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By Dominic Pajak and Sandeep Mistry

Arduino is on a mission to make machine learning easy enough for anyone to use. The other week we announced the availability of TensorFlow Lite Micro in the Arduino Library Manager. With this, some cool ready-made ML examples such as speech recognition, simple machine vision and even an end-to-end gesture recognition training tutorial. For a comprehensive background we recommend you take a look at that article

In this article we are going to walk through an even simpler end-to-end tutorial using the TensorFlow Lite Micro library and the Arduino Nano 33 BLE 33 Sense’s colorimeter and proximity sensor to classify objects. To do this, we will be running a small neural network on the board itself. 

Arduino BLE 33 Nano Sense running TensorFlow Lite Micro

The philosophy of TinyML is doing more on the device with less resources – in smaller form-factors, less energy and lower cost silicon. Running inferencing on the same board as the sensors has benefits in terms of privacy and battery life and means its can be done independent of a network connection. 

The fact that we have the proximity sensor on the board means we get an instant depth reading of an object in front of the board – instead of using a camera and having to determine if an object is of interest through machine vision. 

In this tutorial when the object is close enough we sample the color – the onboard RGB sensor can be viewed as a 1 pixel color camera. While this method has limitations it provides us a quick way of classifying objects only using a small amount of resources. Note that you could indeed run a complete CNN-based vision model on-device. As this particular Arduino board includes an onboard colorimeter, we thought it’d be fun and instructive to demonstrate in this way to start with.

We’ll show a simple but complete end-to-end TinyML application can be achieved quickly and without a deep background in ML or embedded. What we cover here is data capture, training, and classifier deployment. This is intended to be a demo, but there is scope to improve and build on this should you decide to connect an external camera down the road. We want you to get an idea of what is possible and a starting point with tools available.

What you’ll need

About the Arduino board

The Arduino Nano 33 BLE Sense board we’re using here has an Arm Cortex-M4 microcontroller running mbedOS and a ton of onboard sensors – digital microphone, accelerometer, gyroscope, temperature, humidity, pressure, light, color and proximity. 

While tiny by cloud or mobile standards the microcontroller is powerful enough to run TensorFlow Lite Micro models and classify sensor data from the onboard sensors.

Setting up the Arduino Create Web Editor

In this tutorial we’ll be using the Arduino Create Web Editor – a cloud-based tool for programming Arduino boards. To use it you have to sign up for a free account, and install a plugin to allow the browser to communicate with your Arduino board over USB cable.

You can get set up quickly by following the getting started instructions which will guide you through the following:

  • Download and install the plugin
  • Sign in or sign up for a free account

(NOTE: If you prefer, you can also use the Arduino IDE desktop application. The setup for which is described in the previous tutorial.)

Capturing training data

We now we will capture data to use to train our model in TensorFlow. First, choose a few different colored objects. We’ll use fruit, but you can use whatever you prefer. 

Setting up the Arduino for data capture

Next we’ll use Arduino Create to program the Arduino board with an application object_color_capture.ino that samples color data from objects you place near it. The board sends the color data as a CSV log to your desktop machine over the USB cable.

To load the object_color_capture.ino application onto your Arduino board:

  • Connect your board to your laptop or PC with a USB cable
    • The Arduino board takes a male micro USB
  • Open object_color_capture.ino in Arduino Create by clicking this link

Your browser will open the Arduino Create web application (see GIF above).

  • Press OPEN IN WEB EDITOR
    • For existing users this button will be labeled ADD TO MY SKETCHBOOK
  • Press Upload & Save
    • This will take a minute
    • You will see the yellow light on the board flash as it is programmed
  • Open the serial Monitor
    • This opens the Monitor panel on the left-hand side of the web application
    • You will now see color data in CSV format here when objects are near the top of the board

Capturing data in CSV files for each object

For each object we want to classify we will capture some color data. By doing a quick capture with only one example per class we will not train a generalized model, but we can still get a quick proof of concept working with the objects you have to hand! 

Say, for example, we are sampling an apple:

  • Reset the board using the small white button on top.
    • Keep your finger away from the sensor, unless you want to sample it!
    • The Monitor in Arduino Create will say ‘Serial Port Unavailable’ for a minute
  • You should then see Red,Green,Blue appear at the top of the serial monitor
  • Put the front of the board to the apple. 
    • The board will only sample when it detects an object is close to the sensor and is sufficiently illuminated (turn the lights on or be near a window)
  • Move the board around the surface of the object to capture color variations
  • You will see the RGB color values appear in the serial monitor as comma separated data. 
  • Capture at a few seconds of samples from the object
  • Copy and paste this log data from the Monitor to a text editor
    • Tip: untick AUTOSCROLL check box at the bottom to stop the text moving
  • Save your file as apple.csv
  • Reset the board using the small white button on top.

Do this a few more times, capturing other objects (e.g. banana.csv, orange.csv). 

NOTE: The first line of each of the .csv files should read:

Red,Green,Blue

If you don’t see it at the top, you can just copy and paste in the line above. 

Training the model

We will now use colab to train an ML model using the data you just captured in the previous section.

  • First open the FruitToEmoji Jupyter Notebook in colab
  • Follow the instructions in the colab
    • You will be uploading your *.csv files 
    • Parsing and preparing the data
    • Training a model using Keras
    • Outputting TensorFlowLite Micro model
    • Downloading this to run the classifier on the Arduino 

With that done you will have downloaded model.h to run on your Arduino board to classify objects!

The colab will guide you to drop your .csv files into the file window, the result shown above
Normalized color samples captured by the Arduino board are graphed in colab

Program TensorFlow Lite Micro model to the Arduino board

Finally, we will take the model we trained in the previous stage and compile and upload to our Arduino board using Arduino Create. 

Your browser will open the Arduino Create web application:

  • Press the OPEN IN WEB EDITOR button
  • Import the  model.h you downloaded from colab using Import File to Sketch: 
Import the model.h you downloaded from colab
The model.h tab should now look like this
  • Compile and upload the application to your Arduino board 
    • This will take a minute
    • When it’s done you’ll see this message in the Monitor:
  • Put your Arduino’s RGB sensor near the objects you trained it with
  • You will see the classification output in the Monitor:
Classifier output in the Arduino Create Monitor

You can also edit the object_color_classifier.ino sketch to output emojis instead (we’ve left the unicode in the comments in code!), which you will be able to view in Mac OS X or Linux terminal by closing the web browser tab with Arduino Create in, resetting your board, and typing cat /cu/usb.modem[n]. 

Output from Arduino serial to Linux terminal using ANSI highlighting and unicode emojis

Learning more

The resources around TinyML are still emerging but there’s a great opportunity to get a head start and meet experts coming up 2-3 December 2019 in Mountain View, California at the Arm IoT Dev Summit. This includes workshops from Sandeep Mistry, Arduino technical lead for on-device ML and from Google’s Pete Warden and Daniel Situnayake who literally wrote the book on TinyML. You’ll be able to hang out with these experts and more at the TinyML community sessions there too. We hope to see you there!

Conclusion

We’ve seen a quick end-to-end demo of machine learning running on Arduino. The same framework can be used to sample different sensors and train more complex models. For our object by color classification we could do more, by sampling more examples in more conditions to help the model generalize. In future work, we may also explore how to run an on-device CNN. In the meantime, we hope this will be a fun and exciting project for you. Have fun!

This post was originally published by Sandeep Mistry and Dominic Pajak on the TensorFlow blog.

Arduino is on a mission to make machine learning simple enough for anyone to use. We’ve been working with the TensorFlow Lite team over the past few months and are excited to show you what we’ve been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we’ll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager.

The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.

Example 1: Running the pre-trained micro_speech inference example.

Next, we’ll introduce a more in-depth tutorial you can use to train your own custom gesture recognition model for Arduino using TensorFlow in Colab. This material is based on a practical workshop held by Sandeep Mistry and Dan Coleman, an updated version of which is now online

If you have previous experience with Arduino, you may be able to get these tutorials working within a couple of hours. If you’re entirely new to microcontrollers, it may take a bit longer. 

Example 2: Training your own gesture classification model.

We’re excited to share some of the first examples and tutorials, and to see what you will build from here. Let’s get started!

Note: The following projects are based on TensorFlow Lite for Microcontrollers which is currently experimental within the TensorFlow repo. This is still a new and emerging field!

Microcontrollers and TinyML

Microcontrollers, such as those used on Arduino boards, are low-cost, single chip, self-contained computer systems. They’re the invisible computers embedded inside billions of everyday gadgets like wearables, drones, 3D printers, toys, rice cookers, smart plugs, e-scooters, washing machines. The trend to connect these devices is part of what is referred to as the Internet of Things.

Arduino is an open-source platform and community focused on making microcontroller application development accessible to everyone. The board we’re using here has an Arm Cortex-M4 microcontroller running at 64 MHz with 1MB Flash memory and 256 KB of RAM. This is tiny in comparison to Cloud, PC, or mobile but reasonable by microcontroller standards.

Arduino Nano 33 BLE Sense board is smaller than a stick of gum.

There are practical reasons you might want to squeeze ML on microcontrollers, including: 

  • Function – wanting a smart device to act quickly and locally (independent of the Internet).
  • Cost – accomplishing this with simple, lower cost hardware.
  • Privacy – not wanting to share all sensor data externally.
  • Efficiency – smaller device form-factor, energy-harvesting or longer battery life.

There’s a final goal which we’re building towards that is very important:

  • Machine learning can make microcontrollers accessible to developers who don’t have a background in embedded development 

On the machine learning side, there are techniques you can use to fit neural network models into memory constrained devices like microcontrollers. One of the key steps is the quantization of the weights from floating point to 8-bit integers. This also has the effect of making inference quicker to calculate and more applicable to lower clock-rate devices. 

TinyML is an emerging field and there is still work to do – but what’s exciting is there’s a vast unexplored application space out there. Billions of microcontrollers combined with all sorts of sensors in all sorts of places which can lead to some seriously creative and valuable TinyML applications in the future.

What you need to get started

The Arduino Nano 33 BLE Sense has a variety of onboard sensors meaning potential for some cool TinyML applications:

  • Voice – digital microphone
  • Motion – 9-axis IMU (accelerometer, gyroscope, magnetometer)
  • Environmental – temperature, humidity and pressure
  • Light – brightness, color and object proximity

Unlike classic Arduino Uno, the board combines a microcontroller with onboard sensors which means you can address many use cases without additional hardware or wiring. The board is also small enough to be used in end applications like wearables. As the name suggests it has Bluetooth LE connectivity so you can send data (or inference results) to a laptop, mobile app or other BLE boards and peripherals.

Tip: Sensors on a USB stick – Connecting the BLE Sense board over USB is an easy way to capture data and add multiple sensors to single board computers without the need for additional wiring or hardware – a nice addition to a Raspberry Pi, for example.

TensorFlow Lite for Microcontrollers examples

The inference examples for TensorFlow Lite for Microcontrollers are now packaged and available through the Arduino Library manager making it possible to include and run them on Arduino in a few clicks. In this section we’ll show you how to run them. The examples are:

  • micro_speech – speech recognition using the onboard microphone
  • magic_wand – gesture recognition using the onboard IMU
  • person_detection – person detection using an external ArduCam camera

For more background on the examples you can take a look at the source in the TensorFlow repository. The models in these examples were previously trained. The tutorials below show you how to deploy and run them on an Arduino. In the next section, we’ll discuss training.

How to run the examples using Arduino Create web editor

Once you connect your Arduino Nano 33 BLE Sense to your desktop machine with a USB cable you will be able to compile and run the following TensorFlow examples on the board by using the Arduino Create web editor:

Compiling an example from the Arduino_TensorFlowLite library.

Focus on the speech recognition example: micro_speech

One of the first steps with an Arduino board is getting the LED to flash. Here, we’ll do it with a twist by using TensorFlow Lite Micro to recognise voice keywords. It has a simple vocabulary of “yes” and “no”. Remember this model is running locally on a microcontroller with only 256KB of RAM, so don’t expect commercial ‘voice assistant’ level accuracy – it has no Internet connection and on the order of 2000x less local RAM available.

Note the board can be battery powered as well. As the Arduino can be connected to motors, actuators and more this offers the potential for voice-controlled projects.

Running the micro_speech example.

How to run the examples using the Arduino IDE

Alternatively you can use try the same inference examples using Arduino IDE application.

First, follow the instructions in the next section Setting up the Arduino IDE.

In the Arduino IDE, you will see the examples available via the File > Examples > Arduino_TensorFlowLite menu in the ArduinoIDE.

Select an example and the sketch will open. To compile, upload and run the examples on the board, and click the arrow icon:

For advanced users who prefer a command line, there is also the arduino-cli.

Training a TensorFlow Lite Micro model for Arduino

[optimize output image]
Gesture classification on Arduino BLE 33 Nano Sense, output as emojis.

Next we will use ML to enable the Arduino board to recognise gestures. We’ll capture motion data from the Arduino Nano 33 BLE Sense board, import it into TensorFlow to train a model, and deploy the resulting classifier onto the board.

The idea for this tutorial was based on Charlie Gerard’s awesome Play Street Fighter with body movements using Arduino and Tensorflow.js. In Charlie’s example, the board is streaming all sensor data from the Arduino to another machine which performs the gesture classification in Tensorflow.js. We take this further and “TinyML-ifiy” it by performing gesture classification on the Arduino board itself. This is made easier in our case as the Arduino Nano 33 BLE Sense board we’re using has a more powerful Arm Cortex-M4 processor, and an on-board IMU.

We’ve adapted the tutorial below, so no additional hardware is needed – the sampling starts on detecting movement of the board. The original version of the tutorial adds a breadboard and a hardware button to press to trigger sampling. If you want to get into a little hardware, you can follow that version instead.

Setting up the Arduino IDE

Following the steps below sets up the Arduino IDE application used to both upload inference models to your board and download training data from it in the next section. There are a few more steps involved than using Arduino Create web editor because we will need to download and install the specific board and libraries in the Arduino IDE.

  • In the Arduino IDE menu select Tools > Board > Boards Manager…
    • Search for “Nano BLE” and press install on the board 
    • It will take several minutes to install
    • When it’s done close the Boards Manager window
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  • Now go to the Library Manager Tools > Manage Libraries…
    • Search for and install the Arduino_TensorFlowLite library

Next search for and install the Arduino_LSM9DS1 library:

  • Finally, plug the micro USB cable into the board and your computer
  • Choose the board Tools > Board > Arduino Nano 33 BLE
  • Choose the port Tools > Port > COM5 (Arduino Nano 33 BLE) 
    • Note that the actual port name may be different on your computer

There are more detailed Getting Started and Troubleshooting guides on the Arduino site if you need help.

Streaming sensor data from the Arduino board

First, we need to capture some training data. You can capture sensor data logs from the Arduino board over the same USB cable you use to program the board with your laptop or PC.

Arduino boards run small applications (also called sketches) which are compiled from .ino format Arduino source code, and programmed onto the board using the Arduino IDE or Arduino Create. 

We’ll be using a pre-made sketch IMU_Capture.ino which does the following:

  • Monitor the board’s accelerometer and gyroscope 
  • Trigger a sample window on detecting significant linear acceleration of the board 
  • Sample for one second at 119Hz, outputting CSV format data over USB 
  • Loop back and monitor for the next gesture

The sensors we choose to read from the board, the sample rate, the trigger threshold, and whether we stream data output as CSV, JSON, binary or some other format are all customizable in the sketch running on the Arduino. There is also scope to perform signal preprocessing and filtering on the device before the data is output to the log – this we can cover in another blog. For now, you can just upload the sketch and get sampling.

To program the board with this sketch in the Arduino IDE:

  • Download IMU_Capture.ino and open it in the Arduino IDE
  • Compile and upload it to the board with Sketch > Upload

Visualizing live sensor data log from the Arduino board

With that done we can now visualize the data coming off the board. We’re not capturing data yet this is just to give you a feel for how the sensor data capture is triggered and how long a sample window is. This will help when it comes to collecting training samples.

  • In the Arduino IDE, open the Serial Plotter Tools > Serial Plotter
    • If you get an error that the board is not available, reselect the port:
    • Tools > Port > portname (Arduino Nano 33 BLE) 
  • Pick up the board and practice your punch and flex gestures
    • You’ll see it only sample for a one second window, then wait for the next gesture
  • You should see a live graph of the sensor data capture (see GIF below)
Arduino IDE Serial Plotter will show a live graph of CSV data output from your board.

When you’re done be sure to close the Serial Plotter window – this is important as the next step won’t work otherwise.

Capturing gesture training data 

To capture data as a CSV log to upload to TensorFlow, you can use Arduino IDE > Tools > Serial Monitor to view the data and export it to your desktop machine:

  • Reset the board by pressing the small white button on the top
  • Pick up the board in one hand (picking it up later will trigger sampling)
  • In the Arduino IDE, open the Serial Monitor Tools > Serial Monitor
    • If you get an error that the board is not available, reselect the port:
    • Tools > Port > portname (Arduino Nano 33 BLE) 
  • Make a punch gesture with the board in your hand (Be careful whilst doing this!)
    • Make the outward punch quickly enough to trigger the capture
    • Return to a neutral position slowly so as not to trigger the capture again 
  • Repeat the gesture capture step 10 or more times to gather more data
  • Copy and paste the data from the Serial Console to new text file called punch.csv 
  • Clear the console window output and repeat all the steps above, this time with a flex gesture in a file called flex.csv 
    • Make the inward flex fast enough to trigger capture returning slowly each time

Note the first line of your two csv files should contain the fields aX,aY,aZ,gX,gY,gZ.

Linux tip: If you prefer you can redirect the sensor log output from the Arduino straight to a .csv file on the command line. With the Serial Plotter / Serial Monitor windows closed use:

 $ cat /dev/cu.usbmodem[nnnnn] > sensorlog.csv

Training in TensorFlow

We’re going to use Google Colab to train our machine learning model using the data we collected from the Arduino board in the previous section. Colab provides a Jupyter notebook that allows us to run our TensorFlow training in a web browser.

Arduino gesture recognition training colab.

The colab will step you through the following:

  • Set up Python environment
  • Upload the punch.csv and flex.csv data 
  • Parse and prepare the data
  • Build and train the model
  • Convert the trained model to TensorFlow Lite
  • Encode the model in an Arduino header file

The final step of the colab is generates the model.h file to download and include in our Arduino IDE gesture classifier project in the next section:

Let’s open the notebook in Colab and run through the steps in the cells – arduino_tinyml_workshop.ipynb

Classifying IMU Data

Next we will use model.h file we just trained and downloaded from Colab in the previous section in our Arduino IDE project:

  • Open IMU_Classifier.ino in the Arduino IDE.
  • Create a new tab in the IDE. When asked name it model.h
  • Open the model.h tab and paste in the version you downloaded from Colab
  • Upload the sketch: Sketch > Upload
  • Open the Serial Monitor: Tools > Serial Monitor
  • Perform some gestures
  • The confidence of each gesture will be printed to the Serial Monitor (0 = low confidence, 1 =  high confidence)

Congratulations you’ve just trained your first ML application for Arduino!

For added fun the Emoji_Button.ino example shows how to create a USB keyboard that prints an emoji character in Linux and macOS. Try combining the Emoji_Button.ino example with the IMU_Classifier.ino sketch to create a gesture controlled emoji keyboard ?.

Conclusion

It’s an exciting time with a lot to learn and explore in TinyML. We hope this blog has given you some idea of the potential and a starting point to start applying it in your own projects. Be sure to let us know what you build and share it with the Arduino community.

For a comprehensive background on TinyML and the example applications in this article, we recommend Pete Warden and Daniel Situnayake’s new O’Reilly book “TinyML: Machine Learning with TensorFlow on Arduino and Ultra-Low Power Microcontrollers.”

Even though machine learning AKA ‘deep learning’ / ‘artificial intelligence’ has been around for several decades now, it’s only recently that computing power has become fast enough to do anything useful with the science.

However, to fully understand how a neural network (NN) works, [Dimitris Tassopoulos] has stripped the concept down to pretty much the simplest example possible – a 3 input, 1 output network – and run inference on a number of MCUs, including the humble Arduino Uno. Miraculously, the Uno processed the network in an impressively fast prediction time of 114.4 μsec!

Whilst we did not test the code on an MCU, we just happened to have Jupyter Notebook installed so ran the same code on a Raspberry Pi directly from [Dimitris’s] bitbucket repo.

He explains in the project pages that now that the hype about AI has died down a bit that it’s the right time for engineers to get into the nitty-gritty of the theory and start using some of the ‘tools’ such as Keras, which have now matured into something fairly useful.

In part 2 of the project, we get to see the guts of a more complicated NN with 3-inputs, a hidden layer with 32 nodes and 1-output, which runs on an Uno at a much slower speed of 5600 μsec.

This exploration of ML in the embedded world is NOT ‘high level’ research stuff that tends to be inaccessible and hard to understand. We have covered Machine Learning On Tiny Platforms Like Raspberry Pi And Arduino before, but not with such an easy and thoroughly practical example.

Machine learning is starting to come online in all kinds of arenas lately, and the trend is likely to continue for the forseeable future. What was once only available for operators of supercomputers has found use among anyone with a reasonably powerful desktop computer. The downsizing isn’t stopping there, though, as Microsoft is pushing development of machine learning for embedded systems now.

The Embedded Learning Library (ELL) is a set of tools for allowing Arduinos, Raspberry Pis, and the like to take advantage of machine learning algorithms despite their small size and reduced capability. Microsoft intended this library to be useful for anyone, and has examples available for things like computer vision, audio keyword recognition, and a small handful of other implementations. The library should be expandable to any application where machine learning would be beneficial for a small embedded system, though, so it’s not limited to these example applications.

There is one small speed bump to running a machine learning algorithm on your Raspberry Pi, though. The high processor load tends to cause small SoCs to overheat. But adding a heatsink and fan is something we’ve certainly seen before. Don’t let your lack of a supercomputer keep you from exploring machine learning if you see a benefit to it, and if you need more power than just one Raspberry Pi you can always build a cluster to get your task done just a little bit faster, too.

Thanks to [Baldpower] for the tip!

When was the last time you poured water onto your radio to turn it on?

Designed collaboratively by [Tore Knudsen], [Simone Okholm Hansen] and [Victor Permild], Pour Reception seeks to challenge what constitutes an interface, and how elements of play can create a new experience for a relatively everyday object.

Lacking buttons or knobs of any kind, Pour Reception appears an inert acrylic box with two glasses resting on top. A detachable instruction card cues the need for water, and pouring some into the glasses wakes the radio.

Inside, two aluminium plates —  acting as capacitive touch sensors — are connected to an Arduino using the Tact library from NANDSudio. Wekinator — a machine learning tool — enabled [Knudsen] to program various actions to control the radio. Pouring water between the glasses changes stations, rotating and tweaking the glass’ positions adjusts audio quality, and placing a finger in the glass mutes it temporarily.

It’s a great concept for a more engaging piece of tech, if perhaps a little unnerving to be pouring water around household electronics. Best take preventative measures before applying this idea elsewhere.

If there’s one thing that Hollywood knows about hackers, it’s that they absolutely love data visualizations. Sometimes it’s projected on a big wall (Hackers, WarGames), other times it’s gibberish until the plot says otherwise (Sneakers, The Matrix). But no matter what, it has to look cool. No hacker worth his or her salt can possibly work unless they’ve got an evolving Venn diagram or spectral waterfall running somewhere in the background.

Inspired by Hollywood portrayals, specifically one featured in Avengers: Age of Ultron, [Zack Akil] decided it was time to secure his place in the pantheon of hacker wall visualizations. But not content to just show meaningless nonsense on his wall, he set out to create something that was at least showing actual data.

[Zack] created a neural network to work through multi-label classification data in Python using the scikit-learn machine learning suite. The code takes the values from the neutral network training algorithm and converts them to RGB colors by way of an Arduino. Each “node” in the neutral network is 3D printed in translucent filament, and fitted with an RGB LED module. These modules are then connected to each other via side-glow fiber optic tubes, so that the colors within the tubes are mixed depending on the colors of the nodes they are attached to. This allows for a very organic “growing” effect, as colors move through the network node-by-node.

In the end this particular visualization doesn’t really mean anything; the data it’s working on only exists for the purposes of the visualization itself. But [Zack] succeeded in creating a practical visualization of machine learning, and if you’re the kind of person who needs to keep tabs on learning algorithms, some variation of this design may be just what you’re looking for.

If AI isn’t your thing but you still want a wall of RGB LEDs, maybe you can use this phased array antenna visualizer instead. If you’re really hip, maybe you’ll go the analog route and put a big gauge on the wall.


Filed under: Arduino Hacks, led hacks

CatSpotterThumbThe Jetson TX1 Cat Spotter uses advanced neural networking to recognize when there's a cat in the room — and then starts teasing it with a laser.

Read more on MAKE

The post Nvidia Jetson TX1 Cat Spotter and Laser Teaser appeared first on Make: DIY Projects and Ideas for Makers.

When it comes to farming veggies like cucumbers, the sorting process can often be just as hard and tricky as actually growing them. That’s why Makoto Koike is using Google’s TensorFlow machine learning technology to categorize the cucumbers on his family’s farm by size, shape and color, enabling them to focus on more important and less tedious work.

A camera-equipped Raspberry Pi 3 is used to take images of the cucumbers and send them to a small-scale TensorFlow neural network. The pictures are then forwarded to a larger network running on a Linux server to perform a more detailed classification. From there, the commands are fed to an Arduino Micro that controls a conveyor belt system that handles the actual sorting, dropping them into their respective container.

You can read all about the Google AI project here, as well as see it in action below!

mellis-aday

At Arduino Day, I talked about a project I and my collaborators have been working on to bring machine learning to the maker community. Machine learning is a technique for teaching software to recognize patterns using data, e.g. for recognizing spam emails or recommending related products. Our ESP (Example-based Sensor Predictions) software recognizes patterns in real-time sensor data, like gestures made with an accelerometer or sounds recorded by a microphone. The machine learning algorithms that power this pattern recognition are specified in Arduino-like code, while the recording and tuning of example sensor data is done in an interactive graphical interface. We’re working on building up a library of code examples for different applications so that Arduino users can easily apply machine learning to a broad range of problems.

The project is a part of my research at the University of California, Berkeley and is being done in collaboration with Ben Zhang, Audrey Leung, and my advisor Björn Hartmann. We’re building on the Gesture Recognition Toolkit (GRT) and openFrameworks. The software is still rough (and Mac only for now) but we’d welcome your feedback. Installations instructions are on our GitHub project page. Please report issues on GitHub.

Our project is part of a broader wave of projects aimed at helping electronics hobbyists make more sophisticated use of sensors in their interactive projects. Also building on the GRT is ml-lib, a machine learning toolkit for Max and Pure Data. Another project in a similar vein is the Wekinator, which is featured in a free online course on machine learning for musicians and artists. Rebecca Fiebrink, the creator of Wekinator, recently participated in a panel on machine learning in the arts and taught a workshop (with Phoenix Perry) at Resonate ’16. For non-real time applications, many people use scikit-learn, a set of Python tools. There’s also a wide range of related research from the academic community, which we survey on our project wiki.

For a high-level overview, check out this visual introduction to machine learning. For a thorough introduction, there are courses on machine learning from coursera and from udacity, among others. If you’re interested in a more arts- and design-focused approach, check out alt-AI, happening in NYC next month.

If you’d like to start experimenting with machine learning and sensors, an excellent place to get started is the built-in accelerometer and gyroscope on the Arduino or Genuino 101. With our ESP system, you can use these sensors to detect gestures and incorporate them into your interactive projects!



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