Reserved.
% ---------------------------------------------------------------... |
github | daniellerch/aletheia-master | SRM.m | .m | aletheia-master/aletheia-octave/octave/SRM.m | 55,472 | utf_8 | 2dd9ded5028c0dd58acc1dbafdacbcf0 | function f = SRM(IMAGE, channel)
% -------------------------------------------------------------------------
% Copyright (c) 2011 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy, modify, and distribute ... |
github | daniellerch/aletheia-master | S_UNIWARD.m | .m | aletheia-master/aletheia-octave/octave/S_UNIWARD.m | 6,938 | utf_8 | 12eab6d1eb941654f400a98253b0a617 | function stego = S_UNIWARD(coverPath, payload)
% -------------------------------------------------------------------------
% Copyright (c) 2013 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy, modify, a... |
github | daniellerch/aletheia-master | TRIPLES.m | .m | aletheia-master/aletheia-octave/octave/TRIPLES.m | 2,811 | utf_8 | 246cc344502066369572be6f8fb846a5 | function beta_hat = TRIPLES(path, channel)
%
% Triples message length estimator utilizing only the symmetry e_2m+1,2n+1
% = o_2m+1,2n+1 with m,n \in {-5,...,5}.
% This implementation follows the method description in:
% Andrew D. Ker: "A general framework for the structural steganalysis of
% LSB replacement." In... |
github | daniellerch/aletheia-master | J_UNIWARD.m | .m | aletheia-master/aletheia-octave/octave/J_UNIWARD.m | 7,419 | utf_8 | 862bdb2dc9f6bd8a14f585696267cc87 | function J_UNIWARD(cover, payload, stego)
% -------------------------------------------------------------------------
% Copyright (c) 2013 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy, modify, an... |
github | daniellerch/aletheia-master | HILL_COLOR.m | .m | aletheia-master/aletheia-octave/octave/HILL_COLOR.m | 5,448 | utf_8 | 4b65debe28bd32907c6d33ecfc553d73 | function stego = HILL_COLOR(cover_path, payload)
H=0;
cover_3ch = double(imread(cover_path));
stego = zeros(size(cover_3ch));
for index_color=1:3
x = cover_3ch(:,:,index_color);
cost=f_cal_cost(x);
stego(:,:,index_color) = f_sim_embedding(x, cost, payload, H);
end
end
function cost = ... |
github | daniellerch/aletheia-master | HILL.m | .m | aletheia-master/aletheia-octave/octave/HILL.m | 5,287 | utf_8 | 5a1e478de28a0b51c3428f209e0f6047 | function stego = HILL(cover_path, payload)
H=0;
x = imread(cover_path);
cost=f_cal_cost(x);
stego=f_sim_embedding(x, cost, payload, H);
end
function cost = f_cal_cost(cover)
% Copyright (c) 2014 Shenzhen University,
% All Rights Reserved.
% -------------------------------------------------------------------------
% ... |
github | daniellerch/aletheia-master | EBS_COLOR.m | .m | aletheia-master/aletheia-octave/octave/EBS_COLOR.m | 4,870 | UNKNOWN | 003a2f4f562a2f671e2e9327e39b2805 | function EBS_COLOR(cover, payload, stego)
% Script modified by R�mi Cogranne, based upon the ones provided by Vojtech Holub (in 2014)
% use:
% toto = jpeg_read('./test.jpg');
% payload = 0.4;
% S_STRUCT = EBS(toto, payload)
%
try
C_STRUCT = jpeg_read(cover);
catch
error('ERROR (problem with the... |
github | daniellerch/aletheia-master | NSF5.m | .m | aletheia-master/aletheia-octave/octave/NSF5.m | 3,266 | utf_8 | 4ce30e16d5eae39d4b9b653b21f3d113 | function [nzAC,embedding_efficiency,changes] = NSF5(COVER, ALPHA, STEGO)
SEED=sum(100*clock); % random key
% -------------------------------------------------------------------------
% Contact: jan@kodovsky.com | June 2011
% -------------------------------------------------------------------------
% This program s... |
github | daniellerch/aletheia-master | S_UNIWARD_COLOR.m | .m | aletheia-master/aletheia-octave/octave/S_UNIWARD_COLOR.m | 7,177 | utf_8 | 2990fdc10c4bfdb0df3a1d85f8a67b10 | function stego = S_UNIWARD_COLOR(coverPath, payload)
% -------------------------------------------------------------------------
% Copyright (c) 2013 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy, mod... |
github | daniellerch/aletheia-master | NSF5_COLOR.m | .m | aletheia-master/aletheia-octave/octave/NSF5_COLOR.m | 3,331 | utf_8 | 47c5da642adfa3b7099c404ade7a6a67 | function [STEGO,embedding_efficiency,changes] = NSF5_COLOR(cover, payload, stego)
% -------------------------------------------------------------------------
% Contact: jan@kodovsky.com | June 2011
% -------------------------------------------------------------------------
% This program simulates the embedding imp... |
github | daniellerch/aletheia-master | AUMP.m | .m | aletheia-master/aletheia-octave/octave/AUMP.m | 2,725 | utf_8 | 45e5662031d19b8dff0f7251355cf3bf | function beta = AUMP(path, channel)
%
% AUMP LSB detector as described by L. Fillatre, "Adaptive Steganalysis of
% Least Significant Bit Replacement in Grayscale Natural Images", IEEE
% TSP, October 2011.
%
% X = image to be analyzed
% m = pixel block size
% d = q - 1 = polynomial degree for fitting (predictor)... |
github | daniellerch/aletheia-master | SRMQ1.m | .m | aletheia-master/aletheia-octave/octave/SRMQ1.m | 55,091 | utf_8 | 33d050deb6f97a8dae9b351e56119024 | function f = SRMQ1(IMAGE, channel)
% -------------------------------------------------------------------------
% Copyright (c) 2011 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy, modify, and distribut... |
github | daniellerch/aletheia-master | sigma_spam_PSRM.m | .m | aletheia-master/aletheia-octave/octave/sigma_spam_PSRM.m | 14,676 | utf_8 | dc32f2d3b8c164295acc43e9a70410ce | function f = sigma_spam_PSRM(IMAGE, q, Prob)
% -------------------------------------------------------------------------
% Copyright (c) 2011 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy, modify, and... |
github | daniellerch/aletheia-master | WOW.m | .m | aletheia-master/aletheia-octave/octave/WOW.m | 7,349 | utf_8 | 62f1fbf31d4e9bc8bdb35c9b788a0a4c | function [stego, distortion] = WOW(cover_path, payload)
% -------------------------------------------------------------------------
% Copyright (c) 2012 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy, ... |
github | daniellerch/aletheia-master | J_UNIWARD_COLOR.m | .m | aletheia-master/aletheia-octave/octave/J_UNIWARD_COLOR.m | 8,229 | utf_8 | b30f919c16492cfe2115cfd7ded0ecea | function STEGO = J_UNIWARD_COLOR(cover, payload, stego)
% -------------------------------------------------------------------------
% Copyright (c) 2013 DDE Lab, Binghamton University, NY.
% All Rights Reserved.
% -------------------------------------------------------------------------
% Permission to use, copy... |
github | daniellerch/aletheia-master | ensemble_training.m | .m | aletheia-master/aletheia-octave/octave/ensemble_training.m | 25,595 | utf_8 | b967dd7b363b4163b56725f5003306f1 | function [trained_ensemble,results] = ensemble_training(Xc,Xs,settings)
% -------------------------------------------------------------------------
% Ensemble Classification | June 2013 | version 2.0
% -------------------------------------------------------------------------
% The purpose of version 2.0 is to simpl... |
github | daniellerch/aletheia-master | HILL_sigma_spam_PSRM.m | .m | aletheia-master/aletheia-octave/octave/HILL_sigma_spam_PSRM.m | 2,143 | utf_8 | 98d9ae16440c8a4e9a55029502756479 | function f = HILL_sigma_spam_PSRM(IMAGE)
if ischar(IMAGE)
X = double(imread(IMAGE));
else
X = double(IMAGE);
end
q = 1;
P = f_cal_cost(X);
f = sigma_spam_PSRM(X, q, P);
end
function cost = f_cal_cost(cover)
% Copyright (c) 2014 Shenzhen University,
% All Rights Reserved.
% -----------------------------... |
github | dylewsky/MultiRes_Discovery-master | Simple_Toy_Model.m | .m | MultiRes_Discovery-master/Example_Simple_Toy_Model/Simple_Toy_Model.m | 21,750 | utf_8 | 3191468cb3fb9ecdf1f5d0f154223343 | %%% DMD SCALE SEPARATION FOR A SIMPLE MULTISCALE TOY MODEL
%%%
%%% Before running, make sure to download and set up the optdmd package
%%% available here: https://github.com/duqbo/optdmd
%%%
%%% This code reproduces the results of Sec. III of "Dynamic mode decomposition
%%% for multiscale nonlinear physics," by Dylewsk... |
github | dylewsky/MultiRes_Discovery-master | Overlapping_Scale_Oscillators.m | .m | MultiRes_Discovery-master/Example_Overlapping_Scale_Oscillators/Overlapping_Scale_Oscillators.m | 21,904 | utf_8 | cf468a2658d1fe4f0690c6c00986f538 | %%% DMD SCALE SEPARATION FOR A SYSTEM WITH SEPARATE BUT
%%% INTERMITTANTLY-OVERLAPPING TIME SCALES
%%%
%%% Before running, make sure to download and set up the optdmd package
%%% available here: https://github.com/duqbo/optdmd
%%%
%%% This code reproduces the results of Sec. IV of "Dynamic mode decomposition
%%% for mu... |
github | AgriHarmony/soil-hydraulic-simulation-backup-master | SMmodel_GA_WEB.m | .m | soil-hydraulic-simulation-backup-master/SMmodel_code/SMmodel_GA_WEB.m | 4,293 | utf_8 | 550e15e7de23359f535b01408d760f94 | % SMmodel_GA_WEB(name,PAR,FIG)
%
% SMmodel_GA_WEB: Soil Moisture Model based on Green-Ampt for infiltration
% Authors: Luca Brocca, Florisa Melone, Tommaso Moramarco
function [NS,NS_lnQ,NS_radQ,RQ,RMSE] = SMmodel_GA_WEB(name,PAR,FIG);
% Loading input data
PTSM=load([name,'.txt']);
[M,N]=size(PTSM);
D=... |
github | AgriHarmony/soil-hydraulic-simulation-backup-master | cal_SMmodel_GA_WEB.m | .m | soil-hydraulic-simulation-backup-master/SMmodel_code/cal_SMmodel_GA_WEB.m | 1,122 | utf_8 | d36850ec5f279c4fc1e3bd395fb88b83 | function X_OPT=cal_SMmodel_GA_WEB(name,X_ini)
if nargin==1,X_ini=ones(7,1)*.5;,end
% X_ini=rand(5,1);
% [RES,FVAL,EXITFLAG,OUTPUT]=fminsearch(@calibOK,X_ini,...
% optimset('Display','iter','MaxIter',1000,...
% 'MaxFunEvals',1000,'TolFun',0.5*1E-3,'Largescale','off'),name)
[RES,FVAL,EXITFLAG,OUTPUT]=fmi... |
github | AgriHarmony/soil-hydraulic-simulation-backup-master | richards.m | .m | soil-hydraulic-simulation-backup-master/richards_matlab/richards.m | 2,511 | utf_8 | db5175e69e7cd545a8891f6f8d52f1d5 | function richards
% Solution of the Richards equation
% using MATLAB pdepe
%
% $Ekkehard Holzbecher $Date: 2006/07/13 $
%
% Soil data for Guelph Loam (Hornberger and Wiberg, 2005)
% m-file based partially on a first version by Janek Greskowiak
% url source: https://www.mathworks.com/matla... |
github | ChangPaul/B0Mapping-master | region_seg.m | .m | B0Mapping-master/Dependencies/region_seg.m | 6,473 | utf_8 | 3184fa5dbf26411a14d7b731345fcadb | % Region Based Active Contour Segmentation
%
% seg = region_seg(I,init_mask,max_its,alpha,display)
%
% Inputs: I 2D image
% init_mask Initialization (1 = foreground, 0 = bg)
% max_its Number of iterations to run segmentation for
% alpha (optional) Weight of smoothing term
%... |
github | TurtleZhong/MSCKF-1-master | loadCalibrationCamToCam.m | .m | MSCKF-1-master/kitti_extraction/utils/devkit/loadCalibrationCamToCam.m | 1,894 | utf_8 | 88db832a2338f205ea36b1a9f6231aed | function calib = loadCalibrationCamToCam(filename)
% open file
fid = fopen(filename,'r');
if fid<0
calib = [];
return;
end
% read corner distance
calib.cornerdist = readVariable(fid,'corner_dist',1,1);
% read all cameras (maximum: 100)
for cam=1:100
% read variables
S_ = readVariable(fid,['S_' num2s... |
github | TurtleZhong/MSCKF-1-master | loadCalibrationRigid.m | .m | MSCKF-1-master/kitti_extraction/utils/devkit/loadCalibrationRigid.m | 855 | utf_8 | 9148661cd7335b41dace4f57bd25b3a4 | function Tr = loadCalibrationRigid(filename)
% open file
fid = fopen(filename,'r');
if fid<0
error(['ERROR: Could not load: ' filename]);
end
% read calibration
R = readVariable(fid,'R',3,3);
T = readVariable(fid,'T',3,1);
Tr = [R T;0 0 0 1];
% close file
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | ganlubbq/Signal-Processing-master | findpeaks.m | .m | Signal-Processing-master/findpeaks.m | 32,468 | utf_8 | 2b7f5afefbaaafe14b836c32851f6f3d | function [Ypk,Xpk,Wpk,Ppk] = findpeaks(Yin,varargin)
%FINDPEAKS Find local peaks in data
% PKS = FINDPEAKS(Y) finds local peaks in the data vector Y. A local peak
% is defined as a data sample which is either larger than the two
% neighboring samples or is equal to Inf.
%
% [PKS,LOCS]= FINDPEAKS(Y) also returns... |
github | ganlubbq/Signal-Processing-master | findFrequencySets.m | .m | Signal-Processing-master/findFrequencySets.m | 6,594 | utf_8 | e7153b1ff720af9dbc471ef144397eb7 | % ======================================================================
%> @brief findFrequencySets(obj, type, fLow, fHigh, noHarmonics)
%> Changed by CS, no obj
%> Function finds all frequencies that matches a harmonic set
%>
%> @param obj ThicknessAlgorithm class object
%> @param type Type of psd to search: RE... |
github | ganlubbq/Signal-Processing-master | processingBinFile.m | .m | Signal-Processing-master/Test_Dev/processingBinFile.m | 2,307 | utf_8 | 10c8bb9206d91df52481556c83e8c8a9 | %> @file processingBinFile.m
%> @brief transform one .bin file to 96 x 520 x 2000 .mat file
% ======================================================================
%> @brief Input:
% - PathName, Filename
% - round_per_read = 520
%
%> @brief Output:
% - Signal in .mat format
%
%> @brief Author: Chen... |
github | ganlubbq/Signal-Processing-master | MultiChnLoad.m | .m | Signal-Processing-master/Test_Dev/reference/MultiChnLoad.m | 44,220 | utf_8 | fb1445fac828ff1d542f7dd03a5a2520 | function varargout = MultiChnLoad(varargin)
% MULTICHNLOAD MATLAB code for MultiChnLoad.fig
% See also: GUIDE, GUIDATA, GUIHANDLES
% Last Modified by GUIDE v2.5 11-Sep-2017 15:30:56
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_... |
github | ganlubbq/Signal-Processing-master | hydrophoneDataProcessLinear.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/hydrophoneDataProcessLinear.m | 5,486 | utf_8 | 9c3667e31b0474394c570aece3a8b5a3 | function [ psdArray, fVector, distanceArray, meanArray ] = processHydrophoneDataLinear( FFT_LENGTH, fileName )
import ppPkg.*
im = ImportHandler;
% Import data file
header = im.readHeader(fileName);
tmArr = im.importDataFile();
% Init variables
psdArray = zeros(length(tm... |
github | ganlubbq/Signal-Processing-master | hanning.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/hanning.m | 2,240 | utf_8 | e50c3a9e967b4ad6289daa0583051b6b | function w = hanning(varargin)
%HANNING Hanning window.
% HANNING(N) returns the N-point symmetric Hanning window in a column
% vector. Note that the first and last zero-weighted window samples
% are not included.
%
% HANNING(N,'symmetric') returns the same result as HANNING(N).
%
% HANNING(N,'periodic') r... |
github | ganlubbq/Signal-Processing-master | pulseTest.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/pulseTest.m | 8,219 | utf_8 | df4650e4966086aaa576800aac218103 | function res = pulseTest(ctrl, folder, folderToSaveFig)
% Read folder
% Get all subfolder names
% Do a regex
% Keep the ones that matches the regex
listing = dir(folder);
%folderNames;
[pathstr,name,ext] = fileparts(folder);
folderIndex = 1;
for index = 1:siz... |
github | ganlubbq/Signal-Processing-master | RegularizeData3D.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/RegularizeData3D.m | 40,572 | utf_8 | 4c8d0f722d7d1c70c2d58fb1cf81d48b | function [zgrid,xgrid,ygrid] = RegularizeData3D(x,y,z,xnodes,ynodes,varargin)
% RegularizeData3D: Produces a smooth 3D surface from scattered input data.
%
% RegularizeData3D is a modified version of GridFit from the Matlab File Exchange.
% RegularizeData3D does essentially the same thing, but is ... |
github | ganlubbq/Signal-Processing-master | hydrophoneDataProcessSineSweep.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/hydrophoneDataProcessSineSweep.m | 6,377 | utf_8 | 0b06324432fbdf55310db51ea1046637 | function [meanArray ] = hydrophoneDataProcessSineSweep( fileName, startFrequency, stepFrequency )
import ppPkg.*
im = ImportHandler;
% Import data file
header = im.readHeader(fileName);
tmArr = im.importDataFile();
fileName
%% Use moving average filter to smooth the data po... |
github | ganlubbq/Signal-Processing-master | transducerTest.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/transducerTest.m | 6,750 | utf_8 | c7830cdb2a8c1eec2bec64fd33dc07ab | %> @file transducerTest.m
%> @brief Contain functions for processing transducer test measurements
% ======================================================================
%> @brief Contain functions for processing transducer test measurements
%
%>
% ===============================================================... |
github | ganlubbq/Signal-Processing-master | hydrophoneDataProcess.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/hydrophoneDataProcess.m | 7,812 | utf_8 | 015cad9b5dbeee8da2b79fd5733aed31 | function [ meanArray, startIndexArray ] = hydrophoneDataProcess( fileName, varargin)
% Function calculates the mean peak2peak value of the sinus recorded
% by the hydrophone, for each shot the transducer is moved in an angle.
% Function now assumes that transducer is moved with 1 degree for each shot
%
... |
github | ganlubbq/Signal-Processing-master | findFrequencySets.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@ThicknessAlgorithm/findFrequencySets.m | 6,873 | utf_8 | c64c627814ebd16c8d51ff8abb5da649 | % ======================================================================
%> @brief findFrequencySets(obj, type, fLow, fHigh, noHarmonics)
%>
%> Function finds all frequencies that matches a harmonic set
%>
%> @param obj ThicknessAlgorithm class object
%> @param type Type of psd to search: RESONANCE or MAIN
%> @p... |
github | ganlubbq/Signal-Processing-master | importDataVersion4.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@ImportHandler/importDataVersion4.m | 5,342 | utf_8 | f2b9870adc59c60749f6514c7db1a4f5 |
function [tm, numberOfRec, header] = importDataVersion4(obj, h, fileId)
% Read file version. Based on version call correct handler
% Expects that Labview data is little endian.
% Header format
% header.version uint8
% header.seqNo uint32
% header.dataTy... |
github | ganlubbq/Signal-Processing-master | importDataVersion1.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@ImportHandler/importDataVersion1.m | 4,718 | utf_8 | f31eb699831a09c3f8dd1447f150ea99 |
function [tm, numberOfRec, header] = importDataVersion1(obj, h, fileId)
% Read file version. Based on version call correct handler
% Expects that Labview data is little endian.
% Header format
% header.version uint8
% header.seqNo uint32
% header.dataType... |
github | ganlubbq/Signal-Processing-master | guaranteedEllipseFit.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@EllipseFit/guaranteedEllipseFit.m | 7,011 | utf_8 | b971e2ffc43ddbc2156a2ecc3cea081d | % Function: guaranteedEllipseFit
%
% This function implements the ellipse fitting algorithm described in
% Z.Szpak, W. Chojnacki and A. van den Hengel
% "Guaranteed Ellipse Fitting with the Sampson Distance"
% Proc. 12th European Conference on Computer Vision. ECCV
% Firenze,Italy, oct, 2012
%
% ... |
github | ganlubbq/Signal-Processing-master | levenbergMarquardtStep.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@EllipseFit/levenbergMarquardtStep.m | 6,160 | utf_8 | 1120ba6cf4b82e344e57b31cc3dac075 | % Function: levenbergMarquardtStep
%
% This function is used in the main loop of guaranteedEllipseFit in the process
% of minimizing an approximate maximum likelihood cost function of an
% ellipse fit to data. It computes an update for the parameters
% representing the ellipse, using the method of Leven... |
github | ganlubbq/Signal-Processing-master | compute_directellipse_estimates.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@EllipseFit/compute_directellipse_estimates.m | 2,286 | utf_8 | f7046187cec5532b1043fdba910dfbbe | % Function: compute_directellipse_estimates
%
% This function is a wrapper for the numerically stable direct ellipse fit
% due to
%
% R. Halif and J. Flusser
% "Numerically stable direct least squares fitting of ellipses"
% Proc. 6th International Conference in Central Europe on Computer Graphics a... |
github | ganlubbq/Signal-Processing-master | lineSearchStep.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@EllipseFit/lineSearchStep.m | 4,119 | utf_8 | 2a62d73c853d11f08b5e04bb4cc25115 | % Function: lineSearchStep
%
% This function is used in the main loop of guaranteedEllipseFit in the process
% of minimizing an approximate maximum likelihood cost function of an
% ellipse fit to data. It computes an update for the parameters
% representing the ellipse, using the pseudo-inverse of a Gau... |
github | ganlubbq/Signal-Processing-master | direct_ellipse_fit.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@EllipseFit/direct_ellipse_fit.m | 1,238 | utf_8 | b911503812da4726e5ba06646fbee602 | % This code is an implementation of the following paper
%
% R. Halif and J. Flusser
% Numerically stable direct least squares fitting of ellipses
% Proc. 6th International Conference in Central Europe on Computer Graphics and Visualization. WSCG '98
% Czech Republic,125--132, feb, 1998
function a = direct_ellip... |
github | ganlubbq/Signal-Processing-master | compute_guaranteedellipse_estimates.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@EllipseFit/compute_guaranteedellipse_estimates.m | 2,809 | utf_8 | b8db968f07ea32b64e821728f52ac58d | % Function: compute_guaranteedellipse_estimates
%
% This function is a wrapper for an approximate maximum likelihood guaranteed
% ellipse fit due to
%
% Z.Szpak, W. Chojnacki and A. van den Hengel
% "Guaranteed Ellipse Fitting with the Sampson Distance"
% Proc. 12th European Conference on Computer Vi... |
github | ganlubbq/Signal-Processing-master | normalise2dpts.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/+ppPkg/@EllipseFit/normalise2dpts.m | 2,435 | utf_8 | 69ae603cd7c0fa17b28fd805ea19cfb4 | % NORMALISE2DPTS - normalises 2D homogeneous points
%
% Function translates and normalises a set of 2D homogeneous points
% so that their centroid is at the origin and their mean distance from
% the origin is sqrt(2). This process typically improves the
% conditioning of any equations used to solve homographies... |
github | ganlubbq/Signal-Processing-master | processHydrophoneDataLinearFromCsv.m | .m | Signal-Processing-master/release_version_0.9_Fraunhofer/matlab/labTestScripts/processHydrophoneDataLinearFromCsv.m | 3,820 | utf_8 | 3a2bc5cc40fec8ebd09584dfa8a894f7 |
function [ meanArray ] = processHydrophoneDataLinearFromCsv( folderName )
%% Import data from text file.
% Script for importing data from the following text file:
%
% D:\scan\hydrophone_scans\CSV\4MHz\4M_20.csv
%
% To extend the code to different selected data or a different text file,
% generate a functi... |
github | sjgershm/exploration-master | barerrorbar.m | .m | exploration-master/util/barerrorbar.m | 6,797 | utf_8 | 75d58de8b06685309614932e0d5f14f9 | function varargout = barerrorbar(varargin)
% BARERRORBAR Create a bar plot with error bars. BARERRORBAR() uses the
% MATLAB functions BAR() and ERRORBAR() and changes the 'XData' property
% of the errorbar plot so that the error bars are plotted at the center
% of each bar. This does not sup... |
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