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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.

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.

In this session you will learn the basics of Simulink for modIn this session you will learn the basics of Simulink for modeling, simulating, and analyzing multidomain dynamical systems. You will see how to build simulation models using Simulink’s block diagramming interface, customizable set of libraries, and connectivity to MATLAB.eling, simulating, and analyzing multidomain dynamical systems. You will see how to build simulation models using Simulink’s block diagramming interface, customizable set of libraries, and connectivity to MATLAB.

In this webinar, MathWorks will demonstrate modeling and simulation of electrical power systems using Simscape Electrical™. The presentation is developed for students and educators looking to understand the capabilities of Simscape Electrical for the learning and teaching environment.

In webinar will be discussing how to incorporate AI into your project by understanding and implementing the steps of the AI workflow. We will show various demos using Machine Learning and Deep Learning techniques and discuss how MATLAB can work with open-source tools for AI projects.

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

Signal processing and biomedical applications are becoming increasingly complex and computationally intensive. With the increasing adoption of machine learning and deep learning techniques, powerful hardware like multicore CPUs, GPUs, and High-Performance Computing clusters/cloud are common. With Parallel Computing Toolbox™, MATLAB® helps you take advantage of your hardware to speed up your applications without having to rewrite code. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming.

The MathWorks Finance Conference 2022 brings together industry professionals to showcase MathWorks tools in real-world industry use cases and offers practitioner advice through live presentations, Q&A, interactive panel discussions, and in-depth demos.

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.