Below is a comprehensive overview of the core capabilities, new features, and optimization strategies available in MATLAB R2024b. Core Enhancements in R2024b 1. Artificial Intelligence and Deep Learning
The "VDO I" capabilities (implicitly referencing advanced Virtual Design and Optimization integration) allow for tight coupling with simulation-driven design.
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Moving algorithms from prototype to production is more efficient in Version 24.2.0. MATLAB Compiler
New apps and functions for faster data analysis.
Compress deep learning models for deployment on low-power edge devices without significant loss in accuracy. Automated Machine Learning (AutoML) Below is a comprehensive overview of the core
Updated apps like the Deep Network Designer provide interactive environments to build, visualize, and train networks without extensive coding. 2. Signal Processing and Communications
Test Manager scripts execute test suites in cloud CI/CD pipelines (such as GitHub Actions or GitLab CI) with minimal configuration. 4. Connectivity, Cloud, and App Building
| Workload | R2024a | R2024b (same hardware) | Improvement | |----------|--------|------------------------|-------------| | fft on 10^7 complex doubles | 0.48 s | 0.39 s | | | Training ResNet-50 (ImageNet) | 142 min | 118 min | 17% | | Simulink large power grid model | 112 s | 93 s | 17% | | svd (10000×10000 matrix) | 8.2 s | 6.7 s | 18% | Do you intend to deploy your code to
Better visualization of real-time behaviors during hardware-in-the-loop (HIL) testing. DevSecOps and Model Verification
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Native support for out-of-core data processing lets users train machine learning algorithms on massive datasets that exceed system memory limits. Engineering and Signal Processing Advancements
New capabilities include creating multilevel lists and running user-defined code directly from interactive button controls within live scripts. 2. Advanced Data Visualization