Optimized techniques for real-time microwave and millimeter wave SAR imaging

Microwave and millimeter wave synthetic aperture radar (SAR)-based imaging techniques, used for nondestructive evaluation (NDE), have shown tremendous usefulness for the inspection of a wide variety of complex composite materials and structures. Studies were performed for the optimization of uniform and nonuniform sampling (i.e., measurement positions) since existing formulations of SAR resolution and sampling criteria do not account for all of the physical characteristics of a measurement (e.g., 2D limited-size aperture, electric field decreasing with distance from the measuring antenna, etc.) and nonuniform sampling criteria supports sampling below the Nyquist rate. The results of these studies demonstrate optimum sampling given design requirements that fully explain resolution dependence on sampling criteria. This work was then extended to manually-selected and nonuniformly distributed samples such that the intelligence of the user may be utilized by observing SAR images being updated in real-time. Furthermore, a novel reconstruction method was devised that uses components of the SAR algorithm to advantageously exploit the inherent spatial information contained in the data, resulting in a superior final SAR image. Furthermore, better SAR images can be obtained if multiple frequencies are utilized as compared to single frequency. To this end, the design of an existing microwave imaging array was modified to support multiple frequency measurement. Lastly, the data of interest in such an array may be corrupted by coupling among elements since they are closely spaced, resulting in images with an increased level of artifacts. A method for correcting or pre-processing the data by using an adaptation of correlation canceling technique is presented as well --Abstract, page iii

Analysis and Modeling of Passive Stereo and Time-of-Flight Imaging

This thesis is concerned with the analysis and modeling of effects which cause errors in passive stereo and Time-of-Flight imaging systems. The main topics are covered in four chapters: I commence with a treatment of a system combining Time-of-Flight imaging with passive stereo and show how commonly used fusion models relate to the measurements of the individual modalities. In addition, I present novel fusion techniques capable of improving the depth reconstruction over those obtained separately by either modality. Next, I present a pipeline and uncertainty analysis for the generation of large amounts of reference data for quantitative stereo evaluation. The resulting datasets not only contain reference geometry, but also per pixel measures of reference data uncertainty. The next two parts deal with individual effects observed: Time-of-Flight cameras suffer from range ambiguity if the scene extends beyond a certain distance. I show that it is possible to extend the valid range by changing design parameters of the underlying measurement system. Finally, I present methods that make it possible to amend model violation errors in stereo due to reflections. This is done by means of modeling a limited level of light transport and material properties in the scene

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks

Data-driven reconstruction methods for photoacoustic tomography:Learning structures by structured learning

Photoacoustic tomography (PAT) is an imaging technique with potential applications in various fields of biomedicine. By visualising vascular structures, PAT could help in the detection and diagnosis of diseases related to their dysregulation. In PAT, tissue is illuminated by light. After entering the tissue, the light undergoes scattering and absorption. The absorbed energy is transformed into an initial pressure by the photoacoustic effect, which travels to ultrasound detectors outside the tissue.This thesis is concerned with the inverse problem of the described physical process: what was the initial pressure in the tissue that gave rise to the detected pressure outside? The answer to this question is difficult to obtain when light penetration in tissue is not sufficient, the measurements are corrupted, or only a small number of detectors can be used in a limited geometry. For decades, the field of variational methods has come up with new approaches to solve these kind of problems. these kind of problems: the combination of new theory and clever algorithms has led to improved numerical results in many image reconstruction problems. In the past five years, previously state-of-the-art results were greatly surpassed by combining variational methods with artificial neural networks, a form of artificial intelligence.In this thesis we investigate several ways of combining data-driven artificial neural networks with model-driven variational methods. We combine the topics of photoacoustic tomography, inverse problems and artificial neural networks.Chapter 3 treats the variational problem in PAT and provides a framework in which hand-crafted regularisers can easily be compared. Both directional and higher-order total variation methods show improved results over direct methods for PAT with structures resembling vasculature.Chapter 4 provides a method to jointly solve the PAT reconstruction and segmentation problem for absorbing structures resembling vasculature. Artificial neural networks are embodied in the algorithmic structure of primal-dual methods, which are a popular way to solve variational problems. It is shown that a diverse training set is of utmost importance to solve multiple problems with one learned algorithm.Chapter 5 provides a convergence analysis for data-consistent networks, which combine classical regularisation methods with artificial neural networks. Numerical results are shown for an inverse problem that couples the Radon transform with a saturation problem for biomedical images.Chapter 6 explores the idea of fully-learned reconstruction by connecting two nonlinear autoencoders. By enforcing a dimensionality reduction in the artificial neural network, a joint manifold for measurements and images is learned. The method, coined learned SVD, provides advantages over other fully-learned methods in terms of interpretability and generalisation. Numerical results show high-quality reconstructions, even in the case where no information on the forward process is used.In this thesis, several ways of combining model-based methods with data-driven artificial neural networks were investigated. The resulting hybrid methods showed improved tomography reconstructions. By allowing data to improve a structured method, deeper vascular structures could be imaged with photoacoustic tomography.<br/

Software model with verification of the imaging chamber in microwave tomography

Микроталасна томографија је метода за снимање објекта путем микроталаса. Након мерења објекта системом антена у комори решава се инверзни проблем нумеричком симулацијом и оптимизацијом. У овом раду се решава проблем верности моделовања антенског система у симулацији. Избегавањем апроксимација у моделовању антенског система, добија се вернији софтверски модел. Ради постизавања тог циља  жичана квадратна спирална антена је одабрана за коришћење у комори за снимање. Употреба предложене антене у микроталасној томографији је новитет. Реализован је софтверски симулациони модел коморе са предложеном антеном.  У симулацијама је извршено поређење са другим антенама често коришћеним у литератури за дату сврху. Израђен је прототип коморе са предложеним антенама. Извршена су мерења и поређења са предложеним моделом, како би се потврдила веродостојсност модела.Mikrotalasna tomografija je metoda za snimanje objekta putem mikrotalasa. Nakon merenja objekta sistemom antena u komori rešava se inverzni problem numeričkom simulacijom i optimizacijom. U ovom radu se rešava problem vernosti modelovanja antenskog sistema u simulaciji. Izbegavanjem aproksimacija u modelovanju antenskog sistema, dobija se verniji softverski model. Radi postizavanja tog cilja  žičana kvadratna spiralna antena je odabrana za korišćenje u komori za snimanje. Upotreba predložene antene u mikrotalasnoj tomografiji je novitet. Realizovan je softverski simulacioni model komore sa predloženom antenom.  U simulacijama je izvršeno poređenje sa drugim antenama često korišćenim u literaturi za datu svrhu. Izrađen je prototip komore sa predloženim antenama. Izvršena su merenja i poređenja sa predloženim modelom, kako bi se potvrdila verodostojsnost modela.Microwave tomography is method of object imaging by means of microwaves. After object measurement by system of antennas in chamber inverse problem is solved by numeric simulation and optimization. This thesis focuses on problem of trueness in modeling antenna system in simulation. Avoding approximations while modeling antenna system yield better trueness of software model. To achieve this target wire square spiral antenna is utilized in imaging chamber. Usage of proposed antenna in microwave tomography is novelty. Software simulation model of chamber with proposed antenna is designed and evaluated. Comparison with other antennas often used in literature for this purpose is done in simulation. Chamber with antennas is realized at the prototype level. Measurement and comparison with proposed model are done in order to verify its trueness