NEW FAST RECURSIVE ALGORITHMS FOR SIMULTANEOUS RECONSTRUCTION AND IDENTIFICATION OF AR PROCESSES WITH MISSING OBSERVATIONS

This paper deals with the problem of adaptive reconstruction and identification of AR processes with randomly missing observations. The performances of a previously proposed real time algorithm are studied. Two new alternatives, based on other predictors, are proposed. They offer an unbiased estimation of the AR parameters. The first algorithm, based on the h-step predictor, is very simple but suffers from a large reconstruction error. The second one, based on the incomplete past predictor, offers an optimal reconstruction error in the least mean square sense

Identifcation stable et reconstruction robuste de signaux non stationnaires à échantillons manquants

National audienceOn souhaite reconstruire en ligne un signal à échantillons manquants. Lorsque la perte est élevée les méthodes existantes peuvent conduire à l'identifcation de modèles instables. Nous proposons, à notre connaissance, le premier algorithme qui permet le traitement en ligne des signaux à échantillons manquants utilisant la structure en treillis du filtre. La robustesse à un fort taux de perte et la stabilité du modèle ainsi identifié sont garanties. Les performances de ce nouvel algorithme dépassent celles des algorithmes existants et ce d'autant plus que la probabilité de perte est forte

NEW FAST ALGORITHM FOR SIMULTANEOUS IDENTIFICATION AND OPTIMAL RECONSTRUCTION OF NON STATIONARY AR PROCESSES WITH MISSING OBSERVATIONS

International audienceThis paper deals with the problem of adaptive reconstruction and identification of AR processes with randomlymissing observations. A new real time algorithm is proposed. It uses combined pseudo-linear RLS algorithm and Kalman filter. It offers an unbiased estimation of the AR parameters and an optimal reconstruction error in the least mean square sense. In addition, thanks to the pseudo-linear RLS identification, this algorithm can be used for the identification of non stationary AR signals. Moreover, simplifications of the algorithm reduces the calculation time, thus this algorithm can be used in real time applications

Adaptive transmission for lossless image reconstruction

International audienceThis paper deals with the problem of adaptive digital transmission systems for lossless reconstruction. A new system, based on the principle of non-uniform transmission, is proposed. It uses a recently proposed algorithm for adaptive stable identification and robust reconstruction of AR processes subject to missing data. This algorithm offers at the same time an unbiased estimation of the model's parameters and an optimal reconstruction in the least mean square sense. It is an extension of the RLSL algorithm to the case of missing observations combined with a Kalman filter for the prediction. This algorithm has been extended to 2D signals. The proposed method has been applied for lossless image compression. It has shown an improvement in bit rate transmission compared to the JPEG2000 standard

Compression methods for mechanical vibration signals: Application to the plane engines

International audienceA novel approach for the compression of mechanical vibration signals is presented in this paper. The method relies on a simple and flexible decomposition into a large number of subbands, implemented by an orthogonal transform. Compression is achieved by a uniform adaptive quantization of each subband. The method is tested on a large number of real vibration signals issued by plane engines. High compression ratios can be achieved, while keeping a good quality of the reconstructed signal. It is also shown that compression has little impact on the detection of some commonly encountered defects of the plane engine

Signal parameters estimation using time-frequency representation for laser doppler anemometry

International audienceThis paper describes a processing method to estimate parameters of chirp signals for Laser Doppler Anemometry (LDA). The Doppler frequency as well as additional useful parameters are considered here. These parameters are the burst width and the frequency rate. Different estimators based on the spectrogram are proposed. Cramer-Rao bounds are given and performance of the estimators compared to the state of the art using Monte-Carlo simulations for synthesized LDA signals. The characteristics of these signals are provided by a flight test campaign. The proposed estimation procedure takes into account the requirements for a real-time application

Compensation of scintillator sensitivity loss due to irradiation damage

International audienceComputed tomography reconstruction methods consider a normalized input signal. Images must be corrected using both a flat field record and a dark field record. Whereas there is no physical reason why the dark field would evolve during the experiment, the flat field may vary with time. One possible cause for flat field variation is irradiation damage which indeed occurs on the camera considered in this study. This paper considers different methods for taking into account the evolution of flat field due to irradiation damage. The aim is to keep the error resulting from bias on flat field at the same level than signal to noise ratio on fully illuminated areas of images, that is to say an error of 1%. Though this is still a work in progress, the methods proposed will be discussed and compared

Compressed sensing subtracted rotational angiography with multiple sparse penalty

International audienceDigital Subtraction Rotational Angiography (DSRA) is a clinical protocol that allows three-dimensional (3D) visualization of vasculature during minimally invasive procedures. C-arm systems that are used to generate 3D reconstructions in interventional radiology have limited sampling rate and thus, contrast resolution. To address this particular subsampling problem, we propose a novel iterative reconstruction algorithm based on compressed sensing. To this purpose, we exploit both spatial and temporal sparsity of DSRA. For computational efficiency, we use a proximal implementation that accommodates multiple '1-penalties. Experiments on both simulated and clinical data confirm the relevance of our strategy for reducing subsampling streak artifacts

Lattice algorithm for adaptive stable identification and robust reconstruction of non stationary AR processes with missing observations

International audienceThis paper deals with the problem of adaptive reconstruction and identification of non stationary AR processes with randomly missing observations. Existent methods use a direct realization of the filter. Therefore, the estimated parameters may not correspond to a stable all-pole filter. In addition, when the probability of missing a sample is high, existent methods may converge slowly or even fail to converge. We propose, at our knowledge, the first algorithm based on the lattice structure for online processing of signals with missing samples. It is an extension of the RLSL algorithm to the case of missing observations, using a Kalman filter for the prediction of missing samples. The estimated parameters guarantee the stability of the corresponding all-pole filter. In addition it is robust to high probabilities of missing a sample. It offers a fast parameter tracking even for high probabilities of missing a sample. It is compared to the Kalman pseudo linear RLS algorithm, an already proposed algorithm using a direct realization of the filter. The proposed algorithm shows better performance in reconstruction of audio signals

New non-uniform transmission and ADPCM coding system for improving both signal to noise ratio and bit rate

International audienceHere we address the problem of adaptive digitaltransmission systems. New systems based on a nonuniform transmission (NUT) principle are proposed, utilizing a recently proposed algorithm for adaptive identification and reconstruction of AR processes subject to missing data. We propose a new adaptive sampling (nonuniform transmission) method combined with the adaptive reconstruction algorithm. A new NUT-ADPCM coding-decoding system is designed. The proposed system is demonstrated for audio-signal compression and compared to the ADPCM G.726 standard. The new system yields improvements in both signal-to-noise ratio and average bit rat