MATLAB Central BlogsMATLAB Central Blogs Get the inside view on MATLAB & Simulink! https://blogs.mathworks.com Thu, 30 May 2019 13:19:08 +0000 en-US hourly 6 https://wordpress.org/?v=6.2.2 <![CDATA[Closest Pair of Points Problem]]>Thu, 28 Mar 2024 00:00:00 +0000

The Closest Pair of Points problem is a standard topic in an algorithms course today, but when I taught such a course fifty years ago, the algorithm was not yet known.

ContentsCalifornia Dreaming

Imagine you are driving a car on the Harbor Freeway in southern California with typical Los Angeles traffic conditions. Among the many things you might

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The Closest Pair of Points problem is a standard topic in an algorithms course today, but when I taught such a course fifty years ago, the algorithm was not yet known.

ContentsCalifornia Dreaming

Imagine you are driving a car on the Harbor Freeway in southern California with typical Los Angeles traffic conditions. Among the many things you might

...

read more >>

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<![CDATA[The Steve Eddins Interview: 30 years of MathWorking]]>Thu, 28 Mar 2024 00:00:00 +0000

This month, Steve Eddins is retiring from MathWorks after 30+ years on the job. When he joined, MathWorks was only 10 years old and had 190 staff. Recently, we just celebrated our 40th anniversary and have almost 7,000 staff working all over the world. Steve has seen some massive transformations.

This month, Steve Eddins is retiring from MathWorks after 30+ years on the job. When he joined, MathWorks was only 10 years old and had 190 staff. Recently, we just celebrated our 40th anniversary and have almost 7,000 staff working all over the world. Steve has seen some massive transformations.

本日は、MathWorks Japanでデータサイエンス関係を広く担当している田口さんからの投稿です。
はじめに
こんにちは。アプリケーションエンジニアの田口です。
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本日は、MathWorks Japanでデータサイエンス関係を広く担当している田口さんからの投稿です。
はじめに
こんにちは。アプリケーションエンジニアの田口です。
read more >>

]]> <![CDATA[Retirement!]]>Tue, 26 Mar 2024 00:00:00 +0000

The time has come! After more than 30 years of software development at MathWorks, I have decided to retire. Friday, March 29, will be my last day.

For 18 of those 30 years, I’ve been writing here (600 posts!) about image processing and MATLAB. I am grateful to all of you who have been following along.

Here are few highlights.

Some favorite deep dives:

ROIPOLY and POLY2MASK (part 1, part 2, part 3) Fourier transforms Upslope area Continental...

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The time has come! After more than 30 years of software development at MathWorks, I have decided to retire. Friday, March 29, will be my last day.

For 18 of those 30 years, I’ve been writing here (600 posts!) about image processing and MATLAB. I am grateful to all of you who have been following along.

Here are few highlights.

Some favorite deep dives:

ROIPOLY and POLY2MASK (part 1, part 2, part 3) Fourier transforms Upslope area Continental...

read more >>

]]><![CDATA[MATLAB R2024a has been released: Here are my favourite updates]]>Tue, 26 Mar 2024 00:00:00 +0000

The latest version of MATLAB is now available for download and it's our biggest update yet. I have to tell you, I'm really excited by this one! It has got some features that I've been wanting for for a long long time. I'll be doing deeper dives into some of my favourite things over the next few weeks but, for now, here's an overview of some of the features that got me excited for R2024a.
These are just a few of my personal highlights out of thousands of
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The latest version of MATLAB is now available for download and it's our biggest update yet. I have to tell you, I'm really excited by this one! It has got some features that I've been wanting for for a long long time. I'll be doing deeper dives into some of my favourite things over the next few weeks but, for now, here's an overview of some of the features that got me excited for R2024a.
These are just a few of my personal highlights out of thousands of
...

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]]><![CDATA[インクリメンタル学習:適応的かつリアルタイムな機械学習]]>Tue, 26 Mar 2024 00:00:00 +0000

※この投稿は 2024 年 3 月 4 日に Artificial Intelligence へ 投稿されたものの抄訳です。 インクリメンタル学習(Incremental Learning: 逐次学習、追加学習)は、新しく入ってくるデータに適応的にモデルを適合させるという課題に対処する機械学習のアプローチです。インクリメンタル学習は、ストリーミングデータをモデル化する必要があるエンジニアにとって特に有用です。しばしば、機械学習モデルはデバイスに実装しますが、インクリメンタル学習はデータが変化した場合でもモデルが意図した通りに機能し続けることを保証します。 このブログでは、インクリメンタル学習とは何か、なぜ有用なのか、そして MATLAB や Simulink を使用して実装する方法について説明します。

インクリメンタル学習ってなに?

インクリメンタル学習は、データストリームからの非定常データを処理することによって、機械学習モデル(およびディープラーニングモデル)が継続的に学習できるようにする機械学習のアプローチです。インクリメンタル学習を用いることで、新しい知識を統合しつつ以前の知識を維持する、連続的に更新される人工知能(AI)システムを作成できます。 [caption id="attachment_14230" align="alignnone" width="768"]...

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※この投稿は 2024 年 3 月 4 日に Artificial Intelligence へ 投稿されたものの抄訳です。 インクリメンタル学習(Incremental Learning: 逐次学習、追加学習)は、新しく入ってくるデータに適応的にモデルを適合させるという課題に対処する機械学習のアプローチです。インクリメンタル学習は、ストリーミングデータをモデル化する必要があるエンジニアにとって特に有用です。しばしば、機械学習モデルはデバイスに実装しますが、インクリメンタル学習はデータが変化した場合でもモデルが意図した通りに機能し続けることを保証します。 このブログでは、インクリメンタル学習とは何か、なぜ有用なのか、そして MATLAB や Simulink を使用して実装する方法について説明します。

インクリメンタル学習ってなに?

インクリメンタル学習は、データストリームからの非定常データを処理することによって、機械学習モデル(およびディープラーニングモデル)が継続的に学習できるようにする機械学習のアプローチです。インクリメンタル学習を用いることで、新しい知識を統合しつつ以前の知識を維持する、連続的に更新される人工知能(AI)システムを作成できます。 [caption id="attachment_14230" align="alignnone" width="768"]...

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<![CDATA[Deep Learning Toolbox R2024a: Major Update and New Examples]]>Mon, 25 Mar 2024 00:00:00 +0000

On March 20th, MATLAB R2024a was released with many updates for Deep Learning Toolbox. Exciting new features for deep learning help engineers create and use explainable, robust, and scalable deep learning models for automated visual inspection, wireless communications, computer vision, and many more applications.   Some of the new Deep Learning Toolbox capabilities are: Simulink co-execution blocks to simulate Python®-based (PyTorch®, TensorFlow™, ONNX™, and custom) models in a system-wide context. Explainability and verification tools to explain network results and verify the reliability of deep neural networks. Support for more deep learning architectures, including transformers, and training options. Find out more about Deep Learning Toolbox features and new capabilities: Deep Learning Toolbox

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On March 20th, MATLAB R2024a was released with many updates for Deep Learning Toolbox. Exciting new features for deep learning help engineers create and use explainable, robust, and scalable deep learning models for automated visual inspection, wireless communications, computer vision, and many more applications.   Some of the new Deep Learning Toolbox capabilities are: Simulink co-execution blocks to simulate Python®-based (PyTorch®, TensorFlow™, ONNX™, and custom) models in a system-wide context. Explainability and verification tools to explain network results and verify the reliability of deep neural networks. Support for more deep learning architectures, including transformers, and training options. Find out more about Deep Learning Toolbox features and new capabilities: Deep Learning Toolbox

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<![CDATA[Switching a parfor to a parfeval]]>Wed, 20 Mar 2024 00:00:00 +0000

I use parfor a lot when processing large amounts of data. It could be on my local machine, or it could be on a remote cluster. It is very simple to convert a for loop to a parfor loop and often you do not need to change the code in your loop. A parfor is efficient when the work required for each iteration is similar, but if it's not, then, near the end of your loop, some workers can become idle. This is where parfevalcan be helpful. It's a little more complicated to use, but it will keep all workers busy until the work is finished. As a result, you should get your calculation finished sooner. Here I take a parfor loop in one of my existing scripts, and attempt to convert it to use parfeval instead. Features covered in this code-along style video include: parfor parfeval, FevalFuture [bcvid id="6349319049112"] Play the video in full screen mode for a better viewing...

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I use parfor a lot when processing large amounts of data. It could be on my local machine, or it could be on a remote cluster. It is very simple to convert a for loop to a parfor loop and often you do not need to change the code in your loop. A parfor is efficient when the work required for each iteration is similar, but if it's not, then, near the end of your loop, some workers can become idle. This is where parfevalcan be helpful. It's a little more complicated to use, but it will keep all workers busy until the work is finished. As a result, you should get your calculation finished sooner. Here I take a parfor loop in one of my existing scripts, and attempt to convert it to use parfeval instead. Features covered in this code-along style video include: parfor parfeval, FevalFuture [bcvid id="6349319049112"] Play the video in full screen mode for a better viewing...

read more >>

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<![CDATA[Understanding Tolerances in Ordinary Differential Equation Solvers]]>Wed, 20 Mar 2024 00:00:00 +0000

This is a guest blog post by Michael Hosea, a numerical analyst at MathWorks. He works on MATLAB Coder and on
...

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This is a guest blog post by Michael Hosea, a numerical analyst at MathWorks. He works on MATLAB Coder and on
...

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]]> <![CDATA[制御エンタメ化ムーブメントの仕掛け人? こんとろさん、ファイさん 突撃☆インタビュー]]>Tue, 19 Mar 2024 00:00:00 +0000

今回は X (Twitter) でもおなじみの鎌谷さん @ykamataniMWJ がお送りします。

こんにちは。大阪オフィスに生息するアプリケーションエンジニアの鎌谷でございます。
普段は主にシステムズエンジニアリングに関する製品群や、パワーエレクトロニクスアプリケーションにおけるMBD関連製品等のご支援をしております。前回のインタビュー記事、皆さんご覧いただけましたか?
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今回は X (Twitter) でもおなじみの鎌谷さん @ykamataniMWJ がお送りします。

こんにちは。大阪オフィスに生息するアプリケーションエンジニアの鎌谷でございます。
普段は主にシステムズエンジニアリングに関する製品群や、パワーエレクトロニクスアプリケーションにおけるMBD関連製品等のご支援をしております。前回のインタビュー記事、皆さんご覧いただけましたか?
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]]><![CDATA[Building an Intrusion Detection System: A Triumph at the SANReN Cyber Security Challenge]]>Mon, 18 Mar 2024 00:00:00 +0000

Inspiration
Meet the champions: Shani Nezar, Uhone Teffo, Carlo Barnardo, and Heinrich E. Guided, this team trained the most accurate machine learning model among the all 10 teams at the SANReN Cyber Security Challenge! The exploited the ease-to-use capabilities of the MathWorks platform and trained machine learning model via MATLAB Classification Learner App for cyber threat detection. Their proficiency was significantly enhanced by complimentary courses like MATLAB Onramp and Machine Learning
...

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Inspiration
Meet the champions: Shani Nezar, Uhone Teffo, Carlo Barnardo, and Heinrich E. Guided, this team trained the most accurate machine learning model among the all 10 teams at the SANReN Cyber Security Challenge! The exploited the ease-to-use capabilities of the MathWorks platform and trained machine learning model via MATLAB Classification Learner App for cyber threat detection. Their proficiency was significantly enhanced by complimentary courses like MATLAB Onramp and Machine Learning
...

read more >>

]]> <![CDATA[Celebrating Pi Day with cool visualizations]]>Fri, 15 Mar 2024 00:00:00 +0000

Jiro's Pick this week is Happy Pi Day by Zhaoxu Liu / slandarer.
I'm a couple of days late, but I wanted to highlight this set of cool visualizations by Zhaoxu for Pi Day. It's amazing to see so many different types of visualizations
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Jiro's Pick this week is Happy Pi Day by Zhaoxu Liu / slandarer.
I'm a couple of days late, but I wanted to highlight this set of cool visualizations by Zhaoxu for Pi Day. It's amazing to see so many different types of visualizations
...

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<![CDATA[Twenty Years of Parallel MATLAB]]>Fri, 15 Mar 2024 00:00:00 +0000

I have just returned from the MathWorks company meeting celebrating our 40th Anniversary. In one of the presentations, Jos Martin described how Parallel MATLAB was introduced almost twenty years ago. Here are a few slides from Jos's talk.

ContentsWhy There Wasn't Any Parallel MATLAB

In MATLAB News and Notes for spring 1995, I wrote a one-page Cleve's Corner titled "Why there isn't any parallel MATLAB." There were three

...

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I have just returned from the MathWorks company meeting celebrating our 40th Anniversary. In one of the presentations, Jos Martin described how Parallel MATLAB was introduced almost twenty years ago. Here are a few slides from Jos's talk.

ContentsWhy There Wasn't Any Parallel MATLAB

In MATLAB News and Notes for spring 1995, I wrote a one-page Cleve's Corner titled "Why there isn't any parallel MATLAB." There were three

...

read more >>

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<![CDATA[Pi day: Using AI, GPUs and Quantum computing to compute pi]]>Thu, 14 Mar 2024 00:00:00 +0000

14th March is Pi Day, celebrated by geeks everywhere and a great excuse for technical computing bloggers to publish something tenuously related to pi and their favorite technology of choice. This is not the first pi day celebration on a MathWorks blog and it will not be the last. Here are some of our past takes on the subject
2023 Adam Danz celebrated pi day with a pi patch 2018 Cleve Moler Looked at the first 10,000 digits of pi2015 Loren Shure asked ...

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14th March is Pi Day, celebrated by geeks everywhere and a great excuse for technical computing bloggers to publish something tenuously related to pi and their favorite technology of choice. This is not the first pi day celebration on a MathWorks blog and it will not be the last. Here are some of our past takes on the subject
2023 Adam Danz celebrated pi day with a pi patch 2018 Cleve Moler Looked at the first 10,000 digits of pi2015 Loren Shure asked ...

read more >>

]]> <![CDATA[Computing π... Simscape Multibody Style]]>Thu, 14 Mar 2024 00:00:00 +0000

On this π day 2024, I decided to tag along with Mike Croucher from The MATLAB Blog and show one way to compute π.
While Mike went for advanced maneuvers involving the MATLAB AI Chat Playground, Parallel Computing Toolbox, and even
...

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On this π day 2024, I decided to tag along with Mike Croucher from The MATLAB Blog and show one way to compute π.
While Mike went for advanced maneuvers involving the MATLAB AI Chat Playground, Parallel Computing Toolbox, and even
...

read more >>

]]> <![CDATA[MATLAB Portfolio Backtesting - A new app now on GitHub!]]>Tue, 12 Mar 2024 00:00:00 +0000

The following blog was written by Sara Galante, Senior Finance Application Engineer at Mathworks.  MathWorks has a new application to implement backtesting strategies available on GitHub. This custom application provides an intuitive GUI to import portfolio data, create backtesting strategies, and test and compare them against each other. The backtesting application can also automatically generate the code to run said strategies with the click of a button, making integration into the portfolio optimization workflow simpler. The GitHub documentation and MATLAB files are available here. To help you get started watch this video on how to use the BackTesting App in MATLAB:

[bcvid id="6348829792112"] Workflow and Features  
Import Data Module: 1.1. Import prices data directly from...

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The following blog was written by Sara Galante, Senior Finance Application Engineer at Mathworks.  MathWorks has a new application to implement backtesting strategies available on GitHub. This custom application provides an intuitive GUI to import portfolio data, create backtesting strategies, and test and compare them against each other. The backtesting application can also automatically generate the code to run said strategies with the click of a button, making integration into the portfolio optimization workflow simpler. The GitHub documentation and MATLAB files are available here. To help you get started watch this video on how to use the BackTesting App in MATLAB:

[bcvid id="6348829792112"] Workflow and Features  
Import Data Module: 1.1. Import prices data directly from...

read more >>

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<![CDATA[MATLAB extension for Visual Studio Code: Yes, it works with GitHub Copilot]]>Tue, 12 Mar 2024 00:00:00 +0000

After publishing my recent blog post about MATLAB code execution in Visual Studio Code, I've had a lot of questions asking about GitHub Copilot support. I've even had fellow MathWorkers telling me that this should have been covered in the previous article because "Everyone keeps asking us about Copilot support"

After publishing my recent blog post about MATLAB code execution in Visual Studio Code, I've had a lot of questions asking about GitHub Copilot support. I've even had fellow MathWorkers telling me that this should have been covered in the previous article because "Everyone keeps asking us about Copilot support"

※この投稿は 2024 年 3 月 5 日に The MATLAB Blog へ 投稿されたものの抄訳です。
昨年の4月に、The MATLAB Blog で Visual
...

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※この投稿は 2024 年 3 月 5 日に The MATLAB Blog へ 投稿されたものの抄訳です。
昨年の4月に、The MATLAB Blog で Visual
...

read more >>

]]> <![CDATA[Creating a Class to Generate a Non-Expiring Token: Part 2]]>Tue, 05 Mar 2024 00:00:00 +0000

Here, I make one or two more changes to my class that creates a non-expiring SharePoint token that I made in part 1. Then I go about updating my other functions to make use of the new class. Features covered in this code-along style video include: Class Definitions [bcvid id="6348272769112"] Play the video in full screen mode for a better viewing...

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Here, I make one or two more changes to my class that creates a non-expiring SharePoint token that I made in part 1. Then I go about updating my other functions to make use of the new class. Features covered in this code-along style video include: Class Definitions [bcvid id="6348272769112"] Play the video in full screen mode for a better viewing...

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<![CDATA[大学生が MATLAB で学び、伝え、成長する!学生アンバサダーの活躍]]>Tue, 05 Mar 2024 00:00:00 +0000

今回は Education Customer Success の村田さんがお送りします! こんにちは、大学向け包括ライセンスの導入サポートをしている村田です。 皆さんは、現役大学生による「MATLAB 学生アンバサダー」をご存知ですか?MATLAB は、学生生活でも存分に使ってもらいたい中で、特に楽しんで MATLAB を使いこなし、その楽しさを周りにもシェアしてくれているアンバサダーのひとりである、電気通信大学の原木さんにインタビューしました。   Q1.原木さんの自己紹介をお願いします。 電気通信大学 大学院情報理工学研究科 機械知能システム学専攻に所属している原木響也と申します。2021年3月から2024年2月まで MATLAB 学生アンバサダーとして活動させていただきました。 MATLABとの出会いは? 電気通信大学での講義にて出会いました。当初は「シミュレーションができる凄いソフトウェアなんだな~」としか思っていませんでした。しかし、交換留学先で MATLAB を使った演習などを行うことで、MATLAB には自分のアイデアを簡単に試せる環境が整っていることに気づきました。 例えば、“大学の講義資料に載っているグラフを自分で作って試してみたいとき”、“授業中に見たシミュレーションを別のパラメータで動かしたいとき”、“友達に課題の解き方をアニメーション付きで教えてあげるとき”など、MATLAB を使うととても便利でした。 [caption id="attachment_10804" align="aligncenter" width="850"] MATLAB EXPOの準備でMathWorks(東京オフィス)を訪れた際の記念写真[/caption] Q2.MATLAB 学生アンバサダーになったきっかけは? 私がMATLAB 学生アンバサダーになった理由は、もっと MATLAB...

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今回は Education Customer Success の村田さんがお送りします! こんにちは、大学向け包括ライセンスの導入サポートをしている村田です。 皆さんは、現役大学生による「MATLAB 学生アンバサダー」をご存知ですか?MATLAB は、学生生活でも存分に使ってもらいたい中で、特に楽しんで MATLAB を使いこなし、その楽しさを周りにもシェアしてくれているアンバサダーのひとりである、電気通信大学の原木さんにインタビューしました。   Q1.原木さんの自己紹介をお願いします。 電気通信大学 大学院情報理工学研究科 機械知能システム学専攻に所属している原木響也と申します。2021年3月から2024年2月まで MATLAB 学生アンバサダーとして活動させていただきました。 MATLABとの出会いは? 電気通信大学での講義にて出会いました。当初は「シミュレーションができる凄いソフトウェアなんだな~」としか思っていませんでした。しかし、交換留学先で MATLAB を使った演習などを行うことで、MATLAB には自分のアイデアを簡単に試せる環境が整っていることに気づきました。 例えば、“大学の講義資料に載っているグラフを自分で作って試してみたいとき”、“授業中に見たシミュレーションを別のパラメータで動かしたいとき”、“友達に課題の解き方をアニメーション付きで教えてあげるとき”など、MATLAB を使うととても便利でした。 [caption id="attachment_10804" align="aligncenter" width="850"] MATLAB EXPOの準備でMathWorks(東京オフィス)を訪れた際の記念写真[/caption] Q2.MATLAB 学生アンバサダーになったきっかけは? 私がMATLAB 学生アンバサダーになった理由は、もっと MATLAB...

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