Kevin T. McDonnell

Color Design for Illustrative Visualization

Professional designers and artists are quite cognizant of the rules that guide the design of effective color palettes, from both aesthetic and attention-guiding points of view. In the field of visualization, however, the use of systematic rules embracing these aspects has received less attention. The situation is further complicated by the fact that visualization often uses semi-transparencies to reveal occluded objects, in which case the resulting color mixing effects add additional constraints to the choice of the color palette. Color design forms a crucial part in visual aesthetics. Thus, the consideration of these issues can be of great value in the emerging field of illustrative visualization. We describe a knowledge-based system that captures established color design rules into a comprehensive interactive framework, aimed to aid users in the selection of colors for scene objects and incorporating individual preferences, importance functions, and overall scene composition. Our framework also offers new knowledge and solutions for the mixing, ordering and choice of colors in the rendering of semi-transparent layers and surfaces. All design rules are evaluated via user studies, for which we extend the method of conjoint analysis to task-based testing scenarios. Our frameworks use of principles rooted in color design with application for the illustration of features in pre-classified data distinguishes it from existing systems which target the exploration of continuous-range density data via perceptual color maps.

Illustrative parallel coordinates

Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edge‐bundling through splines; (b) visualization of “branched ” clusters to reveal the distribution of the data; (c) opacity‐based hints to show cluster density; (d) opacity and shading effects to illustrate local line density on the parallel axes; and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non‐experts.

CEASAR: a smooth, accurate and robust centerline extraction algorithm

We present CEASAR, a centerline extraction algorithm that delivers smooth, accurate and robust results. Centerlines are needed for accurate measurements of length along winding tubular structures. Centerlines are also required in automatic virtual navigation through human organs, such as the colon or the aorta, as they are used to control movement and orientation of the virtual camera. We introduce a concise but general definition of a centerline, and provide an algorithm that finds the centerline accurately and rapidly. Our algorithm is provably correct for general geometries. Our solution is fully automatic, which frees the user from having to engage in data preprocessing. For a number of test datasets, we show the smooth and accurate centerlines computed by our CEASAR algorithm on a single 194 MHz MIPS R10000 CPU within five minutes.

Antibacterial activity of polymer coated cerium oxide nanoparticles.

Cerium oxide nanoparticles have found numerous applications in the biomedical industry due to their strong antioxidant properties. In the current study, we report the influence of nine different physical and chemical parameters: pH, aeration and, concentrations of MgSO4, CaCl2, KCl, natural organic matter, fructose, nanoparticles and Escherichia coli, on the antibacterial activity of dextran coated cerium oxide nanoparticles. A least-squares quadratic regression model was developed to understand the collective influence of the tested parameters on the anti-bacterial activity and subsequently a computer-based, interactive visualization tool was developed. The visualization allows us to elucidate the effect of each of the parameters in combination with other parameters, on the antibacterial activity of nanoparticles. The results indicate that the toxicity of CeO2 NPs depend on the physical and chemical environment; and in a majority of the possible combinations of the nine parameters, non-lethal to the bacteria. In fact, the cerium oxide nanoparticles can decrease the anti-bacterial activity exerted by magnesium and potassium salts.

A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multidimensional Scaling

Although the euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging intercluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multidimensional scaling (MDS) where one can often observe nonintuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly in high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our biscale framework distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate euclidean distance.

Visual Correlation Analysis of Numerical and Categorical Data on the Correlation Map

Correlation analysis can reveal the complex relationships that often exist among the variables in multivariate data. However, as the number of variables grows, it can be difficult to gain a good understanding of the correlation landscape and important intricate relationships might be missed. We previously introduced a technique that arranged the variables into a 2D layout, encoding their pairwise correlations. We then used this layout as a network for the interactive ordering of axes in parallel coordinate displays. Our current work expresses the layout as a correlation map and employs it for visual correlation analysis. In contrast to matrix displays where correlations are indicated at intersections of rows and columns, our map conveys correlations by spatial proximity which is more direct and more focused on the variables in play. We make the following new contributions, some unique to our map: (1) we devise mechanisms that handle both categorical and numerical variables within a unified framework, (2) we achieve scalability for large numbers of variables via a multi-scale semantic zooming approach, (3) we provide interactive techniques for exploring the impact of value bracketing on correlations, and (4) we visualize data relations within the sub-spaces spanned by correlated variables by projecting the data into a corresponding tessellation of the map.

A network-based interface for the exploration of high-dimensional data spaces

The navigation of high-dimensional data spaces remains challenging, making multivariate data exploration difficult. To be effective and appealing for mainstream application, navigation should use paradigms and metaphors that users are already familiar with. One such intuitive navigation paradigm is interactive route planning on a connected network. We have employed such an interface and have paired it with a prominent high-dimensional visualization paradigm showing the N-D data in undistorted raw form: parallel coordinates. In our network interface, the dimensions form nodes that are connected by a network of edges representing the strength of association between dimensions. A user then interactively specifies nodes/edges to visit, and the system computes an optimal route, which can be further edited and manipulated. In our interface, this route is captured by a parallel coordinate data display in which the dimension ordering is configured by the specified route. Our framework serves both as a data exploration environment and as an interactive presentation platform to demonstrate, explain, and justify any identified relationships to others. We demonstrate our interface within a business scenario and other applications.

Microwave assisted lipase catalyzed solvent-free poly-ε-caprolactone synthesis

Abstract Microwave (MW) assisted enzymatic polymerizations is an area that is largely unexplored. In the current study, the effect of MW reaction parameters on poly-ε-caprolactone (PCL) properties has been investigated using a statistical design. A {3,5} modified mixture experimental design was used to identify the parameter values that gave the desired properties of PCL. The three process parameters that were tested are temperature, MW intensity, and the reaction time. Experimental results showed that in the range of values tested, temperature had the highest positive influence on the properties of PCL, whereas high MW irradiation is not desirable. A cubic regression model was developed and optimal process parameters were obtained using this model. Conducting the polymerization reaction under optimal conditions (90°C, 240 min, 50 W), PCL with M n of 20,624 and polydispersity index of 1.2 were obtained. The regression model was validated by carrying out validation experiments and by 3D visualization.

Bacterial and Archaea Community Present in the Pine Barrens Forest of Long Island, NY: Unusually High Percentage of Ammonia Oxidizing Bacteria

Of the few preserved areas in the northeast of United States, the soil in the Pine Barrens Forests presents a harsh environment for the microorganisms to grow and survive. In the current study we report the use of clustering methods to scientifically select the sampling locations that would represent the entire forest and also report the microbial diversity present in various horizons of the soil. Sixty six sampling locations were selected across the forest and soils were collected from three horizons (sampling depths). The three horizons were 0–10 cm (Horizon O); 11–25 cm (Horizon A) and 26–40 cm (Horizon B). Based on the total microbial substrate utilization pattern and K-means clustering analysis, the soil in the Pine Barrens Forest can be classified into four distinct clusters at each of the three horizons. One soil sample from each of the four clusters were selected and archaeal and bacterial populations within the soil studied using pyrosequencing method. The results show the microbial communities present in each of these clusters are different. Within the microbial communities present, microorganisms involved in nitrogen cycle occupy a major fraction of microbial community in the soil. High level of diversity was observed for nitrogen fixing bacteria. In contrast, Nitrosovibrio and Nitrosocaldus spp are the single bacterial and archaeal population respectively carrying out ammonia oxidation in the soil.

GPU-accelerated volume splatting with elliptical RBFs

Radial Basis Functions (RBFs) have become a popular rendering primitive, both in surface and in volume rendering. This paper focuses on volume visualization, giving rise to 3D kernels. RBFs are especially convenient for the representation of scattered and irregularly distributed point samples, where the RBF kernel is used as a blending function for the space in between samples. Common representations employ radially symmetric RBFs, and various techniques have been introduced to render these, also with efficient implementations on programmable graphics hardware (GPUs). In this paper, we extend the existing work to more generalized, ellipsoidal RBF kernels, for the rendering of scattered volume data. We devise a post-shaded kernel-centric rendering approach, specifically designed to run efficiently on GPUs, and we demonstrate our renderer using datasets from subdivision volumes and computational science.