A well-known difficulty in using the articulatory representation for applications in the areas of speech coding, synthesis and recognition is the poor accuracy in the estimation of the articulatory parameters from the acoustic signal of speech. The difficulty is especially serious for most classes of consonantal sounds. This paper presents a statistical method of estimating the articulatory tra...

We address here the use of EEG and fMRI, and their combination, in order to estimate the full spatiotemporal patterns of activity on the cortical surface in the absence of any particular assumptions on this activity such as stimulation times. For handling such a high-dimension inverse problem, we propose the use of (1) a global forward model of how these measures are functions of the "neural ac...

This study aims at comparing simulation-based approaches for estimating both the state and unknown parameters in nonlinear state-space models. Numerical results on different toy models show that combination of a Conditional Particle Filter (CPF) with Backward Simulation (BS) smoother Stochastic Expectation-Maximization (SEM) algorithm is promising approach. The CPFBS run small number particles ...

: It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network str...

Electromagnetic articulography (EMA) data provides the movement of sensors attached to different articulators of a subject when the subject is speaking. EMA data often contains missing segments due to sensor failure. In this work, we propose an equality constrained Kalman smoother to estimate the missing samples in the EMA data. We incorporate the dynamics of the articulatory movement for missi...

Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalm...

Motility is an important property of immune system cells. It provides cells with the ability to perform their function not only at the right time, but also at the right place. In this paper, we introduce the problem of modeling and estimating an effective force field directing cell movement by the analysis of intravital video microscopy. A computational approach is proposed for solving this pro...

This paper considers identifying effective cortical connectivity from scalp EEG. Recent studies use time-varying multivariate autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve th...

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