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Application of support vector machine in geodesy for the classification of vertical displacements

Mrówczyńska M.

Computer Assisted Methods in Engineering and Science

2014

Vol. 21, no. 1

77--84

The article presents basic rules for constructing and training neural networks, called the Support Vector Machine technique. SVM networks can mainly be used for solving tasks of classification of linearly and nonlinearly separable data and regression as well as identifying signals and recognising increases. In this paper SVM networks have been used for classifying linearly separable data in order to formulate a model of displacements of points representing a monitored object. The problem of learning networks requires the use of quadratic programming in search of an optimum point of a Lagrange function with respect to optimised parameters. Estimated parameters determine the location of the hyperplane which maximises the separation margin of both classes.

Support Vector Machine and Probability Neural Networks in a Device-Free Passive Localization (DFPL) Scenario

Deak G., Curran K., Condell J., Deak D, Kiedrowski P.

Image Processing & Communications

2012

Vol. 17, no. 4

9--16

The holy grail of tracking people indoors is being able to locate them when they are not carrying any wireless tracking devices. The aim is to be able to track people just through their physical body interfering with a standard wireless network that would be in most peoples home. The human body contains about 70% water which attenuates the wireless signal reacting as an absorber. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person’s location. This paper is focused on taking the principle of Device-free Passive Localisation (DfPL) and applying it to be able to actually distinguish if there is more than one person in the environment. In order to solve this problem, we tested a Support Vector Machine (SVM) classifier with kernel functions such as Linear, Quadratic, Polynomial, Gaussian Radial Basis Function (RBF) and Multilayer Perceptron (MLP), and a Probabilistic Neural Network (PNN) in order to detect movement based on changes in the wireless signal strength.