We are concerned with the quantum inverse scattering problem. The corresponding Marchenko integral equation is solved by using the collocation method together with piece-wise polynomials, namely, Hermite splines. The scarcity of experimental data and the lack of phase information necessitate the generation of the input reflection coefficient by choosing a specific profile and then applying our method to reconstruct it. Various aspects of the single and coupled channels inverse problem and details about the numerical techniques employed are discussed. We proceed to apply our approach to synthetic seismic reflection data. The transformation of the classical one-dimensional wave equation for elastic displacement into a Schr¨odinger-like equation is presented. As an application of our method, we consider the synthetic reflection travel-time data for a layered substrate from which we recover the seismic impedance of the medium. We also apply our approach to experimental seismic reflection data collected from a deep water location in the North sea. The reflectivity sequence and the relevant seismic wavelet are extracted from the seismic reflection data by applying the statistical estimation procedure known as Markov Chain Monte Carlo method to the problem of blind deconvolution. In order to implement the Marchenko inversion method, the pure spike trains have been replaced by amplitudes having a narrow bell-shaped form to facilitate the numerical solution of the Marchenko integral equation from which the underlying seismic impedance profile of the medium is obtained. / Physics / D.Phil.(Physics)

We study the Cauchy problem of the derivative nonlinear Schrodinger equation in one space dimension. Using the method of inverse scattering, we prove global well-posedness of the derivative nonlinear Schrodinger equation for initial conditions in a dense and open subset of weighted Sobolev space that can support bright solitons.

Electromagnetic imaging is the problem of determining material properties from scattered fields measured away from the domain under investigation. Solving this inverse problem is a challenging task because (i) it is ill-posed due to the presence of (smoothing) integral operators used in the representation of scattered fields in terms of material properties, and scattered fields are obtained at a finite set of points through noisy measurements; and (ii) it is nonlinear simply due the fact that scattered fields are nonlinear functions of the material properties. The work described in this thesis tackles the ill-posedness of the electromagnetic imaging problem using sparsity-based regularization techniques, which assume that the scatterer(s) occupy only a small fraction of the investigation domain. More specifically, four novel imaging methods are formulated and implemented. (i) Sparsity-regularized Born iterative method iteratively linearizes the nonlinear inverse scattering problem and each linear problem is regularized using an improved iterative shrinkage algorithm enforcing the sparsity constraint. (ii) Sparsity-regularized nonlinear inexact Newton method calls for the solution of a linear system involving the Frechet derivative matrix of the forward scattering operator at every iteration step. For faster convergence, the solution of this matrix system is regularized under the sparsity constraint and preconditioned by leveling the matrix singular values. (iii) Sparsity-regularized nonlinear Tikhonov method directly solves the nonlinear minimization problem using Landweber iterations, where a thresholding function is applied at every iteration step to enforce the sparsity constraint. (iv) This last scheme is accelerated using a projected steepest descent method when it is applied to three-dimensional investigation domains. Projection replaces the thresholding operation and enforces the sparsity constraint. Numerical experiments, which are carried out using synthetically generated or actually measured scattered fields, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.

Electromagnetic (EM) imaging schemes are inherently non-linear and ill-posed. Albeit there exist remedies to these fundamental problems, more efficient solutions are still being sought. To this end, in this thesis, the non-linearity is tackled in- corporating a multitude of techniques (ranging from Born approximation (linear), inexact Newton (linearized) to complete nonlinear iterative Landweber schemes) that can account for weak to strong scattering problems. The ill-posedness of the EM inverse scattering problem is circumvented by formulating the above methods into a minimization problem with a sparsity constraint. More specifically, four novel in- verse scattering schemes are formulated and implemented. (i) A greedy algorithm is used together with a simple artificial neural network (ANN) for efficient and accu- rate EM imaging of weak scatterers. The ANN is used to predict the sparsity level of the investigation domain which is then used as the L0 - constraint parameter for the greedy algorithm. (ii) An inexact Newton scheme that enforces the sparsity con- straint on the derivative of the unknown material properties (not necessarily sparse) is proposed. The inverse scattering problem is formulated as a nonlinear function of the derivative of the material properties. This approach results in significant spar- sification where any sparsity regularization method could be efficiently applied. (iii) A sparsity regularized nonlinear contrast source (CS) framework is developed to di- rectly solve the nonlinear minimization problem using Landweber iterations where the convergence is accelerated using a self-adaptive projected accelerated steepest descent algorithm. (iv) A 2.5D finite difference frequency domain (FDFD) based in- verse scattering scheme is developed for imaging scatterers embedded in lossy and inhomogeneous media. The FDFD based inversion algorithm does not require the Green’s function of the background medium and appears a promising technique for biomedical and subsurface imaging with a reasonable computational time. Numerical experiments, which are carried out using synthetically generated mea- surements, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.

Image quality metrics for several inverse scattering methods and algorithms are presented. Analytical estimates and numerical simulations provide a basis for poor image quality diagnostics. The limitations and noise behavior of reconstructed images are explored analytically and empirically using a contrast ratio. Theoretical contrast ratio estimates using the canonical PEC circular cylinder are derived. Empirical studies are conducted to confirm theoretical estimates and to provide examples of image quality vs SNR for more complex scatterer profiles. Regularized sampling is shown to be more noise sensitive than tomographic reconstructive methods.

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<p>This thesis contributes significantly to the advanced applications of scattering parameter sensitivity analysis including the design optimization of high-frequency printed structures and in microwave imaging. In both applications, the methods exploit the computational efficiency of the self-adjoint sensitivity analysis (SASA) approach where only one EM simulation suffices to obtain both the responses and their gradients with respect to the optimizable variables.</p> <p>An<em> S</em>-parameter self-adjoint sensitivity formula for multiport planar structures using the method of moments (MoM) current solution is proposed. It can be easily implemented with existing MoM solvers. The shape perturbation which is required in computing the system-matrix derivatives are accommodated by changing the material properties of the local mesh elements. The use of a pre-determined library system matrix further accelerates the design optimization because the writing/reading of the system matrix to/from the disk is avoided. The design optimization of a planar ultra-wide band (UWB) antenna and a double stub tuner are presented as validation examples.</p> <p>In the application of the sensitivity-based imaging, the SASA approach allows for real-time image reconstruction once the field distribution of the reference object (RO) is known. Here, the RO includes the known background medium of the object under test (OUT) and the known antennas. The field distribution can be obtained using simulation or measurement.</p> <p>The spatial resolution is an important measure of the performance of an imaging technique. It represents the smallest detail that can be detected by a given imaging method. The resolution of the sensitivity-based imaging approach has not been studied before. In this thesis, the resolution limits are systematically studied with planar raster scanning and circular array data acquisition. In addition, the method’s robustness to noise is studied. A guideline is presented for an acceptable signal-to-noise ratio (SNR) versus the spatial and frequency sampling rates in designing a data-acquisition system for the method.</p> <p>This thesis validates the sensitivity-based imaging with measured data of human tissue phantoms for the first time. The differences in dielectric properties of the targets are qualitatively reflected in the reconstructed image. A preliminary study of imaging with inexact background information of the OUT is also presented.</p> / Doctor of Philosophy (PhD)

La présente thèse est consacrée à l'étude de quelques problèmes inverses pour l'équation de Helmholtz jauge-covariante, dont des cas particuliers comprennent l'équation de Schrödinger pour une particule élémentaire chargée dans un champ magnétique et l'équation d'onde harmonique en temps qui décrive des ondes acoustiques dans un fluide en écoulement. Ces problèmes ont comme motivation des applications dans des tomographies différentes, qui comprennent la tomographie acoustique, la tomographie qui utilise des particules élémentaires et la tomographie d'impédance électrique. En particulier, nous étudions des problèmes inverses motivés par des applications en tomographie acoustique de fluide en écoulement. Nous proposons des formules et équations qui permettent de réduire le problème de tomographie acoustique à un problème de diffusion inverse approprié. En suivant, nous développons un algorithme fonctionnel-analytique pour la résolution de ce problème de diffusion inverse. Cependant, en général, la solution de ce problème n'est unique qu'à une transformation de jauge appropriée près. À cet égard, nous établissons des formules qui permettent de se débarrasser de cette non-unicité de jauge et retrouver des paramètres du fluide, en mesurant des ondes acoustiques à des plusieurs fréquences. Nous présentons également des exemples des fluides qui ne sont pas distinguable dans le cadre de tomographie acoustique considérée. En suivant, nous considérons le problème de diffusion inverse sans information de phase. Ce problème est motivé par des applications en tomographie qui utilise des particules élémentaires, où seulement le module de l'amplitude de diffusion peut être mesuré facilement. Nous établissons des estimations dans l'espace de configuration pour les reconstructions sans phase de type Borne, qui sont requises pour le développement des méthodes de diffusion inverse précises. Finalement, nous considérons le problème de détermination d'une surface de Riemann dans le plan projectif à partir de son bord. Ce problème survient comme une partie du problème de Dirichlet-Neumann inverse pour l'équation de Laplace sur une surface inconnue, qui est motivé par des applications en tomographie d'impédance électrique. / This work is devoted to study of some inverse problems for the gauge-covariant Helmholtz equation, whose particular cases include the Schrödinger equation for a charged elementary particle in a magnetic field and the time-harmonic wave equation describing sound waves in a moving fluid. These problems are mainly motivated by applications in different tomographies, including acoustic tomography, tomography using elementary particles and electrical impedance tomography. In particular, we study inverse problems motivated by applications in acoustic tomography of moving fluid. We present formulas and equations which allow to reduce the acoustic tomography problem to an appropriate inverse scattering problem. Next, we develop a functional-analytic algorithm for solving this inverse scattering problem. However, in general, the solution to the latter problem is unique only up to an appropriate gauge transformation. In this connection, we give formulas and equations which allow to get rid of this gauge non-uniqueness and recover the fluid parameters, by measuring acoustic fields at several frequencies. We also present examples of fluids which are not distinguishable in this acoustic tomography setting. Next, we consider the inverse scattering problem without phase information. This problem is motivated by applications in tomography using elementary particles, where only the absolute value of the scattering amplitude can be measured relatively easily. We give estimates in the configuration space for the phaseless Born-type reconstructions, which are needed for the further development of precise inverse scattering algorithms. Finally, we consider the problem of determination of a Riemann surface in the complex projective plane from its boundary. This problem arises as a part of the inverse Dirichlet-to-Neumann problem for the Laplace equation on an unknown 2-dimensional surface, and is motivated by applications in electrical impedance tomography.

In this thesis, having in mind applications to the fault-detection/diagnosis of electrical networks, we consider some inverse scattering problems for the Zakharov-Shabat equations and time-independent Schrödinger operators over star-shaped graphs. The first chapter is devoted to describe reflectometry methods applied to electrical networks as an inverse scattering problems on the star-shaped network. Reflectometry methods are presented and modeled by the telegrapher's equations. Reflectometry experiments can be written as inverse scattering problems for Schrödinger operator in the lossless case and for Zakharov-Shabat system for the lossy transmission network. In chapter 2 we introduce some elements of the inverse scattering theory for 1 d Schrödinger equations and the Zakharov-Shabat system. We recall the basic results for these two systems and we present the state of art of scattering theory on network. The third chapter deals with some inverse scattering for the Schrödinger operators. We prove the identifiability of the geometry of the star-shaped graph: the number of the edges and their lengths. Next, we study the potential identification problem by inverse scattering. In the last chapter we focus on the inverse scattering problems for lossy transmission star-shaped network. We prove the identifiability of some geometric informations by inverse scattering and we present a result toward the identification of the heterogeneities, showing the identifiability of the loss line factor.

The objective of this thesis work is to detect and to characterize three-dimensional targets in a disordered medium, using electromagnetic excitations. This research domain is of great interest in many applications, such as subsoil probing, medical imaging, non-destructive testing and geophysical exploration, etc. In order to extract the target information from the heterogeneities of the medium, we propose to use one of the time reversal technique, namely the DORT method (French acronym for Décomposition de l'Opérateur de Retournement Temporel). This method permits us to generate different waves that focus selectively on each target in high noisy environment. Moreover, this method is also combined with a non-linear inversion algorithm, which permits not only to localize but also to characterize the targets. The reconstruction resolution appears to be better than the ones obtained with the DORT or the inversion procedure alone, especially in the illumination direction. It is also shown that using full-polarized data is indispensable for achieving better performances rather than in scalar configuration. Moreover, in the half-space configuration, it is mandatory to use the frequency-diversity data to get an accurate reconstruction. These theoretical developments are also confronted to experimental data measured in the optical domain. A full-polarization Tomographic Diffractive Microscopy (TDM) is implemented and a resolution about one-fourth of the wavelength is thus obtained. Furthermore, the DORT method is applied in TDM to realize selective focalization and characterization. In the presence of multiple targets, selective characterization of each scatterer is achieved.This thesis work also deals with the characterization problem using transient data. Different inversion algorithms are validated using synthetic and experimental hyper-frequency data.