How to Build an Autonomous Anything


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.

What’s New in MATLAB and Simulink Products in R2016b and R2017a


Learn about the latest capabilities that have been introduced in MATLAB® and Simulink® platforms. Topics include areas of big data, machine learning, speeding up simulations, and productivity improvement techniques.

Brain Imaging Data Analysis with MATLAB: From Pictures to Knowledge


The human brain is the most complex organ in the known universe, and understanding its function has become one of the major challenges for modern biology. A better understanding of how the brain works will hopefully lead to more efficient treatments for numerous neurological and psychiatric diseases. Over the last decade, imaging techniques have become critically important in providing neuroscience with empirical data about brain structure and function. Nevertheless, the complexity and amount of imaging data produced by these techniques has posed great challenges for neuroscientists.

This presentation discusses several recent neuroscience applications for which MATLAB® has become a valuable tool for quantification and interpretation of imaging data. Applications include registration and segmentation techniques for image processing, machine learning approaches for the analysis of neuronal network function, and optimization strategies for reconstruction of neuronal activity from noisy imaging data. Finally, the presentation discusses how MATLAB can be combined with recent big data computing frameworks, notably Apache Spark™, to achieve scalable processing of large volumes of imaging data in a cluster environment.

Dr. Henry Lütcke Dr. Henry Lütcke, ETH Zürich

Solar Impulse, First Round-The-World Solar Flight


Solar Impulse is the only airplane of perpetual endurance that can fly day and night on solar power, without a drop of fuel. It is a fine example of how pioneering spirit, innovation, and clean technologies can change the world.

Bertrand Piccard, André Borschberg, and their team was to attempt the first-ever round-the-world solar flight.

They completed the first phase in 2015, flying from Abu Dhabi to Hawaii—the longest solo flight ever undertaken by a single pilot. The second phase, from Hawaii back to Abu Dhabi, was completed in 2016.

This presentation focuses on flight dynamics and flight operations, and shows how Model-Based Design and code verification processes were instrumental in enabling the Solar Impulse team to meet their ambitious objectives.

Ralph Paul Ralph Paul, Ralph E. Paul FD-Tec

Verification and Virtual Commissioning of Configurable Handling Systems


Manufacturing companies that aim to increase production rates and to reduce costs need a method to virtually test system behavior. Configurable material handling systems make this particularly challenging, given that a myriad of processes may be involved. These processes include, among others, machining, deburring, measuring, and storage. Virtual commissioning requires the ability to integrate simulation models of the mechanical, electronic, and control algorithms.

Reishauer AG, a producer of high-precision gear grinding machines, addresses this challenge by using Simscape™ to unite CAD designs with mechatronic simulations. Multibody simulation models imported from CAD assemblies are connected to models of PMSM motors, elastic belt drives, and cascaded controllers to simulate the entire system within the Simulink® environment. To ensure realistic behavior, these models had to be parameterized by input from data sheets. Subsequently, these models had to be compared with actual measurements to verify how closely they would fit reality. This methodology helped to determine which physical effects were critical for achieving the required accuracy.

This presentation covers how an integrated solution makes it possible to perform virtual commissioning, and how to verify designs early in the development process.

Oliver Stamm Oliver Stamm, Reishauer