The Rise of Engineering-Driven Analytics

9:15–9:30

Engineering data has become essential in business-critical systems and applications. Audio, image, real-time video, motion, machine performance metrics, and other sensor-generated data is being combined with business, transactional, and other IT data to create opportunities for sophisticated analytics on more complex phenomena. The flexibility to run those analytics, either on massive data sets in IT or cloud infrastructures or as the data is acquired on smart sensors and embedded devices, is enabling organizations in many industries to develop intelligent products, devices, and services that expand the business impact of their data and analytics. In this talk, you will see numerous examples of this in action and learn about new capabilities in MATLAB® and Simulink® that empower you to design and develop these systems and be a leading force in this new analytics-driven age.

Phil Rottier, MathWorks


Keynote Presentation

9:45–10:15


What’s New in MATLAB Release 2016a and 2016b

10:15–10:45

In this session, Ned Gulley introduces new capabilities in the MATLAB® product family in Release 2016a and 2016b. Ned shares his insights into how MATLAB is designed to be the language of choice for millions of engineers and scientists worldwide. Attend this session for a unique opportunity to learn from one of the key designers of MATLAB.

Ned Gulley, MathWorks


Lunchtime Talk: Science Capital

13:15–13:45

Science Capital is a concept that the Science Museum has been working on with Kings College London as part of the Enterprising Science research programme. It describes a person’s relationship with science and can broadly be used to define whether a person feels that science is for them—or not. Science Museum is exploring how it might change what it does, such that visitors’ science capital increases as a result of a visit to the museum. The goal is to take the 27% of people that have low science capital and help them see that science is for them.

Dr. Kenny Webster, Science Museum


Big Data

11:15–11:45

Data is everywhere and each year we store more and more of it. Huge data sets present an amazing opportunity for discovering new things about our world, the products we make, and how people interact with them. However, big data sets also present some real challenges. How do we understand them? How do we interrogate them? How do we even read them? In this talk, we will look at the new tools in the MathWorks 2016 product releases that help you work with even bigger data.

Ben Tordoff, MathWorks


MATLAB and Advanced Analytics at Shell

11:45–12:15

The advanced analytics group at Shell have been working with MathWorks to define approaches to radically shorten the process of operationalisation of engineering analytics projects. In this talk, we share our approach to building analytics stacks to serve algorithms for process monitoring and predictive analytics based on a three-step approach: definition of use case, proof of concept, and platform.


Machine Learning and Deep Learning

12:15–12:45

Machine learning is ubiquitous. From medical diagnosis, speech, and object recognition to engine health monitoring and predictive maintenance, machine learning techniques are being used to make critical engineering and business decisions every moment of the day. In this session, we look at different machine learning techniques in MATLAB®, and in particular, we address the computer vision problem of object recognition using deep learning.

Jon Cherrie, MathWorks


The Adoption of MATLAB Apps and Toolboxes at Jaguar Land Rover

14:00–14:30

Engineering teams across Jaguar Land Rover rely on MATLAB based apps and toolboxes to develop a wide range of advanced vehicles. This session discusses:

  • How MATLAB® is used throughout the product development cycle
  • Best practices for MATLAB tool development, and how they are shared and encouraged
  • How MATLAB tools are distributed throughout the organisation as apps and toolboxes using an in-house store

Dave Barry, Jaguar Land Rover


Developing and Sharing MATLAB Apps and Toolboxes

14:30–15:15

In this session, David discusses how to develop MATLAB® tools that can be used across teams in your organisation, focusing on:

  • Architectural and implementation patterns to encourage robustness and reusability
  • Design conventions to help users learn tools fast
  • Tools to manage the creation and distribution of toolboxes

David Sampson, MathWorks


MATLAB Algorithm Development and Verification for Eurofighter Typhoon Praetorian

15:45–16:15

The Praetorian Defensive Aids Sub-System (DASS) currently installed on the Eurofighter Typhoon provides protection against air-to-air and surface-to-air threats by monitoring and proactively responding to the operational environment. It contains Electronic Support Measures, missile warning, on-board electronic countermeasures, and towed radar decoys to detect, evaluate, and counter threats at maximum range.

In this session, Neil provides an overview of the Praetorian system and explains how MATLAB® is being used as a simulation and modelling tool. He explains how automatic code generation can be configured to create loadable "apps" to provide more system flexibility and reduce the high cost associated with traditional development, verification, and maintenance of such systems.

Neil Brearley, Leonardo


Modelling and Simulating RF Sensor Systems

16:15–17:00

Wireless communications and radar systems are pervasive across many application fields, such as consumer electronics, aerospace and defence, and automotive. The need to improve performance while reducing the overall area and power imposes challenging system requirements. MATLAB® and Simulink® are powerful tools for the development of RF systems. In this session, Marc uses live demonstrations and examples to demonstrate how to:

  • Analyse and visualise RF sensor data
  • Develop sensor processing algorithms
  • Model RF front ends and antenna array systems
  • Stream real-world RF signals into MATLAB

Marc Willerton, MathWorks


What's New in Simulink Release R2016a and R2016b

11:15–11:45

This session covers the most recently added capabilities in the Simulink® product family for Model-Based Design for control systems. The added capabilities cover the cornerstones of Model-Based Design, including plant modelling, control design, real-time testing, automatic code generation, and verification and validation activities.

Join this session to discover the latest features to ensure you maximise your modelling effectiveness while minimising your effort.

Mark Walker, MathWorks


Fast-Paced Development in F1 Control and Analysis Systems

11:45–12:15

In this session, you will learn how Simulink® is used by McLaren Applied Technologies (MAT) and F1 teams in the racing environment. Various toolchains used for development, testing, hardware-in-the-loop simulation, driver-in-the-loop simulation, and trackside cosimulation are discussed.

You will also learn about the interactions between MAT, teams, and engine manufacturers, and the use of Simulink models as the preferred method of documenting supplied software. You will discover how models are supplied in support of change requests. (During race weekends, teams use vTAG and other simulators using models for analysis.)

Lastly, you will learn about the interaction with the FIA from a regulatory point of view, including how Simulink models are used to inspect code for legality.

Charles Hawkins, McLaren Applied Technologies


New Capabilities in Testing

12:15–12:45

The ability to easily author and manage testing is a key enabler for efficient Model-Based Design. Recent releases of MATLAB® and Simulink® products have seen rapid developments to facilitate testing of models and generated code. In this session, Fraser shows how you can use Simulink Test™ to:

  • Create and manage test harnesses
  • Rapidly author temporal and logic based tests
  • Manage different types of testing: regression, equivalence, and requirement-based
  • Integrate coverage analysis, requirements linking, and report generation

Fraser Macmillen, MathWorks


Physical Modelling Integration and Cosimulation in a Real-Time Environment

14:00–14:30

Aircraft vehicle systems are becoming increasingly complex and costly to develop, with more time consumed fixing or upgrading systems due to the level of integration that is inherent within them. Traditional approaches to vehicle design, where physical integration testing only occurs once system designs are mature and physical equipment/hardware exists for integration onto rigs, are no longer suitable. There is a need to better understand the physical interactions between systems much earlier in the design life cycle, which is achieved through high-fidelity modelling and cosimulation of systems in a real-time environment. This opens up the potential to then perform rapid prototyping activities and hardware-in-the-loop testing when hardware becomes available.

Andrew Ramsay, BAE Systems


Connecting to Hardware and Rapid Prototyping

14:30–15:15

In this session, Nicolas discusses how you can use MATLAB® and Simulink® to:

  • Test your system by running plant models in real-time
  • Quickly iterate on your designs without access to production hardware
  • Connect test setup to external actuators and sensors

Nicolas Gautier, MathWorks


Applying MathWorks Tools to Automotive Embedded Software Development

15:45–16:15

The development of high-integrity software for automotive applications is of increasing importance with the rapidly increasing complexity of software intensive systems in future vehicles. As part of the overall strategy of technology development for its parent company, Changan UK has made extensive use of MathWorks tools in the development of advanced powertrain control software.

In this session, you will gain an understanding of the methods, process, and tools employed by Changan UK in a first series production project. This includes adoption of best practices for development of high-integrity software and compliance with industry standards such as ISO 26262. Future developments, including considerations regarding cybersecurity, are also covered.

Neil Robson, Changan UK


Verification of Automatically Generated Code

16:15–17:00

Code verification is often left to the end of the project, when changes are expensive to implement. What if you could verify the behaviour of your system throughout the development life cycle? Using the same test scenarios developed for your simulation model, you can continuously verify the behaviour of the generated code as you refine and extend it. The session covers:

  • Development, management, and execution of reusable test cases across simulation and code generation workflows
  • Use of dynamic and static verification techniques
  • Collection of code coverage to measure the completeness of your testing from within Simulink®
  • Collection of execution profiling metrics and results annotations on the model
  • Analysis of standards conformance and proving correctness of the production code

Richard Anderson, MathWorks


Introduction to MATLAB

11:15–12:15

In this session, Thomas introduces MATLAB®, the interactive environment and high-level language for numerical computation, visualisation, and programming. Topics discussed in this session include:

  • Importing data from Microsoft® Excel®, text files, databases, and hardware devices
  • Exploring and visualising data using interactive tools
  • Performing mathematical analysis on the data
  • Automating your analysis and creating reports

Thomas Todd, MathWorks


Introduction to Parallel Computing

12:15–12:45

Are you curious about parallel computing? Have you ever wondered if it is right for you? Do you wish you could use cloud computing because you do not have the right hardware? In this session, Elwin helps you answer the questions:

  • Who should think about using parallel computing?
  • What can it do for me?
  • Why should I use it?
  • When should I use it?
  • Where can I use it?

Elwin Chan, MathWorks


Introduction to Simulink and Stateflow

14:00–15:15

In this session, GianCarlo introduces the Simulink® product family. Topics include:

  • Basic concepts for Simulink and Stateflow®
  • Problems that are appropriate for Simulink rather than MATLAB®
  • Appropriate applications of Simulink and Stateflow, to effectively capture your complete system modelling needs
  • How you can use code generation to take your designs from desktop to hardware

This presentation is ideal for Simulink beginners and MATLAB users interested in learning more about Simulink.

GianCarlo Pacitti, MathWorks


Modelling Physical Systems in Simscape

15:45–17:00

Do you still rely on hardware prototypes for your development process? How much value are you extracting from the models you develop? In this session, you will learn how Simscape™ helps engineers reach for the run button and enables them to use simulation to save time and money. You will learn how Simscape models of the physical system:

  • Are easy to build
  • Cover multiple domains (electrical, mechanical, hydraulic, etc.)
  • Connect directly to Simulink® without cosimulation
  • Leverage MATLAB® capabilities for finding optimal designs

Steve Miller, MathWorks


Signal Processing

11:15–12:15

Processing signals from a variety of sensors is a key task for many engineers today—whether performing offline analysis of captured data to understand system performance, or performing real-time signal processing on sensor data in an embedded system. In this session Graham demonstrates the latest signal processing capabilities of MATLAB® and Simulink®, with examples using signals from a range of sources including accelerometers, microphones, and RF receivers.

You will learn how you can:

  • Apply advanced techniques for exploring data and extracting features
  • Use a range of spectral analysis methods to analyse signals in the frequency domain
  • Design digital filters and analyse their responses
  • Stream real-world signals into MATLAB and Simulink
  • Create real-time prototypes of signal processing applications on low-cost hardware platforms

Graham Reith, MathWorks


Hardware-in-the-Loop: Real-Time Simulation

12:15–12:45

Model-Based Design often involves developing plant models of physical equipment, and developing and deploying control algorithms. Offline non-real-time techniques are effective at verifying and validating control algorithms, but how can the algorithms be tested in real time without physical equipment being available or without potentially endangering hardware? In this session, we investigate how MATLAB® and Simulink® can be used for hardware-in-the-loop (HIL) testing. HIL testing incorporates the development of real-time plant models and their deployment to real-time hardware. HIL test rigs are effectively real-time simulators which, when viewed from their terminals, are indistinguishable from the real-world equipment they represent. Come to this session to find out why, when, and how your workflow will benefit from HIL.

Andrew Bennett, MathWorks


Simulink for Teams: High-Productivity Workflows

14:00–15:15

Most engineers work in teams, requiring tools that support the creation of a shared team environment and the ability to partition large designs into manageable components.

In this session, Gavin shows how recent advances in MATLAB® and Simulink® can support:

  • Sharing work across a team
  • Componentisation
  • Peer reviews of software changes
  • Automation and reporting
  • Impact analysis to understand the implications of a change

Gavin Walker, MathWorks


Developing Robust MATLAB Code and Apps

15:45–17:00

Whether writing a MATLAB® application from scratch, or restructuring existing scripts and prototypes into functions and classes, you want to ensure that users can run your code without encountering unexpected behaviour or errors. You also want to prevent bugs being introduced as your application grows in complexity and features.

This master class covers:

  • Handling and reporting errors and other diagnostics
  • Coding patterns to manage increasing complexity
  • Writing unit and performance tests using the MATLAB Unit Testing Framework
  • Tools for improving development productivity and checking code for common bugs
  • Version control, tracking changes, and continuous integration

Paul Peeling, MathWorks