Sydney Abstracts

Are You Ready for AI? Is AI Ready for You?

9:05 a.m.–9:45 a.m.

AI, or artificial intelligence, is powering a massive shift in the roles that computers play in our personal and professional lives. Most technical organisations expect to gain or strengthen their competitive advantage through the use of AI. But are you in a position to fulfill that expectation, to transform your research, your products, or your business using AI?

This presentation looks at the techniques that compose AI (deep learning, computer vision, robotics, and more), enabling you to identify opportunities to leverage it in your work. You will also learn how MATLAB® and Simulink® are giving engineers and scientists AI capabilities that were previously available only to highly-specialised software developers and data scientists.

Stéphane Marouani

Stéphane Marouani, MathWorks

Introduction to Simulink and Model-Based Design

8:30 a.m.–9:00 a.m.

Explore Simulink®, an environment for multidomain simulation and Model-Based Design for dynamic and embedded systems. You will see 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.

This presentation is for people who are new users of or are unfamiliar with Simulink.

Ruth-Anne Marchant

Ruth-Anne Marchant, MathWorks


What’s New in MATLAB and Simulink

9:45 a.m.–10:15 a.m.

Learn about the new capabilities in the latest releases of MATLAB® and Simulink® that will help your research, design, and development workflows become more efficient. MATLAB highlights include updates for writing and sharing code with the Live Editor, developing and sharing MATLAB apps with App Designer, and managing and analysing data. Simulink highlights include updates to the Simulation Manager that allow you to run multiple simulations in parallel and new smart editing capabilities to build up models even faster. There are also new tools that make it easier to automatically upgrade your projects to the latest release.

Mandar Gujrathi

Mandar Gujrathi, MathWorks


Crawling Before Running: Advanced Analytics in Orthopaedics Research with MATLAB – A Focus on Data Quality

11:05 a.m.–11:45 a.m.

Machine learning and artificial intelligence are terms that are applied to the future of healthcare at a dizzying rate in both lay media and scientific literature. Sports and orthopaedic medicine are areas that would benefit immensely from the deployment of advanced algorithms to establish treatment value and predict patient outcomes. However, the reality is far removed from the vision, in that the quality of data is lacking to feed such algorithms, and previous efforts lack large-scale integration of the diverse data sources required in this area.

In this presentation, Milad Ebrahimi from EBM Analytics will walk through a range of simple deployments of MATLAB® that focus on establishing the quality of data collected in orthopaedic and sports medicine, as well as establishing feedback loops with distributed sites to improve data collection processes and organisation. He will also present a vision for the future where MATLAB keeps up with an agile startup to integrate complex data and reduce the time to deploy accurate algorithms built on quality data.

Milad Ebrahimi

Milad Ebrahimi, EBM Analytics

Corey Scholes, EBM Analytics


Scaling up MATLAB Analytics with Kafka and Cloud Service

11:35 a.m.–12:00 p.m.

As the size and variety of your engineering data has grown, so has the capability to access, process, and analyse those (big) engineering data sets in MATLAB®. With the rise of streaming data technologies and large-scale cloud infrastructure, the volume and velocity of this data has increased significantly, and this has motivated new approaches to handle data-in-motion. This presentation and demo highlights the use of MATLAB as a data analytics platform with best-in-class stream processing frameworks and cloud infrastructure to express MATLAB based workflows that enable decision-making in “near-real-time” through the application of machine learning models. It demonstrates how to use MATLAB Production Server™ to deploy these models on streams of data from Apache® Kafka®. The demonstration shows a full workflow from the development of a machine learning model in MATLAB to deploying it to work with a real-world sized problem running on the cloud.

Branko Dijkstra

Branko Dijkstra, MathWorks


TIBCO Systems of Insight: Deeper Insight and Best Action Changes Everything

12:00 p.m.–12:20 p.m.

Visualizing data helps us explore structure and relationships in data, and it provides a basis for communicating information. Where statistical analysis can be used to systematically comb through data and quantitatively identify patterns, combining it with visual analytics can be especially powerful. Learn how the TIBCO® Systems of Insight fuses our visual, predictive, and streaming analytics technologies, providing a tool suite for optimising business operations. Like a digital nervous system, this insight platform lets you build intelligence in your business, generates actionable insights in real time, and lets you capitalise on them. The platform lets you source the right data, analyse it with various techniques, and implement the resulting insights as a closed-loop solution with built-in continuous learning.

Vaz Balasingham

Emmanuel Schweitzer, TIBCO

Unlocking the Power of Machine Learning

1:15 p.m.–1:45 p.m.

Machine learning is driving innovation in many application areas, including predictive maintenance, digital health and patient monitoring, financial portfolio forecasting, and advanced driver assistance. Developing machine learning models and deploying them on embedded systems or cloud infrastructure often still requires significant expertise with signal processing, big data, and model optimisation.

In the context of obtaining insights from real-world data, this talk addresses how MATLAB® empowers engineers and scientists without significant signal processing and machine learning expertise to tackle challenges like:

  • Importing, visualising, and preprocessing time-series and other data
  • Detecting and extracting features in time, frequency, and time-frequency domains from signals
  • Exploring advanced signal processing and transfer learning techniques for time-series classification
  • Evaluating multiple models and working with large amounts of data
  • Optimising performance, including hyperparameter tuning
  • Deploying models in production IT systems or on embedded devices
Mandar Gujrathi

Mandar Gujrathi, MathWorks


Demystifying Deep Learning

1:45 p.m.–2:15 p.m.

Deep learning can achieve state-of-the-art accuracy for many tasks considered algorithmically unsolvable using traditional machine learning, including classifying objects in a scene or recognising optimal paths in an environment. Gain practical knowledge of the domain of deep learning and discover new MATLAB® features that simplify these tasks and eliminate the low-level programming. From prototype to production, you’ll see demonstrations on building and training neural networks and hear a discussion on automatically converting a model to CUDA® to run natively on GPUs.

Mandar Gujrathi

Mandar Gujrathi, MathWorks


Master Class: Taking MATLAB Development to the Next Level

2:30 p.m.–3:00 p.m.

This presentation focuses on MathWorks Consulting Services. Branko Dijkstra shares customer success stories and how companies saved time and money while learning more about MATLAB® and Simulink®. Practical examples will be presented, including the challenges, the implemented solutions, and the overall results of the consulting engagements.

Branko Dijkstra

Branko Dijkstra, MathWorks


Predictive Maintenance: From Development to IoT Deployment

3:00 p.m.–3:30 p.m.

Interest in predictive maintenance is increasing as more and more companies see it as a key application for data analytics that run on the Internet of Things. This talk covers the development of these predictive maintenance algorithms, as well as their deployment on the two main nodes of the IoT—the edge and the cloud.

Branko Dijkstra

Branko Dijkstra, MathWorks

Designing Mechatronic Systems

1:15 p.m.–1:45 p.m.

Mechatronic systems include a wide range of components, including motors, op-amps, and shaft encoders. Simulating these components together with mechanical and control systems is critical to optimising system performance. To ensure that testing is efficient, MathWorks offers a number of ways to easily balance the trade-off of model fidelity and simulation speed. The ability to generate C code from the model enables engineers to use Model-Based Design for the entire system (plant and controller).

Ruth-Anne Marchant

Ruth-Anne Marchant, MathWorks


Developing Algorithms for Robotics and Autonomous Systems

1:45 p.m.–2:15 p.m.

Robotics researchers and engineers use MATLAB® and Simulink® to design and tune algorithms for perception, planning, and controls; model real-world systems; and automatically generate code—all from one software environment. In this presentation, you learn how to develop autonomous systems that are complex with multiple sensors, need continuous planning and decision making, as well as have controls and motion requirements. An approach to adopt these interconnected technologies and make them work seamlessly is Model-Based Design. It focuses on the use of system models throughout the development process for design, analysis, simulation, automatic code generation, and verification. Through the lens of an industrial automation example, see how techniques in perception, such as deep learning, can be integrated with algorithms for motion planning and control of a commercial robotic arm.

Ruth-Anne Marchant

Ruth-Anne Marchant, MathWorks