Non-stationary signals are very common in nature, consider for example speech, music or heart rate. Using the concept of time-frequency analysis this thesis studies the performance of different time-frequency distributions of both simulated and real non-stationary signals. The signals studied are linear and non-linear frequency modulated (FM) signals. Two methods are studied to increase perform...

By adding retinal and pursuit eye-movement velocity one can determine the motion of an object with respect to the head. It would seem likely that the visual system carries out a similar computation by summing extra-retinal, eye-velocity signals with retinal motion signals. Perceived head-centred motion may therefore be determined by differences in the way these signals encode speed. For example...

The distribution of complex random signals is typically improper. It has recently been established that conventional strictly linear models are only second order optimum for signals with proper distributions, while so called “widelylinear models” are optimum for the generality of complex signals, both proper and improper. Widely-linear models, however, are over-parameterised when the underlying...

large-scale climatic signals including ocean-atmosphere interactions, are the main factors influencing the earth’s climatic oscillations and are the most important indices in predicting of climate variables. in this research, precipitation in the next month was predicted by applying artificial neural network (ann), neuro-fuzzy network (nfn), and multiple linear regression (mlr) in semnan synopt...

neural mass models are computational nonlinear models that simulate the activity of a population of neurons as an average neuron, in such a way that different inhibitory post synaptic potential (ipsp) and excitatory post synaptic potential (epsp) signals could be reproduced. these models have been developed either to simulate the recognized neural mechanisms or to predict some physiological fac...