An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Convex relaxation of mixture regression with efficient algorithms

We develop a convex relaxation of maximum a posteriori estimation of a mixture of regression models. Although our relaxation involves a semidefinite matrix variable, we reformulate the problem to eliminate the need for general semidefinite programming. In particular, we provide two reformulations that admit fast algorithms. The first is a max-min spectral reformulation exploiting quasi-Newton descent. The second is a min-min reformulation consisting of fast alternating steps of closed-form updates. We evaluate the methods against Expectation-Maximization in a real problem of motion segmentation from video data.

Substrate treatment and drying conditions effect on the properties of roll-to-roll gravure printed PEDOT:PSS thin films

The optical, structural and electrical properties of poly(3,4- ethylenedioxythiophene):poly(4-styrenesulfonic acid) (PEDOT:PSS) thin films printed by roll-to-roll gravure have been investigated. Corona treatment has been applied to enhance the adhesion of PEDOT:PSS on PolyEthylene Terephthalate (PET) web. It has been found that there was a stronger in-depth surface modification of PET with the increase of corona efficiency; however, the adhesion of PEDOT:PSS was not actually affected. Also, Spectroscopic Ellipsometry and Atomic Force Microscopy have been used to extract information on the mechanisms that define PEDOT:PSS properties. The increase of the drying temperature of the PEDOT:PSS films has been found to reduce the remaining water inside the films and lead to the decrease of the PEDOT:PSS particles size. © 2011 Elsevier B.V. All rights reserved.