Acquire and analyze data from Analog Devices ADALM1000 devices.

Control instruments using Rohde & Schwarz VISA (R&SVISA)

Design, analyze, and prototype for Embedded Linux devices

This support package enables you to deploy a deep learning processor on Intel® FPGA and SoC based hardware from MATLAB

Pretrained NasNet-Large network model for image classification

Pretrained Resnet-101 network model for image classification

Pretrained Inception-ResNet-v2 network model for image classification

Pretrained GoogLeNet network trained on Places365 data set for image classification

Pretrained NasNet-Mobile network model for image classification

Implementation of several variants of the vision transformer (ViT) model.

Pretrained ShuffleNet model for image classification

A collection of standard drive cycles and maneuvers

Land cover basemap data for geographic visualization

Gray land, white water basemap data for geographic visualization

Generate testbenches for ASIC and advanced FPGA designs

Build, Simulate, and Run Quantum Algorithms

Generate code for the FPGA portion of RFSoC devices

Control Ryze Tello drones from MATLAB and acquire sensor and image data