Accelerating Cosmic Microwave Background map-making procedure through preconditioning

Estimation of the sky signal from sequences of time ordered data is one of the key steps in Cosmic Microwave Background (CMB) data analysis, commonly referred to as the map-making problem. Some of the most popular and general methods proposed for this problem involve solving generalised least squares (GLS) equations with non-diagonal noise weights given by a block-diagonal matrix with Toeplitz blocks. In this work we study new map-making solvers potentially suitable for applications to the largest anticipated data sets. They are based on iterative conjugate gradient (CG) approaches enhanced with novel, parallel, two-level preconditioners. We apply the proposed solvers to examples of simulated non-polarised and polarised CMB observations, and a set of idealised scanning strategies with sky coverage ranging from nearly a full sky down to small sky patches. We discuss in detail their implementation for massively parallel computational platforms and their performance for a broad range of parameters characterising the simulated data sets. We find that our best new solver can outperform carefully-optimised standard solvers used today by a factor of as much as 5 in terms of the convergence rate and a factor of up to $4$ in terms of the time to solution, and to do so without significantly increasing the memory consumption and the volume of inter-processor communication. The performance of the new algorithms is also found to be more stable and robust, and less dependent on specific characteristics of the analysed data set. We therefore conclude that the proposed approaches are well suited to address successfully challenges posed by new and forthcoming CMB data sets.Comment: 19 pages // Final version submitted to A&

Accelerating Cosmic Microwave Background map-making procedure through preconditioning

Estimation of the sky signal from sequences of time ordered data is one of the key steps in Cosmic Microwave Background (CMB) data analysis, commonly referred to as the map-making problem. Some of the most popular and general methods proposed for this problem involve solving generalised least squares (GLS) equations with non-diagonal noise weights given by a block-diagonal matrix with Toeplitz blocks. In this work we study new map-making solvers potentially suitable for applications to the largest anticipated data sets. They are based on iterative conjugate gradient (CG) approaches enhanced with novel, parallel, two-level preconditioners. We apply the proposed solvers to examples of simulated non-polarised and polarised CMB observations, and a set of idealised scanning strategies with sky coverage ranging from nearly a full sky down to small sky patches. We discuss in detail their implementation for massively parallel computational platforms and their performance for a broad range of parameters characterising the simulated data sets. We find that our best new solver can outperform carefully-optimised standard solvers used today by a factor of as much as 5 in terms of the convergence rate and a factor of up to $4$ in terms of the time to solution, and to do so without significantly increasing the memory consumption and the volume of inter-processor communication. The performance of the new algorithms is also found to be more stable and robust, and less dependent on specific characteristics of the analysed data set. We therefore conclude that the proposed approaches are well suited to address successfully challenges posed by new and forthcoming CMB data sets.Comment: 19 pages // Final version submitted to A&

Compressive Sensing Theory for Optical Systems Described by a Continuous Model

A brief survey of the author and collaborators' work in compressive sensing applications to continuous imaging models.Comment: Chapter 3 of "Optical Compressive Imaging" edited by Adrian Stern published by Taylor & Francis 201

X-ray computer tomography based numerical modelling of fibre reinforced composites

Non-crimp fabric reinforced polymers are commonly used to manufacture the load carrying parts in wind turbine blades. Since wind turbine blades have a large material usage, the favourable stiffness to price ratio of non-crimp fabric reinforced polymers is highly attractive for manufactures. Additionally, they are easy to manufacture, which is essential for mould sizes of up to approximately 100 m. Smaller turbine blades up to 75 m use glass fibres, lager blades require carbon fibres to meet the stiffness requirements.\ua0Wind turbine blades are ever increasing in length since the generated power is proportional to the length squared. In addition to the challenge to reduce the material usage, longer blades demand higher stiffness. Furthermore, wind turbines are one of the man-made structures that have to endure the highest numbers of load cycles. Even though wind turbine blades are mainly loaded in tension there are compressive loads present on the leeward side of the blade. Those three main material requirements demand highly tailored high-performance materials. At the same time wind turbine manufactures are under a high cost pressure as governments all over the world are cutting subsidies. As for any other high-performance material a constant production quality is essential. However, in particular composites are susceptible for manufacture flaws.\ua0X-ray computer tomography allows for the detection of some of the defects present after manufacture. X-ray computer tomography is a very promising tool for materials quality control and quantification when combined with numerical modelling. In the last years the image acquisition and analysis process has seen enormous progress that can now be exploited.\ua0In this research project the X-ray computer tomography aided engineering (XAE) process has been established. XAE systemically combines all work-steps from material image acquisition to the final finite element analysis results. The process provides an automated, accurate and fast image analysis and an element-wise and integration point-wise material orientation mapping. The analysis of the detailed stress and strain distributions after manufacture with XAE will allow for more reliable and low-cost wind turbine blades

Resin transfer molding of textile composites

The design and manufacture of textile composite panels, tubes, and angle sections that were provided to NASA for testing and evaluation are documented. The textile preform designs and requirements were established by NASA in collaboration with Boeing and several vendors of textile reinforcements. The following four types of preform architectures were used: stitched uniweave, 2D-braids, 3D-braids, and interlock weaves. The preforms consisted primarily of Hercules AS4 carbon fiber; Shell RSL-1895 resin was introduced using a resin transfer molding process. All the finished parts were inspected using ultrasonics

Учет априорной информации при итерационной реконструкции изображений литейных изделий

Methods of restoring images and properties of non-destructive testing objects based on solving inverse problems (problems of restoring distribution functions of unknown characteristics of an object based on the results of indirect measurements) are considered. Management methods are based on solving inverse problems and allow you to get the most complete information about the distributed properties of an object. The need to attract additional information imposes serious restrictions on the development of universal applied algorithms for solving incorrectly set tasks. As a rule, individual additional information is available for each specific non-destructive testing task. An effective numerical algorithm for solving an incorrectly posed problem should be focused on taking this information into account at each stage of the solution search. When solving an applied problem, it is also necessary that the algorithm corresponds to both the measuring capabilities and the capabilities of available computing tools. The problem of low-projection X-ray tomography is always associated with a lack of initial data and can only be solved using a priori information. To introduce the necessary additional information into the numerical algorithm, the methods of iterative reconstruction of tomographic images are identified as the most suitable. One of the approaches to the presentation of this kind of information is described. A practical solution to this problem will expand the scope of the X-ray tomography method.Рассматриваются методы восстановления изображений и свойств объектов неразрушающего контроля, основанные на решении обратных задач (задач восстановления функций распределения неизвестных характеристик объекта по результатам косвенных измерений). Методы управления основаны на решении обратных задач и позволяют получить наиболее полную информацию о распределенных свойствах объекта. Необходимость привлечения дополнительной информации накладывает серьезные ограничения на разработку универсальных прикладных алгоритмов решения некорректно поставленных задач. Для каждой конкретной задачи неразрушающего контроля, как правило, имеется индивидуальная дополнительная информация. Эффективный численный алгоритм решения некорректно поставленной задачи должен быть ориентирован на учет этой информации на каждом этапе поиска решения. При решении прикладной задачи также необходимо, чтобы алгоритм соответствовал как измерительным возможностям, так и возможностям доступных вычислительных средств. Проблема низкопроекционной рентгеновской томографии всегда связана с недостатком исходных данных и может быть решена только с использованием априорной информации. Для введения необходимой дополнительной информации в численный алгоритм в качестве наиболее подходящих определены методы итеративной реконструкции томографических изображений. Описан один из подходов к представлению такого рода информации. Практическое решение указанной проблемы расширит область применения метода рентгеновской томографии

Image-based numerical modelling of heterogeneous materials

In science there has always been a desire to visualise the invisible. Since the discovery of X-rays in 1895, imaging research has made remarkable progress. Nowadays, state-of-the-art technology allows to visualise the micro-structure of objects in three dimensions. However, merely visualising the structure is often insufficient. The quantitative information regarding morphology and structure is of great interest. Therefore, in addition to significant advancements in X-ray image acquisition and three-dimensional reconstruction, image analysis has become an active research field in recent years. Modern image analysis methods enable to extract even invisible information from image data. The heterogeneous micro-structure of composites imposes advanced material characterisation as even for the largest composite structures, such as wind turbine blades or airplane wings, the material properties are dictated on the micro-scale. Image-based modelling offers exceptional capabilities in analysing the micro-structure at the fibre level and numerically predicting material behaviour even at larger scales. However, image-based modelling is a complex process and all work-steps must be in line with the final modelling goal. Therefore, X-ray computed tomography aided engineering has been introduced to emphasise the importance of a holistic point of view on the image-based modelling process. The developed X-ray computed tomography aided engineering methodology has been developed based on micro X-ray computed tomography scans for non-crimp fabric glass-fibre reinforced composites. It is demonstrated that local fibre orientations and fibre volume fractions can be accurately imaged and transferred onto a finite element model. Thereby, the tensile modulus of the scanned samples can be accurately predicted and possible stress concentration regions detected. However, conventional micro X-ray computed tomography presents a major drawback. Achieving the required high resolutions to visualise carbon or glass fibres, typically ranging between 5 to 20 μm, limits the scanning field of view, which remains in the millimetre range. This drawback is overcome with new approaches in image-based modelling involving advances in imaging and image analysis. Therefore, targeted approaches for accurate image-based modelling are presented which increase the possible scanning field-of-view of fibrous composites by up to three to six orders of magnitude

Interferometry-based Free Space Communication And Information Processing

This dissertation studies, analyzes, and experimentally demonstrates the innovative use of interference phenomenon in the field of opto-electronic information processing and optical communications. A number of optical systems using interferometric techniques both in the optical and the electronic domains has been demonstrated in the filed of signal transmission and processing, optical metrology, defense, and physical sensors. Specifically it has been shown that the interference of waves in the form of holography can be exploited to realize a novel optical scanner called Code Multiplexed Optical Scanner (C-MOS). The C-MOS features large aperture, wide scan angles, 3-D beam control, no moving parts, and high beam scanning resolution. A C-MOS based free space optical transceiver for bi-directional communication has also been experimentally demonstrated. For high speed, large bandwidth, and high frequency operation, an optically implemented reconfigurable RF transversal filter design is presented that implements wide range of filtering algorithms. A number of techniques using heterodyne interferometry via acousto-optic device for optical path length measurements have been described. Finally, a whole new class of interferometric sensors for optical metrology and sensing applications is presented. A non-traditional interferometric output signal processing scheme has been developed. Applications include, for example, temperature sensors for harsh environments for a wide temperature range from room temperature to 1000 degree C

Measurements, Models, Systems and Design

531 s.