Xefteris Vasileios-Rafael (Phd Candidate)

Thesis title: Data analysis and multimodal data fusion of sensors with machine learning and networks methods

Supervisor: Goulianas Konstantinos

Advisory Committee Members:

Chatzimisios Periklis, Professor IHU
Vrochidis Stefanos, Researcher (Grade C), Information Technologies Institute (CERTH)

Abstract:

In the last few decades, the rapid development of the network of objects (Internet of Things – IoT) has led to the development of “smart” devices equipped with a plethora of multimodal sensors. These various sensors can offer a variety of data, which, depending on their nature, provide information about specific characteristics. Beyond the analysis of data from different sensors separately, the fusion of sensor data is of vital importance in multimodal sensor analysis applications, as it can utilize the different information provided by the various sensors to enhance the final performance of the system. Often, machine learning techniques and neural networks are used for the purpose of analyzing and fusing multimodal sensor data, as they can be trained to achieve very high levels of performance, depending on the application. The proposed doctoral thesis aims to develop applications for the analysis and fusion of multimodal sensor data based on machine learning methods and neural networks