MathWorks has released a range of new capabilities in MATLAB and Simulink, including those in support of artificial intelligence, deep learning and the automotive industry. In addition, R2019b introduces new products in support of robotics and new training resources for event-based modelling, and updates and bug fixes across the MATLAB and Simulink product families.
Among the MATLAB highlights in R2019b is the introduction of Live Editor Tasks, which enables users to interactively explore parameters, preprocess data, and generate MATLAB code that becomes part of the live script. Now, MATLAB users can focus on the task instead of the syntax or complex code, and automatically run generated code to quickly iterate on parameters through visualisation.
R2019b highlights of Simulink include the new Simulink Toolstrip, which helps users access and discover capabilities as they are needed. In the Simulink Toolstrip tabs are arranged according to workflow and sorted by frequency of use, saving navigation and search time.
Artificial Intelligence and Deep Learning
In R2019b, Deep Learning Toolbox builds on the flexible training loops and networks introduced earlier this year. New capabilities enable users to train advanced network architectures using custom training loops, automatic differentiation, shared weights, and custom loss functions. In addition, users can now build generative adversarial networks (GANs), Siamese networks, variational autoencoders, and attention networks. Deep Learning Toolbox also can now export to ONNX format networks that combine CNN and LSTM layers and networks that include 3D CNN layers.
R2019b also introduces significant capabilities in support of the automotive industry across multiple products, including:
– Automated Driving Toolbox: Support for 3D simulation, including the ability to develop, test, and verify driving algorithms in a 3D environment, and a block that enables users to generate the velocity profile of a driving patch given kinematic constraints.
– Powertrain Blockset: Ability to generate a deep learning SI engine model for algorithm design and performance, fuel economy, and emissions analysis. Also new are HEV P0, P1, P3, and P4 Reference Applications, fully assembled models for HIL testing, tradeoff analysis, and control parameter optimization of hybrid electric vehicles.
– Sensor Fusion and Tracking Toolbox: Ability to perform track-to-track fusion and architect decentralised tracking systems.
– Polyspace Bug Finder: increased support of AUTOSAR C++14 coding guidelines to check for misuse of lambda expressions, potential problems with enumerations, and other issues.
In addition to new features in Robotics System Toolbox, R2019b introduces two new products:
– Navigation Toolbox (new) for designing, simulating, and deploying algorithms for planning and navigation. It includes algorithms and tools for designing and simulating systems that map, localise, plan, and move within physical or virtual environments.
– ROS Toolbox (new) for designing, simulating, and deploying ROS-based applications. The toolbox provides an interface between MATLAB and Simulink and the Robot Operating System (ROS and ROS2) that enables users to compose a network of nodes, model and simulate the ROS network, and generate embedded system software for ROS nodes.
R2019b offers Stateflow Onramp, an interactive tutorial to help users learn the basics of how to create, edit, and simulate Stateflow models. Like the existing Onramps for MATLAB, Simulink and deep learning, this self-paced learning course includes video tutorials and hands-on exercises with automated assessments and feedback.