Vector Graphics for Real-time 3D Rendering

Algorithms are presented that enable the use of vector graphics representations of images in texture maps for 3D real time rendering. Vector graphics images are resolution independent and can be zoomed arbitrarily without losing detail or crispness. Many important types of images, including text and other symbolic information, are best represented in vector form. Vector graphics textures can also be used as transparency mattes to augment geometric detail in models via trim curves. Spline curves are used to represent boundaries around regions in standard vector graphics representations, such as PDF and SVG. Antialiased rendering of such content can be obtained by thresholding implicit representations of these curves. The distance function is an especially useful implicit representation. Accurate distance function computations would also allow the implementation of special effects such as embossing. Unfortunately, computing the true distance to higher order spline curves is too expensive for real time rendering. Therefore, normally either the distance is approximated by normalizing some other implicit representation or the spline curves are approximated with simpler primitives. In this thesis, three methods for rendering vector graphics textures in real time are introduced, based on various approximations of the distance computation. The first and simplest approach to the distance computation approximates curves with line segments. Unfortunately, approximation with line segments gives only C0 continuity. In order to improve smoothness, spline curves can also be approximated with circular arcs. This approximation has C1 continuity and computing the distance to a circular arc is only slightly more expensive than computing the distance to a line segment. Finally an iterative algorithm is discussed that has good performance in practice and can compute the distance to any parametrically differentiable curve (including polynomial splines of any order) robustly. This algorithm is demonstrated in the context of a system capable of real-time rendering of SVG content in a texture map on a GPU. Data structures and acceleration algorithms in the context of massively parallel GPU architectures are also discussed. These data structures and acceleration structures allow arbitrary vector content (with space-variant complexity, and overlapping regions) to be represented in a random-access texture

Radial Basis Functions: Biomedical Applications and Parallelization

Radial basis function (RBF) is a real-valued function whose values depend only on the distances between an interpolation point and a set of user-specified points called centers. RBF interpolation is one of the primary methods to reconstruct functions from multi-dimensional scattered data. Its abilities to generalize arbitrary space dimensions and to provide spectral accuracy have made it particularly popular in different application areas, including but not limited to: finding numerical solutions of partial differential equations (PDEs), image processing, computer vision and graphics, deep learning and neural networks, etc. The present thesis discusses three applications of RBF interpolation in biomedical engineering areas: (1) Calcium dynamics modeling, in which we numerically solve a set of PDEs by using meshless numerical methods and RBF-based interpolation techniques; (2) Image restoration and transformation, where an image is restored from its triangular mesh representation or transformed under translation, rotation, and scaling, etc. from its original form; (3) Porous structure design, in which the RBF interpolation used to reconstruct a 3D volume containing porous structures from a set of regularly or randomly placed points inside a user-provided surface shape. All these three applications have been investigated and their effectiveness has been supported with numerous experimental results. In particular, we innovatively utilize anisotropic distance metrics to define the distance in RBF interpolation and apply them to the aforementioned second and third applications, which show significant improvement in preserving image features or capturing connected porous structures over the isotropic distance-based RBF method. Beside the algorithm designs and their applications in biomedical areas, we also explore several common parallelization techniques (including OpenMP and CUDA-based GPU programming) to accelerate the performance of the present algorithms. In particular, we analyze how parallel programming can help RBF interpolation to speed up the meshless PDE solver as well as image processing. While RBF has been widely used in various science and engineering fields, the current thesis is expected to trigger some more interest from computational scientists or students into this fast-growing area and specifically apply these techniques to biomedical problems such as the ones investigated in the present work

Advanced Fault Detection Methods for Permanent Magnets Synchronous Machines

The trend in recent years of transport electrification has significantly increased the demand for reliability and availability of electric drives, particularly in those employing Permanent Magnet Synchronous Machines (PMSM), often selected due to their high efficiency and energy density. Fault detection has been identified as one of the key aspects to cover such demand. Stator winding faults are known to be the second most common type of fault, after bearing fault. An extensive literature review has shown that, although a number of methods has been proposed to address this type of fault, no tool of general application, capable of dealing effectively with fault detection under transient conditions unrelated to the fault, has been proposed up to date. This thesis has made contributions to modelling, real-time emulation and stator winding fault detection of PMSM. Fault detection has been carried out through model-based and signal-based methods with a specific aim at operation during transient conditions. Furthermore, fault classification methods already available have been implemented with features computed by proposed signal-based fault detection methods. The main conclusion drawn from this thesis is that model-based fault detection methods, particularly those based on residuals, appear to be better suited for transient conditions analysis, as opposed to signal-based fault detection methods. However, it is expected that a combination of the two (model/signal) would yield the best results

Computer-Aided Geometry Modeling

Techniques in computer-aided geometry modeling and their application are addressed. Mathematical modeling, solid geometry models, management of geometric data, development of geometry standards, and interactive and graphic procedures are discussed. The applications include aeronautical and aerospace structures design, fluid flow modeling, and gas turbine design

Articulating Space: Geometric Algebra for Parametric Design -- Symmetry, Kinematics, and Curvature

To advance the use of geometric algebra in practice, we develop computational methods for parameterizing spatial structures with the conformal model. Three discrete parameterizations – symmetric, kinematic, and curvilinear – are employed to generate space groups, linkage mechanisms, and rationalized surfaces. In the process we illustrate techniques that directly benefit from the underlying mathematics, and demonstrate how they might be applied to various scenarios. Each technique engages the versor – as opposed to matrix – representation of transformations, which allows for structure-preserving operations on geometric primitives. This covariant methodology facilitates constructive design through geometric reasoning: incidence and movement are expressed in terms of spatial variables such as lines, circles and spheres. In addition to providing a toolset for generating forms and transformations in computer graphics, the resulting expressions could be used in the design and fabrication of machine parts, tensegrity systems, robot manipulators, deployable structures, and freeform architectures. Building upon existing algorithms, these methods participate in the advancement of geometric thinking, developing an intuitive spatial articulation that can be creatively applied across disciplines, ranging from time-based media to mechanical and structural engineering, or reformulated in higher dimensions

Characterization of multiphase flows integrating X-ray imaging and virtual reality

Multiphase flows are used in a wide variety of industries, from energy production to pharmaceutical manufacturing. However, because of the complexity of the flows and difficulty measuring them, it is challenging to characterize the phenomena inside a multiphase flow. To help overcome this challenge, researchers have used numerous types of noninvasive measurement techniques to record the phenomena that occur inside the flow. One technique that has shown much success is X-ray imaging. While capable of high spatial resolutions, X-ray imaging generally has poor temporal resolution. This research improves the characterization of multiphase flows in three ways. First, an X-ray image intensifier is modified to use a high-speed camera to push the temporal limits of what is possible with current tube source X-ray imaging technology. Using this system, sample flows were imaged at 1000 frames per second without a reduction in spatial resolution. Next, the sensitivity of X-ray computed tomography (CT) measurements to changes in acquisition parameters is analyzed. While in theory CT measurements should be stable over a range of acquisition parameters, previous research has indicated otherwise. The analysis of this sensitivity shows that, while raw CT values are strongly affected by changes to acquisition parameters, if proper calibration techniques are used, acquisition parameters do not significantly influence the results for multiphase flow imaging. Finally, two algorithms are analyzed for their suitability to reconstruct an approximate tomographic slice from only two X-ray projections. These algorithms increase the spatial error in the measurement, as compared to traditional CT; however, they allow for very high temporal resolutions for 3D imaging. The only limit on the speed of this measurement technique is the image intensifier-camera setup, which was shown to be capable of imaging at a rate of at least 1000 FPS. While advances in measurement techniques for multiphase flows are one part of improving multiphase flow characterization, the challenge extends beyond measurement techniques. For improved measurement techniques to be useful, the data must be accessible to scientists in a way that maximizes the comprehension of the phenomena. To this end, this work also presents a system for using the Microsoft Kinect sensor to provide natural, non-contact interaction with multiphase flow data. Furthermore, this system is constructed so that it is trivial to add natural, non-contact interaction to immersive visualization applications. Therefore, multiple visualization applications can be built that are optimized to specific types of data, but all leverage the same natural interaction. Finally, the research is concluded by proposing a system that integrates the improved X-ray measurements, with the Kinect interaction system, and a CAVE automatic virtual environment (CAVE) to present scientists with the multiphase flow measurements in an intuitive and inherently three-dimensional manner

Fast algorithm for real-time rings reconstruction

The GAP project is dedicated to study the application of GPU in several contexts in which real-time response is important to take decisions. The definition of real-time depends on the application under study, ranging from answer time of μs up to several hours in case of very computing intensive task. During this conference we presented our work in low level triggers [1] [2] and high level triggers [3] in high energy physics experiments, and specific application for nuclear magnetic resonance (NMR) [4] [5] and cone-beam CT [6]. Apart from the study of dedicated solution to decrease the latency due to data transport and preparation, the computing algorithms play an essential role in any GPU application. In this contribution, we show an original algorithm developed for triggers application, to accelerate the ring reconstruction in RICH detector when it is not possible to have seeds for reconstruction from external trackers

Third International Conference on Inverse Design Concepts and Optimization in Engineering Sciences (ICIDES-3)

Papers from the Third International Conference on Inverse Design Concepts and Optimization in Engineering Sciences (ICIDES) are presented. The papers discuss current research in the general field of inverse, semi-inverse, and direct design and optimization in engineering sciences. The rapid growth of this relatively new field is due to the availability of faster and larger computing machines

Modern Machine Learning for LHC Physicists

Modern machine learning is transforming particle physics, faster than we can follow, and bullying its way into our numerical tool box. For young researchers it is crucial to stay on top of this development, which means applying cutting-edge methods and tools to the full range of LHC physics problems. These lecture notes are meant to lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications as fast as possible. They start with an LHC-specific motivation and a non-standard introduction to neural networks and then cover classification, unsupervised classification, generative networks, and inverse problems. Two themes defining much of the discussion are well-defined loss functions reflecting the problem at hand and uncertainty-aware networks. As part of the applications, the notes include some aspects of theoretical LHC physics. All examples are chosen from particle physics publications of the last few years. Given that these notes will be outdated already at the time of submission, the week of ML4Jets 2022, they will be updated frequently.Comment: First version, we very much appreciate feedbac