Best Practices for Establishing a Model-Based Design Culture

The transition to Model-Based Design requires careful management, both to demonstrate its short-term benefits and to establish a culture that enables the full realization of the theoretical benefits of this approach. In this session, we introduce the concepts of Model-Based Design, highlight some of its benefits, and discuss in detail 10 best practices for adopting Model-Based Design across an organization. These best practices have been gleaned from successful and not-so-successful transformations to Model-Based Design at companies from a variety of industries.

Using Physical Modeling Tools to Design Power-Optimized Aircraft

A number of initiatives are underway to make tomorrow’s aircraft more efficient while reducing aircraft emissions. Projects such as the Power-Optimized Aircraft and the Clean Sky Joint Technology Initiative are focused on finding more efficient ways of transporting power throughout an aircraft while improving the environmental impact of air transport. Efforts like these require an optimized system design. This session focuses on achieving this goal by modeling a flight actuation system and performing tradeoff studies that can lead to more realistic requirements and optimized system performance.

Developing Communications and ISR Systems Using MATLAB and Simulink

A video surveillance UAV application provides attendees with an example of the integrated design and modeling of three subsystems in a single development environment. An antenna pointing control subsystem, a video imaging subsystem, and a communications link are jointly modeled in Simulink^® with several components implemented as Embedded MATLAB™ blocks. Real-world tradeoffs of control loop response, platform motions, bit error rates, and video processing complexity serve to illustrate the ease with which Simulink enables multidomain modeling.

Solving Data Analysis Challenges Using MATLAB and Statistics Products

Engineers often have significant quantities of data that need to be analyzed. Complicating the need to rapidly analyze the data are anomalies (drop-outs, sensor failures, etc.), which often lead to manual and laborious tasks to discover, categorize, and deal with missing or bad data. An example application is presented in order to demonstrate how MATLAB^® and statistics add-on products can be used to improve data quality and enhance understanding of the data through quantitative statistical methods.

Model-Based Design for Safety-Critical Systems

MathWorks products enable Model-Based Design, which improves engineering productivity with safety-critical systems, including those that must meet DO-178B certification standards. This session presents a workflow to demonstrate how MathWorks tools can be used 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.

Introduction to Parallel Computing with MATLAB

This master class shows you how the new products and features for MATLAB enable you to take advantage of recent advances in computer hardware, from multiprocessor machines to computer clusters. You will learn how to utilize multiple cores in your desktop machine through the new parallelism capabilities of MATLAB and Parallel Computing Toolbox™. We also introduce the use of MATLAB Distributed Computing Server™ on a computer cluster to speed up your algorithms and handle larger data sets.

Embedded MATLAB: Designing Embeddable Algorithms and Automatically Generating C Code with MATLAB

In this master class, we showcase new capabilities of MathWorks products that enable you 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 be compliant with the Embedded MATLAB language subset
• Generate C code from your Embedded MATLAB code directly from 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

Introduction to Object-Oriented Programming in MATLAB

R2008a included a major update to object-oriented programming in MATLAB, enabling easier development and maintenance of large applications and data structures. Using engineering examples, this master class demonstrates how to define classes and work with objects, highlighting the benefits of this programming approach over traditional procedural techniques. Features covered include class definitions, properties, property attributes, methods, method attributes, and inheritance. No knowledge of object-oriented programming is required.

New Concepts and Tools for Effective Verification and Validation Based on Model Analysis

Verification and validation is critical for implementation of Model-Based Design in production programs. This master class introduces new concepts and tools for effective verification and validation based on model analysis techniques. You will learn how to:

• Verify that your models meet requirements and modeling standards
• Prove correctness of the generated code and trace this information back to the model
• Use automation and tools to aid with design reviews and document generation