Autonomous technology will touch nearly every part of our lives, changing the products we build and the way we do business. It’s not just in self-driving cars, robots, and drones; it’s in applications like predictive engine maintenance, automated trading, and medical image interpretation. Autonomy—the ability of a system to learn to operate independently—requires three elements:
- Massive amounts of data and computing power
- A diverse set of algorithms, from communications and controls to vision and deep learning
- The flexibility to leverage both cloud and embedded devices to deploy the autonomous technology
In this talk, Michelle shows you how engineers and scientists are combining these elements, using MATLAB® and Simulink®, to build autonomous technology into their products and services today—to build their autonomous anything.
Solar Orbiter is an ESA satellite, due for launch in October 2018, which will capture unprecedented insight into the Sun’s heliosphere to help scientists understand more about how solar weather can affect Earth’s atmosphere and satellite communications.
Working under the leadership of Airbus Defence and Space, Tessella designed the algorithms for the Attitude and Orbit Control Subsystem (AOCS). These algorithms support for example the gravitational slingshot manoeuvres during flybys of Earth and Venus, and maintain pointing stability to support data capture from the on-board instruments. They also ensure that the sunshield protects the spacecraft from intense solar radiation at all times, especially during its closest approach to the Sun.
In this session, Colin Maule and Andrew Pollard discuss how they have used MATLAB® and Simulink® throughout the project to support simulation and analysis and to solve the complex challenges associated with the control system. They also discuss the techniques and workflows used to organize this large, long-running project in the presence of uncertain and variable inputs.
Yes, we can communicate with anyone, anytime, anywhere. Yes, we are all connected. Yes, because of the internet, physical distance has almost disappeared. And yes, we could say we live in a global community — maybe even a village.
But what if we want to meet in real-life? What if the virtual world isn’t enough? What if you want to shake hands with your best friend in Paris or work in Amsterdam while you live in Groningen?
In this presentation, Mars Geuze will explain how they use MATLAB® and Simulink® to revolutionize transportation by developing and realizing the Hyperloop.
Engineers and scientists are increasingly using simulation to develop products in today’s market. Gone are the days when software and hardware can be developed and tested directly on the physical prototypes. An environment that can model both the algorithmic and physical components of a system is needed to fully understand and develop the systems of tomorrow.
Key elements of an Enterprise Simulation Platform are:
- Multi-domain authoring, modeling, simulation, and analysis capabilities
- Intellectual Property integration addressing specific domain needs
- Scalability for small to large organizations
Join this session to discover how you can use Simulink® as your Enterprise Simulation Platform.
Application Track A
The increasing complexity in many industrial, automotive, robotics, and aerospace applications demands high-bandwidth motor control. This often requires implementation in FPGAs or systems-on-chips (SoCs). Model-Based Design facilitates close collaboration among the different engineering disciplines needed for these applications.
Industrial companies, suppliers, and customers are facing a major challenge—the ever-increasing complexity of embedded systems. Driven by the need for continuous innovation, their products need to become smarter through the use of electronics and software. This requires these companies to revisit their software and hardware development processes. Traditional methods are no longer fit to meet today’s requirements for product development in industrial supply chains.
3T has partnered with MathWorks to enable companies to adopt new development approaches. This talk discusses two examples of how Model-Based Design was used as a platform for:
- Collaboration: to solve integration challenges while designing an emergency braking system for SCARA robots.
- Innovation: to enable 3T customers to develop the next generation of speed cameras.
Most system design errors are introduced in the original specification but not found until the test phase. In this session, you will learn how to apply verification and validation techniques at every stage of the development process to catch design errors before they can derail your project.
Application Track B
Machine learning is ubiquitous. From deep learning in medical diagnosis, speech, and object recognition to data-driven modeling for engine health monitoring and predictive maintenance, machine learning techniques are used to make critical engineering and business decisions every day. In this session, we discuss both machine learning and deep learning techniques in MATLAB® with emphasis on real-world examples in both predictive maintenance and computer vision applied to object detection and recognition.
Analyzing Building Acoustics and Vibrations: Solving Sound Leakages at the Tivoli-Vredenburg Concert Hall
Rebuilding the concert hall Tivoli-Vredenburg in Utrecht to include five concert halls led to sound leakages that severely restricted its use. Level Acoustics & Vibration was asked to analyze the problem. As researchers in the field of acoustics and vibration, they determined the cause of the sound leakage and solved the issue. In this session, Arnold Koopman and Sven Lentzen show how they developed a new tool to solve problems like this, based on object-oriented programming with MATLAB® and Java®. They illustrate the advantage of moving from scripting languages, such as Fortran, which are still often used in the building industry, to higher-abstraction-level languages.
NLE services 750,000 connections providing energy, internet, television, and other services. From all these connections, NLE collects massive amounts of business data to predict customer behavior and loyalty to NLE. NLE uses MATLAB® to perform Monte Carlo simulations to make these predictions. To scale up NLE implemented their simulation software into Spark and integrated MATLAB, Amazon Web Services Elastic Compute Cloud spot instances, and Cloudera.