Driving direction prediction can be useful in different applications such as driver warning and route recommendation. In this paper, a framework is proposed to predict the driving direction based on weighted Markov model. First the city POI (Point of Interesting) map is generated from trajectory data using weighted PageRank algorithm. Then, a weighted Markov model is trained for the near term d...

In this contribution three examples of techniques that can be used for state order estimation of hidden Markov models are given. The methods are also exem-pliied using real laser range data, and the computational burden of the three methods is discussed. Two techniques, Maximum Description Length and Maximum a Posteriori Estimate, are shown to be very similar under certain circumstances. The th...

SUMMARY This paper describes a new presentation of continuous speech in terms of the probability of all phoneme types as a function of time. The presentation is called a phoneme probability presentation (PPP) and can be used for phoneme recognition of continuous speech. As a technique ,,0 produce the PPP, we have employed hidden Markov models (HMM) with time duration information. This informati...

We present a system for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. The sources are modeled using hidden Markov models (HMM) and separated using factorial HMM methods. Without prior speaker models for the sources in the mixture it is difficult to exactly resolve the individual sources because there is n...

The segmentation of ECG signal is a useful tool for the diagnosis of cardiac diseases. However, the state-of-the-art methods use hidden Markov models which do not adequately model the transitions between successive waves. This paper uses two methods which attempt to overcome this limitation: a HMM state scission scheme which prevents ingoing and outgoing transitions in the middle of the waves a...

This paper deals with a tool which may help maintenance manager to schedule maintenance activities. To help him, we show that by using events which can be observed on a process, like maintenance events, we can predict failures before they occur. Principles are based on the hypothesis that failure is preceded by a typical sequence of events. We also show that Hidden Markov Models can be used acc...

The Infinite Hidden Markov Model (IHMM) extends hidden Markov models to have a countably infinite number of hidden states (Beal et al., 2002; Teh et al., 2006). We present a generalization of this framework that introduces nearly block-diagonal structure in the transitions between the hidden states, where blocks correspond to “subbehaviors” exhibited by data sequences. In identifying such struc...

We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et al., 2009 [53]). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of t...

This paper presents an empirical study of the joint wavelet statistics for textures and other random imagery. There is a growing realization that modeling wavelet coefficients as independent, or at best correlated only across scales, assuming independence within a scale, may be a poor assumption. While recent developments in wavelet-domain Hidden Markov Models (notably HMT-3S) account for withi...