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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.

The need for advanced civil and defense systems has been brought into sharp focus. In civil, the drive for zero climate impact is driving development of advanced propulsion, autonomous systems, and urban...

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.

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 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.