Events - Seminars

Technical Computing with MATLAB and Simulink at Boise State University

Seminar Overview

Join MathWorks for free MATLAB seminars, October 19 - 20, 2010 in the Boise State Student Union – Hatch C/D.

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Agenda
Presenter:	Jiro Doke, Ph.D., Senior Application Engineer
1:15 p.m.	Registration and sign-in. Walk-ins are welcome.
1:30 – 4:30 p.m.	Tuesday, October 19, 2010 Session 1: Technical Computing with MATLAB and Simulink (Ball Tracking Demo) In this session, we will demonstrate how to acquire, analyze, and visualize data in MATLAB and then design and model a system using Simulink. Our example involves building, modeling, and deploying a system that rotates a camera to track an object. Introduction to MATLAB Case Study: Image Analysis Accessing data (from files or hardware) Analyzing and visualizing data Sharing your work Modeling and Simulation with Simulink Case Study: Object Tracking System Developing video processing algorithms Designing event-driven logic using Stateflow® Case Study: Modeling and Controlling a DC Motor Modeling dynamic systems with Simulink Designing a feedback controller Implementing the controller using code generation
9:15 a.m.	Registration and sign-in. Walk-ins are welcome.
9:30 a.m. – 12:00 p.m.	Wednesday, October 20, 2010 Session 2: Speeding Up and Parallel Computing with MATLAB Speeding Up MATLAB Applications (9:30– 10:30 a.m.) In this session, we will demonstrate simple ways to improve and optimize your code that can boost execution speed by orders of magnitude. We will also address common pitfalls in writing MATLAB code, and explore the use of the MATLAB Profiler to find bottlenecks. You will learn to: Understand memory usage and vectorization in MATLAB Address bottlenecks in your programs Optimize I/O to streamline your code Parallel Computing with MATLAB (10:45 a.m. – 12:00 p.m.) In this session we will demonstrate how to perform parallel and distributed computing in MATLAB to boost execution speed on computationally intensive problems, including how to handle larger data sets. We will highlight our add-on tools that offer built-in ability to take advantage of parallel computing resources without requiring any extra coding. We will introduce you to parallel processing constructs such as parallel for-loops, distributed arrays, and message-passing functions. We will also show you how to take advantage of common trends in computer hardware, from multiprocessor machines to computer clusters. This session will cover: Toolboxes with built-in support for parallel computing Go faster: Use larger compute pool Work with larger data sets: Use larger memory pool Scale up to a cluster: Interactive to scheduled jobs Numerous tips on developing parallel code