'Cubic spline' presentation slideshows

MAE 152 Computer Graphics for Scientists and Engineers

MAE 152 Computer Graphics for Scientists and Engineers. Splines and Bezier Curves. Introduction. |Representing Curves A number of small line-segments joined Interpolation Parametric equations Types of curve we study Natural Cubic Splines and Bezier Curves Derivation Implementation.

1.07k views • 76 slides

Interpolation Techniques; Simulation & Analysis

CS 584 Multimedia Communication. Interpolation Techniques; Simulation & Analysis. Asmar Azar Khan 2005-06-0003. Objective. Study and analyze different interpolation techniques Performance analysis of these techniques on different performance metrics

585 views • 42 slides

Chapter 10: Graphics

Chapter 10: Graphics. MATLAB for Scientist and Engineers Using Symbolic Toolbox. You are going to. Review the basics of plotting simple 2-D/3-D graphs and animations Create graphs with different attributes Generate advanced animated graphs with timing control

498 views • 40 slides

Introduction to Smoothing Splines

Introduction to Smoothing Splines. Tongtong Wu Feb 29, 2004. Outline. Introduction Linear and polynomial regression, and interpolation Roughness penalties Interpolating and Smoothing splines Cubic splines Interpolating splines Smoothing splines Natural cubic splines

649 views • 39 slides

Model Comparison for Tree Resin Dose Effect On Termites

Model Comparison for Tree Resin Dose Effect On Termites. Lianfen Qian Florida Atlantic University Co-author: Soyoung Ryu, University of Washington. Outline. Introduction Longitudinal Data: Termites Data Set Model Comparison Partially Linear Model Piecewise Linear Models

546 views • 37 slides

A GPU Accelerated Explicit Finite-volume Euler Equation Solver with Ghost-cell Approach

Session: Supercomputer/GPU and Algorithms (GPU-2). A GPU Accelerated Explicit Finite-volume Euler Equation Solver with Ghost-cell Approach. F.-A. Kuo 1,2 , M.R. Smith 3 , and J.-S. Wu 1* 1 Department of Mechanical Engineering National Chiao Tung University Hsinchu , Taiwan

525 views • 36 slides

Lecture 9. Model Inference and Averaging

Lecture 9. Model Inference and Averaging. Instructed by Jinzhu Jia. Outline. Bootstrap and ML method Bayesian method EM algorithm MCMC (Gibbs sampler) Bagging General model average Bumping. The Bootstrap and ML Methods. One Example with one dim data:

558 views • 36 slides

Numerical Methods

573 views • 29 slides

Estimation of selectivity in Stock Synthesis : lessons learned from the tuna stock assessment

Estimation of selectivity in Stock Synthesis : lessons learned from the tuna stock assessment. Shigehide Iwata* 1 Toshihde Kitakado * 2 Yukio Takeuchi* 1 *1 National Research Institute of far seas fisheries *2 Tokyo University of Marine Science and Technology. Background (1).

479 views • 29 slides

Arc-length computation and arc-length parameterization

Arc-length computation and arc-length parameterization. Arc-length computation. Parametric spatial curve used to define the route of an object Q(t)=(x(t),y(t),z(t)) Arc-length computation necessary for motion control along a curve Control the speed at which the curve is traced

463 views • 29 slides

Basis Expansions and Generalized Additive Models (2)

Basis Expansions and Generalized Additive Models (2). Splines Generalized Additive Model MARS. Piecewise Polynomials. Lower -right: Cubic spline. Spline. An order-M spline with knots ? j , j = 1,...,K is a piecewise-polynomial of order M, and has continuous derivatives up to order M ? 2.

312 views • 26 slides

real work by Adam Langley some slide sorting and comments by Ian Taylor

MULTIFAN-CL and Stock Synthesis A comparison of the 2010 Indian Ocean yellowfin tuna assessment including tagging data. real work by Adam Langley some slide sorting and comments by Ian Taylor. Introduction.

320 views • 25 slides

A Parametric Model for Oriented, Navigable Surfaces in Virtual Environments

A Parametric Model for Oriented, Navigable Surfaces in Virtual Environments. Hongling Wang Joseph Kearney Department of Computer Science University of Iowa. Virtual Environments as Behavioral Laboratories. Gaining widespread acceptance Driving (Uc, Rizzo, Shi, Anderson, and Dawson, 2004;

249 views • 24 slides

The Diurnal Temperature Smart Tool

The Diurnal Temperature Smart Tool. Angie Enyedi National Weather Service Jacksonville, FL. What is it and why the need for it? How is it implemented? What are the results?. What is it?.

320 views • 22 slides

Parallel Analysis of Egg Data with hadoop on futuregrid

Parallel Analysis of Egg Data with hadoop on futuregrid. Project Member: Rewati Ovalekar Project Guide : Gregor von Laszweski , Lizhe Wang. BACKGROUND. Importance of EEG Data: Used in detecting and diagnosing brain related dis-orders

397 views • 19 slides

HIV incidence determination in clade B epidemics: A multi-assay approach

HIV incidence determination in clade B epidemics: A multi-assay approach. Oliver Laeyendecker, Brookmeyer R, Cousins MM, Mullis CE, Konikoff J, Donnell D, Celum C, Buchbinder SP, Seage GR, Kirk GD, Mehta SH, Astemborski J, Jacobson LP, Margolick JB, Brown J, Quinn TC, and Eshleman SH.

233 views • 16 slides

Computer Graphics: Programming, Problem Solving, and Visual Communication

295 views • 15 slides

Introduction

Introduction.

133 views • 13 slides

Lecture on Numerical Analysis

Lecture on Numerical Analysis. Dr.-Ing. Michael Dumbser . 15 / 09 / 2008. Interpolation. f. Set of given function values at given points. These may come e.g. from measurements made in the real world (temperature, snow thickness, rainfall) or may come from laboratory experiments.

276 views • 12 slides

Lecture 07: Splines

186 views • 6 slides