Attendees who visited the demo stations were able to discuss their challenges and ideas with MATLAB® and Simulink® experts, and see demos showcasing the latest features including:
Deep Learning and Reinforcement Learning
MATLAB makes deep learning easy and accessible even if you are not an expert. Learn how you can perform deep learning tasks with just a few lines of code. Also discover how you can design and build your own convolutional neural network models, access the latest models, or import pretrained models.
Running and Deploying MATLAB Code on GPUs
Computationally intensive MATLAB code for deep learning, computer vision, and autonomous systems can be accelerated using GPUs on your development computer. Learn and discuss how you can do this, and also generate optimized CUDA® code from your MATLAB code to deploy your algorithms onto embedded GPU hardware.
Advanced App Development
Engineers and scientists use MATLAB to build advanced data analytics systems and make them accessible to end-users as MATLAB Apps. Discuss and understand how you can architect and develop complex applications and user interfaces, the tools available to help you get started, and features that support collaboration on MATLAB projects as a team.
Integrating MATLAB into Enterprise Systems, Clusters, or Clouds
MATLAB and Simulink applications can be scaled up with clusters and clouds. Learn and understand how you can use parallel computing tools to accelerate computationally intensive MATLAB programs by running them in large-scale, high-performance computing resources such as computer clusters and cloud computing services (e.g. Amazon EC2® and Microsoft® Azure®).
Model-Based Design: Implementing MATLAB and Simulink Designs on FPGA, GPU and SoC Devices
Verilog® and VHDL can be generated from designs in MATLAB and Simulink. Additionally, hardware-software co-design is enabled by providing C/C++ code generation for your software models. GPU Coder™ generates optimized CUDA® code from MATLAB® code for deep learning, embedded vision, and autonomous systems. Discuss and learn how you can design and generate code and additionally leverage targeted support for popular development boards.
Modelling System Architectures with System Composer
System Composer™ enables the definition, analysis, and specification of architectures and compositions for model-based systems engineering and software design. Learn and discuss how you can use System Composer to allocate requirements while refining an architecture model that can then be designed and simulated in Simulink®.
Automated Driving and ADAS Development in MATLAB and Simulink
Automated driving spans a wide range of automation levels, from advanced driver assistance systems (ADAS) to fully autonomous driving. Learn and discuss how MATLAB® and Simulink® support designing automated driving system functionality, including computer vision algorithm development, sensor fusion and tracking, and controls development.
Model-Based Design: Multidomain Physical Systems
Systems are becoming more and more complex with multiple physical domains interacting. The software domain is a major portion of modern systems and needs to be considered as well. Learn and discuss how Simulink provides a complete simulation environment that lets you integrate multiple physical domains and control software to capture the behaviour of the full system.
Model-Based Design: Rapid Prototyping and Hardware-in-the-Loop Simulations (Real-Time Testing)
Designs (models) of control software and physical systems in Simulink can be reused for the purpose of real-time testing. Learn and discuss how you can move from pure desktop simulation to real-time execution of your models on hardware specifically designed and optimized for rapid prototyping or hardware-in-the-loop (HIL) simulations.
Model-Based Design: Verification and Validation on the Model and Code Level
Early verification on the model level, and later on the code level, to ensure correctness of model and code are strategic activities within Model-Based Design. Learn and discuss capabilities such as model checking, coverage measurement, requirements management, and static analysis at the code level to increase the quality of your design. Understand how Model-Based Design with MATLAB and Simulink supports compliance with your safety standard, such as ISO 26262, DO-178C, and other standards.