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"Verhaegen, Michel"
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Data driven identification of networks of dynamic systems
\"The identification of network connected dynamic systems is currently a hot research topic within the community of systems and control. Other engineering areas, social sciences and system biology are putting a lot of effort in the study of network connected systems. Modeling such networks and the identification of these models from acquired measurements is crucial in the analysis or understanding of the dynamics. Based on these models, synthesis to modify the behavior of the network can also be performed. This book gives a unique overview of state of the art research in the field of identifying networks of linear dynamical systems. This overview combines many of the pioneering contributions from the authors with those of other researchers that play a crucial role in the development of this new field\"-- Provided by publisher.
Book
Imaging & identification of malaria parasites using cellphone microscope with a ball lens
We have optimized the design and imaging procedures, to clearly resolve the malaria parasite in Giemsa-stained thin blood smears, using simple low-cost cellphone-based microscopy with oil immersion. The microscope uses a glass ball as the objective and the phone camera as the tube lens. Our optimization includes the optimal choice of the ball lens diameter, the size and the position of the aperture diaphragm, and proper application of immersion, to achieve diagnostic capacity in a wide field of view. The resulting system is potentially applicable to low-cost in-the-field optical diagnostics of malaria as it clearly resolves micron-sized features and allows for analysis of parasite morphology in the field of 50 × 50 μm, and parasite detection in the field of at least 150 × 150 μm.
Journal Article
Closed-loop subspace identification methods: an overview
In this study, the authors present an overview of closed-loop subspace identification methods found in the recent literature. Since a significant number of algorithms has appeared over the last decade, the authors highlight some of the key algorithms that can be shown to have a common origin in autoregressive modelling. Many of the algorithms found in the literature are variants on the algorithms that are discussed here. In this study, the aim is to give a clear overview of some of the more successful methods presented throughout the last decade. Furthermore, the authors retrace these methods to a common origin and show how they differ. The methods are compared both on the basis of simulation examples and real data. Although the main focus in the literature has been on the identification of discrete-time models, identification of continuous-time models is also of practical interest. Hence, the authors also provide an overview of the continuous-time formulation of the identification framework.
Journal Article
Optimal model-based sensorless adaptive optics for epifluorescence microscopy
We report on a universal sample-independent sensorless adaptive optics method, based on modal optimization of the second moment of the fluorescence emission from a point-like excitation. Our method employs a sample-independent precalibration, performed only once for the particular system, to establish the direct relation between the image quality and the aberration. The method is potentially applicable to any form of microscopy with epifluorescence detection, including the practically important case of incoherent fluorescence emission from a three dimensional object, through minor hardware modifications. We have applied the technique successfully to a widefield epifluorescence microscope and to a multiaperture confocal microscope.
Journal Article
Convex combination of alternating projection and Douglas–Rachford operators for phase retrieval
We present the convergence analysis of convex combination of the alternating projection and Douglas–Rachford operators for solving the phase retrieval problem. New convergence criteria for iterations generated by the algorithm are established by applying various schemes of numerical analysis and exploring both physical and mathematical characteristics of the phase retrieval problem. Numerical results demonstrate the advantages of the algorithm over the other widely known projection methods in practically relevant simulations.
Journal Article
Some New Characterizations of Intrinsic Transversality in Hilbert Spaces
Motivated by a number of questions concerning transversality-type properties of pairs of sets recently raised by Ioffe and Kruger, this paper reports several new characterizations of the intrinsic transversality property in Hilbert spaces. New results in terms of normal vectors clarify the picture of intrinsic transversality, its variants and sufficient conditions for subtransversality, and unify several of them. For the first time, intrinsic transversality is characterized by an equivalent condition which does not involve normal vectors. This characterization offers another perspective on intrinsic transversality. As a consequence, the obtained results allow us to answer a number of important questions about transversality-type properties.
Journal Article
Robust Fault Detection Observer and Fault Estimation Filter Design for LTI Systems Based on GKYP Lemma
This paper addresses the robust fault detection observer and fault estimation filter design issues for linear time invariant (LTI) systems with parameter uncertainties in a polytope and the systems are subject to unknown inputs as well. The observer and filter design are investigated in the H∞/H– index framework in the finite frequency interval by using the generalized KYP lemma. The threshold design and the worst undetectable fault size estimation are discussed. A numerical example is given to illustrate the effectiveness of the derived algorithms.
Journal Article
Controller Design for a High-Sampling-Rate Closed-Loop Adaptive Optics System with Piezo-Driven Deformable Mirror
Adaptive Optics (AO) systems are widely used in many scientific and medical applications, such as astronomy, laser systems and microscopes, in order to improve the resolution of the image by actively sensing and compensating the optical aberration in the system. This paper aims at improving the performance of a closed-loop AO system with Piezo-driven Deformable Mirror (PDM) and high-sampling-rate Wavefront Sensor (WFS) by means of model-based control. The improvement is achieved by reducing the hysteresis in the PDM with a hysteresis compensator and identifying a linear dynamic model of the AO system from the measurement data with a closed-loop subspace identification approach. Based on the identified model of the AO system and the model of the disturbance, a dynamic controller is designed. Experimental results show that the variance of the residual error of the proposed closed-loop AO system has been reduced by 30% with respect to the conventional AO control approach.
Journal Article
Adaptive and Real-time Optimal Control for Adaptive Optics Systems
An optimal control method to reject turbulence-induced wavefront distortions in an Adaptive Optics system is discussed. Details of a data-driven control approach are presented where the emphasis is put on the estimation of the optimal predictor of the wavefront disturbance. Several algorithms capable of finding the predictor parameters from the sensor signals are discussed. These algorithms could also track time-varying disturbance characteristics in an adaptive control setting. In a simulation experiment with turbulence data, recursive type and batch-wise estimation algorithms are compared.
Journal Article