Learning Phase-Based Descriptions for Action Recognition

Action recognition continues to receive significant attention from the research community, with new neural network architectures being developed continuously. Optical flow is by far the most popular input motion representation to these architectures, leaving a lot of undiscovered potential for other types of motion representations. Eulerian...

Side-Channel Attacks using Convolutional Neural Networks: A Study on the performance of Convolutional Neural Networks on side-channel data

Side-Channel Attacks, are a prominent type of attacks, used to break cryptographic implementations on a computing system. They are based on information "leaked" by the hardware of a computing system, rather than the encryption algorithm itself. Recent studies showed that Side-Channel Attacks can be performed using Deep Learning models. In this...

Deep Learning for Monitoring the Health Condition of RailwayCrossings

In this thesis, a method to monitor the health condition of railway crossings based on vibration data recorded by the accelerometers installed on the crossing is proposed. Due to various types of trains and other exogenous factors, responses obtained from accelerometers vary, even when the crossing has the same state condition. As a consequence,...

Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions

In hyperspectral (HS) imaging, for every pixel a spectrum of wavelengths is captured. These spectra represent material properties, i.e. the spectral signatures. So, classification of HS imagery is based on material properties. This thesis describes a framework to perform pixelwise classification of HS images of a fixed scene subject to varying...