The Rise of Engineering-Driven Analytics


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 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 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, Sameer discusses numerous examples of this in action. Learn about new capabilities in MATLAB® and Simulink® that can empower you to design and develop these systems and be a leading force in this new analytics-driven age.

Sameer Prabhu, Industry Marketing Director, MathWorks

From Data Science to Data Stories: Bridging the Gap to Digital Transformation


Predictive analytics and data science are gaining importance and proven impact despite the hype. Surprisingly, the data science universe and the business universe keep coexisting without too much overlap. We claim that data-driven solutions will see a greater success in business and industry only when they are understood and internalized by domain experts (not just data scientists), and when domain experts take ownership of the solutions. This can only happen if predictive analytics outcomes are communicated to domain experts in human language with a narrative. Otherwise, they have little chance to be sustainably deployed.

Digital transformation and data-driven strategy are proven to increase EBITs, but are assumed to require epic efforts in terms of upfront investment and unique talent acquisition. Budgets are almost always spent on collecting the data with little to no plans on what to do with it later, which makes the transformation incomplete. Interestingly, the technology exists to turn all of this data into immediate actions without epic efforts and with existing human capital. Datastories’ claim is that turning data science into data stories is the missing ingredient in completing the data-driven transformation and making it an enjoyable and natural next step to make.

Katya Vladislavleva, CEO and Chief Data Scientist, DataStories (Evolved Analytics)

Academia Meets Industry: The Road to Le Mans


Student Race Team InMotion’s ultimate goal is to design, build, and race their series-hybrid endurance racer, the IM01, during the 24 hours of Le Mans race. On the road to Le Mans, InMotion will first attempt to break several lap-records with the full-electric KP&T IM/e. In order to develop and test both the KP&T IM/e and the IM01, InMotion is using MATLAB® and Simulink® to speed up the development process.

Marco Lenssen, Automotive Technology InMotion

Johan van Uden, ICT Automatisering

What’s New in MATLAB and Simulink


Learn about the latest capabilities offered in MATLAB® and Simulink®. New MATLAB features include the Live Editor to accelerate the way you work in MATLAB and the App Designer for a new way to create interactive MATLAB applications. New Simulink features include a start page to start and resume work faster and an automatic solver option to set up and simulate models more quickly.

Stephan van Beek, MathWorks

Mohamed Anas, MathWorks

System Design with MATLAB and Simulink


New to Model-Based Design? Want to find out what it is all about? Through the use of a motor control example, this presentation discusses the steps for successful use of MATLAB® and Simulink® for Model-Based Design. Using the latest features, key workflow steps are detailed such as desktop simulation, prototyping in real time, implementation through code generation, and continuous verification and validation. Learn powerful and new ways to use models to speed up system development.

Paul Lambrechts, MathWorks

Virtual Overlay Metrology for Fault Detection Supported with Integrated Metrology and Machine Learning


To build up the structures that make up a microchip, some as small as 5nm, a silicon wafer moves though a lithography apparatus multiple times. This puts increased emphasis on reducing the influence known contributors have toward the on-product overlay budget. Using MATLAB® and Simulink®, ASML applied a machine learning technique known as function approximation to gain insight to how known contributors, such as those collected with scanner metrology, influence the on-product overlay budget. The result is a sufficiently trained function that can approximate overlay for all wafers exposed with the lithography system.

Get More from Your Data with Data Analytics


Scientists, engineers, and decision makers continue to explore and improve ways to turn large volumes of data into meaningful information. In this session, Guangyuan talks about recent developments in MATLAB® to support data analytics workflows including accessing and exploring new types of data, building predictive models with machine learning, and operationalizing analytics in enterprise systems.

Guangyuan Yang, MathWorks

MATLAB as the Core Engine of Goal Monitor


Goal Monitor is a core system that helps Rabobank clients manage their portfolios and reach their goals. Its purpose is to show the expected future value of the client’s portfolio and provide recommendations if this deviates from their goal. This presentation discusses the process that was used to choose MATLAB® as the core engine for Goal Monitor. Trevin explains how Rabobank came to consider MATLAB and what the biggest advantages are for the company and its clients.

Trevin Lam, Rabobank

Sharing and Deploying MATLAB Applications


Learn how you can use MATLAB Compiler™ and MATLAB Compiler SDK™ to generate standalone applications, Microsoft® Excel® add-ins, Hadoop libraries, and components for integration with C, C++, Java®, .NET, and Python. Extend these capabilities for large-scale deployment to IT production systems or web servers by using MATLAB Production Server™.

Guangyuan Yang, MathWorks

Development of a Multi-Axle Harvesting Machine Using Model-Based Design


This presentation outlines the challenges for software development of a one-of-a-kind multi-axle harvesting machine. Simulation models in Simscape™ are used to develop and test (MIL, SIL, and HIL) algorithms developed in Simulink®. The code is deployed using Simulink PLC Coder™ and integrated into a CODESYS® environment (IFM PLC). This design flow emphasises the strong advantages of Model-Based Design when software needs to be developed without any hardware availability.

Multitarget Production Code Generation to Optimize Hardware Resources


Automatic code generation is increasingly used in mass production for a variety of applications including controls, radar, imaging, and robotics. These applications involve a wide range of embedded processors and hardware devices, and may require adherence to industry standards such as DO-178, ISO 26262, IEC 61508, and AUTOSAR. This talk describes how MATLAB® and Simulink® generate hardware-optimized code in C, HDL, and Structured Text, in accordance with industry standards.

Stephan van Beek, MathWorks

Faster and More Accurate Control of Switched Reluctance Electric Motors Using Zynq SoC


In the last decades, hybrid and electric vehicle powertrains have emerged in the automotive market. They have proved to be among the most viable alternatives for the reduction of emissions and fossil fuel usage in transportation. Since then, the vehicle electrification scenario has initiated a boost in the development of many cutting-edge solutions, such as switched reluctance (SR) electric motors, which appear as a great technology for automotive electrification as they are free from rare earth metals and a very cost-effective solution. This presentation discusses the evolution of an SR motor control algorithm enabled by using Model-Based Design with MATLAB® and Simulink® for a platform based on Zynq-7000 SoC.

Steven Bervoets, Punch Powertrain

Increasing Design Confidence with Model and Code Verification


Verification and validation techniques applied throughout the development process enable you to find errors before they can derail your project. Most system design errors are introduced in the original specification, but aren't found until the test phase. Learn how you can apply MATLAB® and Simulink® verification and validation products throughout your development and certification process to find bugs early and reduce development time and effort..