Our Events

MATLAB EXPO brings together engineers, researchers, and scientists to hear real-world examples, get hands-on demonstrations, and learn more about the latest features and capabilities of MATLAB and Simulink.

In this session we will discuss state-of-the-art approaches for visual inspection and present multiple case studies on how these approaches have been applied in industry.

Want to quickly and easily analyze UAV autopilot flight logs? Explore the Flight Log Analyzer Tool in MATLAB for customized plots and efficient analysis.

This webinar will provide a complete environment for the development of intelligent systems and the making of data-driven decisions. With MATLAB and Simulink, you can build models to use AI techniques such as deep learning, reinforcement learning, and evolutionary algorithms.

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.

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.

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.