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Animation and Games Development

Animation and Games Development

Animation and Games Development. 242-515 , Semester 1 , 2014-2015. Objective to give some background on the course. 7. Basic Maths. Overview. Vectors Matricies The Dot Product The Cross Product Transformations as Matricies Multiple Coordinate Spaces Models in a Scene Graph.

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859 views • 72 slides
Chapter 4.1 Mathematical Concepts

Chapter 4.1 Mathematical Concepts

Chapter 4.1 Mathematical Concepts. Applied Trigonometry. Trigonometric functions Defined using right triangle. h. y. a. x. Applied Trigonometry. Angles measured in radians Full circle contains 2 p radians. Applied Trigonometry.

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628 views • 62 slides
ENGG2013 Unit 5 Linear Combination & Linear Independence

ENGG2013 Unit 5 Linear Combination & Linear Independence

ENGG2013 Unit 5 Linear Combination & Linear Independence. Jan, 2011. Last time. How to multiply a matrix and a vector Different ways to write down a system of linear equations Vector equation Matrix-vector product Augmented matrix. Column vectors. Review: matrix notation.

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625 views • 55 slides
CSCE 552 Fall 2012

CSCE 552 Fall 2012

CSCE 552 Fall 2012. Math. By Jijun Tang. Applied Trigonometry. Trigonometric functions Defined using right triangle. h. y. a. x. Applied Trigonometry. Angles measured in radians Full circle contains 2 p radians. arcs. Vectors and Scalars.

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537 views • 53 slides
CS I400/B659: Intelligent Robotics

CS I400/B659: Intelligent Robotics

CS I400/B659: Intelligent Robotics. Transformations and Matrix Algebra. Agenda. Principles, Ch. 3.5-8. Rigid Objects. Biological systems, virtual characters. q 2. q 1. Articulated Robot. Robot: usually a rigid articulated structure Geometric CAD models, relative to reference frames

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426 views • 42 slides
ENGG2013 Unit 17 Diagonalization Eigenvector and eigenvalue

ENGG2013 Unit 17 Diagonalization Eigenvector and eigenvalue

ENGG2013 Unit 17 Diagonalization Eigenvector and eigenvalue. Mar, 2011. EXAMPLE 1. Q6 in midterm. u(t): unemployment rate in the t-th month. e(t)= 1-u(t) The unemployment rate in the next month is given by a matrix multiplication Equilibrium: Solve.

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341 views • 31 slides
Computation on meshes, sparse matrices, and graphs

Computation on meshes, sparse matrices, and graphs

Computation on meshes, sparse matrices, and graphs. Some slides are from David Culler, Jim Demmel, Bob Lucas, Horst Simon, Kathy Yelick, et al., UCB CS267. Parallelizing Stencil Computations. Parallelism is simple Grid is a regular data structure

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289 views • 28 slides
PiCAP: A Parallel and Incremental Capacitance Extraction Considering Stochastic Process Variation

PiCAP: A Parallel and Incremental Capacitance Extraction Considering Stochastic Process Variation

PiCAP: A Parallel and Incremental Capacitance Extraction Considering Stochastic Process Variation. Fang Gong 1 , Hao Yu 2 , and Lei He 1 1 Electrical Engineering Dept., UCLA 2 Berkeley Design Automation Presented by Fang Gong. Outline. Background and Motivation Algorithms

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517 views • 25 slides
ENGG2013 Unit 6 Matrix in action

ENGG2013 Unit 6 Matrix in action

ENGG2013 Unit 6 Matrix in action. Jan, 2011. Linear transformation. A.k.a. Linear mapping , linear function . A way to map an m -dimensional object to an n -dimensional object. 2-D to 3-D transformation. 3-D to 2-D transformation. Historical note.

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287 views • 18 slides
Distributed Computation of a Sparse Matrix Vector Product

Distributed Computation of a Sparse Matrix Vector Product

Distributed Computation of a Sparse Matrix Vector Product. Lecture 18 MA/CS 471 Fall 2003. Recall. We wish to solve Ax=b for some sparse A In each of the three iterative schemes we need to evaluate a matrix vector product of the form: Qv for some sparse matrix Q .

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330 views • 15 slides

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