Validating Stereoscopic Volume Rendering

The evaluation of stereoscopic displays for surface-based renderings is well established in terms of accurate depth perception and tasks that require an understanding of the spatial layout of the scene. In comparison direct volume rendering (DVR) that typically produces images with a high number of low opacity, overlapping features is only beginning to be critically studied on stereoscopic displays. The properties of the specific images and the choice of parameters for DVR algorithms make assessing the effectiveness of stereoscopic displays for DVR particularly challenging and as a result existing literature is sparse with inconclusive results. In this thesis stereoscopic volume rendering is analysed for tasks that require depth perception including: stereo-acuity tasks, spatial search tasks and observer preference ratings. The evaluations focus on aspects of the DVR rendering pipeline and assess how the parameters of volume resolution, reconstruction filter and transfer function may alter task performance and the perceived quality of the produced images. The results of the evaluations suggest that the transfer function and choice of recon- struction filter can have an effect on the performance on tasks with stereoscopic displays when all other parameters are kept consistent. Further, these were found to affect the sensitivity and bias response of the participants. The studies also show that properties of the reconstruction filters such as post-aliasing and smoothing do not correlate well with either task performance or quality ratings. Included in the contributions are guidelines and recommendations on the choice of pa- rameters for increased task performance and quality scores as well as image based methods of analysing stereoscopic DVR images

An investigation into the dynamical and statistical properties of dominant ocean surface waves using close-range remote sensing

Denne avhandlingen er basert på forskningsresultat som behandler statistiske og dynamiske egenskaper av dominante vinddrevne overflatebølger i åpent hav. Med uttrykket dominante bølger refererer vi her til de største bølgene, med størst energi, i en gitt sjøtilstand. Bølgedrevne prosesser er viktige både i klimasammenheng via atmosfære--hav interaksjon som drives i stor grad av bølgebrytning, samt for kommersiell og rekreasjonell offshorevirksomhet p.g.a. risikoen for å bli utsatt for f.eks. ekstreme enkeltbølger. Både bølgebrytning og ekstrembølgestatistikk er i skrivende stund ufullstendig representert i teoretiske og numeriske modeller. Arbeidet som presenteres i denne avhandlingen undersøker de ovennevnte temaene ved bruk av bølgeobservasjoner som er primært samlet inn på Ekofiskfeltet i den sentrale delen av Nordsjøen. Observasjonsdatasettene består av en langtidstidsserie av laser-altimetermålinger og stereoskopiske videodata fra Ekofisk, samt videomålinger av brytende bølger fra et forskningstokt i nordre Stillehavet. Forskningsresultatene er presentert i artikkelform med to publiserte verk og ett innlevert manuskript. Det blir påvist en tydelig forbindelse mellom økt bølgebrytning og dominante bølgegrupper, et resultat som tidligere har blitt påvist i laboratorie- og modelleksperiment, men sjeldent ved bruk av feltobservasjoner. Tredimensjonale stereo-rekonstruksjoner viser også at ekstreme bølgekammer, både brytende og ikke-brytende, følger nylig utviklet teori om ikke-lineær bølgegruppedynamikk. Dette funnet har konsekvenser f.eks. for estimering av geometriske og kinematiske bølgeegenskaper såsom steilhet og kamhastighet fra endimensjonale tidsseriemålinger. Som følge av en langtidsanalyse av endimensjonal bølgestatistikk blir det vist at enrettet, langkammet og bratt sjø mest sannsynlig leder til ekstreme enkeltbølger med statistiske egenskaper som avviker systematisk fra ordinære statistiske modeller. Tredimensjonal, kortsiktig tid-rom-statistikk av ekstreme bølgekammer blir også undersøkt v.h.a. stereomålingene fra Ekofisk. Her blir det vist at statistiske modeller utvidet fra endimensjonale til tredimensjonale bølgefelt i snitt er velegnet til å beskrive forekomsten av de høyeste bølgekammene, spesielt for relativt store tid-rom segment.The research presented in this thesis characterizes statistical and dynamical aspects of dominant wind-generated surface gravity waves inferred from field observations in intermediate-to-deep water. Dominant waves are the most energetic waves in a sea state, and as such, understanding their behavior is important in both engineering and geophysical contexts. Large waves impart considerable impact forces on marine structures such as oil and gas platforms and offshore wind turbines, and these forces may multiply manyfold when waves break. Wave breaking in deep water, often referred to as whitecapping, is also a key, though incompletely understood, process regulating the transfer of momentum, gas and heat across the air-sea interface, and must thus be accurately parameterized in large-scale weather and climate models. Current theory holds that the wave breaking process is closely linked kinematically and dynamically to the group structure inherent in ocean surface wave fields. Wave group dynamics is also believed to govern the characteristic shape and motion of so-called extreme or rogue waves, whose correct statistical description is central to many offshore activities. The work presented herein shows, using state-of-the-art stereoscopic imaging techniques employed at the Ekofisk platform complex in the central North Sea, that large-scale wave breaking activity in the open ocean is strongly enhanced in dominant wave groups. The topic of wave group-modulated wave breaking has received considerable attention in the past two decades from theoretical, numerical and laboratory perspectives; however, quantitative field studies of the phenomenon remain comparatively rare. The current results also support the general notion that the dominant waves in a given sea state regulate the breaking of shorter waves. The statistics of extreme wave crest elevations is investigated using a novel long-term laser altimeter data set, also located at the Ekofisk field. The validity of the extreme values is verified using a newly developed despiking methodology, and the quality controlled data set, which covers storm events over an 18-year period, is used to investigate the effects of wave steepness and directionality on crest height statistics. Narrow directional spread combined with high wave steepness is found to lead to crest height statistics that deviate the most from standard linear and second-order formulations. Finally, geometric wave shape and crest speed dynamics are analyzed for the highest wave crests encountered in three-dimensional, spatially and temporally resolved segments of the stereo-reconstructed sea surface fields. The directly measured crest steepness is found to conform to the classical breaking limit of Stokes, whereas crest steepness estimated from one-dimensional time series measurements using the linear gravity-wave dispersion relation are systematically higher. This may be at least in part explained by the observation that the directly measured crest speed just before, during and after the moment of maximum crest elevation slows down compared to the linear gravity-wave phase speed estimate. For the first time, the crest speed slowdown is shown with field measurements to apply to both breaking and non-breaking dominant wave crests.Doktorgradsavhandlin

A Deep Learning Approach to Evaluating Disease Risk in Coronary Bifurcations

Cardiovascular disease represents a large burden on modern healthcare systems, requiring significant resources for patient monitoring and clinical interventions. It has been shown that the blood flow through coronary arteries, shaped by the artery geometry unique to each patient, plays a critical role in the development and progression of heart disease. However, the popular and well tested risk models such as Framingham and QRISK3 current cardiovascular disease risk models are not able to take these differences when predicting disease risk. Over the last decade, medical imaging and image processing have advanced to the point that non-invasive high-resolution 3D imaging is routinely performed for any patient suspected of coronary artery disease. This allows for the construction of virtual 3D models of the coronary anatomy, and in-silico analysis of blood flow within the coronaries. However, several challenges still exist which preclude large scale patient-specific simulations, necessary for incorporating haemodynamic risk metrics as part of disease risk prediction. In particular, despite a large amount of available coronary medical imaging, extraction of the structures of interest from medical images remains a manual and laborious task. There is significant variation in how geometric features of the coronary arteries are measured, which makes comparisons between different studies difficult. Modelling blood flow conditions in the coronary arteries likewise requires manual preparation of the simulations and significant computational cost. This thesis aims to solve these challenges. The "Automated Segmentation of Coronary Arteries (ASOCA)" establishes a benchmark dataset of coronary arteries and their associated 3D reconstructions, which is currently the largest openly available dataset of coronary artery models and offers a wide range of applications such as computational modelling, 3D printed for experiments, developing, and testing medical devices such as stents, and Virtual Reality applications for education and training. An automated computational modelling workflow is developed to set up, run and postprocess simulations on the Left Main Bifurcation and calculate relevant shape metrics. A convolutional neural network model is developed to replace the computational fluid dynamics process, which can predict haemodynamic metrics such as wall shear stress in minutes, compared to several hours using traditional computational modelling reducing the computation and labour cost involved in performing such simulations

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Advanced Techniques for Design and Manufacturing in Marine Engineering

Modern engineering design processes are driven by the extensive use of numerical simulations; naval architecture and ocean engineering are no exception. Computational power has been improved over the last few decades; therefore, the integration of different tools such as CAD, FEM, CFD, and CAM has enabled complex modeling and manufacturing problems to be solved in a more feasible way. Classical naval design methodology can take advantage of this integration, giving rise to more robust designs in terms of shape, structural and hydrodynamic performances, and the manufacturing process.This Special Issue invites researchers and engineers from both academia and the industry to publish the latest progress in design and manufacturing techniques in marine engineering and to debate the current issues and future perspectives in this research area. Suitable topics for this issue include, but are not limited to, the following:CAD-based approaches for designing the hull and appendages of sailing and engine-powered boats and comparisons with traditional techniques;Finite element method applications to predict the structural performance of the whole boat or of a portion of it, with particular attention to the modeling of the material used;Embedded measurement systems for structural health monitoring;Determination of hydrodynamic efficiency using experimental, numerical, or semi-empiric methods for displacement and planning hulls;Topology optimization techniques to overcome traditional scantling criteria based on international standards;Applications of additive manufacturing to derive innovative shapes for internal reinforcements or sandwich hull structures

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Design of an endoscopic 3-D Particle-Tracking Velocimetry system and its application in flow measurements within a gravel layer

In this thesis a novel method for 3-D flow measurements within a permeable gravel layer is developed. Two fiberoptic endoscopes are used in a stereoscopic arrangement to acquire image sequences of the flow field within a single gravel pore. The images are processed by a 3-D Particle-Tracking Velocimetry (3-D PTV) algorithm, which yields the three-dimensional reconstruction of Lagrangian particle trajectories. The underlying image processing algorithms are significantly enhanced and adapted to the special conditions of endoscopic imagery. This includes methods for image preprocessing, robust camera calibration, image segmentation and particle-tracking. After a performance and accuracy analysis, the measurement technique is applied in extensive systematic investigations of the flow within a gravel layer in an experimental flume at the Federal Waterways Engineering and Research Institute in Karlsruhe. In addition to measurements of the pore flow within three gravel pores, an extended experimental setup enables the simultaneous observation of the near-bed 3-D flow field in the turbulent open-channel flow above the gravel layer and of grain motions in a sand layer beneath the gravel layer. The interaction of the free surface flow and the pore flow can be analyzed for the first time with a high temporal and spatial resolution. The experiments are part of a research project initiated by an international cooperation called Filter and Erosion Research Club (FERC). The longterm goal of this project is to quantify the influence of turbulent velocity and pressure fluctuations on the bed stability of waterways. The obtained experimental data provide new insight into the damping behaviour of a gravel bed and can be used for comparison with numerical, analytical and phenomenological models