Multigate doppler signal analysis using 3-D regularized long AR modelling.

Autoregressive (AR) modelling has already been proposed as an alternative to fast Fourier transform to process ultrasound (US) Doppler signals. Previous works introduced long AR models, set up under a regularization framework. The latter may be in 1-D (frequency) or 2-D (frequency and space or time). This study generalizes the spectrum regularization in the three dimensions frequency, space and time. The problem of the penalization function is addressed, and a new convex solution is proposed, taking into account possible nonstationarity of the Doppler signal. The parameter tuning is based on simulations using a standard Doppler signal model. The first results show that this processing improves the spectral estimation, and is well suited to flow interpretation.

Bayesian estimation for molecular profile reconstruction in proteomics based on liquid chromatography and mass spectrometry.

There is a need to precisely measure concentration of proteins in biological substance for early diagnosis of disease or knowledge of fundamental biological processes. Many apparatus have been proposed, and now data processing methods have to be investigated. This paper focuses on data processing of proteomic experiments combining nano liquid chromatography and mass spectrometry. Experimental fluctuations of this process raise an interest for robust methods. Consequently, we propose a model of this acquisition system and a probabilistic Bayesian method to estimate the proteins' concentrations and system parameters.

An adaptive approach to computing the spectrum and mean frequency of Doppler signals.

Modern ultrasound Doppler systems are facing the problem of processing increasingly shorter data sets. Spectral analysis of the strongly nonstationary Doppler signal needs to shorten the analysis window while maintaining a low variance and high resolution spectrum. Color flow imaging requires estimation of the Doppler mean frequency from even shorter Doppler data sets to obtain both a high frame rate and high spatial resolution. We reconsider these two estimation problems in light of adaptive methods. A regularized parametric method for spectral analysis as well as an adapted mean frequency estimator are developed. The choice of the adaptive criterion is then addressed and adaptive spectral and mean frequency estimators are developed to minimize the mean square error on estimation in the presence of noise. Two suboptimal spectral and mean-frequency estimators are then derived for real-time applications. Finally, their performance is compared to that of both the FFT based periodogram and the AR parametric spectral analysis for the spectral estimator, and, to both the correlation angle and the Kristoffersen's [8] estimators for the mean frequency estimator using Doppler data recorded in vitro.