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A unified approach to the classification of visual data systems.

Description of an approach to attaining a unified means of characterizing film, TV, and optical data systems. The concept is based on the premise that all of these imaging systems can be described by an equation similar to the ideal imaging system described by Rose (1948). This technique permits the direct comparison of film and TV performance without converting speed, film resolution lines, TV resolution lines, highlight output current and video bandwidth into compatible units, only to find some essential element of the conversion has been omitted from the particular specification in use. Most important, it permits system performance criteria to be based on input and output criteria without extensive manipulation of the elements between input and output.

Black, J.

AWE: Aviation Weather Data Visualization

The two official sources for aviation weather reports both require the pilot to mentally visualize the provided information. In contrast, our system, Aviation Weather Environment (AWE) presents aviation specific weather available to pilots in an easy to visualize form. We start with a computer-generated textual briefing for a specific area. We map this briefing onto a grid specific to the pilot's route that includes only information relevant to his flight route that includes only information relevant to his flight as defined by route, altitude, true airspeed, and proposed departure time. By modifying various parameters, the pilot can use AWE as a planning tool as well as a weather briefing tool.

Spirkovska, Lilly

Data Visualization of Invisible Airflow Hazards During Helicopter Takeoff and Landing Operations

Many aircraft accidents each year are caused by encounters with unseen airflow hazards near the ground such as vortices, downdrafts, wind shear, microbursts, or other turbulence. While such hazards frequently pose problems to fixed-wing airplanes, they are especially dangerous to helicopters, which often have to operate in confined spaces and under operationally stressful conditions. We are developing flight-deck visualizations of airflow hazards during helicopter takeoff and landing operations, and are evaluating their effectiveness with usability studies. Our hope is.that this work will lead to the production of an airflow hazard detection system for pilots that will save lives.

Aragon, Cecilia R.

Venus Quadrangle Geological Mapping: Use of Geoscience Data Visualization Systems in Mapping and Training

We are currently investigating new technological developments in computer visualization and analysis in order to assess their importance and utility in planetary geological analysis and mapping [1,2]. Last year we reported on the range of technologies available and on our application of these to various problems in planetary mapping [3]. In this contribution we focus on the application of these techniques and tools to Venus geological mapping at the 1:5M quadrangle scale. In our current Venus mapping projects we have utilized and tested the various platforms to understand their capabilities and assess their usefulness in defining units, establishing stratigraphic relationships, mapping structures, reaching consensus on interpretations and producing map products. We are specifically assessing how computer visualization display qualities (e.g., level of immersion, stereoscopic vs. monoscopic viewing, field of view, large vs. small display size, etc.) influence performance on scientific analysis and geological mapping. We have been exploring four different environments: 1) conventional desktops (DT), 2) semi-immersive Fishtank VR (FT) (i.e., a conventional desktop with head-tracked stereo and 6DOF input), 3) tiled wall displays (TW), and 4) fully immersive virtual reality (IVR) (e.g., "Cave Automatic Virtual Environment," or Cave system). Formal studies demonstrate that fully immersive Cave environments are superior to desktop systems for many tasks [e.g., 4].

Head, James W.

Neural Network Machine Learning and Dimension Reduction for Data Visualization

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

Liles, Charles A.