Kalman filtering-smoothing is a fundamental tool in statistical time series analysis: it implements the optimal Bayesian filter in the linear-Gaussian setting, and serves as a key step in the inference algorithms for a wide variety of nonlinear and non-Gaussian models. However, using this kind of filter in small embedded systems is not a good choice due to the computational intensive maths. For that reason there are several variations of the original Kalman filter which may…

The aim of this article is to detect the edges with a given direction in an image. To that end create a function [ E ] = oriented_edges( I, thr, a, da ) that takes as input a double grayscale image Ι, a threshold value thr, a direction a, and an angle da. The output of the function is a binary image Ε where the pixels that meet the following requirements should have the value 1:…

PCA is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences. Below are the steps of the algorithm:

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close all; % clear all; clc; % data = load('Data/eeg.mat'); % data = data.data{1,2}; |

Step 1 – Initialize the dataset, 6 vectors of 32 sample data

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% % Step 1 - Initialize the dataset, 6 vectors of 32 sample data % X = data(1:32,1:6); |

Step 2 – Subtract the mean from each of the data dimensions. The mean subtracted is the average across each dimension. [math]Y= X – (O * Mean(X)) [/math], where [math]O…