The design and implementation of an interactive graphics system for graph manipulation are discussed. The activation fcr such a system is examined, and the relevant literature is described and evaluated. A number of ways to improve and extend the system are presented. The system provides the basic graph drawing operations cf adding, deleting, labeling, and changing both vertices and edges. Also included are a number of graph manipulation operations which, among ether things, allow a user to subdivide edges, associate vertices, reverse the direction cf arcs, move vertices about the screen, cr even move whole graphs about the sc teen. A facility is provided whereby the screen can te divided into as many as four regions, thus allowing users tc display more than one graph at a time. Graphs can be saved on disk and later restored. The image on the graphics screen can te easily plotted tc obtain a hard copy of graphs. A few routines which perform graph-theoretic operations have teen implemented. Among these are a routine for finding the minimum and maximum degrees of a graph, and a routine fcr finding the blocks, cutnodes, and bridges cf a graph. Moreover, the system is designed to allow users to add their own routines. / Science, Faculty of / Computer Science, Department of / Graduate

This thesis describes Polygon Filling System, an interactive graphics system, which fills user defined areas on a raster scan graphics display device. The areas may be concave or convex and may be nested within each other. Issues related to the underlying grid model, hexagonal or square tesselation, are discussed. A formal approach to polygon filling is compared to a heuristic domain dependent approach. Connectivity problems arise in processing boundaries. Two user rules are defined. Adherence to these rules ensures correct processing of data. These rules are appropriate for cartographic and remote sensing applications. The implementation of the system and its key data structures are described. / Science, Faculty of / Computer Science, Department of / Graduate

Time-series data draw extensive attention from many research domains, such as financial and biomedical engineering. Researchers often face the difficultly of visualizing multiple time-series data simultaneously. The predominant techniques suffer from visual clutter either due to over-plotting or an overwhelming number of small graphs that carries a high cognitive load. This research study proposes a Colored slope, a combination of Tufte's slope graph and time-series heatmap, to visualize multiple time-series data at once, balancing scalability and accuracy. Colored slope inherits the complementary advantages from each method, regarding visualizing temporal changes within a period and identifying precise values. The efficacy, effectiveness, and graphical perception of the Colored slope on visualizing multiple time-series data with publicly accessible stock data were evaluated and compared it to popular time-series visualizations, including line graphs, time-series heatmap, and small multiple spark area graphs. Analyzing the experimental data, this study concludes that Colored slope contributes to (1) allowing users to identify the extreme values (maximum and minimum), co-variability, the general trend of the data, and rate of change effectively for an optimally large number of visual marks(time-series data); (2) capable of display more instances of time-series data with a less visual clutter problem. Finally, several possible applications and limitations with the Colored slope are demonstrated.

Diffusion tensor images and higher-order diffusion images are the foundation for neuroscience researchers who are trying to gain insight into the connectome, the wiring scheme of the brain. Although modern imaging devices allow even more detailed anatomical measurements, these pure anatomical connections are not sufficient for understanding how the brain processes external stimuli. Anatomical connections constraint the causal influences between several areas of the brain, as they mediate causal influence between them. Therefore, neuroscientists developed models to represent the causal coherence between several pre-defined areas of the brain, which has been measured using fMRI, MEG, or EEG. The dynamic causal modeling (DCM) technique is one of these models and has been improved to use anatomical connection as informed priors to build the effective connectivity model. In this paper, we present a visualization method allowing neuroscientists to perceive both, the effective connectivity and the underlying anatomical connectivity in an intuitive way at the same time. The metaphor of moving information packages is used to show the relative intensity of information transfer inside the brain using a GPU based animation technique. We provide an interactive way to selectively view one or multiple effective connections while conceiving their anatomical connectivity. Additional anatomical context is supplied to give further orientation cues.

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