销售matlab软件百科 matlab 2020a 本地化服务

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Public Clouds
Use MATLAB on virtual machines in public cloud environments like Amazon Web Services (AWS) and Microsoft Azure. These vendors provide access to on-demand computing resources. They also offer wide-ranging, prebuilt services for data storage, data streaming, elastic scaling, load balancing, security, and more.
If you are not a cloud expert, or if you want a head start, use a MathWorks published reference architecture. Templates in these reference architectures automatically create and configure the cloud infrastructure for running MATLAB. You can also adapt or extend the reference architectures to better meet your specific needs.

Matlab在测试测量中的应用
Acquire, analyze, and explore data and automate tests
The MATLAB® environment for analysis provides the tools you need to acquire data and automate tasks. Within MATLAB and Simulink® you can control and acquire data from plug-in data acquisition boards, test instruments, Web cameras and frame grabbers, as well as send and receive messages over CAN buses.
Once you acquire data, you can interactively explore it and perform live visualization and data analysis. You can then build test systems, verify designs and concepts, and automate repetitive tasks. You can also deploy test applications built in MATLAB onto other computers.

MATLAB在数据科学中的应用：
Explore data; build machine learning models;
do predictive analytics
MATLAB® makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models to enterprise IT systems.
Access data stored in flat files, databases, data historians, and cloud storage, or connect to live sources such as data acquisition hardware and financial data feeds
Manage and clean data using datatypes and preprocessing capabilities for programmatic and interactive data preparation, including apps for ground-truth labeling
Document data analysis with MATLAB graphics and the Live Editor notebook environment
Apply domain-specific feature engineering techniques for sensor, text, image, video, and other types of data
Explore a wide variety of modeling approaches using machine learning and deep learning apps
Fine-tune machine learning and deep learning models with automated feature selection and hyperparameter tuning algorithms
Deploy machine learning models to production IT systems, without recoding into another language
Automatically convert machine learning models to standalone C/C++ code

Engineering teams use Model-Based Design with MATLAB® and Simulink® to verify and validate embedded systems. Teams author requirements directly in their models and can then use those models to generate production code for certification.
Author requirements in your model, and verify and trace them to the design, tests, and code.
Prove that your design meets requirements, and automatically generate tests.
Check compliance of models and code using static analysis and formal methods.
Find bugs, security vulnerabilities, and prove the absence of critical run-time errors.
Produce reports and artifacts, and certify to standards (such as DO-178 and ISO 26262).
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