A great challenge in the volume-rendering field is to achieve high-quality images in an acceptable amount of time. In the area of volume rendering, there is always a trade-off between speed and quality. Applications where only high-quality images are acceptable often use the ray-casting algorithm, but this method is computationally expensive and typically achieves low frame rates. The work presented here is RZSweep, a new volume-rendering algorithm for uniform rectilinear datasets, that gives high-quality images in a reasonable amount of time. In this algorithm a plane sweeps the vertices of the implicit grid of regular datasets in depth order, projecting all the implicit faces incident on each vertex. This algorithm uses the inherent properties of a rectilinear datasets. RZSweep is an object-order, back-toront, direct volume rendering, face projection algorithm for rectilinear datasets using the cell approach. It is a single processor serial algorithm. The simplicity of the algorithm allows the use of the graphics pipeline for hardware-assisted projection, and also, with minimum modification, a version of the algorithm that is graphics-hardware independent. Lighting, color and various opacity transfer functions are implemented for giving realism to the final resulting images. Finally, an image comparison is done between RZSweep and a 3D texture-based method for volume rendering using standard image metrics like Euclidian and geometric differences.

Forest management is a costly and time-consuming activity. Remote sensing has the potential to improve the process by making it cheaper and more efficient, but only if appropriate characteristics can be determined from computer-models. This thesis describes the implementation of a forest visualization system and a corresponding user study that tests the accuracy of parameter estimation and forest characterization. The study uses data obtained from field-surveys to generate a computer-modeled forest. Five different stands were tested. Based on the quantitative results obtained, generally, there is no statistically significant difference in parameter estimation when comparing field-recorded movies and computer-generated movies.

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Network analysts have long used two-dimensional security visualizations to make sense of network data. As networks grow larger and more complex, two-dimensional visualizations become more convoluted, potentially compromising user situational awareness of cyber threats. To combat this problem, augmented reality (AR) can be employed to visualize data within a cyber-physical context to restore user perception and improve comprehension; thereby, enhancing cyber situational awareness. Multiple generations of prototypes, known collectively as Mobile Augmented Reality for Cyber Security, or MARCS, were developed to study the impact of AR on cyber situational awareness. First generation prototypes were subjected to a formative pilot study of 44 participants, to generate user-centric performance data and feedback, which motivated the design and development of second generation prototypes and provided initial insight into the potentially beneficial impact of AR on cyber situational awareness. Second generation prototypes were subjected to a summative secondary study by 50 participants, to compare the impact of AR and non-AR visualizations on cyber situational awareness. Results of the secondary study suggest that employing AR to visualize cyber threats in a cyber-physical context collectively improves user threat perception and comprehension, indicating that, in some cases, AR security visualizations improve user cyber situational awareness over non-AR security visualizations. / Master of Science

Dimension reduction (DRs) are significant in data analysis to reduce the complexity of high dimensional data while preserving information to the greatest extent. However, the complex processes involved in DRs attribute to their inability to reason the relationship between the projection and the original data features (dimensions). "Why points are clustered?" and "What feature/s caused the points to scatter?" are some of the common questions. As a solution, we use gradients of the projection to generate visual explanations of the DRs. Utilizing these gradients, we show the point-wise sensitivities of the projection with respect to the original data features to explain the reasoning of DR. The combination of the gra- dients with various visualization techniques contribute to the exploration of the impact of dimensions on the projection. To overcome the curse of dimensionality, we propose inter- active techniques that facilitate the combination and comparison of features impact on the projection through gradients. Encapsulating the gradients and the visualization techniques, we present a web-based framework that facilitates an overview of impacts of all features and allows users to selectively explore notable features. / Master of Science / Data is prevalent in almost every facets of our lives. A simple data may comprise a few rows and couple of columns. Such data can be easily visualized and understood through simple visualization tools such as charts and graphs. However the real world data consists of large number of rows and columns (features, dimensions). As the number of features increase, so does the complexity to visualize and understand the data. One of the methods to reduce the high dimension data to low dimension is the dimension reduction (DR). DR methods generate a simpler form of data usually in 2D format which can be easily understood by human eyes. Even though the result from DR is simple, the complex process involved in the reduction of dimension makes the result (projection) difficult to understand. To better understand this projection, we propose visualization techniques that allow users to simulate change in original features and visualize the corresponding change in the projection.

Multi-dimensional functions are characterized by a large number of parameters on which the functional value depends. They are commonly used in engineering problems such as image analysis and system modeling. Multi-dimensional function spaces are very difficult to understand due to their multi-dimensional nature and the presence of a large number of data points in the functional space. A point called the focal point is selected by the user in the vast multi-dimensional parameter space. Rays called "focal rays" emanate from the focal point in all directions to the boundaries of the functional space. The focal rays contain functional data points. The focal point is mapped onto the center of the visualization with the focal rays arranged radially around it. The degree of detail decreases as we move away from the focal point toward the edges of the visualization in accordance with the focus+context technique. The functional values are mapped onto a color scheme with shades of green representing positive function values, and shades of red representing negative function values. Interactive features such as the ability to change the focal point, highlighting of functional values aid the user in exploring and analyzing the functional space. The algorithm, practical applications of the visualization approach and results of formative evaluation studies are also elaborated in this thesis. The contributions of this thesis are fourfold, namely, providing an overview of the functional space, equal treatment of all dimensions, improved scalability and a smooth blending of details with the overview. / Master of Science