Pretrained Inception-ResNet-v2 network model for image classification

Generate code optimized for Infineon AURIX TC4x Microcontrollers

Generate code optimized for Infineon AURIX TC3x Microcontrollers

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

Generate testbenches for ASIC and advanced FPGA designs

Debug and test HDL code on Intel FPGAs and SoC FPGAs

Import, visualize, process, analyze and export hyperspectral data

Pre-trained Segment Anything Model for instance segmentation

Gray land, white water basemap data for geographic visualization

Create driving scenarios using imported road data from Zenrin Japan Map API 3.0 (Itsumo NAVI API 3.0).

Pre-trained SOLOv2 for instance segmentation.

Add analytics to your Kafka streaming pipeline with MATLAB Production Server, and interact with Kafka streams using the MATLAB desktop.

Generate C code for single and multicore devices on bare metal, RTOS, and middleware services

Gray land, white water basemap data for geographic visualization

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

Simple example on support vector machine

A collection of standard drive cycles (vehicle speed vs. time)

モデル予測制御（MPC）は、複雑なシステムにおける高性能な制御機能を実現するための、実用的な手段として注目されている。しかし、MPCの内部モデル設計の工数、また内部モデルと実システムとのずれによる不安定化が課題となっている。そこで、逐次最小二乗法を用いた実時間モデル推定を行い、そ