Mourad Elloumi

Quantitative detection of bcr-abl transcripts in chronic myeloid leukemia.

The optimal management of malignant haematological disorders depend on the degree of tumor load reduction after therapy. Chronic myeloid leukemia constitutes a clinical model for molecular detection and therapy surveillance of malignant disease since this entity was the first leukemia shown to be associated with a specific bcr-abl fusion gene in the patients leukemia cells. Molecular monitoring of bcr-abl transcript levels by real-time quantitative PCR is increasingly used to assess treatment response in patients with chronic myeloid leukemia (CML). This has become particularly relevant in the era of imatinib therapy when residual levels of leukaemia usually fall below the level of detection by bone marrow cytogenetic analysis. We monitored bcr-abl transcript levels by quantitative real time PCR in 50 tunisian patients treated with imatinib for chronic myeloid leukemia in chronic phase for a median of 29 months (3-60) after they started imatinib.

An iterative parametric estimation method for Hammerstein large-scale systems: a simulation study of hydraulic process

This paper aims at developing an iterative method which permits to estimate the parameters of single-input single-output (SISO) large-scale nonlinear systems, described by Hammerstein mathematical models. We particularly focus on the dynamic large-scale nonlinear systems, which are made up of several interconnected nonlinear monovariable subsystems. Each subsystem can operate in a stochastic environment and be described by a discrete-time Hammerstein mathematical model with known structure variables (order, delay) and unknown time-varying parameters. The problem formulation is achieved based on the prediction error method and the least-squares techniques. The convergence analysis of the recursive algorithm is provided using the differential equation approach and its performance is illustrated by treating two simulation examples.

Design of Self-Tuning Regulator for Large-Scale Interconnected Hammerstein Systems

This paper deals with the self-tuning regulator for large-scale stochastic nonlinear systems, which are composed of several interconnected nonlinear monovariable subsystems. Each interconnected subsystem is described by discrete Hammerstein model with unknown and time-varying parameters. This self-tuning control is developed on the basis of the minimum variance approach and is combined by a recursive algorithm in the estimation step. The parametric estimation step is performed on the basis of the prediction error method and the least-squares techniques. Simulation results of the proposed self-tuning regulator for two interconnected nonlinear hydraulic systems show the reliability and effectiveness of the developed method.

Improved Discrete Techniques of Time-Delay and Order Estimation for Large-Scale Interconnected Nonlinear Systems

The selection of a suitable model structure is an essential step in system modeling. Model structure is defined by determining the class, the time delay, and the model order. This paper proposes improved structural estimation procedures for large-scale interconnected nonlinear systems which are composed of a set of interconnected Single-Input Single-Output (SISO) Hammerstein structures and described by discrete-time stochastic models with unknown time-varying parameters. An extensive Determinant Report (DR) algorithm is developed to determine the order of the process. An improved discrete-time technique based on Recursive Extended Least Squares with Varying Time (RELSVT) delay method is proposed to estimate the time delays of the considered system. The developed theoretical analysis and simulation results prove the validity and performance of the proposed algorithms.

Adaptive tracking control algorithms for large-scale interconnected nonlinear systems

The present work proposes two control algorithms for a class of large-scale deterministic systems, which are composed into a set of interconnected monovariable systems and defined by discrete oriented block model with variable parameters. The purpose of these control algorithms is to formulate the tracking problem permitting each interconnected system output to pursue a certain time-varying reference trajectory. Each control algorithm is developed with a direct or indirect scheme and is incorporated into a recursive estimator, used to estimate the system parameters. This estimation step is established using the prediction error approach and the least-squares method. A numerical simulation example is cured to validate the developed theoretical results.

Identification and self-tuning regulation of a solar system

This paper deals with the parameter estimation and the self-tuning regulation of a solar system, which is composed of a solar cell followed by a boost converter. The considered system is described by an input-output stochastic mathematical model at discrete-time, with unknown but constant or slowly time-varying parameters. This parameter estimation is conducted based on the method of prediction error and least squares techniques. The posed regulation problem consists in the development of a self-tuning regulator on the basis of the generalized minimum-variance approach. Thus, a generalized minimum-variance self-tuning regulator is developed. A numerical simulation of the self-tuning regulation of the considered stochastic system is treated.