TL;DR

GTC Keynote from Jensen Huang, CEO of NVIDIA

Jensen Huang, CEO of NVIDIA, at GTC March 2024 discusses the evolution of computing and the emergence of generative AI, a new kind of software that can be used to create other software. He also unveils a new line of products from NVIDIA, including NIM (NVIDIA Inference Microservice), and Omniverse, a digital twin platform that can be used to create and simulate robotic systems.

• Generative AI is a new kind of software that can be used to create other software. This is a major breakthrough that has the potential to revolutionize many industries.
• NVIDIA is a leader in the development of generative AI, and the company is committed to making this technology accessible to everyone. Nims is a new product from NVIDIA that makes it easy for companies to integrate generative AI into their workflows.
• Omniverse is another new product from NVIDIA that is designed to help companies create and simulate robotic systems. Omniverse is a digital twin platform that can be used to create virtual models of real-world systems. These models can then be used to train robots and to test new products and processes.
• NVIDIA believes that generative AI and Omniverse have the potential to transform many industries, including healthcare, manufacturing, and transportation. The company is committed to working with its partners to bring these technologies to market.

The Power of the Ecosystem

The one awesome part of GTC is that NVIDIA is bringing together partners and building a strong ecosystem. There have been so many blogs, press releases, and videos to capture all of the news, but one is the idea that it is going to take many developers, partners, and tech stacks to come together in order to deliver on the promise of AI. I was proud to see that MathWorks is in the ecosystem.

One thing that has captured my interest is the Omniverse Cloud APIs, which I believe are set to revolutionize autonomous system development through advanced sensor simulation. These APIs offer developers crucial tools for creating and testing realistic scenarios, thus enhancing the safety and effectiveness of autonomous technologies. NVIDIA’s initiative not only accelerates the development process but also aims to democratize access to cutting-edge technologies, fostering innovation in the autonomous sector.

By bringing together an expansive ecosystem of simulators, verification and validation (V&V) tools, content and sensor developers, the Omniverse Cloud APIs enable a universal environment for AI system development.

Developers and software vendors such as CARLA, MathWorks, MITRE, Foretellix, and Voxel51 underscore the broad appeal of these APIs for autonomous vehicles.

Here’s a video showing how Foretellix leverages NVIDIA Omniverse Cloud APIs to generate high-fidelity sensor simulations for autonomous vehicle development.

2024 is shaping up to be exciting. AI tech, platforms, partners, and developer interest are hitting at once.

I am a panelist on the Makers & Manufactures track. This panel is moderated by Manisha Rajaghatta and includes a great lineup: Emily Roundy, Collins Aerospace, Dean Schifilliti, BAE Systems, Gaby Waldman, Autodesk, and Hans Scharler, MathWorks.

The event will also be streaming on Twitch, so this will be my first official Twitch live stream. I am excited about participating on the panel and attending the rest of the great sessions. See you there!

The coding contest is all about using 280 characters or less of MATLAB code to show off an interesting and beautiful image. We will be displaying all of the images allowing folks to remix the code and make new ones. As part of the celebration, MathWorks will be donating up to $20,000 to the humanitarian aid organization, Direct Relief, based on participation. Along with the support of Direct Relief, we will be giving winners a custom MathWorks shirt. I WANT THIS SHIRT!

Check out the MathWorks Community Contests at MATLAB Central. Follow along for news and updates. And, if you win, show off your t-shirt.

Butterflies have recently captured George’s attention.

It was fun exploring the lifecycle of a butterfly going from eggs to a caterpillar to a pupa to finally a beautiful butterfly. I am sure there are life lessons in this metamorphosis, but that is for another day.

George learned by observation. I learned by observation. Butterflies have a unique flight path. They have had a lot of time to learn how to fly and they treat the air like water. What can we learn from butterflies? Well, to go next level, you need some butterflies, a wind tunnel, and some high-speed cameras to slow down the butterfly’s motion and see what they are doing with their wings. Researchers at Lund University did just that.

In a complex experiment, the researchers used MATLAB to calculate aerodynamic forces and analyze the butterfly kinematics. They discovered that the butterflies’ flexible wings provide the impulse and increased efficiency that results in their distinct flight pattern.

It turns out that the butterfly wing is flexible. This allows the butterfly to form a cup to create a tremendous amount of thrust. Planes, jets, and drones all have rigid wings. Understanding how butterflies fly could lead to innovations with other flying machines.

Read more about the butterfly project on MathWorks.com. Science!

Our Ninja Foodi Air Fryer became our family’s go-to gadget during the stay-at-home order. When you order takeout, you probably noticed that the food isn’t the same. The plastic containers are nice for containing during transport, but they have a side effect of steaming your food. Pop your sides into an air fryer and they are back to their former glory… and sometimes even better. We also found a bunch a recipes made for the air fryer. This is going to change your whole existence and I never, ever use hyperbole.

A few years ago, I worked on a predictive maintenance project for duct fans. An air flyer is like a normal oven with the addition of a fan. There are fans in almost every major system and when they fail, the system that they are attached to will eventually fail. Fan blades can get bent, dusty, covered in food particles and oils, and damaged over time. The idea behind predictive maintenance is to predict the remaining useful life (RUL) of a machine and perform maintenance and repairs just-in-time. When applied at scale, predictive maintenance strategies provide a substantial return on investment. Studies show that condition-monitoring and predictive maintenance pays for itself and can be developed by anyone using MATLAB and the Predictive Maintenance Toolbox.

Check out my Condition-based Maintenance of a Duct Fan using ThingSpeak over at Hackster for a complete tutorial, video, and the source code. I adapted this project for an air fryer, but you could literally apply this technique to any machine that you want to monitor or to predict its remaining useful life. The idea is that fans and monitors vibrate when they are operational. As they degrade, the vibration changes over time. If you train a model with the data, you can use the model to predict the machine’s state, condition, and remaining lifespan. The predicted information can be used to change the maintenance schedule of the machine and optimize repair timing. It’s better to replace a part when the machine is scheduled to be down versus when the machine is in production.

I am using the Particle Internet Button for this project. The button gives me a nice convenient packaging of a Wi-Fi device for connectivity to ThingSpeak, an accelerometer to measure vibration, and LEDs for status lights.

The system uses ThingSpeak channels to store the vibration data, MATLAB / Predictive Maintenance Toolbox to build our machine learning model, MATLAB to analyze live data, and ThingSpeak to visualize our machine state and predictions.

Here’s an overview of the steps involved:

• Data Collection – Your first challenge is to collect data from the system. Attach the vibration sensor package to the machine, connect it to the network, and send the data to an IoT service like ThingSpeak.
• Data Parsing, Exploration, Feature Extraction, and Training – Now that you have data, you have to figure out what it all means. Use MATLAB, the Predictive Maintenance Toolbox, and the Statistics and Machine Learning Toolbox to parse the data, extract interesting features, and to train a machine learning model.
• Visualize and Report – Present your findings with charts and gauges. Use ThingSpeak Alerts to send automated email messages about the machine’s health.
• Profit.

Here’s what some raw data from air fyer looks like.

Vibration data is hard to interpret, but with machine learning, it’s possible to classify what the vibration data means and understand what the changes mean over time. You may even have noticed the sound of these appliances change over time. This is your brain picking up on the changes and telling you, “IT IS TIME TO CLEAN YOUR NASTY AIR FRYER.” Imagine a machine learning model always monitoring the vibration data and alerting you on the changes. Things will be working when they are supposed to be working.

Run two experiments with your fan and collect the data:

• Experiment 1: Simulating failure due to blocking the airflow (1000 data points) – Collect 1000 data points: 250 – OFF, 250 –ON, and 500 – BLOCKAGE
• Experiment 2: Simulating failure due to rotor imbalance (1000 data points) – Collect 1000 data points: = 250 – OFF, 250 –ON, 500 – IMBALANCED

When we explore the vibration data, you may notice some things:

• The variance of the data in the time domain increases as the fan is turned on and as the failure develops in both experiments.
• In the frequency domain for the experiment 1 data, the peak shifts from 30 Hz to 25 Hz.
• In experiment 2, the peak in the frequency domain remains at the same point but the amplitude at that frequency increases.

Using MATLAB’s built in Classification Leaner App in the Statistics and Machine Learning Toolbox makes it easy to train a machine learning model with your data.

When everything is running in operation, the system will monitor the vibration data, analyze using the MATLAB analytics that we developed in the tutorial, and predict the time to service. As you use appliances and machines they degrade over time. You don’t always need to clean and maintain. The trick is to do it when you need to do it before catastrophic failure… or going to Amazon and buying a new one.

If you are looking to try this out for yourself, check out the Condition-based Maintenance of a Duct Fan project published on on Hackster. We have included the source code for the device as well as the MATLAB code to make the system to work. This can be applied to other systems and the process applies to many applications of predictive maintenance. Now that you know that these techniques exist, you might be able to find some useful ways to apply them.

Apparently, I like connecting appliances to the internet.

You can export the MATLAB Live Script as HTML (by clicking the LIVE EDITOR tab, then Save and Export as HTML…) and put the exported HTML in an HTML block on WordPress.

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To learn how to build a ThingSpeak People Counter with MATLAB, check out File Exchange. To learn about the new ThingSpeak gauge widgets, check out the MathWorks Documentation for ThingSpeak or the MathWorks IoT Blog. Thanks for stopping by!

You never know who you are going to run into. I was so happy to meet one of the first users of ThingSpeak! This is Andy Leer of Leer Media. Andy was introduced to my IoT projects back in 2008 at Hack Pittsburgh! He was instrumental in me being able to kickstart my startup and help support my growing community. Andy provided meeting space for my IoT Meetup in Pittsburgh.

A couple of the Mythbusters also showed up at the Bay Area Maker Faire. I got to talk about MathWorks software with both Adam Savage and Grant Imahara. Grant remembers using MATLAB quite a bit in his education and later his engineering projects.

Click to view slideshow.

I have been getting ready for this year’s Bay Area Maker Faire. Stop by the MathWorks booth to see our latest hardware projects and demos. We have giveaways and info on the latest products. See you soon at the Maker Faire!