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You will learn how to author scenarios for simulation on realistic road networks designed in RoadRunner. You can use this workflow to simulate autonomous driving with built-in agents as well as author and integrate custom agents designed in MATLAB, Simulink, or CARLA. The scenarios can be exported to OpenSCENARIO for simulation and analysis in external tools if desired.

This series will look at two current trends affecting mining companies: the need to improve energy efficiency in order to reduce carbon emissions, and the growing benefits of autonomous mine operations. You will hear from experts in the field and MathWorks Australia engineers about the innovations and practical frameworks you can use to address these trends in your own operations.

Model-Based Design affords many advantages over traditional development by offering high-level design abstractions and automatic generation of production code. Modeling and code generation for AUTOSAR software components lets you automate the process of specifying and synchronizing lengthy identifiers in designs

This session demonstrates an end-to-end MATLAB workflow for developing anomaly detection models in the context of a pill production quality control data set comprising a large collection of images. The objective is to verify the quality of pills using automated visual inspection techniques.

This presentation considers the alternative construction of the design space based on experiment data and a grey-box model of the reactions. The models are subsequently used to optimize the production process by changing the process variables. In addition, the effect of uncertainty and variability of the parameters on the process performance is also examined with a Monte-Carlo simulation.

Learn how to use MATLAB for hyperspectral imaging and aerial lidar data processing for terrain classification and vegetation detection in agricultural applications.

Deep Learning and Machine Learning are powerful tools to build applications for signals and time-series data across a broad range of industries. These applications range from predictive maintenance and health monitoring to financial portfolio forecasting and advanced driver assistance systems.

MATLAB® and Simulink® with deep learning frameworks, TensorFlow and PyTorch, provide enhanced capabilities for building and training your machine learning models. Via interoperability, you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.

In this webinar, we will show how MATLAB and Simulink can be used to validate renewable energy models to help address regulatory requirements for commissioning, such as voltage-ride through requirements. As a result of the increasing number of utility-scale solar, wind, and energy storage systems, regulatory requirements have recently been expanded to include many renewable energy installations.