Abdourrahmane M. Atto

Control of discrete event systems with respect to strict duration: Supervision of an industrial manufacturing plant

In this paper, we propose a (max,+)-based method for the supervision of discrete event systems subject to tight time constraints. Systems under consideration are those modeled as timed event graphs and represented with linear (max,+) state equations. The supervision is addressed by looking for solutions of constrained state equations associated with timed event graph models. These constrained state equations are derived by reducing duration constraints to elementary constraints whose contributions are injected in the systems state equations. An example for supervisor synthesis is given for an industrial manufacturing plant subject to a strict temporal constraint, the thermal treatment of rubber parts for the automotive industries. Supervisors are calculated and classified according to their performance, considering their impact on the production throughput.

Supervision of an industrial plant subject to a maximal duration constraint

This paper presents a method for the supervision of an industrial plant. This supervision is aimed at guaranteeing the respect of a maximal duration constraint for some specific processing, and is addressed by considering a discrete event system model for this industrial plant. In this associated model, the time constraint is reduced to elementary constraints whose contributions are taken into account in the state equation of the system, yielding a constrained state equation for the plant. Supervisors are then synthesized by looking for solutions of this constrained state equation.

Non-stationary texture synthesis from random field modeling

This paper presents a generalized non-stationary and fractional model for texture synthesis. The model is based on convolution and modulation operations of fractional Brownian fields and its associated spectral representation contains many poles with unit norm. Synthesized textures generated from this model can exhibit several non-trivial fringes which can be visualized in natural textures such those involved in high resolution transmission electron microscopy.

Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases

The paper addresses image feature characterization and the structuring of large and heterogeneous image databases through the stochasticity or randomness appearance. Measuring stochasticity involves finding suitable representations that can significantly reduce statistical dependencies of any order. Wavelet packet representations provide such a framework for a large class of stochastic processes through an appropriate dictionary of parametric models. From this dictionary and the Kolmogorov stochasticity index, the paper proposes semantic stochasticity templates upon wavelet packet sub-bands in order to provide high level classification and content-based image retrieval. The approach is shown to be relevant for texture images.

Multivariate Linear Time-Frequency modeling and adaptive robust target detection in highly textured monovariate SAR image

Usually, in radar imaging, the scatterers are supposed to respond the same way regardless of the angle from which they are viewed and have the same properties within the emitted spectral bandwidth. Nevertheless, new capacities in SAR imaging (large bandwidth, large angular extent) make this assumption obsolete. An original application of the Linear Time-Frequency Distributions (LTFD) in SAR imaging allows to highlight the spectral and angular diversities of these reflectors. This methodology allows to transform a monovariate SAR image onto multivariate SAR image. Robust detection schemes in Gaussian or non-Gaussian background (Adaptive Matched Filter (AMF), Adaptive Normalized Matched Filter (ANMF), Anomaly Kelly Detector) associated with classical or robust Covariance Matrix Estimates (Sample Covariance Matrix (SCM), M-estimators) can then be applied exploiting these diversities. The combined two-methodologies show their very good performance for target detection.

Robust adaptive detection of buried pipes using GPR

The Ground Penetrating Radar (GPR) consists in an electromagnetic signal which is transmitted at different positions through the ground in order to obtain an image of the subsoil. In particular, the GPR is used to detect buried objects like pipes. Their detection and localisation are intricate for three main reasons. First, the noise is important in the resulting image due to the presence of several rocks and/or layers. Second, the wave speed and the response of the pipe depend on the characteristics of the different layers. Finally, the signal attenuation could be important because of the depth of pipes. In this paper, we propose to derive an adaptive detector where the steering vector is parametrised by the wave speed in the ground and the noise follows a Spherically Invariant Random Vector (SIRV) distribution in order to obtain a robust detector. To estimate the covariance matrix, we propose to use robust maximum likelihood-type estimators called M-estimators. To handle the large size of data, we consider regularised versions of such M-estimators. Simulations will allow to estimate the relation Probability of False Alarm (PFA)-Threshold. Application on real datasets will show the relevancy of the proposed analysis for detecting buried objects like pipes.

ARFBF model for non stationary random fields and application in HRTEM images

This paper presents a new model called Autoregressive Fractional Brownian Field (ARFBF) for analyzing textures which contain stationary and non-stationary components. The paper also proposes two estimation methods for the parameter of an isotropic fractional Brownian field based on Wavelet Packet (WP) spectrum: the Log-Regression on Diagonal WP spectrum (Log-RDWP) and the Log-Regression on Polar representation of WP spectrum (Log-RPWP). The Log-RPWP method provides a better estimation performance for small size images. We show the interest of ARFBF model and Log-RPWP for characterizing High-Resolution Transmission Electron Microscopy (HRTEM) images.

Adaptive multitemporal filtering of polarimetric SAR images

This paper proposes an approach for temporal adaptive filtering of Polarimetric Synthetic Aperture Radar (PolSAR) image time series by integrating a change detection technique. The filtering strategy is based on the detection of changed and unchanged areas derived by applying an appropriate similarity test. A time series including 7 descending fine-quad polarization RADARSAT2 images acquired from January 29, 2009 to Jun 22, 2009 over Chamonix-MontBlanc test-site which includes different kinds of change is used to validate the proposed method.

Simulation of image time series from dynamical fractional brownian fields

The paper addresses random field time series analysis and simulation. The analysis constrains a spatial isotropic fractional Brownian field to a dynamic temporal behavior from separable time varying Hurst parameters. The constrained dynamic applies by embedding the wavelet packet spectrum of the input random field into different spectra associated with the same random family (exponential spectrum decay). The paper highlights the relevance of the approach for representing and simulating isotropic light source and cloud dynamics.

Vector and matrix LP norms in polarimetric radar filtering

The paper addresses multi-channel complex image filtering. It provides regularization cost functions associated to non-conventional vector and matrix iv norms for promoting geometry properties. The approach is shown to be efficient for filtering PolSAR images.