Satellite-based Synthetic Aperture Radar (SAR) images can be used as a source of remote sensed imagery regardless of cloud cover and day-night cycle. However, the speckle noise and varying image acquisition conditions pose a challenge for change detection classifiers. This paper proposes a new method of improving SAR image processing to produce higher quality difference images for the classification algorithms. The method is built on a neural network-based mapping transformation function that produces artificial SAR images from a location in the requested acquisition conditions. The inputs for the model are: previous SAR images from the location, imaging angle information from the SAR images, digital elevation model, and weather conditions. The method was tested with data from a location in North-East Finland by using Sentinel-1 SAR images from European Space Agency, weather data from Finnish Meteorological Institute, and a digital elevation model from National Land Survey of Finland. In order to verify the method, changes to the SAR images were simulated, and the performance of the proposed method was measured using experimentation where it gave substantial improvements to performance when compared to a more conventional method of creating difference images.

Network security and intrusion detection are important in the modern world where communication happens via information networks. Traditional signature-based intrusion detection methods cannot find previously unknown attacks. On the other hand, algorithms used for anomaly detection often have black box qualities that are difficult to understand for people who are not algorithm experts. Rule extraction methods create interpretable rule sets that act as classifiers. They have mostly been combined with already labeled data sets. This paper aims to combine unsupervised anomaly detection with rule extraction techniques to create an online anomaly detection framework. Unsupervised anomaly detection uses diffusion maps and clustering for labeling an unknown data set. Rule sets are created using conjunctive rule extraction algorithm. This research suggests that the combination of machine learning methods and rule extraction is a feasible way to implement network intrusion detection that is meaningful to network administrators.

Time-frequency analysis is critical to study event-related potentials (ERPs) now. ERPs are usually generated through averaging over a number of trials, and such averaging limits the application of a nonlinear time-frequency analysis method-Hilbert-Huang transformation (HHT). This is because HHT usually requires very long recordings to sufficiently decompose the complicated signal into oscillations and the averaged ERP trace tends to possess only hundreds of samples. Thus, this study designs the concatenated trial based HHT to release the limitation on the decomposition. Such a paradigm may reveal better temporal and spectral properties of an ERP than the conventional wavelet transformation does. Moreover, under the proposed method, it is found that the children with attention deficit hyperactivity disorders may have more temporally, spectrally and spatially distributed brain activities than the control children do.