In this article, we will briefly discuss the SVR model. We will discuss three types of SVR namely, S-SVR (Scaling-SVR), Z-SVR (Z-score-SVR), and R-SVR (Range-SVR). Afterwards, we will discuss its application in predicting the Average Localisation Error (ALE) in node localisation process in Wireless Sensor Network (WSNs).
Broadly, learning algorithms are divided into supervised and unsupervised learning. …
In this article, we will discuss a simple code to plot a Linear Regression (LR) curve. The code is written in MATLAB and can be downloaded from my MATLAB repository. You can write to me (firstname.lastname@example.org) if you have any question or visit my web page for more updates.
In LR, our main objective is to find the best fitting straight line through the observed values. The best fitting line is called the regression line. The formula for LR is
y = m *x + c
where y is the predicted value, m is the slope of the line, and c is the intercept. …
In this article, we will discuss the detailed process of surface soil moisture (top 5 cm) estimation using satellite images. This article is divided into five sections. First, we will see the satellite images used then we will see the study area. Afterwards, we will go through the models. Then we will see the detailed methodology. Lastly, we will see the results, discussion and conclusion section.
In this study, we have used the microwave and optical satellite images (Table 1) to estimate soil moisture. We have downloaded publicly available Sentinel-1A images of two consecutive pass, i.e., 17 and 29 January 2019 from the European Space Agency. We have also downloaded Landsat 8 images from the United States Geological Survey pertaining to the date closest to the Sentinel-1A images. …
In this article, we will first discuss the basics of PCA and how we can use PCA in MATLAB. After that, we will try to answer a fundamental question in PCA. You can download the MATLAB code from my MATLAB repository.
Question. What is PCA?
PCA is a mathematical procedure that transforms a no. of possibly correlated variables into smaller no. of uncorrelated variables called principal components (PC’s).
The first PC accounts for the highest variability in the data and the succeeding components have less variability than the preceding one.
Question. How we can use PCA in MATLAB?
[coeff, score, latent, ~, explained] =…