Pragmatic Digital Transformation

Organizations with digital transformation initiatives are making the shift from visionary ambitions to practical projects.  These organizations have defined their high-level digital transformation objectives, and are now looking to their engineers and scientists to achieve them. This will involve learning new technologies, collaborating with unfamiliar groups, and proposing new products and services.

To meet this challenge, technical organizations must master how to systematically use data and models, not only during the research and development stages, but also across groups throughout the lifecycle of the offering.  An effective digital transformation plan needs to consider changes in people’s skills, processes, and technology. 

Join us as [insert presenter] describes this pragmatic approach to digital transformation and demonstrates how engineering and scientific teams are leveraging data and models to achieve their digital transformation objectives.

Learn how engineering and scientific teams are leveraging data and models to achieve their organization’s digital transformation objectives.

What’s New in MATLAB and Simulink R2020a

Learn about new capabilities in the MATLAB^® and Simulink^® product families to support your research, design, and development workflows. This talk highlights features for deep learning, wireless communications, automated driving, and other application areas. You will see new tools for preprocessing and analyzing data; developing motor control algorithms; creating interactive apps; packaging and sharing simulations; and modeling, simulating, and verifying designs.

AI in Engineering Systems

• Machine Learning: Proven Applications and New Features
• Automated Vision-Based Inspection and Defect Detection for Industrial Applications
• Deploying Deep Learning on Embedded CPUs, GPUs, and FPGAs
• Configure and Use MATLAB in the Cloud to Develop, Scale, and Deploy AI Applications
• Predictive Maintenance Solution for Smart Manufacturing
• Digital Twins for Industrial IoT

Autonomous Systems

• Automated Driving Development Using MATLAB and Simulink
• Real-Time Prototyping and Testing for ADAS: Lane Keeping and Following Assist Systems
• Sensor Fusion and Navigation for Autonomous Systems
• Reinforcement Learning Workflows with MATLAB and Simulink
• Connectivity for Autonomous Systems: Developing 5G/LTE/WLAN Systems

Electrification

• Full Vehicle Simulation for Powertrain Electrification
• Spinning Brushless Motors with Simulink
• Implementing and Real-Time Testing a Controller for a Grid-Tied Inverter
• Developing Battery Management Systems using Simulink
• Implementation of Power and Motor Control Systems on SoC FPGAs

Model-Based Design

• Bridging the Gap Between Systems Engineers' Architecture Models and Model-Based Design
• Share Simulink Simulations as Standalone Applications, Web Apps, and Enterprise Applications
• Automated Requirements-Based Testing
• Industry-Standard Production C Code Generation from Simulink Models
• Importance of Static Code Analysis in Continuous Integration (CI)
• Digital Twins for Industrial IoT