seismic waves are non-stationary due to its propagation through the earth. time-frequency transformsare suitable tools for analyzing non-stationary seismic signals. spectral decomposition can reveal thenon-stationary characteristics which cannot be easily observed in the time or frequency representationalone. various types of spectral decomposition methods have been introduced by some researche...

Spatio-temporal pattern formation over the square and rectangular domain has received significant attention from researchers. A wide range of stationary non-stationary patterns produced by two interacting populations is abundant in literature. Fragmented habitats are widespread reality due to irregularity landscape. This work considers a prey-predator model capable producing time-varying comple...

The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

‎in this work we introduce and study discrete time periodically correlated stable‎ ‎processes and multivariate stationary stable processes related to periodic and cyclic‎ ‎flows‎. ‎our study involves producing a spectral representation and a‎ ‎spectral identification for such processes‎. ‎we show that the third‎ ‎component of a periodically correlated stable process has a component related to a...