Dynamic-Event-Based Fault Detection for Markov Jump Systems Under Hidden-Markov Mode Observation

The fault detection (FD) problem is investigated for event-triggered discrete-time Markov jump systems (MJSs) with hidden-Markov mode observation. A dynamic-event-triggered mechanism, which includes some existing ones as special cases, is proposed to reduce unnecessary data transmissions to save network resources. Mode observation of the MJS by the FD filter (FDF) is governed by a hidden Markov process. By constructing a Markov-mode-dependent Lyapunov function, a sufficient condition in terms of linear matrix inequalities (LMIs) is obtained under which the filtering error system of the FD is stochastically stable with a prescribed H∞ performance index. The parameters of the FDF are explicitly given when these LMIs have feasible solutions. The effectiveness of the FD method is demonstrated by two numerical examples.

Research of Face Recognition Based on Wavelet Transform and EHMM

A method was presented which was based on Wavelet Transform and Embedded Hidden Markov Mode (EHMM). The proposed algorithm can reduce the affections such as illuminations which affects the recognition rate using the method of Principal Components Analysis (PCA).Analyzed the critical problems that affect recognition rates in Wavelet Transform. Experimental results show that the presented method can get better results.