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Keynote: The ROI of Adopting Model-Based Design

Jon Friedman, MathWorks

Companies must both deliver high-quality systems to the market today and develop exciting innovations for tomorrow, all while controlling costs. To accomplish this paradoxical challenge, innovative companies have adopted Model-Based Design over traditional development methods. Model-Based Design starts with a set of requirements that are used to develop executable specifications in the form of models rather than textual specifications. Engineers use these models to clarify requirements and specifications, quickly evaluate design alternatives through simulation, and then automatically generate production code. In this session, we present a set of case studies that demonstrate how leading companies in the aerospace and defense industry meet market challenges to win new contracts and develop the next generation of innovations. Their return on investment goes beyond the achieved improvement in quality and time to market; it extends to the additional capacity made available to meet their next design challenge.

Modeling and Simulation of Electrical Power Systems Using MATLAB and Simulink

Carlos Osorio, MathWorks

System and power engineers face many challenges, including validating power consumption requirements and modeling power distribution systems that include degradation effects of power source and storage devices. This session focuses on the use of Simulink and its suite of physical modeling libraries for the design and analysis of electrical power systems for spacecraft. We demonstrate how you can use dynamic simulation to overcome these challenges and explore how MathWorks tools can help you in every step of the design process.

Introduction to Parallel Computing with MATLAB

Dave Forstot, MathWorks

Learn how you can use Parallel Computing Toolbox™ to speed up MATLAB applications by using hardware you already have. Learn how minimal programming efforts can speed up your applications on widely available desktop systems equipped with multicore processors and GPUs.

Modeling Communication Interference Scenarios

Timothy Reeves, MathWorks

This session demonstrates how to model the effects an electronic interferer on an end-to-end communication system using MATLAB and Simulink. The example shows how to interactively select different interfering signal types, power levels, and locations and then see the effects on system-level metrics such as bit error rate. It also shows how to include mitigation algorithms such as adaptive beam-forming.

Model-Based Design for High-Integrity Systems

Will Campbell, MathWorks

MathWorks products enable Model-Based Design, which improves engineering productivity on safety-critical systems, including those that must meet DO-178B certification standards. This session presents a workflow using MathWorks tools for requirements validation, algorithm design, traceability, code generation, test generation, formal methods verification, and processor in-the-loop testing. Interfaces to requirements management and configuration management tools are also presented.

Master Classes

Designing Embeddable Algorithms and Automatically Generating C Code with MATLAB

Timothy Reeves, MathWorks

In this master class, we showcase new capabilities of MathWorks products you can use to generate C code from your Embedded MATLAB® code. You will learn about these capabilities by going through an example for the design of a video processing system. Through demonstrations, you will learn how to:

  • Create and modify your MATLAB algorithms to comply with the MATLAB language subset supported for code generation
  • Generate C code from your Embedded MATLAB code directly from the MATLAB desktop
  • Call your Embedded MATLAB code as a new block within Simulink to integrate and simulate your algorithm as part of a larger system model

MATLAB for Telemetry Data Analysis

Dave Forstot, MathWorks

In this master class, we show how you can use MATLAB to solve the unique challenges posed by telemetry data analysis. Specifically, you will learn how you can use MATLAB and related toolboxes to:

  • Import poorly formatted data
  • Manage data in the MATLAB workspace using advanced techniques
  • Quickly visualize data interactively and programmatically
  • Evaluate techniques for dealing with missing or poorly sampled data
  • Automate importing, analysis, and visualization of data
  • Develop a user interface for others to utilize analysis and visualization routines