Texture-based volume rendering of adaptive mesh refinement data

Many phenomena in nature and engineering happen simultaneously on rather diverse spatial and temporal scales. In other words, they exhibit a multi-scale character. A special numerical multilevel technique associated with a particular hierarchical data structure is adaptive mesh refinement (AMR). This scheme achieves locally very high spatial and temporal resolutions. Due to its popularity, many scientists are in need of interactive visualization tools for AMR data. In this article, we present a 3D texture-based volume-rendering algorithm for AMR data that directly utilizes the hierarchical structure. Thereby fast rendering performance is achieved even for high-resolution data sets. To avoid multiple rendering of regions that are covered by grids of different levels of resolution, we propose a space partitioning scheme to decompose the volume into axis-aligned regions of equal-sized cells. Furthermore the problems of interpolation artifacts, opacity corrections, and texture memory limitations are addressed. Published online: November 6, 2002 Correspondence to: R. K?hler     

Two-dimensional time-dependent vortex regions based on the acceleration magnitude

Acceleration is a fundamental quantity of flow fields that captures Galilean invariant properties of particle motion. Considering the magnitude of this field, minima represent characteristic structures of the flow that can be classified as saddle- or vortex-like. We made the interesting observation that vortex-like minima are enclosed by particularly pronounced ridges. This makes it possible to define boundaries of vortex regions in a parameter-free way. Utilizing scalar field topology, a robust algorithm can be designed to extract such boundaries. They can be arbitrarily shaped. An efficient tracking algorithm allows us to display the temporal evolution of vortices. Various vortex models are used to evaluate the method. We apply our method to two-dimensional model systems from computational fluid dynamics and compare the results to those arising from existing definitions.     

Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration

In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.     

Uncertain 2D Vector Field Topology

We introduce an approach to visualize stationary 2D vector fields with global uncertainty obtained by considering the transport of local uncertainty in the flow. For this, we extend the concept of vector field topology to uncertain vector fields by considering the vector field as a density distribution function. By generalizing the concepts of stream lines and critical points we obtain a number of density fields representing an uncertain topological segmentation. Their visualization as height surfaces gives insight into both the flow behavior and its uncertainty. We present a Monte Carlo approach where we integrate probabilistic particle paths, which lead to the segmentation of topological features. Moreover, we extend our algorithms to detect saddle points and present efficient implementations. Finally, we apply our technique to a number of real and synthetic test data sets.     

Cores of swirling particle motion in unsteady flows

In nature and in flow experiments particles form patterns of swirling motion in certain locations. Existing approaches identify these structures by considering the behavior of stream lines. However, in unsteady flows particle motion is described by path lines which generally gives different swirling patterns than stream lines. We introduce a novel mathematical characterization of swirling motion cores in unsteady flows by generalizing the approach of Sujudi/Haimes to path lines. The cores of swirling particle motion are lines sweeping over time, i.e., surfaces in the space-time domain. They occur at locations where three derived 4D vectors become coplanar. To extract them, we show how to re-formulate the problem using the Parallel Vectors operator. We apply our method to a number of unsteady flow fields.     

Interactive volume rendering of large sparse data sets using adaptive mesh refinement hierarchies

In this paper, we present an algorithm that accelerates 3D texture-based volume rendering of large, sparse data sets, i.e., data sets where only a traction of the voxels contain relevant information. In texture-based approaches, the rendering performance is affected by the fill-rate, the size of texture memory, and the texture I/O bandwidth. For sparse data, these limitations can be circumvented by restricting most of the rendering work to the relevant parts of the volume. In order to efficiently enclose the corresponding regions with axis-aligned boxes, we employ a hierarchical data structure, known as an AMR (adaptive mesh refinement) tree. The hierarchy is generated utilizing a clustering algorithm. A good balance is thereby achieved between the size of the enclosed volume, i.e., the amount to render in graphics hardware and the number of axis-aligned regions, i.e., the number of texture coordinates to compute in software. The waste of texture memory by the power-of-two restriction is minimized by a 3D packing algorithm which arranges texture bricks economically in memory. Compared to an octree approach, the rendering performance is significantly increased and less parameter tuning is necessary.     

The electronic nose as a rapid sensor for volatile compounds in treated domestic wastewater

An electronic nose consisting of 12 metal oxide sensors was used to monitor volatile compounds in effluent of a domestic wastewater treatment plant. Effluent and reference (deionized water) samples were heated to 60 and 90 degrees C to promote the volatilization and to increase the sensitivity. An effluent measuring campaign of 12 weeks was conducted and the repeatability and reproducibility of the procedure and the apparatus were determined. Processing the obtained fingerprints with principal component analysis (PCA) allowed interpretation and differentiation of the samples in terms of origin and quality, relative to the reference. To minimize the variance due to sensitivity fluctuations of the apparatus and to detect effluents with deviating qualities, two new concepts were defined, i.e. the relative sensorial odour perception (in short: rSOP) and the relative fingerprint. Correlations between the relative overall electronic nose output, expressed as rSOP, and selected routine parameters were weak except for the parameter "volatile suspended solids" (VSS), indicating adsorption of volatile organic compounds (VOCs) onto the organic particles. The results clearly demonstrate the possibility to use the electronic nose as a rapid alarm generator towards volatile compounds, e.g. in specific advanced treatment processes to produce reclaimed water from effluent of the domestic wastewater treatment plant under scrutiny.     

Effects of nicotine and depressive traits on affective priming of lateralized emotional word identification.

Based on evidence suggesting that depressive traits, emotional information processing, and the effects of nicotine may be mediated by lateralized brain mechanisms, analyses assessed the influence of depressive traits and nicotine patch on emotional priming of lateralized emotional word identification in 61 habitual smokers. Consistent with hypotheses, nicotine as compared to placebo patch enhanced right visual field (RVF) emotional word identification while decreasing performance of emotional word identification in the left visual field (LVF). Nicotine also enhanced positive affect and decreased negative affect. Consistent with the Heller model of depression, scoring high in depressive traits was associated with a general decrease in LVF emotional word identification. Additionally, this general LVF deficit was especially pronounced for positive word identification in individuals scoring high in trait depression. Positive primes facilitated positive target identification in the RVF and negative primes facilitated negative target identification in the LVF. Thus, nicotine promoted a LVF word-identification deficit similar to that observed in those with depressive traits. However, nicotine also enhanced RVF processing and reduced negative affect, whereas it enhanced positive affect. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Louisiana Spirit Specialized Crisis Counseling: Counselor perceptions of training and services.

This study examined perceptions of training, supervision, consultation, services provided, and role satisfaction among program personnel (N = 93) providing assistance to children, adults, and families affected by Hurricanes Katrina and Rita through the Louisiana Spirit Specialized Crisis Counseling Services (SCCS) Program. Findings indicated that there was uniform high satisfaction with the training, supervision, and consultation, which included the skills-based intervention, Skills for Psychological Recovery, provided by the SCCS Practice Directorate that included staff from Louisiana State University Health Sciences Center, the National Child Traumatic Stress Network, and the National Center for PTSD. Counselors also made high ratings of their perceptions of the helpfulness of services to survivors and overall satisfaction with their work. The vast majority of counselors, who had themselves been seriously affected by the hurricanes, indicated that their own hurricane-related experiences and losses were extremely helpful to them in relating to survivors and providing services. Fully 90% indicated that they had significantly benefited professionally from their work and would be interested in providing SCCS services in the event of another disaster. Provider perceptions of personal strengths derived from the hurricanes were associated with high levels of SCCS program satisfaction and conversely, difficulties providing SCCS services were associated with lower levels of program satisfaction. Multiple regression analyses indicated that provider's perceived negative impact of the hurricane on survivors predicted increased perceived helpfulness of SCCS provider trainings and services to survivors. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Prevalence of synthetic cannabinoids in blood samples from Norwegian drivers suspected of impaired driving during a seven weeks period

From early year 2000 different herbal products containing synthetic cannabinoids (SC) have appeared on the drug market all over the world, and new substances are frequently introduced. The prevalence of SC use in different populations is however still mainly unknown, also in Norway. This information is difficult to obtain, but studies of drivers suspected of driving under the influence of drugs (DUID), might provide important information.