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A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. A generalised fuzzy approach to statistical modelling techniques is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to coupled hidden Markov models. The CHMM based on the fuzzy c-means (FCM) and fuzzy entropy (FE) is referred to as FCM-FE-CHMM in this paper. By building up a generalised fuzzy objective function, several new formulae solving Training algorithms are theoretically derived for FCM-FE-CHMM. The fuzzy modelling techniques are very flexible since the degree of fuzziness, the degree of fuzzy entropy.

Aiming at enhancing the portability of the smart home management, it is an effective way to use the mobile terminal such as the mobile phone to control the smart home remotely. In addition, properly designed application on the mobile phone can give convenience to monitor the smart home as well as improve the user experience. In this paper, after a comprehensive analysis to the functions of the smart home, a related application based on Android is presented using the scheme of modular design. The application has the functions of monitoring the remote data and controlling the appliances in a long distance. Meanwhile, it can also realize the access control. The test of the application shows that it can receive the sensor data timely and make a proper control of the appliances, which means that it has realized the desired targets.

A hidden Markov model (HMM) encompasses a large class of stochastic process models and has been successfully applied to a number of scientific and engineering problems, including speech and other pattern recognition problems, and biological sequence analysis. A major restriction is found, however, in conventional HMM, i.e., it is ill-suited to capture the interactions among different models. A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. This paper study is focused on the coupled discrete HMM, there are two state variables in the network. By generalizing forward-backward algorithm, Viterbi algorithm and Baum-Welch algorithm commonly used in conventional HMM to accommodate two state variables, several new formulae solving the 2-chain coupled discrete HMM probability evaluation, decoding and training problem are theoretically derived.

It’s very important that Bridge Structural Health Monitoring to bridge management and maintenance. In this paper,the authors study the Application of GIS and GPS in Bridge Structural Health Monitoring. Introduce the application field of GIS in bridge structural health monitoring and the method of GPS-RTK Technology to measure the bridge three dimension dynamic coordinate, proposed the System General Framework of GIS in Bridge Structural Health Monitoring,mainly study the problems that acquisition and collection of Monitoring information,the database management scheme of the system, the method that of GIS manage Spatial and attribute information which related to bridge structural health monitoring and to process and analysis the Monitoring data. Also analysis the feasibility of using GPS and GIS technology in a Bridge Structural Health Monitoring.