Farhad Asadi and Hossein Sadati
Temporal Signal Processing by Inference Bayesian Approach for Detection of Abrupt Variation of Statistical Characteristics of Noisy Signals
312 - 320
2020
14
7
International Journal of Mechanical and Mechatronics Engineering
https://publications.waset.org/pdf/10011317
https://publications.waset.org/vol/163
World Academy of Science, Engineering and Technology
In fields such as neuroscience and especially in cognition modeling of mental processes, uncertainty processing in temporal zone of signal is vital. In this paper, Bayesian online inferences in estimation of changepoints location in signal are constructed. This method separated the observed signal into independent series and studies the change and variation of the regime of data locally with related statistical characteristics. We give conditions on simulations of the method when the data characteristics of signals vary, and provide empirical evidence to show the performance of method. It is verified that correlation between series around the change point location and its characteristics such as Signal to Noise Ratios and mean value of signal has important factor on fluctuating in finding proper location of change point. And one of the main contributions of this study is related to representing of these influences of signal statistical characteristics for finding abrupt variation in signal. There are two different structures for simulations which in first case one abrupt change in temporal section of signal is considered with variable position and secondly multiple variations are considered. Finally, influence of statistical characteristic for changing the location of change point is explained in details in simulation results with different artificial signals.
Open Science Index 163, 2020