MATLAB is a high-level language and interactive programming environment for numerical computation and visualization developed by MathWorks.

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Do you want to run Monte Carlo simulations of your Simulink model? Or share simulations as standalone executables? Or just have a better way of navigating around your Simulink model? Learn about these new features and more about what’s new in Simulink R2021b.

#matlab #simulink

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How to Cluster Data in MATLAB

Clustering is the process of grouping a set of data given a certain criterion. In this way it is possible to define subgroups of data, called clusters, that share common characteristics. Determining the internal structure of the data is important in exploratory data analysis, but is also used for anomaly detection and preprocessing for supervised learning.

MATLAB’s Statistics and Machine Learning Toolbox offers a wide set of functions that help to cluster your data. If you are not familiar with clustering, you can start with k-means algorithm which groups data based on their squared euclidean distance. K-means requires the a priori knowledge of how many clusters are present in your data.

A common criterion for the estimate of the optimal K is the Calinski-Harabasz method which assigns a score to each possible value of K. The Calinski-Harabasz score is defined as ratio between the within clusters dispersion and the between clusters dispersion. The optimal number of clusters is the one associated with the highest score.

K-means is a simple and yet effective algorithm for clustering but it is just one of the many algorithm that the Statistics and Machine Learning MATLAB Toolbox offers, find them out at https://mathworks.com/discovery/cluster-analysis.html

- Learn more about the K-means algorithm: https://www.mathworks.com/help/stats/kmeans.html

- Evaluate the optimal number of clusters: https://www.mathworks.com/help/stats/evalclusters.html

- Density based clustering: https://www.mathworks.com/help/stats/clusteranalysis.html

0:00 Intro

1:06 K-means algorithm

2:38 Finding the optimal value of K

4:10 Other methods

#matlab

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A python package for music and audio analysis.

Documentation

See https://librosa.org/doc/ for a complete reference manual and introductory tutorials.

The advanced example gallery should give you a quick sense of the kinds of things that librosa can do.

The latest stable release is available on PyPI, and you can install it by saying

```
pip install librosa
```

Anaconda users can install using `conda-forge`

:

```
conda install -c conda-forge librosa
```

To build librosa from source, say `python setup.py build`

. Then, to install librosa, say `python setup.py install`

. If all went well, you should be able to execute the demo scripts under `examples/`

(OS X users should follow the installation guide given below).

Alternatively, you can download or clone the repository and use `pip`

to handle dependencies:

```
unzip librosa.zip
pip install -e librosa
```

or

```
git clone https://github.com/librosa/librosa.git
pip install -e librosa
```

By calling `pip list`

you should see `librosa`

now as an installed package:

```
librosa (0.x.x, /path/to/librosa)
```

`librosa`

uses `soundfile`

and `audioread`

to load audio files. Note that `soundfile`

does not currently support MP3, which will cause librosa to fall back on the `audioread`

library.

If you're using `conda`

to install librosa, then most audio coding dependencies (except MP3) will be handled automatically.

If you're using `pip`

on a Linux environment, you may need to install `libsndfile`

manually. Please refer to the SoundFile installation documentation for details.

To fuel `audioread`

with more audio-decoding power (e.g., for reading MP3 files), you may need to install either *ffmpeg* or *GStreamer*.

*Note that on some platforms, **audioread** needs at least one of the programs to work properly.*

If you are using Anaconda, install *ffmpeg* by calling

```
conda install -c conda-forge ffmpeg
```

If you are not using Anaconda, here are some common commands for different operating systems:

- Linux (apt-get):
`apt-get install ffmpeg`

or`apt-get install gstreamer1.0-plugins-base gstreamer1.0-plugins-ugly`

- Linux (yum):
`yum install ffmpeg`

or`yum install gstreamer1.0-plugins-base gstreamer1.0-plugins-ugly`

- Mac:
`brew install ffmpeg`

or`brew install gstreamer`

- Windows: download ffmpeg binaries from this website or gstreamer binaries from this website

For GStreamer, you also need to install the Python bindings with

```
pip install pygobject
```

Please direct non-development questions and discussion topics to our web forum at https://groups.google.com/forum/#!forum/librosa

If you want to cite librosa in a scholarly work, there are two ways to do it.

If you are using the library for your work, for the sake of reproducibility, please cite the version you used as indexed at Zenodo:

If you wish to cite librosa for its design, motivation etc., please cite the paper published at SciPy 2015:

McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. "librosa: Audio and music signal analysis in python." In Proceedings of the 14th python in science conference, pp. 18-25. 2015.

Author: librosa

Source Code: https://github.com/librosa/librosa

License: ISC License

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Discover new capabilities in Simulink® that will help you get your work done. In part one of this video series, we walk through new features that will make model editing easier and simulations faster.

#matlab #simulink

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Pricing a weather option involves getting a good model for the climate event. Learn how to price a weather option using the following steps:

- Get data into MATLAB using a REST API.

- Clean and analyze the temperature timeseries.

- Model the temperature using a linear model for the deterministic part and an arma/garch process for the volatility.

- Forecast the temperature and price of a weather option.

#matlab

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In this tutorial, learn how to leverage hardware and software components for rapid control prototyping (RCP) with MATLAB® and Simulink®.

- Use App Designer, external mode, Simulation Data Inspector, and the MATLAB Data API to instrument your real-time applications.

- Simplify tasks and provide ready-to-run reference applications with add-ons.

- Select the correct real-time target computer, I/O connectivity, and communication interfaces for your requirements.

#matlab

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Learn how to deal with technical challenges encountered when developing motor control systems for TI multicore MCUs.

Are you facing challenges in developing motor control systems for multicore microcontrollers, such as partitioning control algorithms, managing inter-processor communication, or synchronization between cores?

Attend this webinar to learn how Model-Based Design helps you design motor control applications for multicore MCUs such as TI C2000 F2837xD / F2838xD devices.

In this webinar, a MathWorks engineer will highlight issues that can arise when using a multicore MCU for motor control applications. You will learn how to use Simulink with SoC Blockset and Motor Control Blockset to design a two-core implementation of a field-oriented control (FOC) algorithm – higher control-loop rates will be achieved by allocating the current and velocity controllers to the two processor cores.

A TI engineer will discuss TI’s multicore devices and review the architectures that are being used in today’s motor control applications.

- Modeling a velocity control system of a PMSM motor with field-oriented control
- Modeling the TI C2000 F2837xD architecture and simulating the complete system with the dual-core MCU
- Modeling MCU peripheral behavior, such as ADC-PWM synchronization
- Generating the motor control application using Embedded Coder with SoC Blockset
- Deploying the compiled application to the TI Delfino F28379D LaunchPad
- Performing simultaneous real-time multicore on-device profiling
- #matlab

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Learn how to use system identification to fit and validate a linear model to data that has been corrupted by noise and external disturbances

Noise and disturbances can make it difficult to determine if the error between an identified model and the real data comes from incorrectly modeled essential dynamics, data influenced by a random disturbance process, or some combination of the two. Discover how to account for the random disturbances by fitting a first-order autoregressive moving average (ARMA1) model to the disturbance path. This can give you a better overall system model fit and confidence that the essential dynamics were captured correctly.

#matlab

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Get an introduction to system identification that covers what it is and where it fits in the bigger picture. See how the combination of data-driven methods and physical intuition can improve the model with so-called grey-box methods. Explore the components of a mathematical model and look at the differences between the white-box method and the black-box method for developing a model of a physical system. Finally, learn the difference between identifying a dynamic model from data versus just fitting a curve to the data.

#matlab

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As we know, Numpy is a famous Python library for supporting matrix computations in Python, along with a large collection of high-level mathematical functions to operate on vector, matrix, and tensor arrays . A common feature of Numpy and Matlab is their broad use in matrix computations and numerical computing. In this post, we will discuss how to generate random numbers and arrays in both Matlab and Numpy.

**Random Numbers and Arrays****Comparing Rand Functions in Numpy and Matlab****Generating Normal-Distributed Random Numbers in Numpy and Matlab**

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Learn how to integrate handwritten OpenCV code using the OpenCV Interface for Simulink®. The demo showcases importing a custom lane detection algorithm written with OpenCV. Simulink image type signal and conversion blocks are used to represent and extract the image data. The workflow is extended seamlessly from simulation to code generation.

#opencv #simulink #image #matlab

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Python was the natural choice. Matlab and Python both can do quite different and incredible things, which makes Matlab versus Python an interesting question. If you’re learning computer science online with any language, you’re already in a great place. Coding is a hugely valuable skill, no matter which language you go with.

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The C++ Interface in MATLAB® allows you to call C++ libraries directly from MATLAB without writing any additional C++ code. In this video, you will see a demo of how to create an interface to an open-source C++ library and then call the library functionality from MATLAB.

#matlab #cplusplus

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Join us live to see AI models compete for the top prize. Who is the fastest? Who is the most accurate? Who will take the crown?

In this part of the series, we’ll compare top deep learning models on different data sets. We’ll learn about the algorithms as they compete in regression and classification challenges. We’ll discuss techniques for judging the models in categories like accuracy, speed, and overall quality, using examples in MATLAB.

As always, we’ll answer your questions, live!

#deeplearning #matlab

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MATLAB is a high-level language where you are able to perform calculations, visualize data, and many more. You will be amazed to know more regarding the software once you start working with it. Even if you are not a coder or don't know anything about coding you need not worry as Matlab is beginner-friendly software. You will even be able to ask the software directly in case you face any issue in executing any commands. Matlab is being used in high-demand domains like Data Analysis, Control systems, Signal Processing, Machine Learning, and many more. Having a firm grip on the software can get you great opportunities.

00:00:00 Introduction to Matlab

00:08:21 What is Matlab?

00:12:02 Matlab GUI

00:17:33 Understanding MATLAB Variables

00:28:13 Types of Variables

00:45:35 Understanding Constants

00:53:40 Common Operations

1:03:46 Creating Scripts

1:10:58 Basic Math Operations

1:18:22 MATLAB Functions

1:21:44 Defining Functions

1:25:02 Basic Linear Algebra

1:30:30 Summary

#matlab