An AI-based classification algorithm as part of a system model created in the Simulink environment. Libraries of blocks for the inference of learned models based on deep learning / machine learning will enable joint testing of artificial intelligence algorithms with other algorithms (control systems, signal and image processing, …) and simulation models of dynamic systems.
The interactive configuration tool enables easy and quick setup of ADAS sensors and subsequent visualization in 3D scenery. The graphics are generated by the Unreal engine for the most faithful rendering of the scene.
This example deals with the so-called „Multibody Dynamics“ motion analysis in FEM software COMSOL Multiphysics. The simulation calculates the movement of the balls under the action of magnetic and frictional forces.
Abnormal system state detection using an autoencoder that is trained only with data from „normal“ operation without previously known data from fault states.
This example presents a model of a PEM membrane hydrogen electrolyser created using Simscape. The model contains a block simulating the MEA (membrane electrode assembly) written in the Simscape language and systems that include reservoirs, treatment and distribution of gases.
Sample split model for programmable logic (FPGA) and processor (ARM). Use of the HDL Workflow Advisor tool to simplify the workflow and configure the interface.
The combination of virtual reality with the MATLAB / Simulink environment offers many application possibilities, from the visualization of dynamic systems to the learning of neural networks. We will show you several examples of the use of virtual reality.
Determination of gravitational acceleration (g) using a pendulum. We will use mobile phone sensors and real-time signal processing. MATLAB Mobile and FFT (fast Fourier transform) will come to the fore.