Engineering Science and Mechanics 4714
SCIENTIFIC VISUAL DATA ANALYSIS AND MULTIMEDIA
(ADP TITLE: SCI VISUAL DATA ANALYSIS)

Spring Semester 1999
ESM 4714 (Graduate and Undergraduate credit)
Index No. 5351
T,Th 5:00-6:15pm
McBryde Room 231

I. Catalog Description

4714 SCI VISUAL DATA ANALSIS

Classical and advanced methods of visual data analysis within scientific applications context; emphasis on examples of scientific investigation with visual tools, and new visual methods with computer graphics; visual data analysis of numerical experimental and analytical results including: gradients, function-extraction, chaos, nth-order tensor glyph representations, molecular synthesis.

(3H, 3C), II.

Pre: Math 1015-1016 or Math 1205-1206, any course in C, Fortran, or Pascal, working knowledge of Unix.

Co: Senior or graduate project that demonstrates a need for visual data analysis.

ESM 4714
SCI VISUAL DATA ANALYSIS

II. Learning Objectives:

Upon successful completion of this course, the student will be able to:

III. Justification

Computer graphics have become increasingly useful for many disciplines, including art and architecture, as well as for engineering and science. Engineers and scientists use graphics software for analysis and interpretation in addition to presentation. The introduction of computer graphics as an analytical research tool was largely motivated by the National Science Foundation which created four supercomputing centers in the late 80s. Scientists and engineers have increasingly used supercomputers to simulate complex phenomena, typically three dimensional, that resulted in massive data sets. Similarly, computer-controlled experiments in the laboratory have also created massive three dimensional data sets such as MRI and X-Ray CAT scans. These massive data sets created a new problem in that traditional graphical techniques designed for presentation were inadequate for analysis and interpretation. Hence the name "visualization", short for visual data analysis, was coined to emphasize analysis and interpretation.

The need for visual data analysis has penetrated almost every discipline that encounters these large data sets. Because large data sets are encountered in the course of a senior project or graduate level research project, this course is oriented as a problem solving class project. Students are required to define a research problem as their class project that can benefit from implementing visual methods and techniques introduced in the class.

No credit is given for developing or programming visual interfaces although it is expected that students must demonstrate proficiency in programming as a prerequisite.

With resources provided in the ACITC: 1) Scientific Visualization and Modeling Classroom and 2) University Visualization and Animation Laboratory, students learn how to use state-of-the-art visual tools in a systematic and rational way, independent of the source of data (experimental, numerical, or analytical) to gain insight into their data or complex analytic functions. Access to NSF supercomputer time is also available from the NCSA for class projects that require numerical solutions to complex boundary value problems.

IV. PREREQUISITES & COREQUISITES:

Prerequisites: Undergraduate calculus, e.g., Math 1015-1016, or Math 1205-1206. Working knowledge of UNIX, and either C, Fortran, or Pascal.

Corequistes: Existing project (e.g. Senior Design Project, Masters Thesis or Ph.D. dissertation) that will benefit from their class project.

V. TEXT AND SPECIAL TEACHING AIDS:

  1. Required Texts: Robert Wolff and Larry Yaegar, VISUALIZATION OF NATURAL PHENOMENA, New York: Springer-Verlag, 1993, 374.

  2. Optional Texts:

  3. Class notebook: "Three Visual Methods", lectures, exercises, assignments, data sets and handouts accessed at http://www.sv.vt.edu/classes/ESM4714/ESM4714.html.

VI. SYALLBUS:

                                                                   Percent of Course

1.  Historical perspective on visual tools and methods in scientific research:    5 %
     general principles for using graphical methods for visual insight

2.   Scientific visual data analysis                                             15 %
	       
      A.  General principles and methods
           1.  Data compression into 4-D space: gradients of scalar functions
           2.  N-dimensional space for extracting new relationships
           3.  Visual representation of N-th order tensors

      B.  Introduction to visual tools                                           25 %
           1.  Constructing scientific data sets and data types and conversions 
           2.  General visual tools 
           3.  Interactive data languages and visual programming systems 

      C.  Examples                                                                5 %
           1.   Fluid mechanics, solid mechanics and dynamics 

3.  Multimedia development 

      A.  Foundations of multimedia                                              13 %
            1.   Introduction to multimedia                				
            2.   Introduction to layout and design         			

       B.  Authoring tools                                                       12 %
            1.    Macromedia director
            2.    Macromedia authorware professional
 
       C.  Technical Labs                                                        25 %
            1.   Tour of the multimedia lab
            2.   Interface design and image scanning			         
            3.   Digital audio, digital video, navigation and scripting
            4.   Final project production                  					

4.   Final Class Presentations

Return to Visualization Home Page

Send comments to: kriz@wave.esm.vt.edu
Ronald D. Kriz
Virginia Tech
College of Engineering
http://www.sv.vt.edu/classes/ESM4714/ESM4714.syllabus99.html

Revised January 3, 1999