Pick of the Week

What Does a Spacecraft Instrument Really See?

Mike Croucher — Wed, 15 Jul 2026 15:38:47 +0000

Mike’s pick this week is spacecraft-vector-intersection by George Xystouris nominated by Kostas Leptokaropoulos

What does an instrument actually see during flight? For fixed cameras this may be straightforward, but for rotating particle instruments, or for any case where a generalized vector such as sunlight or plasma flow matters, the answer can depend strongly on the geometry of the spacecraft itself. Dr George Xystouris created a MATLAB-based tool to answer this deceptively simple spacecraft-science question.

The tool uses a 3D model of the spacecraft to calculate the field of view of an instrument and identify when parts of the spacecraft obstruct that view. This is scientifically important because unrecognized obstructions can affect the interpretation of directional measurements, including particle fluxes and angular distributions.

Figure 1. Cassini 3D model with the CAPS instrument outlined; model credit: NASA Visualization Technology Applications and Development.

Cassini CAPS as an example

In the example prepared by George, the method is applied to the Cassini spacecraft, using a NASA 3D model and focusing on the Cassini Plasma Spectrometer (CAPS). CAPS measured in-situ charged-particle flux, kinetic energy, and travel direction at the spacecraft location. Because CAPS rotated between -80° and +104° in the spacecraft XY plane, not every detector necessarily had a clear view of space at every scan angle. For the ion mass spectrometer, the geometry question becomes especially practical: the detector contains eight vertically stacked elements, and nearby instruments or spacecraft structures can intermittently block the top or bottom detectors at the extremes of the scan. The same workflow can also show how the instrument view changes when the spacecraft configuration changes. In Cassini’s case, the release of the Huygens probe opened the field of view of the bottom two detectors.

Figure 2. Example visibility map illustrating how spacecraft geometry influences the instrument field of view.

Why MATLAB?

A key challenge in this problem is working reliably with complex spacecraft 3D geometry. MATLAB provided the core environment for importing, visualizing, and processing the model, and for converting the visibility calculations into clear 2D maps. Community-developed tools added support for a wider range of 3D object formats, making the workflow more flexible for different spacecraft models.

What makes this useful beyond Cassini?

The same method can be applied to other spacecraft, provided that a suitable 3D model is available. That makes it useful in two complementary ways: during mission design, it can help assess candidate instrument placements and possible obstructions; for legacy missions, it can support reanalysis of data where spacecraft geometry may influence the scientific interpretation.

Get the code and read more

Code: spacecraft-vector-intersection on GitHub
Paper: Royal Astronomical Society: Techniques and Instruments article
Cassini 3D model: NASA Cassini 3D model resource

Sankey diagrams in MATLAB

Mike Croucher — Thu, 09 Jul 2026 16:25:13 +0000

Mike's pick this week is sankey plot by Zhaoxu Liu / slandarer nominated by Yair Altman. 

Sankey diagrams visualize flows, of energy, money, materials, people, anything that moves from one place to another, where the width of each arrow is proportional to the quantity flowing. According to Wikipedia, Sankey diagrams are named after Irish Captain Matthew Henry Phineas Riall Sankey, who used this type of diagram in 1898 in to show the energy efficiency of a steam engine. 

slandarer's file exchange entry brings these diagrams to MATLAB and they are beautiful! Here are a few examples from the package taken from the 20+ included

links={'a1','A',1.2;'a2','A',1;'a1','B',.6;'a3','A',1; 'a3','C',0.5;

    'b1','B',.4; 'b2','B',1;'b3','B',1; 'c1','C',1;

    'c2','C',1;  'c3','C',1;'A','AA',2; 'A','BB',1.2;

    'B','BB',1.5; 'B','AA',1.5; 'C','BB',2.3; 'C','AA',1.2};

SK = SSankey(links(:,1), links(:,2), links(:,3));

SK.draw()

%% sankey demo20 :Rotate the Sankey diagram by 90 degrees to make it vertical

figure('Name','sankey demo20', 'Units','normalized', 'Position',[.05,.1,.4,.7])

A12 = [1,2,1; 1,2,3; 5,0,1];

A23 = [1,4,2; 3,1,0; 0,3,2];

adjMat = mergeAdjMat({A12, A23});

SK = SSankey([],[],[], 'AdjMat',adjMat);

SK.NodeList = {'AAA','BBB','CCC','prop1','prop2','prop3','set1','set2','set3'};

SK.RenderingMethod = 'left';

SK.ValueLabelLocation = 'left';

SK.ValueLabelFormat = @(X) ['sample number = ',num2str(X)];

SK.ColorList = [ 36, 59, 90; 199, 49, 68; 161,137,111; 

                 73, 84,114; 209, 90,105; 226,209,191;

                 30, 30, 30;  30, 30, 30;  30, 30, 30]./255;

SK.Align = 'down';

SK.draw()

SK.setLabelLocation(3, 'right')

SK.setLink(1:15, 'FaceAlpha',.5)

% Rotate the entire diagram to vertical orientation 

rotateSankey(SK)

The package has been downloaded thousands of times and slandarer has been very responsive to user requests in the discussions and review sections of the file exchange entry leading to several new versions since it's first release in 2023.

IMVIEW Image Display Function

Mike Croucher — Fri, 05 Jun 2026 15:36:48 +0000

Mike's pick this week is IMVIEW Image Display Function by Steve Eddins.

Steve Eddins worked at MathWorks for over 30 years and was the author of the long running Image Processing with MATLAB blog. Now retired, he still interacts regularly with several of us at MathWorks and currently serves as a member of our Community Advisory Board (CAB). He also continues to blog about MATLAB on his personal blog, Harmonic Notes.

From Steve's description of the function: "imview displays a binary, grayscale, indexed, or RGB image. It is intended to be used instead of imshow in most cases. It behaves better with other MATLAB graphics functions, it uses a higher quality image interpolation, and it displays and makes it easy to change the zoom level."

As a bonus, Steve has blogged extensively about the development of imview so we get the benefit of his insights in why and how he built it. Here are the posts so far in reverse chronological order.

DeepSqueak – a deep learning-based system for detection and analysis of ultrasonic vocalizations

Mike Croucher — Thu, 14 May 2026 12:39:37 +0000

Mike's pick this week is DeepSqueak by Kevin R Coffey. 

Introducing Alston's singing mouse

This week I learned that there is a type of mouse that sings, Alston's singing mouse Scotinomys teguina)! Here's a video of the singing from the Live Science YouTube channel.

In a recent Nature paper, Emily Isko et al investigated what's different in the brains of these mice compared to the laboratory mouse (Mus musculus) making use of DeepSqueak along the way.

DeepSqueak: Using Machine Vision to Accelerate Bioacoustics Research

Isko's paper joins over 400 articles that cite the paper behind DeepSqueak. Developed by academics at The University of Washington and available on GitHub and The MATLAB File Exchange (and, therefore, from directly within MATLAB itself), DeepSqueak is a USV (ultrasonic vocalizations) detection and analysis software suite that can perform human quality USV detection and classification automatically, rapidly, and reliably.

Once you've added it to MATLAB, you can launch the graphical user interface with the command DeepSqueak

Congratulations to Kevin and his team for enabling so much amazing science!

Artemis II Trajectory Simulation

Mike Croucher — Fri, 10 Apr 2026 17:16:36 +0000

Mike's pick this week is artemis2_trajectory_simulation by Jacob Honer.

Later this evening the Artemis II crew will splashdown back on Earth following a successful Lunar flyby. The mission has captured the imagination of the world including that of Jacob Horner, A PhD student at Michigan State University. In Jacob's own words:

"I used MATLAB to put together a 2D Earth-Moon mission simulation in MATLAB that recreates the high-level flow of an Artemis-style free-return trajectory: start in low Earth orbit, complete a parking orbit, perform translunar injection, make a small correction burn, swing around the far side of the Moon, and head back toward Earth.

The model uses a simplified planar gravity setup, numerical integration with ode45, and a search-and-optimization loop to tune the burns and trajectory until the flyby and return lined up the way I wanted. The GIF shows the full result.

Even for a simplified model like this, it takes a lot of iteration to get a trajectory to behave the way you want. Like many real-world problems, there often is no closed-form solution for the exact outcome you’re after, which is what makes numerical methods so important. That’s one reason I always try to emphasize them to my students: numerical methods are not just useful for one narrow class of problems, they are tools you can use for almost all computational problems!"

Jacob has made his code available on GitHub and when you run it, you'll get the animation shown below.

For more about Artemis II and MATLAB, check out my post from earlier in the week: NASA’s Artemis II mission and MATLAB.

Insert Token: 15 Arcade Games in MATLAB with Claude and the MCP Server

Mike Croucher — Wed, 25 Mar 2026 11:56:26 +0000

This week's pick is Arcade by Vasilis Bellos

MATLAB's MCP Server allows AI agents, such as Claude or Amp, to use MATLAB on your machine. In turn, this allows you to develop MATLAB scripts and applications faster than you ever have before. We talk about these new workflows a lot in the Generative AI discussions forum on MATLAB Central. 

MATLAB user Vasilis Bellos has clearly been having a lot of fun with these tools since he has released Arcade: 15 arcade games built entirely in MATLAB - 8 timeless classics and 7 originals. No toolboxes, no external dependencies, no imported assets. Every pixel is drawn using native MATLAB graphics.

Features a neon-styled launcher with an animated starfield menu, persistent high scores, frame-rate-independent physics, and automatic display scaling that ties everything together. Pick up and play with your mouse or keyboard.

I've spent the morning playing these and will be spending the afternoon looking at how Claude and Vasilis got those cool Neon vector effects out of MATLAB Graphics. 

MATLAB user John D’Errico finds largest known reversible prime with 12,346 digits

Mike Croucher — Mon, 16 Mar 2026 15:14:38 +0000

Mike's pick this week is a candidate for the new world-record reversible prime number found by John D'Errico.

A reversible prime, (also known as an emirp), is a prime number that yields a different prime when its digits are reversed. An example is 13. Reversing this gives 31 and both are prime

isprime([13 31])

ans = 1×2 logical array
1 1

Checking if small numbers are emirp is relatively straightforward but things get rather more complex when we start looking at larger numbers. Matt Parker's Numberphile recently discussed the discovery of the (then) largest known emirp. It had 10,002 decimal digits. Wikipedia currently lists 117,954,861 × 1011,111 + 1 as the largest known emirp with which has 11,120 digits.

How MATLAB user, John D'Errico, found a 12,346 digit emirp

The short version of the story is that John looked for emirps of the form 10^n + a and used symbolic toolbox and parallel computing toolbox to make use of all 16 cores on his desktop computer to burn through the search space. The result is that he found that 10^12338*79191501 + 1 is a 12,346 digit emirp, currently the largest known (although I'd prefer us to get independent verification before we suggest an update the Wikipedia page).

Why he chose that particular form of emirp, all of this code and full discussion of his journey can be found in this MATLAB discussion thread. I found it fascinating to follow him on his journey from 'This is a promising candidate' to 'We can say that it's highly probable that this is an emirp' to 'This is definitely an emirp'! Great work John!

We fully expect, and hope, that this record will not stand for long. Happy hunting!

MATLAB on the Steam Deck

Pick of the Week Team — Fri, 06 Mar 2026 15:44:44 +0000

Toshi’s pick this week is MATLAB on the Steam Deck from Reddit by Mindless_Egg7324

A recent Reddit post showed something unexpected and fun: a user successfully installed MATLAB on a Steam Deck, and the community loved it—nearly 500 upvotes and an entire thread of enthusiastic reactions.

The Steam Deck runs SteamOS, which is based on Arch Linux. While MATLAB officially supports a set of validated Linux distributions—including Ubuntu, Debian, Red Hat Enterprise Linux, and SUSE Linux Enterprise Desktop/Server—Arch Linux and SteamOS aren’t on the official support list.

That makes this achievement even more interesting. It works—not because it’s a standard setup, but because someone decided to try it anyway.

This is obviously not practical, but the community had a lot of fun praising the audacity of the OP and cracking jokes on what they might do with it.

The comments captured that feeling:

The excitement came from seeing MATLAB in a completely unexpected place—a gaming handheld, running a non‑standard Linux distro.

It’s a great reminder of how inventive the community can be.

MATLAB Deep Learning Image Classifier App

Mike Croucher — Fri, 27 Feb 2026 17:33:10 +0000

Mike's pick this week is the Deep Learning Image Classifier App by MathWorks Deep Learning Toolbox Team.

Most people who start learning about Deep Learning begin with image classification workflows. I certainly did! My first deep learning experiments involved cats, dogs, digits and tiny little pictures of clothes! 

The MATLAB Deep Learning Image Classifier is a free App Designer app for training image classification deep neural networks in MATLAB. Using this app, you can:

Import, visualize, and augment data
Quickly transfer learn with the SqueezeNet pretrained network
Modify pretrained networks for transfer learning with Deep Network Designer
Import networks from the workspace
Explain predictions with explainability techniques like Grad-CAM and LIME
Generate MATLAB code for training an image classifier

Its a great way to get started with deep learning workflows without worrying about the code; although the code is just a click away when you are ready for it. 

The short video below gives an example of the app in use. In this case, we start off with a pre-trained image classification network called Squeezenet. Squeezenet can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. It cannot, however, classify MathWorks swag!

Using a technique called Transfer Learning, we can adapt squeezenet to recognize the different types of MathWorks swag and the entire workflow can be done in the app.

This just scratches the surface of what this app can do! Since it is all available on GitHub, you can even adapt the app itself to cover workflows that haven't been considered yet.

Agentic AI + MATLAB: A Workflow Worth Exploring

Pick of the Week Team — Sat, 07 Feb 2026 00:09:46 +0000

Toshi’s pick this week is Exploring LLMs in Chemical Engineering: Thermodynamic Modeling meets Vibe Coding from LinkedIn by Antonio Mazzei

Have you ever wondered what happens when you combine agentic AI workflows with MATLAB? A couple months ago, I saw a fantastic example from Antonio Mazzei that shows just how powerful this combination can be.

Antonio used a “vibe coding” approach with Gemini CLI to connect to the MATLAB MCP Core Server and build an interactive script for thermodynamic modeling. The goal was to visualize the 1-parameter Margules model for Excess Gibbs Free Energy—a classic chemical engineering problem.

What’s exciting here isn’t just the math—it’s the workflow. By letting an LLM act as an agent, Antonio didn’t just generate code; he created a system that understands context, iterates on solutions, and integrates with MATLAB. This is a glimpse into the future of technical computing.

The best part? The MATLAB MCP Core is open source and available on GitHub, so you can try this yourself here. Whether you’re in chemical engineering, finance, or any modeling-heavy domain, this approach opens up new possibilities for automation and creativity.

Your turn: Have you experimented with LLMs for modeling or simulation tasks? Share your experience in the comments—I’d love to hear how you’re using AI to push boundaries.