Forecasting of stock returns by using manifold wavelet support vector machine

An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.T...     

Toward an SCA-OSGi based middleware for radio frequency identification applications

The radio frequency identification (RFID) technology progressing is becoming a increasing interest in several application areas. To accommodate diverse and changing requirements from applications,RFID middleware should be reconfigurable. However,current RFID middleware is limited in its ability to support reconfiguration. To solve this problem,we adopt the service-oriented component-based approach in building the middleware. Component-based design enables decomposition of middleware functionality and is eas...     

Object tracking and tracing: Hidden semi-Markov model based probabilistic location determination

The enhancement of radio frequency identification (RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry. However, RFID systems do not always function reliably under complex and variable deployment environment. In many cases, RFID systems provide only probabilistic observations of object states. Thus, an approach to predict, record and track real world object states based upon probabilistic RFID observations is required. Hidden Markov model (HMM) has been used in the field of probabilistic location determination. But the inherent duration probability density of a state in HMM is exponential, which may be inappropriate for modeling of object location transitions. Hence, in this paper, we put forward a hidden semi-Markov model (HSMM) based approach for probabilistic location determination. We evaluated its performance comparing with that of the HMM-based approach. The results show that the HSMM-based approach provides a more accurate determination of real world object states based on observation data.