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Optional Primer Sessions

An Overview of MATLAB and the MATLAB Product Family

Daryl Ning, MathWorks

What is MATLAB®? Why do so many companies use MATLAB as their primary tool for data analysis? Why do so many universities use MATLAB for both teaching and research?

MATLAB is an interactive development environment as well as a technical computing language. This means you can use a graphical user interface, or a programmatic textual interface to perform your complex data analysis and visualisation, as well as your algorithm and application development. During this demo, we show how MATLAB fits into a typical data analyst’s workflow, from data access, to data analysis and exploration, through to application deployment and reporting.

If you've never been exposed to MATLAB, or you are still new to MATLAB, this session provides a high-level overview of the major capabilities and provides an ideal foundation for the presentations that follow.

Introduction to Simulink for Modelling and Simulation

Sam Oliver, MathWorks

Why are so many companies using Simulink® as an integral part of their development processes? Why do all the universities that use MATLAB for both teaching and research, also use Simulink as part of this process?

If you’re new to Simulink, or just need a refresher, attend this session where we introduce you to Simulink, an environment for multi-domain simulation and Model-Based Design for dynamic and embedded systems. Product demos give you a high-level overview of the major capabilities and how you can use Simulink to design, simulate, implement, and test a variety of time-varying systems, including communications, controls, signal processing, video processing, and image processing.

Keynote Presentation

MathWorks – from Research to Production

Andrew Clay, MathWorks

Over the last quarter century, engineers, scientists, economists, financial quantitative analysts, and others have turned their workflows upside down as a result of vast shifts in the engineering, scientific, economic, financial, and computing landscapes. These shifts include:

  • The economic availability (relative to 1980) of vast computing power to suit all requirements, from the desktop to the high-performance grid or cluster, has allowed them to rethink how they do things.
  • The growing sophistication of technical computing tools has revolutionised the practice of research (whether academic or commercial), data analysis and manipulation, algorithm development, and collaboration and communication whilst completing these tasks.
  • More rapid and flexible methods for deploying or distributing the results of research and development enables collaboration and interaction with groups that may not previously have been part of a process, including different departments or groups in an organisation, customers, or suppliers.
  • The growing use of microprocessors, driven by embedded software, continues to increase explosively as our access to computing power enables us to design and build ever more complex systems and system of systems.

The growth of MathWorks, the MATLAB technical computing platform, and the Simulink simulation platform have been intertwined with these changes. Technical computing is now part of “how things are done.” As a result MathWorks technology has evolved to provide a platform able to support a broad workflow – from research to production. This workflow often spans multiple individuals and in many cases, organisations.

In this keynote presentation, Andrew Clay will discuss how MathWorks technologies have evolved to support this workflow. He will show how they remain powerful ‘point solution’ enablers but have also evolved so that they can be considered comprehensive platforms enabling broad workflows, from research to production.

Technical Sessions

Streamline Your Data Analysis Workflow with MATLAB

Bobby Nedelkovski, MathWorks

This presentation reviews the data analysis workflow and how MATLAB technology can be leveraged to streamline and automate your analyses. This session features a walk-through explanation of the data analysis workflow with MATLAB featuring the access, visualisation and analysis, and reporting stages. Addition highlights include discussion of industry- and application-specific MATLAB add-ons to complement your data analysis tasks and reduce your development costs.

Data Analysis and Modelling with MATLAB: A Practical Approach

Bobby Nedelkovski, MathWorks

Scientists, engineers, and analysts are presented with increasing quantities of data, so being able to sort through the mass of information quickly and easily is critical. This demo takes you through a practical example of how to apply MATLAB to the data analysis workflow discussed in the Streamline Your Data Analysis Workflow with MATLAB presentation.

Model-Based Design Turns 10

Sam Oliver, MathWorks

Are you having trouble meeting your product or system development deadlines? Does finding issues late in the development process result in budget blowouts? Are you interested in streamlining your development process? Model-Based Design has been integrated into many companies’ development processes both to streamline them and to address these critical issues.

NASA’s definition of Model-Based Design is the following:
“Model-Based Design is a mathematical and visual method of addressing problems associated with designing complex control systems. It is used in motion control, industrial equipment, aerospace, and automotive applications.

“[Model-Based Design] provides an efficient approach for establishing a common framework for communication throughout the design process while supporting the development cycle ("V" diagram). In Model-Based Design, development is manifested in these steps: modeling a system, analyzing and synthesizing a controller for the system, simulating the system, and integrating all these phases by implementing the system. The model-based design paradigm is significantly different from traditional design methodology. Rather than using complex structures and extensive software code, designers can use [Model-Based Design] to define models with advanced functional characteristics using continuous-time and discrete-time building blocks. These built models used with simulation tools can lead to rapid prototyping, software testing, and verification. Not only is the testing and verification process enhanced, but also, in some cases, hardware-in-the-loop simulation can be used with the new design paradigm to perform testing of dynamic effects on the system more quickly and much more efficiently than with traditional design methodology.”

This session provides a high-level overview of Model-Based Design by introducing the workflow and its application using customer success stories. It highlights the benefits Model-Based Design has provided to customers from a variety of industries over the last 10 years.

Pathways to Production – Taking MATLAB and Simulink Algorithms from Research and Design to Production

Sam Oliver, MathWorks

MATLAB and Simulink are used extensively in the research, analysis, and design of algorithms and systems. As high-level language design environments, they provide advantages in the rapid development of ideas when compared to lower level-languages such as C, HDL, Structured Text, or Java.

So what happens when you want to run your algorithms away from the MATLAB or Simulink environments, targeting a prototype or production application? Attend this session to understand the options available to rapidly transition from design to prototype or production systems, without having to go through the time-consuming and error-prone process of manually converting your designs to other environments.

This session covers deployment options for both production and prototype systems. Some examples include:

  • Developing a MATLAB based application with a graphical user interface, Web application, or back-end production system
  • Using Simulink to develop algorithms for an embedded system, hardware design, or real-time testing environment
  • Converting a MATLAB algorithm to generic C code for integration into your existing production systems

Industry Sessions

Research to Reality

Linda Davis, Institute for Telecommunications Research, University of South Australia

The Institute for Telecommunications Research (ITR) is internationally recognised for its research and technology development for wireless communications. ITR conducts its research in three main areas: satellite communications, high-speed data communications, flexible networks encompassing fixed and mobile, satellite- and terrestrial-based applications. ITR has experts in information theory, security, networks, distributed systems, signal processing, optical, and wireless communication systems. ITR has a long history of collaborative projects with industry and transforming research to reality.

At the core of ITR’s fundamental research and product development are tasks of analysis, design, optimisation, and performance simulation. In this talk Linda highlights how ITR uses MATLAB and Simulink to support both theoretical work and product prototyping. She also gives an example of integrated tools (MATLAB, Simulink, and third-party tools) used as the development environment for hardware including custom FPGAs, DSPs, or an off-the-shelf platform such as Lyrtech's small form factor software defined radio.

Industry Case Study

Daniel Williams, Principal Advisor, Strategic Scheduling, QueenslandRail

The Rooftop loading model is used throughout the world in the assessment and development of passenger rail timetables. This session describes how a team of mathematics graduates implemented this model at QueenslandRail using MATLAB to examine the loading dynamics on the network.

This tool can be used to assess the impacts of a minor train plan alteration or a major timetable revision.

Real-Time Research Platform Applied to Sound Processing Research in Cochlear™ Implants and Hearing Aids

John Heasman, Cochlear Ltd.

The multichannel cochlear implant is a unique technological achievement, representing the application of a novel combination of science, technology, and medicine. It brings functional hearing to severely or profoundly deaf individuals, transforming not only their lives but those of their families.

In 1985 Cochlear released the first commercial multichannel Nucleus® implant system. Four further generations have been released in the 25 years since. Innovations in mechanical design, electronics, and signal processing have brought successive improvements in device reliability and clinical outcomes. As of April 2011, over 144,000 registered Cochlear Nucleus® Implant Systems were in use globally.

Recent advances in acoustic signal processing for cochlear implants have produced incremental, but significant, improvements in cochlear implant recipients’ speech understanding. In the past, these improvements were constrained by laborious, time consuming algorithmic implementation using proprietary digital signal processor (DSP) devices. This investment in process can limit the time available for creative work and hence restrict technological innovation.

Cochlear circumvented this innovation bottleneck with a rapid prototyping platform built with MathWorks products. The accelerated development process greatly reduced the time from conception to realization and increased the potential for future innovation.
This session describes how Simulink and xPC Target were integrated into a PC-based system with real-time capability. It will also provide examples of day-to-day contributions to people with Cochlear implants or hearing aids.

Variable Stability Flight Simulation in Aerospace Engineering Education – Development Expedited by Simulink

Dr. Peter W. Gibbens, Senior Lecturer in Aerospace Engineering, School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney

At the University of Sydney, faculty at the School of Aerospace, Mechanical and Mechatronic Engineering use a motion-based flight simulation facility to help train engineering students in aspects of aircraft design and systems development. It also offers students experiential learning about impacts on flight dynamics, handling qualities, and aircraft operation. The variable stability flight simulator enables students to study how variations in aerodynamic parameters affect flight stability and handling characteristics.  Students can design and implement flight control systems and assess the behaviour of these systems in real-time operation by simulating aircraft flight and systems.

This presentation gives an overview of the simulator and discusses its educational purpose and uses. MATLAB and Simulink are key to the success of the programme. MATLAB is the students’ main analytical and development tool. Simulink and xPC Target provide a real-time implementation framework. The presentation also describes the system hardware and software, and the interactions between them, with particular emphasis on modelling and communications. Dr. Gibbens will address educational outcomes and describe why these tools provide an effective solution to the problem.


Automatically Converting MATLAB Code to C Code Using MATLAB Coder

Daryl Ning, MathWorks

Many engineers, scientists, and researchers rely on the flexibility of the MATLAB language to prototype their ideas and algorithms. As a high-level language, MATLAB provides an easy-to-use environment that facilitates rapid design iterations compared to lower-level languages like C. What happens when these algorithms need to run independently of MATLAB and any MATLAB runtime libraries? For example, what if these algorithms need to be integrated within some external C environment or run on an embedded system?

New to R2011a, MATLAB Coder allows you to close the gap between a MATLAB prototype and implementation by automatically generating C code. With this capability, MATLAB algorithms can be exported to C environments completely independent of MATLAB. You can also compile the generated C code into a MATLAB friendly executable called a MEX file. This is useful for verification purposes, and may also provide speed improvements on your existing MATLAB code (most likely in the case of fixed-point algorithms).

This presentation walks you through the process of converting MATLAB code to C code, with demos that show:

  • Floating-point C code generation
  • Fixed-point C code generation
  • MEX (MATLAB executable) file generation
  • Dealing with inputs of variable sizes
  • Integrating generated C code into an external C environment

Note: Some of you may know this technology as Embedded MATLAB®, and if so, you are partially correct. MATLAB Coder is the current and more mature technology for automatic C code generation. This presentation shows some advances in the technology that make the generated C code more suitable outside of embedded applications, and also a more user-friendly interface.

Data Analysis for Design with MATLAB

Bobby Nedelkovski, MathWorks

Data analysis for design and quality assurance is a critical task in many industries. Scientists, analysts, and engineers working in fields as diverse as automotive, industrial automation, finance, mining and exploration, and biopharmaceuticals will benefit from attending this overview of data analysis for design with MATLAB.

This presentation reviews the MATLAB data analysis workflow for accessing, analysing, and modelling data, and documenting results. The presenter uses a Six Sigma approach to both measure and ensure quality in product designs. Demos show several ways to use MATLAB, including:

  • Modelling a physical system with historical data
  • Conducting a designed experiment for model derivation
  • Optimising system parameters
  • Measuring model robustness with Monte Carlo simulation and parallel computing

This presentation describes how you can use MATLAB to complement your data analysis tasks and to develop designs that not only work but designs that you can trust.

Using MATLAB for Signal Analysis

Daryl Ning, MathWorks

In signal processing, whether in academia, research, or industry, MATLAB is the de facto standard analysis tool. Whether you are a data analyst exploring time-series measurements or a seasoned DSP engineer, MATLAB provides rich functionality and a friendly development environment to help you become successful in your work. This presentation covers some of the major capabilities useful in a signal processing workflow, from data access through data exploration.

Product demos show how MATLAB can help users tackle a range of signal processing problems and challenges. If you’re still new to MATLAB, don’t be afraid to attend this presentation. Several demos illustrate how MATLAB can be used as an interactive point-and-click environment to simplify the analysis process, and then generate the required MATLAB code. Topics include:

  • Spectral analysis
  • Efficient techniques to handle data streams
  • Filter design and implementation techniques
  • Interfaces to instruments and data acquisition hardware (we use an Agilent arbitrary waveform generator feeding into a PC sound card)

Brief discussions address add-on products for applications such as communications, image and video processing, and array signal processing.

Model-Based Design for High-Integrity and Business-Critical System Development

Sam Oliver, MathWorks

Do you want to understand how to refine your system development practices to improve quality? Are you identifying critical design issues or bugs late in the design process? Does this create difficulty when meeting project deadlines or budgets? Are you required to meet system quality standards such as ISO 26262, IEC 61508, IEC 62304, DO-178B, or MISRA? Learn how many organisations are using a development process based upon an executable system-level model, otherwise known as Model-Based Design, to improve the quality of their designs and code through verification and validation using Simulink.

Model-Based Design is a powerful development method that allows you to reduce time to market and improve quality. This session builds on the modelling and simulation capabilities of the Simulink platform and introduces tools and techniques that enable you to verify and validate your models early in the development process, trace requirements to your models and code, check modelling standards automatically, manage regression and unit testing, and apply formal methods to prove the absence of run-time errors in both models and source code. The adoption of these formal verification and validation techniques ensures that your embedded systems meet your quality and safety goals.