Documents Found!
As seen in
Less Work, Better Grades
Join
Course Hero
Access
best resources
Ace
your classes
Ace your courses with Course Hero!
|
|
|
Study Smarter, Score Higher
Here are the top 5 related documents
...
Internet Engineering Task Force
Internet Draft Schulzrinne
draft-schulzrinne-sip-911-01.txt Columbia U.
March 25, 2001
Expires: December 2001
Emergency Call Ser...
...From sm2545 at cs.columbia.edu Fri Mar 10 11:59:08 2006
From: sm2545 at cs.columbia.edu (Saurabh Mathur)
Date: Fri, 10 Mar 2006 11:59:08 -0500 (EST)
Subject: [CS3203-Discrete Maths] Homework 2
Message-ID:
...From sm2545 at cs.columbia.edu Thu Feb 2 03:51:22 2006
From: sm2545 at cs.columbia.edu (Saurabh Mathur)
Date: Thu, 2 Feb 2006 03:51:22 -0500 (EST)
Subject: [CS3203-Discrete Maths] TA hours
Message-ID:
...From sm2545 at cs.columbia.edu Mon Apr 3 17:07:33 2006
From: sm2545 at cs.columbia.edu (Saurabh Mathur)
Date: Mon, 3 Apr 2006 17:07:33 -0400 (EDT)
Subject: [CS3203-Discrete Maths] Review Session
Message-ID:
Document Content (unformatted)
Course Hero has millions of student submitted documents similar to the one
below including study guides, homework solutions, papers, exam answer keys and textbook solutions.
Mapping, Texture Bump Mapping, OpenGL CS116B Chris Pollett May 2, 2004. Outline Surface Texture Mapping Texture Reduction Patterns Procedural Texturing Methods Bump Mapping Frame Mapping OpenGL Surface Texture Mapping Texture Space (s,t) Object Space (u,v) parametrization of a surface (x(u,v), y(u,v), z(u,v)) transformation Image Space: (x,y) Pixel Coordinates texture surface transformation viewing and projection Typically, the texture space and the object space are rectangles so can map using the equations: u(s,t) = a_u*s+b_u*t +c_u v(s,t) = a_v*s =b_v*t +c_u. The object space to image space mapping depends on the surface we are parametrizing. For example, for a cylinder we might use x=r*cos u, y=r*cos u, z=v. We can map either from texture space to image space (texture scanning) by composition or can map reverse direction (pixel-order scanning). The latter is useful to avoid pixel round off errors. Volume Texturing Similar to surface texturing except now texture is 3D. So given by 3 coordinates (s,t,r). Might want to do for cut-away displays, scenes like inside a fish-tank,etc. Texture Reduction Patterns As objects get far away, it doesn t make sense to do lots of calculations to apply a texture to them. It also can cause distortion in how the texture looks. To avoid this we can create different textures of different levels of detail to use depending on the scale of the object. These texture reduction are patterns often called MIP maps (multum in parvo). Procedural Texturing Methods Another technique for adding a texture pattern to an object is to use a procedural definition for the textures that are to be applied. That is have a little program that calculates something that looks like wood graining, marble, etc. Bump Mapping Texture are not very effective when trying to model rough surfaces such as oranges, strawberries, or raisins. The problem is the light intensity given in a texture for such an object does not depend on the light in the scene. But these objects change a lot according to the lighting. Bump mapping is a technique to make realistic bumpy surfaces that can be used instead. More Bump Mapping Let P(u,v) be a point on a surface. Then N = Pu x Pv is the normal at (u,v). Let n= N/|N|. We can add a bump to the surface using an equation: P (u,v) =P(u,v) + b(u,v)n. Here b(u,v) is a bump function. Can show the perturbed normal is now approximately: N = N + bv(Pu x n) + bu(Pv x n) We now use this normal to do our lighting calculations. Frame Mapping This is an extension to bump mapping. We not only perturb the surface normal, we also perturb the local coordinate system at the point. To do this we tweak the tangent vector T and calculate a binormal as B= T x N. This is useful in modeling anisotropic surfaces, such as wood grains, cross threading in clothing, and streaks on marbles.
Find millions of documents here - Study Guides, Homework Solutions, Papers, Exam Answer Keys and more.
Course Hero has millions of course related materials that will enable you to learn better,
faster and get an A in all your courses.
Below is a small sample set of documents:
Below is a small sample set of documents:
San Jose State >> CS >> 116b (Spring, 2008)
Blobby Objects and Splines CS116B Chris Pollett Feb 2, 2005. Outline Blobby Objects Spline Representations Blobby Objects By a blobby object we mean a nonrigid object. That is things, like cloth, rubber, liquids, water droplets, etc. These obj...
San Jose State >> CS >> 116b (Spring, 2008)
Shadows and Faking Intensity CS116B Chris Pollett Apr 6, 2004. Outline Shadows Camera Parameters Displaying Light Intensities Halftone Patterns and Dithering Shadows Visibility detection algorithms can be used to locate regions that are not i...
San Jose State >> CS >> 122 (Fall, 2008)
INFORMATION INTEGRATION ANAND KANANKAM CS-257 ID- 122 DATA CUBES Data cube is a multi-dimensional structure , it as a data abstraction that allows one to view aggregated data from a number of perspectives. It is surrounded by a collection of sub...
San Jose State >> CS >> 134 (Spring, 2008)
Listeners CS134 Chris Pollett Oct 13, 2004. Outline cController From keypress to critter Listeners Shooting with Listeners Viewer listeners Listeners initializing critters cController In MFC, void CView:OnKeyDown(UINT nChar, UINT nRepCnt, U...
San Jose State >> CS >> 134 (Spring, 2008)
2D Shooting Games CS134 Chris Pollett Oct 20, 2004. Outline The Spacewar game Specification Design Spacewar code The 2D Game Stub The Worms games The Spacewar game Spacewar was the first computer game Real version was 2-player, written in...
San Jose State >> CS >> 134 (Spring, 2008)
Mouse, cursors, and keyboard CS134 Chris Pollett Nov. 17, 2004. Outline Mouse Messages Cursor Tools The Mouse Wheel Focus and Autofocus The Keyboard Mouse Messages Windows generates a number of mouse related messages: WM_MOUSEMOVE, WM_ONLBUT...
San Jose State >> CS >> 134 (Spring, 2008)
Simulating Physics, Generating 2D terrains CS134 Chris Pollett Sep. 20, 2004 Introduction Physics Parallelism The Laws of Motion Force and acceleration Implementing Forces Preserving Physics Terrain Generation Physics Games are more success...
San Jose State >> CS >> 140 (Fall, 2008)
Operations Management Chapter 14 Material Requirements Planning (MRP) and ERP PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 7e Operations Management, 9e 2008 Prentice Hall, Inc. 14 1 Outline Global Compa...
San Jose State >> CS >> 146 (Fall, 2008)
Make-up Program CS146 For those students who didnt do well in Program 3 and Program 4, this make-up program will help you to enhance your program grade. Due date: April 28, 2003 For any n 5, let Input: (a1, a2, a3, a4, a5) where a1 + a2 + a3 + a4 + ...
San Jose State >> CS >> 147 (Fall, 2008)
2008Fall CS 147 Midterm 2 Study Guide October 23 Thursday 20% of the material covered in Midterm 1 and 80% of the new topics in 1-complement and 2-complement arithmetic Sequential logic and flip-flops Design and analysis of sequential circui...
San Jose State >> CS >> 147 (Fall, 2008)
Study guide of Final Exam CS 147, Spring 2005 Exam Time: Sec 1 Tuesday May 24 2005 09:45AM to 12:00PM Sec 2 Monday May 23 2005 12:15PM to 02:30PM Note: Please bring 882E Scantron! TYPES OF QUESTIONS: - Multiple Choice (4-6 answers possible, enter o...
San Jose State >> CS >> 147 (Fall, 2008)
CS 147 Practice Problems 3. The following questions are practice problems associated with the lecture material on the subject of Decoders and Multiplexers. 1. 2.Given the function f(W, X, Y, Z) = (Sigma)m(2, 3, 4, 6, 7, 15) + (Sigma)d(0, 5, 12, 13) ...
San Jose State >> CS >> 147 (Fall, 2008)
Study guide of Final Exam CS 147Fall 2007 Exam Time: Cs147 Wednesday Dec 12 2007 09:45AM to 12:00AM Cs157 Tuesday, Dec 18 2007 07:15AM-09:30AM Note: Please bring 882E Scantron! TYPES OF QUESTIONS: - Multiple Choice (4-6 answers possible, enter one ...
San Jose State >> CS >> 151 (Spring, 2008)
More of the Case Study CS151 Chris Pollett Nov. 7, 2005. Outline Iterations 2-4 State Pattern More on Iteration2 Recall the goal of iteration 2 was to add menus, dialogs, and to support saving and loading. Last day, we showed how menus could be...
San Jose State >> CS >> 151 (Spring, 2008)
Object-Oriented Modeling Using UML CS151 Chris Pollett Aug. 29, 2005. Outline Objects and Classes Modeling Relationships and Structures Some Terms and Concepts Objects and classes are fundamental to OO development. They both can be viewed from ...
San Jose State >> CS >> 151 (Spring, 2008)
I/O Framework and Case Study CS151 Chris Pollett Nov. 2, 2005. Outline Character Streams Random Access Files Design case study Planning Iterations Character Streams Java internally represents strings as Unicode which uses two bytes/char....
San Jose State >> CS >> 151 (Spring, 2008)
More on Design by Abstraction CS151 Chris Pollett Oct. 19, 2005. Outline Generalizing Design Pattern - Strategy Abstract Coupling Design Pattern - Iterator Case Study Generalizing Generalizing is a process that takes a solution to a specific ...
San Jose State >> CS >> 152 (Fall, 2008)
Scanning (lexical analysis) could be done by parser but a special-purpose scanner is generally more efficient how to recognize tokens longest substring white space maximum length? The scanner needs to identify categories of tokens for the parser. Cat...
San Jose State >> CS >> 152 (Fall, 2008)
Languages and grammars A (formal) language is defined as a set of finite strings over an alphabet. An alphabet is just a finite set of symbols. The question then becomes which strings over a given alphabet are legal programs in a given programming la...
San Jose State >> CS >> 152 (Fall, 2008)
A grammar for a fragment of English S NP NP PP VP VP VP -> -> -> -> -> -> -> NP VP Det N Det N PP P NP V V NP V NP PP A very simple grammar S -> x S -> x S S An ambiguous grammar for algebraic expressions E -> E + E E -> E * E E -> x E -> y E -> ( E...
San Jose State >> CS >> 152 (Fall, 2008)
Translation steps (idealized) character string lexical analysis (scanning, tokenizing) string of tokens syntactic analysis (parsing) parse tree semantic analysis, ...
San Jose State >> CS >> 154 (Spring, 2008)
PDAs CS154 Chris Pollett Mar 19, 2007. Outline Pushdown Automata Equivalence Pushdown Automata Our goal is a machine model corresponding CFG. This might help to develop parsers. To do this we will consider machines that have a stack: x Stack y...
San Jose State >> CS >> 154 (Spring, 2008)
Proofs, Strings, and Finite Automata CS154 Chris Pollett Feb 5, 2007. Outline Proofs and Proof Strategies Strings Example: For every graph G, the sum of the degrees of all the nodes in G is an even number. Might approach problem by checking ...
San Jose State >> CS >> 154 (Spring, 2008)
More Context Free Languages CS154 Chris Pollett Mar 6, 2006. Outline Pushdown Automata Equivalence Pushdown Automata Our machine model is a generalization of finite automata. We allow our machines to have a stack: x Stack y z state control ...
San Jose State >> CS >> 154 (Spring, 2008)
LaTeX, automata,computability, and notation CS154 Chris Pollett Jan. 25, 2006. Outline What is LaTeX? Automata, Computability, and Complexity Mathematical Notation and Terminology What is LaTeX? LaTeX is a markup language which can be used to s...
San Jose State >> CS >> 156 (Spring, 2008)
Propositional Knowledge Bases M satisfies a knowledge base means that M is a truth assignment which makes each formula in the knowledge base true. M can be thought of as a possible world in which KB happens i.e., a model for the knowledge base. We wa...
San Jose State >> CS >> 156 (Spring, 2008)
Prolog Handout Page 3 of 9 X=penguin If we were to hit return at this point Prolog would print yes. and give a prompt. If instead we typed `;\' (which in Prolog means logical OR just as `,\' means logical AND) and return, Prolog would try to find an...
San Jose State >> CS >> 156 (Spring, 2008)
Cut in prolog a:- b, c, d, !, e, f, g ! = a cut can backtrack on subgoals before the cut If ever fail on a subgoal after the cut symbol then not only do we fail at this particular rule, but fail on the goal a. Example not(x) :- call(x), !, fail. not(...
San Jose State >> CS >> 156 (Spring, 2008)
...
San Jose State >> CS >> 157a (Fall, 2008)
Assertions, Views, and Programming CS157A Chris Pollett Oct. 31, 2005. Outline Assertions Views Database Programming Assertions It is useful to be able to specify general constraints in SQL - i.e., other than domain and key constraint. SQL all...
San Jose State >> CS >> 157a (Fall, 2008)
More Database Programming CS157A Chris Pollett Nov. 2, 2005. Outline JDBC SQLJ Introduction Last day we went over some JDBC and SQLJ code examples from prior classes. Today, we will discuss JDBC and SQLJ in more detail JDBC Connecting to a Dat...
San Jose State >> CS >> 157a (Fall, 2008)
Rrgh{ Ovtv- Jorn .\\ R-q-su l+ Racuut . -/- >,) I I z_ ouTev- \\,2nron tQesu \\ tReguLTtA a a. 6 n ut \\ R ociTLP- (JNJ\\bNS Bc- D I -/-. - N*wrbe\" Nav^e-\\ Sc.\\\'\" )lloNv - [Jo -: ^Ac nl\' N kvm A\ {ttoB\\zA{\\Dt\'l \' ^-\' rol\\-\ w t^\' i+h -\' d...
San Jose State >> CS >> 157a (Fall, 2008)
The Relational Algebra CS157A Chris Pollett Oct. 3, 2005. Outline Overview of the Relation Algebra Select Operations Project Operations Composition and Rename Operations Union, Intersection and Minus Cartesian Product Join Overview of the Re...
San Jose State >> CS >> 157b (Spring, 2008)
indexing and hashing Azita Keshmiri CS 157B Basic concept An index for a file in a database system works the same way as the index in text book. For example if we want to learn about a particular topic, we can search for the topic in the index a...
San Jose State >> CS >> 157b (Spring, 2008)
By Shantanu Narang CS 157b Relational Model Object Model Object Oriented Databases(OOD) Object Query Language(OQL) OOD pros and cons Relation database Java Representation Relation Tuple Composite Attribute Multi Valued Attributes Foreign Key C...
San Jose State >> CS >> 157b (Spring, 2008)
More Multidimensional Indexes CS157B Chris Pollett Mar. 7, 2005. Outline Tree-Like Structures for Multidimensional Data Bitmap Indexes Query Execution Different Kinds of Tree Indexes Multiple Key Indexes kd-trees Quad trees R-trees Multipl...
San Jose State >> CS >> 157b (Spring, 2008)
Checkpointing, Redo, Undo/Redo Logging CS157B Chris Pollett Apr.20, 2005. Outline Checkpointing Redo Logging Undo/redo Logging Checkpointing So far recovery requires that the entire log file be looked at. Even if a transaction has written a ...
San Jose State >> CS >> 158a (Fall, 2008)
e)\"r) 4 F\'Et-, t- ,g)lra,\" qffi T\';\' ;\' ;1\" c;Lrca,ga W,#.:*r,f\';T -6;\' o\'-rco< q:^Ti!T ; : t{ c-W s. .\\. o/-l_J, -r- t \\j- o,(,r* \\Ot\"^-!-/; - 6.8.Lacp-*tb{o, t-9 :QLu*J;.t r\'t: \',-,>*,u> g-5 6 6c d-&td)n D r^T ?REsedI toN J...
San Jose State >> CS >> 158a (Fall, 2008)
Wires and Optics CS158a Chris Pollett Feb 7, 2007. Outline Maximum Data Rate of a Channel Guided Transmission Media (wires and optics) Maximum Data Rate of a Channel Nyquist in the 1920s derived an equation expressing the maximum data rate for a...
San Jose State >> CS >> 158a (Fall, 2008)
Optics and Wireless Media CS158a Chris Pollett Feb 12, 2007. Outline More on Optical Fiber Wireless Transmission More on Optical Fiber Optical fiber is made from highly transparent glass, which in turn is made from essentially sand Attenuation ...
San Jose State >> CS >> 158a (Fall, 2008)
The Application Layer CS158a Chris Pollett May 9, 2007. Outline DNS E-mail More on HTTP The Domain Name System (DNS) to give an IP address and To refer to a process on the internet we need a port. These numbers on not very convenient for peo...
San Jose State >> CS >> 174 (Fall, 2008)
Introduction to Flex and Air CS174 Chris Pollett Nov. 24, 2008. Outline Flash, Flex, and Adobe Air Installation Simple Program Components Flash Flash was originally a product to make it easy to produce vector based animations on the web create...
San Jose State >> CS >> 174 (Fall, 2008)
PHP, JSON, REST CS174 Chris Pollett Oct 17, 2007. Outline Web Services REST JSON Example More PHP Web Services One important use of PHP is to allow you to write web services. For HW3, you are asked to write a web service, lets spend a mom...
San Jose State >> CS >> 174 (Fall, 2008)
Credit Card Transactions, Version Control Systems CS174 Chris Pollett Nov. 12, 2008. Outline Credit Card Transactions Version Control Systems Credit Card Transactions One use for HTTPS is so that you can do credit card transactions on the web. ...
San Jose State >> CS >> 174 (Fall, 2008)
Webservices, Proxies, Rest, File Uploads, Security. CS174. Chris Pollett. Nov. 3, 2008. Outline. Web Services . REST . JSON Example. More PHP. Web Services. One important use of AJAX and PHP is to allow you to write web services. A we...
San Jose State >> CS >> 185c (Fall, 2008)
...
San Jose State >> CS >> 185c (Fall, 2008)
Today code ...
San Jose State >> CS >> 185c (Fall, 2008)
...
San Jose State >> CS >> 185c (Fall, 2008)
Serial Ports A precursor to hot sync Standards: RS232, USB, FireWire Some comparisons of speed: RS232 ~ 115KBps USB ~ 12Mbps (Ver. 2.0 ~ 50Mbps) FireWire ~ 50Mbps Ultra IDE ~ 33Mbps We will be focusing on RS232 & USB Some Differences between USB a...
San Jose State >> CS >> 254 (Fall, 2008)
Undecidability CS254 Chris Pollett Sep 18, 2006. Outline Diagonalization Undecidability of the Halting Problem Facts about recursive and r.e. lanaguages Universal Turing Machines It is natural to write Turing Machine programs as strings, say ...
San Jose State >> CS >> 254 (Fall, 2008)
n \\J A = I t i* ,-ac,:{,Ce.^tt-.-.,., k;o ,\', } idza ; Pz-ooJ t. Q;u- a^ inTd- %. fu \' /\"1 \\ n, F;.,< 2-LiYs 6*t + y1. A* a\',-.t h\'*2. l\'e\' tL U r I t-t 6e7te{ \\ \'tt\"\'\\s i.1,.*vt ) 2. {\"bA,W \"tt lr\"r Lifs o het lAo-, /e: z-t-rTr J. i e...
San Jose State >> CS >> 254 (Fall, 2008)
Reductions CS254 Chris Pollett Oct 16, 2006. Outline Reductions Completeness Closure Under Reductions Introductions to Reductions The class NP contains an infinite number of languages. We have already mentioned that TSP(D) is in NP. Similarly,...
San Jose State >> CS >> 254 (Fall, 2008)
Completeness CS254 Chris Pollett Oct 23, 2006. Outline Polynomially Verifiable Complete problems for P and NP Polynomially Verifiable Languages NP is sometimes called the class of languages which are polynomial time verifiable. Call a relation ...
San Jose State >> CS >> 255 (Fall, 2008)
Distributed algorithms (based on Johnsonbaugh, Chapter 12, as linked to from the CS 255 home page) There are many models of distributed computation. The architecture of these models is defined in terms of a network of nodes, each representing a proce...
San Jose State >> CS >> 255 (Fall, 2008)
Finding the max min are found simultan...
San Jose State >> CS >> 255 (Fall, 2008)
Polynomial reducibility Our intuitive definition of what it means for one problem to be easier than another is this: A problem A is easier than a problem B iff a solution algorithm for B would give us a solution algorithm for A, with no significant l...
San Jose State >> CS >> 255 (Fall, 2008)
Approximation algorithms One way of dealing with intractable (optimization) problems is to find a polynomial-time algorithm that guarantees a good approximate solution. There are several notions of a \"good approximation\". One rarely encountered noti...
San Jose State >> CS >> 267 (Spring, 2008)
BHARGAV R VADHER (14) CHAPTER 8: DECISION MAKING COMPUTER SCIENCE DEPARTMENT SAN JOSE STATE UNIVERSITY November 26, 2007 Here I will explain the problem of patient, whether he has flue or not, based on three conditional attribute and one decision a...
San Jose State >> CS >> 267 (Spring, 2008)
DISSIMILIRATY ANALYSIS Introduction In DISSIMILIRATY ANALYSIS we are going to discuss Knowledge Representation Systems in which neither condition nor decision attributes are distinguished. We are basically not interested in dependencies among attr...
San Jose State >> CS >> 298 (Fall, 2008)
CS298 Proposal Online Video Chatting Tool Sapna Blesson (sapna.blesson@yahoo.com) Advisor: Dr. Chris Pollett Committee Members: Dr. Mark Stamp (stamp@cs.sjsu.edu) and Dr. Sin Min Lee (lee@cs.sjsu.edu) Abstract: The aim of this project is to develop ...
San Jose State >> CS >> 298 (Fall, 2008)
Fraud Tolerant Distributed Computing By Shruti Parihar Advisor: Dr. Mark Stamp Committee: Dr. Chris Pollett Dr. David Blockus Department of Computer Science, One Washington Square San Jos, California USA 95192 Fraud Tolerant Distributed Computin...
San Jose State >> CS >> 40 (Spring, 2008)
ARTICLE IN PRESS Management S C A N D I N AV I A N J O U R N A L O F Scand. J. Mgmt. 21 (2005) 1940 www.elsevier.com/locate/scaman Transaction cost economics and business administration Oliver E. Williamson1 University of California, Berkeley, USA...
San Jose State >> CS >> 40 (Spring, 2008)
...
San Jose State >> NURS >> 147a (Fall, 2008)
SAN JOSE STATE UNIVERSITY, SCHOOL OF NURSING Community Psychiatric Nursing, NURS 147A. Section 1, TUESDAYS, Spring 08 Jan. 29 May 13, 2008 Chia-Ling Mao PhD, RN, 408-924-3152, FAX 408-924-3135 School of Nursing 408-924-3131 OFFICE HOURS:MONDAY, 2:30...
San Jose State >> NURS >> 204 (Fall, 2008)
SAN JOSE STATE UNIVERSITY School of Nursing DIVERSE POPULATIONS AND HEALTH CARE NURS. 204, 3 Units Spring 2003 (12764) IRC 302 Thurs. 4:30 7:20pm Phyllis M. Connolly, PhD, RN, CS HB 416; 408-924-3144 Office phone; connollyDR@son.sjsu.edu Fax 408-924...
San Jose State >> NURS >> 204 (Fall, 2008)
SAN JOSE STATE UNIVERSITY School of Nursing DIVERSE POPULATIONS AND HEALTH CARE NURS. 204, 3 Units Phyllis M. Connolly PhD, RN, CS GUIDELINES FOR PRESENTATION OF ASSIGNED READINGS The purpose of this assignment is to increase effective class particip...
San Jose State >> NURS >> 204 (Fall, 2008)
DIVERSE POPULATIONS AND HEALTH CARE Nurs 204, 3 Units THE INDIVIDUAL PAPER 75 POINTS Vulnerable, Culturally Distinct, Population Assessment . NAME: DUE: Submit 2 copies of your paper and this grading sheet. POINTS _5 _5 _5 OBJECTIVES 1. Describe a ...
San Jose State >> NURS >> 204 (Fall, 2008)
DIVERSE POPULATIONS AND HEALTH CARE Nurs 204, 3 Units THE INITIAL BRIEF PAPER 25 POINTS A Brief Personal Cultural Assessment. NAME: OBJECTIVES 1. Prepare a paper of 2-3 pages that is clearly written in APA format. Include an _5 abstract and a referen...
San Jose State >> DSGD >> 103a (Spring, 2008)
103a p2 San Jose State University DSGD 103a Advanced Typography S 07 Instructor Chuck Byrne Project #2 Problem Redesign the logotype, format and typography of Scientific American Magazine. C4 C1 Schedule Feb 26 Studio L...
San Jose State >> DSGD >> 107b (Spring, 2008)
107b San Jose State University DSGD 107b, Sec 01, Code 32178 Special Topics Design and Innovation Spring 07 Prerequisites DsGD 107a portfolio review. Instructor Chuck Byrne Office Room: 213, Art Bldg. Phone: 924-5448 Do not leave messages about missi...
What are you waiting for?
Course Hero is not sponsored or endorsed by any college or university.
Copyright © 2010. Course Hero, Inc.