Effect of Disease Severity on the Performance of Cirrus Spectral-Domain OCT for Glaucoma Diagnosis

PURPOSE. To evaluate the effect of disease severity on the diagnostic accuracy of the Cirrus Optical Coherence Tomograph (Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, CA) for glaucoma detection. METHODS. One hundred thirty-five glaucomatous eyes of 99 patients and 79 normal eyes of 47 control subjects were recruited from the longitudinal Diagnostic Innovations in Glaucoma Study (DIGS). The severity of the disease was graded based on the visual field index (VFI) from standard automated perimetry. Imaging of the retinal nerve fiber layer (RNFL) was obtained using the optic disc cube protocol available on the Cirrus HD-OCT. Pooled receiver operating characteristic (ROC) curves were initially obtained for each parameter of the Cirrus HD-OCT. The effect of disease severity on diagnostic performance was evaluated by fitting an ROC regression model, with VFI used as a covariate, and calculating the area under the ROC curve (AUCs) for different levels of disease severity. RESULTS. The largest pooled AUCs were for average thickness (0.892), inferior quadrant thickness (0.881), and superior quadrant thickness (0.874). Disease severity had a significant influence on the detection of glaucoma. For the average RNFL thickness parameter, AUCs were 0.962, 0.932, 0.886, and 0.822 for VFIs of 70%, 80%, 90%, and 100%, respectively. CONCLUSIONS. Disease severity had a significant effect on the diagnostic performance of the Cirrus HD-OCT and thus should be considered when interpreting results from this device and when considering the potential applications of this instrument for diagnosing glaucoma in the various clinical settings. (Invest Ophthalmol Vis Sci. 2010;51:4104-4109) DOI:10.1167/iovs.094716

Partial utility-driven scheduling for flexible SLA and pricing arbitration in clouds

Cloud SLAs compensate customers with credits when average availability drops below certain levels. This is too inflexible because consumers lose non-measurable amounts of performance being only compensated later, in next charging cycles. We propose to schedule virtual machines (VMs), driven by range-based non-linear reductions of utility, different for classes of users and across different ranges of resource allocations: partial utility. This customer-defined metric, allows providers transferring resources between VMs in meaningful and economically efficient ways. We define a comprehensive cost model incorporating partial utility given by clients to a certain level of degradation, when VMs are allocated in overcommitted environments (Public, Private, Community Clouds). CloudSim was extended to support our scheduling model. Several simulation scenarios with synthetic and real workloads are presented, using datacenters with different dimensions regarding the number of servers and computational capacity. We show the partial utility-driven driven scheduling allows more VMs to be allocated. It brings benefits to providers, regarding revenue and resource utilization, allowing for more revenue per resource allocated and scaling well with the size of datacenters when comparing with an utility-oblivious redistribution of resources. Regarding clients, their workloads’ execution time is also improved, by incorporating an SLA-based redistribution of their VM’s computational power.

Estimating Effective Slowdown of Tasks in Energy-Aware Clouds

Consolidation consists in scheduling multiple virtual machines onto fewer servers in order to improve resource utilization and to reduce operational costs due to power consumption. However, virtualization technologies do not offer performance isolation, causing applications’ slowdown. In this work, we propose a performance enforcing mechanism, composed of a slowdown estimator, and a interference- and power-aware scheduling algorithm. The slowdown estimator determines, based on noisy slowdown data samples obtained from state-of-the-art slowdown meters, if tasks will complete within their deadlines, invoking the scheduling algorithm if needed. When invoked, the scheduling algorithm builds performance and power aware virtual clusters to successfully execute the tasks. We conduct simulations injecting synthetic jobs which characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our strategy can be efficiently integrated with state-of-the-art slowdown meters to fulfil contracted SLAs in real-world environments, while reducing operational costs in about 12%.

TSKY: a dependable middleware solution for data privacy using public storage clouds

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Optical measurements in disperse two-phase flows : application to rain formation in cumulus clouds

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2012

Dark Clouds or Silver Linings? Knightian Uncertainty and Climate Change

This paper examines the impact of Knightian uncertainty upon optimal climate policy through the prism of a continuous-time real option modelling framework. We analytically determine optimal intertemporal climate policies under ambiguous assessments of climate damages. Additionally, numerical simulations are provided to illustrate the properties of the model. The results indicate that increasing Knightian uncertainty accelerates climate policy, i.e. policy makers become more reluctant to postpone the timing of climate policies into the future.

Coltop3D: A new software for structural analysis with high resolution 3D point clouds and DEM

Coltop3D is a software that performs structural analysis by using digital elevation model (DEM) and 3D point clouds acquired with terrestrial laser scanners. A color representation merging slope aspect and slope angle is used in order to obtain a unique code of color for each orientation of a local slope. Thus a continuous planar structure appears in a unique color. Several tools are included to create stereonets, to draw traces of discontinuities, or to compute automatically density stereonet. Examples are shown to demonstrate the efficiency of the method.

Symmetry breaking in small rotating clouds of trapped ultracold Bose atoms

We study the signatures of rotational and phase symmetry breaking in small rotating clouds of trapped ultracold Bose atoms by looking at rigorously defined condensate wave function. Rotational symmetry breaking occurs in narrow frequency windows, where energy degeneracy between the lowest energy states of different total angular momentum takes place. This leads to a complex condensate wave function that exhibits vortices clearly seen as holes in the density, as well as characteristic local phase patterns, reflecting the appearance of vorticities. Phase symmetry (or gauge symmetry) breaking, on the other hand, is clearly manifested in the interference of two independent rotating clouds.

Comparison of Roadway Roughness Derived from LIDAR and SFM 3D Point Clouds, HR-3001, 2015

This report describes a short-term study undertaken to investigate the potential for using dense three-dimensional (3D) point clouds generated from light detection and ranging (LIDAR) and photogrammetry to assess roadway roughness. Spatially continuous roughness maps have potential for the identification of localized roughness features, which would be a significant improvement over traditional profiling methods. This report specifically illustrates the use of terrestrial laser scanning (TLS) and photogrammetry using a process known as structure from motion (SFM) to acquire point clouds and illustrates the use of these point clouds in evaluating road roughness. Five roadway sections were chosen for scanning and testing: three gravel road sections, one portland cement concrete (PCC) section, and one asphalt concrete (AC) section. To compare clouds obtained from terrestrial laser scanning and photogrammetry, the coordinates of the clouds for the same section on the same date were matched using open source computer code. The research indicates that the technologies described are very promising for evaluating road roughness. The major advantage of both technologies is the large amount of data collected, which allows the evaluation of the full surface. Additional research is needed to further develop the use of dense 3D point clouds for roadway assessment.

Rockfall susceptibility assessment and remote geological mapping with LiDAR point clouds

Characterizing the geological features and structures in three dimensions over inaccessible rock cliffs is needed to assess natural hazards such as rockfalls and rockslides and also to perform investigations aimed at mapping geological contacts and building stratigraphy and fold models. Indeed, the detailed 3D data, such as LiDAR point clouds, allow to study accurately the hazard processes and the structure of geologic features, in particular in vertical and overhanging rock slopes. Thus, 3D geological models have a great potential of being applied to a wide range of geological investigations both in research and applied geology projects, such as mines, tunnels and reservoirs. Recent development of ground-based remote sensing techniques (LiDAR, photogrammetry and multispectral / hyperspectral images) are revolutionizing the acquisition of morphological and geological information. As a consequence, there is a great potential for improving the modeling of geological bodies as well as failure mechanisms and stability conditions by integrating detailed remote data. During the past ten years several large rockfall events occurred along important transportation corridors where millions of people travel every year (Switzerland: Gotthard motorway and railway; Canada: Sea to sky highway between Vancouver and Whistler). These events show that there is still a lack of knowledge concerning the detection of potential rockfalls, making mountain residential settlements and roads highly risky. It is necessary to understand the main factors that destabilize rocky outcrops even if inventories are lacking and if no clear morphological evidences of rockfall activity are observed. In order to increase the possibilities of forecasting potential future landslides, it is crucial to understand the evolution of rock slope stability. Defining the areas theoretically most prone to rockfalls can be particularly useful to simulate trajectory profiles and to generate hazard maps, which are the basis for land use planning in mountainous regions. The most important questions to address in order to assess rockfall hazard are: Where are the most probable sources for future rockfalls located? What are the frequencies of occurrence of these rockfalls? I characterized the fracturing patterns in the field and with LiDAR point clouds. Afterwards, I developed a model to compute the failure mechanisms on terrestrial point clouds in order to assess the susceptibility to rockfalls at the cliff scale. Similar procedures were already available to evaluate the susceptibility to rockfalls based on aerial digital elevation models. This new model gives the possibility to detect the most susceptible rockfall sources with unprecented detail in the vertical and overhanging areas. The results of the computation of the most probable rockfall source areas in granitic cliffs of Yosemite Valley and Mont-Blanc massif were then compared to the inventoried rockfall events to validate the calculation methods. Yosemite Valley was chosen as a test area because it has a particularly strong rockfall activity (about one rockfall every week) which leads to a high rockfall hazard. The west face of the Dru was also chosen for the relevant rockfall activity and especially because it was affected by some of the largest rockfalls that occurred in the Alps during the last 10 years. Moreover, both areas were suitable because of their huge vertical and overhanging cliffs that are difficult to study with classical methods. Limit equilibrium models have been applied to several case studies to evaluate the effects of different parameters on the stability of rockslope areas. The impact of the degradation of rockbridges on the stability of large compartments in the west face of the Dru was assessed using finite element modeling. In particular I conducted a back-analysis of the large rockfall event of 2005 (265'000 m3) by integrating field observations of joint conditions, characteristics of fracturing pattern and results of geomechanical tests on the intact rock. These analyses improved our understanding of the factors that influence the stability of rock compartments and were used to define the most probable future rockfall volumes at the Dru. Terrestrial laser scanning point clouds were also successfully employed to perform geological mapping in 3D, using the intensity of the backscattered signal. Another technique to obtain vertical geological maps is combining triangulated TLS mesh with 2D geological maps. At El Capitan (Yosemite Valley) we built a georeferenced vertical map of the main plutonio rocks that was used to investigate the reasons for preferential rockwall retreat rate. Additional efforts to characterize the erosion rate were made at Monte Generoso (Ticino, southern Switzerland) where I attempted to improve the estimation of long term erosion by taking into account also the volumes of the unstable rock compartments. Eventually, the following points summarize the main out puts of my research: The new model to compute the failure mechanisms and the rockfall susceptibility with 3D point clouds allows to define accurately the most probable rockfall source areas at the cliff scale. The analysis of the rockbridges at the Dru shows the potential of integrating detailed measurements of the fractures in geomechanical models of rockmass stability. The correction of the LiDAR intensity signal gives the possibility to classify a point cloud according to the rock type and then use this information to model complex geologic structures. The integration of these results, on rockmass fracturing and composition, with existing methods can improve rockfall hazard assessments and enhance the interpretation of the evolution of steep rockslopes. -- La caractérisation de la géologie en 3D pour des parois rocheuses inaccessibles est une étape nécessaire pour évaluer les dangers naturels tels que chutes de blocs et glissements rocheux, mais aussi pour réaliser des modèles stratigraphiques ou de structures plissées. Les modèles géologiques 3D ont un grand potentiel pour être appliqués dans une vaste gamme de travaux géologiques dans le domaine de la recherche, mais aussi dans des projets appliqués comme les mines, les tunnels ou les réservoirs. Les développements récents des outils de télédétection terrestre (LiDAR, photogrammétrie et imagerie multispectrale / hyperspectrale) sont en train de révolutionner l'acquisition d'informations géomorphologiques et géologiques. Par conséquence, il y a un grand potentiel d'amélioration pour la modélisation d'objets géologiques, ainsi que des mécanismes de rupture et des conditions de stabilité, en intégrant des données détaillées acquises à distance. Pour augmenter les possibilités de prévoir les éboulements futurs, il est fondamental de comprendre l'évolution actuelle de la stabilité des parois rocheuses. Définir les zones qui sont théoriquement plus propices aux chutes de blocs peut être très utile pour simuler les trajectoires de propagation des blocs et pour réaliser des cartes de danger, qui constituent la base de l'aménagement du territoire dans les régions de montagne. Les questions plus importantes à résoudre pour estimer le danger de chutes de blocs sont : Où se situent les sources plus probables pour les chutes de blocs et éboulement futurs ? Avec quelle fréquence vont se produire ces événements ? Donc, j'ai caractérisé les réseaux de fractures sur le terrain et avec des nuages de points LiDAR. Ensuite, j'ai développé un modèle pour calculer les mécanismes de rupture directement sur les nuages de points pour pouvoir évaluer la susceptibilité au déclenchement de chutes de blocs à l'échelle de la paroi. Les zones sources de chutes de blocs les plus probables dans les parois granitiques de la vallée de Yosemite et du massif du Mont-Blanc ont été calculées et ensuite comparés aux inventaires des événements pour vérifier les méthodes. Des modèles d'équilibre limite ont été appliqués à plusieurs cas d'études pour évaluer les effets de différents paramètres sur la stabilité des parois. L'impact de la dégradation des ponts rocheux sur la stabilité de grands compartiments de roche dans la paroi ouest du Petit Dru a été évalué en utilisant la modélisation par éléments finis. En particulier j'ai analysé le grand éboulement de 2005 (265'000 m3), qui a emporté l'entier du pilier sud-ouest. Dans le modèle j'ai intégré des observations des conditions des joints, les caractéristiques du réseau de fractures et les résultats de tests géoméchaniques sur la roche intacte. Ces analyses ont amélioré l'estimation des paramètres qui influencent la stabilité des compartiments rocheux et ont servi pour définir des volumes probables pour des éboulements futurs. Les nuages de points obtenus avec le scanner laser terrestre ont été utilisés avec succès aussi pour produire des cartes géologiques en 3D, en utilisant l'intensité du signal réfléchi. Une autre technique pour obtenir des cartes géologiques des zones verticales consiste à combiner un maillage LiDAR avec une carte géologique en 2D. A El Capitan (Yosemite Valley) nous avons pu géoréferencer une carte verticale des principales roches plutoniques que j'ai utilisé ensuite pour étudier les raisons d'une érosion préférentielle de certaines zones de la paroi. D'autres efforts pour quantifier le taux d'érosion ont été effectués au Monte Generoso (Ticino, Suisse) où j'ai essayé d'améliorer l'estimation de l'érosion au long terme en prenant en compte les volumes des compartiments rocheux instables. L'intégration de ces résultats, sur la fracturation et la composition de l'amas rocheux, avec les méthodes existantes permet d'améliorer la prise en compte de l'aléa chute de pierres et éboulements et augmente les possibilités d'interprétation de l'évolution des parois rocheuses.

Rockfall detection from LIDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution is fundamental to understand and model the main factors that predispose to failure. In our study we analysed LiDAR point clouds aiming to: (1) detect and characterise single rockfalls; (2) investigate their spatial distribution. To this end, different cluster algorithms were applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination with the Expectation?Maximization (EM) in order to separate feature points from clutter; 1b) a density based algorithm (DBSCAN) was applied to isolate the single clusters (i.e. the rockfall events); 2) finally we computed the Ripley's K-function to investigate the global spatial pattern of the extracted rockfalls. The method allowed proper identification and characterization of more than 600 rockfalls occurred on a cliff located in Puigcercos (Catalonia, Spain) during a time span of six months. The spatial distribution of these events proved that rockfall were clustered distributed at a welldefined distance-range. Computations were carried out using R free software for statistical computing and graphics. The understanding of the spatial distribution of precursory rockfalls may shed light on the forecasting of future failures.

3D Surface Modeling From Point Clouds

The goal of this thesis is to implement software for creating 3D models from point clouds. Point clouds are acquired with stereo cameras, monocular systems or laser scanners. The created 3D models are triangular models or NURBS (Non-Uniform Rational B-Splines) models. Triangular models are constructed from selected areas from the point clouds and resulted triangular models are translated into a set of quads. The quads are further translated into an estimated grid structure and used for NURBS surface approximation. Finally, we have a set of NURBS surfaces which represent the whole model. The problem wasn’t so easy to solve. The selected triangular surface reconstruction algorithm did not deal well with noise in point clouds. To handle this problem, a clustering method is introduced for simplificating the model and removing noise. As we had better results with the smaller point clouds produced by clustering, we used points in clusters to better estimate the grids for NURBS models. The overall results were good when the point cloud did not have much noise. The point clouds with small amount of error had good results as the triangular model was solid. NURBS surface reconstruction performed well on solid models.

Clouds, Op. 7, No. 4

Soitinnus: Ork.

Una experiencia universitaria de innovación educativa con word clouds.

Resumen en inglés