We present a new technique for analyzing the rate of convergence of local dynamics in bargaining networks. The technique reduces balancing in a bargaining network to optimal play in a randomturn game. We analyze this game using techniques from martingale and Markov chain theory. We obtain a tight polynomial bound on the rate of convergence for a nontrivial class of unweighted graphs (the previo...

To study intelligent auxiliary drive system of port machinery, it needs to identify operational intention of quayside container crane driver. With the background of loading and unloading process of quayside container crane, upon Hidden Markov Model, double HMM model is established. The algorithm of revised Forward-Backward is applied to calculate each likelihood of HMM in operation layer, the m...

Human computer interaction techniques using hand poses are more natural to users than those that rely on devices. In this paper we describe and evaluate two techniques for hand pose recognition, based on a general library for gesture recognition, called CALI. This library was initially designed for calligraphic gesture recognition, however its usage shows that CALI is able to broaden its applic...

Recursive identification methods using time-domain data have been developed in [l, 21 utilizing a gradient-based identification technique for estimating the Markov parameters of a system. This identification technique utilizes the ARMARKOV representation of a time-invariant finite-dimensional system which relates the current output of a system to past outputs as well as current and past inputs....

 In this paper we study the relative entropy rate between a homogeneous Markov chain and a hidden Markov chain defined by observing the output of a discrete stochastic channel whose input is the finite state space homogeneous stationary Markov chain. For this purpose, we obtain the relative entropy between two finite subsequences of above mentioned chains with the help of the definition of...

We review modern approaches to the learning and recognition of complex patterns, including discriminant functions, neural networks, decision trees, and hidden Markov models. We then intro duce several relational learning systems and discuss in detail one specific technique, conditional rule generation. This technique is shown to be very flexible and useful for the learning of static patterns, s...

Due to its simplicity, the completion-of-squares technique is quite popular in linear optimal control. However, this simple technique is limited to linear quadratic Gaussian systems. In this note, by interpreting the completion-ofsquares from a new angle, we extend this technique to performance optimization of general Markov systems with the long run average criterion, leading to a new approach...

In this paper, the authors investigated the various factors, which can affect the performance of a turbofan engine. For this, the various subunits of a turbofan engine like inlet duct, compressor, combustion chamber, liner, turbine, exhaust nozzle etc. are investigated to find its various reliability characteristics through Markov process and supplementary variable technique (SVT). The main adv...

This paper describes an extension of maximum likelihood linear regression (MLLR) to hidden semi-Markov model (HSMM) and presents an adaptation technique of phoneme/state duration for an HMM-based speech synthesis system using HSMMs. The HSMM-based MLLR technique can realize the simultaneous adaptation of output distributions and state duration distributions. We focus on describing mathematical ...