- •1. TABLE OF CONTENTS
- •2. AN INTRODUCTION TO UNIX
- •2.1 OVERVIEW
- •2.2 UNIX
- •2.2.1 Using UNIX Workstations in general:
- •2.2.2 Directories, Files, Etc.
- •2.2.3 Advanced Concepts
- •2.3 THE NETWORK
- •2.4 GOOD MANNERS
- •3. THE INTERNET
- •3.1 NETWORKS
- •3.1.1 Computer Addresses
- •3.2 NETWORK TYPES
- •3.2.1 Permanent Wires
- •3.2.2 Phone Lines
- •3.3 NETWORK PROTOCOLS
- •3.3.1 Mail Transfer Protocols
- •3.3.1.1 - Attachments
- •3.3.1.2 - Mail Lists
- •3.3.2 FTP - File Transfer Protocol
- •3.3.3 News
- •3.3.4 HTTP - Hypertext Transfer Protocol
- •3.3.5 Chat
- •3.3.6 Novell
- •3.3.7 Security
- •3.4 DATA FORMATS
- •3.4.1 HTML - Hyper Text Markup Language
- •3.4.1.1 - Publishing Web Pages
- •3.4.2 URLs
- •3.4.3 Hints
- •3.4.4 Specialized Editors
- •3.4.6 Encryption
- •3.4.7 Compression
- •3.5 PULLING ALL THE PROTOCOLS AND FORMATS TOGETHER WITH BROWSWERS
- •3.6 OTHER STUFF
- •3.6.1 Clients and Servers
- •3.6.2 Java
- •3.6.3 Javascript
- •3.6.5 Searches
- •3.6.6 ActiveX
- •3.6.7 Graphics
- •3.6.8 Animation
- •3.6.9 Video
- •3.6.10 Sounds
- •3.6.11 Other Program Files
- •3.6.12 Fancy Stuff
- •4. TEACHING WITH THE INTERNET
- •4.1 LECTURES
- •4.1.1 Equipment
- •4.1.2 Techniques
- •4.2 ON-LINE NOTES
- •4.3 ON-LINE MARKING
- •4.3.1 Web Pages
- •4.3.2 email
- •4.4 The Time-Line For My First On-Line Course (Fall 1996)
- •5. WWW and HTML
- •5.1 Why Bother?
- •5.2 Where to Find Netscape
- •5.3 How to Get Your Own Home Page
- •5.4 How to Create a file
- •5.5 Resources
- •6. A BASIC INTRODUCTION TO ‘C’
- •6.2 BACKGROUND
- •6.3 PROGRAM PARTS
- •6.4 HOW A ‘C’ COMPILER WORKS
- •6.5 STRUCTURED ‘C’ CODE
- •6.6 ARCHITECTURE OF ‘C’ PROGRAMS (TOP-DOWN)
- •6.7 CREATING TOP DOWN PROGRAMS
- •6.8.1 Objectives:
- •6.8.2 Problem Definition:
- •6.8.3 User Interface:
- •6.8.3.1 - Screen Layout (also see figure):
- •6.8.3.2 - Input:
- •6.8.3.3 - Output:
- •6.8.3.4 - Help:
- •6.8.3.5 - Error Checking:
- •6.8.3.6 - Miscellaneous:
- •6.8.4 Flow Program:
- •6.8.5 Expand Program:
- •6.8.6 Testing and Debugging:
- •6.8.7 Documentation
- •6.8.7.1 - Users Manual:
- •6.8.7.2 - Programmers Manual:
- •6.8.8 Listing of BeamCAD Program.
- •6.9 PRACTICE PROBLEMS
- •7. GUI DESIGN
- •7.1 PRACTICE PROBLEMS
- •8. AN EXAMPLE - BEAMCAD
- •9. PROGRAMMING IN JAVA
- •9.1 OVERVIEW
- •9.2 THE LANGUAGE
- •9.3 OBJECT ORIENTED PROGRAMMING
- •9.4 REFERENCES/BIBLIOGRAPHY
- •10. DATABASES
- •11. MESSAGE PASSING ON NETWORKS
- •12. MATHEMATICAL ELEMENTS OF COMPUTER GRAPHICS
- •12.1 INTRODUCTION
- •12.2 PIXELS
- •12.2.1 The Perspective Transform
- •12.3 LINE DRAWING
- •12.3.1 Hidden Lines
- •12.4 POLYGON DRAWING
- •12.5 SHADED POLYGONS
- •12.6 COLORS
- •12.6.1 Color Maps
- •12.6.1.1 - Quantization with an Octree RGB Cube
- •12.6.1.1.1 - Algorithm and Implementation
- •12.6.1.1.2 - Color Quantization Data Structures
- •12.7 DITHERING
- •12.7.1 A Model for Light Ray Reflection
- •12.7.2 A Model for Light Ray Refraction:
- •12.7.3 A Model for Specular Reflection of Point Light
- •12.8 RAY TRACING
- •12.8.1 Basic Ray Tracing Theory
- •12.8.1.1 - A Model for Diffuse Reflection of Ambient Light
- •12.8.1.2 - A Model for Diffuse Reflection of Point Light:
- •12.8.1.3 - Collision of a Ray with a Sphere:
- •12.8.1.4 - Collision of a Ray With a Plane:
- •12.8.1.5 - Mapping a Pattern
- •12.8.2 Ray Tracer Algorithms
- •12.8.3 Bounding Volumes
- •12.8.4 Shadows
- •12.8.5 Aliasing
- •12.8.6 Advanced topics
- •12.9 RADIOSITY
- •12.10 ADVANCED GRAPHICS TECHNIQUES
- •12.10.1 Animation
- •12.11 REFERENCES
- •12.12 PRACTICE PROBLEMS
- •13. NEW TOPICS
- •13.1 VIRTUAL REALITY
- •13.2 MULTIMEDIA
- •14. VISIONS SYSTEMS
- •14.1 OVERVIEW
- •14.2 APPLICATIONS
- •14.3 LIGHTING AND SCENE
- •14.4 CAMERAS
- •14.5 FRAME GRABBER
- •14.6 IMAGE PREPROCESSING
- •14.7 FILTERING
- •14.7.1 Thresholding
- •14.8 EDGE DETECTION
- •14.9 SEGMENTATION
- •14.9.1 Segment Mass Properties
- •14.10 RECOGNITION
- •14.10.1 Form Fitting
- •14.10.2 Decision Trees
- •14.11 PRACTICE PROBLEMS
- •15. SIMULATION
- •15.1 MODEL BUILDING
- •15.2 ANALYSIS
- •15.3 DESIGN OF EXPERIMENTS
- •15.4 RUNNING THE SIMULATION
- •15.5 DECISION MAKING STRATEGY
- •15.6 PLANNING
- •15.7 NEURAL NETWORK THEORY
- •16. ARTIFICIAL INTELLIGENCE (AI)
- •16.1 OVERVIEW
- •16.2 EXPERT SYSTEMS
- •16.3 FUZZY LOGIC
- •16.4 NEURAL NETWORKS
- •16.4.1 Neural Network Calculation of Inverse Kinematics
- •16.4.1.1 - Inverse Kinematics
- •16.4.1.2 - Feed Forward Neural Networks
- •16.4.1.3 - The Neural Network Setup
- •16.4.1.4 - The Training Set
- •16.4.1.5 - Results
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16. ARTIFICIAL INTELLIGENCE (AI)
16.1 OVERVIEW
•What is Intelligence ? - Nobody knows, but there are lots of ideas.
•So if we don’t know what intelligence is, how can we write programs that are intelligent ? - AI researchers guess at how parts of the brain work, then try to reproduce the effect in a program.
•A Myth is that artificial programs use intelligence to guide themselves, but they are not as stupid as traditional programs.
•Most AI methods take ONE aspect of human thinking, and then model it, such as,
-Logical Reasoning
-Recognition
-Association
•There are many older, and newer AI topics of interest,
-Expert Systems
-Planning Systems
-Search Techniques
-Fuzzy Logic
-Neural Networks
-Genetic Algorithms
-Symbol Manipulation
-etc.
•Each of the AI techniques is distinct in which applications they are useful for, and how they are applied.
•In manufacturing there are many problems which are difficult to solve using computers, such as trying to get a computer to ‘listen for a particular PING when doing inspection’. But, AI can help solve some of these problems.
•AI is not magic, you must still understand the problem before an AI system will help you solve it.
•The List below says a few words about some popular AI topics, and how they relate to applications,
Expert Systems - These systems use exact rules with true or false conditions, and results. “If the engine stops, the car is out of gas”
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Fuzzy Logic - This method still uses rules which do not need true or false conditions. “When I want the engine to go slower, I give it less gas” where slower, and less are somewhat arbitrary values.
Neural Networks - This methods is equivalent to learning by example. “I don’t know why, I just know to go faster, I push the accelerator that much”.
• For the three methods above, how well the problem can be described determines which is too be used. In effect, if the problem is very logical, use expert systems, if you can still make rules, but nothing is true or false use fuzzy logic. Finally, if you can’t define rules for solving a problem, but can do it by intuition, use neural networks.
16.2 EXPERT SYSTEMS
•To implement an expert system, a knowledge engineer will talk to ‘experts’ about how they solve a problem. The Knowledge engineer will then try to develop a set of rules for solving the problem. After the rules are done, they are entered into Expert System Software ($0 - $20,000). Expert system software will then ask questions (or check sensors, or look at data files) to compare rules to conditions, and see the results.
•The rules are in the form shown on the other page
•There are two ways to search rules,
Forward Chaining - Consider what you know now, then check rules to see if all conditions are satisfied. The Results of the rule give you a new conclussion. The rules are all checked again using the Results from the previous rule. This is often used for choosing an action.
Backward Chaining - This method will backtrack from a set of consequents to find which conditions caused them. This method is often used for determining how something was done.
•
16.3 FUZZY LOGIC
• Rules are created which make sense to humans, for example when driving a car some acceleration rules may be,
if LOUD_NOISE and FAST_SPEED then SLOW_SPEED if QUIET_NOISE and FAST_SPEED then SAME_SPEED
• Each or the rule conditions, and results, can be represented with a 1 dimension matrix,