Recent Research

Learn more about some of our recent research results, supplement material and MATLAB code.

Current research projects

Nonlinear Multidimensional Adaptive Modeling, System Identification and Prediction — Especially complex-valued and hyper-complex neural networks for natural phenomena, such as in modeling of environmental signals, complex industrial plants and trajectory tracking.

Wind Modeling and Prediction — Using advanced nonlinear multivariate predictors. Simultaneous modeling of all the components of multidimensional and multivariate signals.

One- and multi-dimensional adaptive denoising — Using adaptive signal processing algorithms and wavelets, with applications to biomedical signals, such as fMRI and medical images.

Blind Source Separation and Extraction — Especially on-line adaptive algorithms for Electroencephalogram, MEG, and fMRI, especially based on some fundamental properties of signals, such as their nonlinear, stochastic or nonstationary nature.

Brain Signal Processing — Signal conditioning and classification in various EEG based mental tasks, such as decision making processes.

Sensor Fusion — Combining information from sensors measuring different physical quantities, especially in modeling and detection of sleep stages, where a combination of EEG, EOG, ECG and respiratory signal is processed. Also sensor fusion in navigation, and robotics.

Fit for Duty — Data fusion based techniques for fatigue detection.

Signal Modality Characterisation — Based on the recently developed DVV method, this new exciting area of research can be used to asses a qualitative performance of adaptive filters as well as for initial characterisation of signal's nature, as for instance, linear, nonlinear, deterministic or stochastic.