RJ11 Phone to RJ45 Jack

RJ11 Phone to RJ45 Jack - Yet another hidden feature is ‘advanced vibration haptics’ so you can feel, for example, whether shots being fired are coming from the left or right. The ROG Phone is roughly the same size as the Razer Phone, so it’s pretty big and also weighs 200g. That’s more than most smartphones but more understandable here considering everything that’s going on and we didn’t find the weight an issue during our time. footage to look more, well we have collected a lot of data from the field directly and from many other blogs so very complete his discussion here about RJ11 Phone to RJ45 Jack, on this blog we also have to provide the latest automotive information from all the brands associated with the automobile. ok please continue reading:

Kalman Filter (KF) 

Linear dynamical system (Linear RJ11 Phone to RJ45 Jack evolution functions)

Extended Kalman Filter (EKF) 

Non-linear dynamical system (Non-linear evolution functions)

Consider the following non-linear system:

Assume that we can somehow determine a reference trajectory 

Then:

where

For the measurement equation, we have:

We can then apply the standard Kalman filter to the linearized model

How to choose the reference trajectory?

Idea of the extended Kalman filter is to re-linearize the model around the most recent state estimate, i.e.

The Extended Kalman Filter (EKF) has become a standard    technique used in a number of 

# nonlinear estimation and 

# machine learning applications

#State estimation

#estimating the state of a nonlinear dynamic system

#Parameter estimation

#estimating parameters for nonlinear system identification

#e.g., learning the weights of a neural network

#dual estimation 

#both states and parameters are estimated simultaneously

#e.g., the Expectation Maximization (EM) algorithm

MATLAB CODE
########################################################################
function [x_next,P_next,x_dgr,P_dgr] = ekf(f,Q,h,y,R,del_f,del_h,x_hat,P_hat);
% Extended Kalman filter
%
% -------------------------------------------------------------------------
%
% State space model is
% X_k+1 = f_k(X_k) + V_k+1   -->  state update
% Y_k = h_k(X_k) + W_k       -->  measurement
%
% V_k+1 zero mean uncorrelated gaussian, cov(V_k) = Q_k
% W_k zero mean uncorrelated gaussian, cov(W_k) = R_k
% V_k & W_j are uncorrelated for every k,j
%
% -------------------------------------------------------------------------
%
% Inputs:
% f = f_k
% Q = Q_k+1
% h = h_k
% y = y_k
% R = R_k
% del_f = gradient of f_k
% del_h = gradient of h_k
% x_hat = current state prediction
% P_hat = current error covariance (predicted)
%
% -------------------------------------------------------------------------
%
% Outputs:
% x_next = next state prediction
% P_next = next error covariance (predicted)
% x_dgr = current state estimate
% P_dgr = current estimated error covariance
%
% -------------------------------------------------------------------------
%

if isa(f,'function_handle') & isa(h,'function_handle') & isa(del_f,'function_handle') & isa(del_h,'function_handle')
    y_hat = h(x_hat);
    y_tilde = y - y_hat;
    t = del_h(x_hat);
    tmp = P_hat*t;
    M = inv(t'*tmp+R+eps);
    K = tmp*M;
    p = del_f(x_hat);
    x_dgr = x_hat + K* y_tilde;
    x_next = f(x_dgr);
    P_dgr = P_hat - tmp*K';
    P_next = p* P_dgr* p' + Q;
else
    error('f, h, del_f, and del_h should be function handles')
    return
end

##############################################################################


For more

https://drive.google.com/folderview?id=0B2l8IvcdrC4oMzU3Z2NVXzQ0Y28&usp=sharing