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#include "cv.h" #include "highgui.h" #include int main(int argc, char** argv) { /* A matrix data */ const float A[] = { 1, 1, 0, 1 }; IplImage* img = cvCreateImage( cvSize(500,5

//:c04: InitialValues.java class Measurement { boolean t; char c; byte b; short s; int i; long l; float f; double d; void print() { System.out.println("Data type Inial value\

//:c04: InitialValues.java class Measurement { boolean t; char c; byte b; short s; int i; long l; float f; double d; void print() { System.out.println("Data type Inial value\

/*********************************** MODULE INFO ************************************ Device : AT90S2333 File name : BC.h Ver nr. : 1.0 Description : Header file fo

function Correlated(M, MFilter) % Kalman filter simulation using correlated process and measurement noise. % This illustrates the improvement in filter results that can be attained % when the cor

function [AltErr, VelErr, BallErr] = HybridBody % Hybrid extended Kalman filter example. % Track a body falling through the atmosphere. % Outputs are: % AltErr = RMS altitude estimation error

function DiscreteKFEx1(N) % Discrete time Kalman filter for position estimation of a Newtonian system. % This example illustrates the effectiveness of the Kalman filter for state % estimation. It

function KalmanConstrained % function KalmanConstrained % This m-file simulates a vehicle tracking problem. % The vehicle state is estimated with a Kalman filter. % In addition, with the a prior

% Measurement and simulation data for the 2D EIT Package % % Real measurements on a "2D tank" % % bubble1.dat, bubble2.dat, bubble3.dat, bubble4.dat % % Precomputed mesh data % % meshdata.ma

function [V, converged, i] = state_est(branch, Ybus, Yf, Yt, Sbus, V0, ref, pv, pq, mpopt) %STATE_EST Solves a state estimation problem. % [V, converged, i] = state_est(branch, Ybus, Yf, Yt, Sbus,