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Edge Impulse has joined 1% for Planet, pledging to donate 1% of our revenue to support nonprofit organizations focused on the environment. To complement this effort we launched the ElephantEdge competition, aiming to create the world’s best elephant tracking device to protect elephant populations that would otherwise be impacted by poaching. In this similar vein, this blog will detail how Lacuna Space, Edge Impulse, a microcontroller and LoraWAN can promote the conservation of endangered species by monitoring bird calls in remote areas.

Over the past years, The Things Networks has worked around the democratization of the Internet of Things, building a global and crowdsourced LoraWAN network carried by the thousands of users operating their own gateways worldwide. Thanks to Lacuna Space’ satellites constellation, the network coverage goes one step further. Lacuna Space uses LEO (Low-Earth Orbit) satellites to provide LoRaWAN coverage at any point around the globe. Messages received by satellites are then routed to ground stations and forwarded to LoRaWAN service providers such as TTN. This technology can benefit several industries and applications: tracking a vessel not only in harbors but across the oceans, monitoring endangered species in remote areas. All that with only 25mW power (ISM band limit) to send a message to the satellite. This is truly amazing!

Most of these devices are typically simple, just sending a single temperature value, or other sensor reading, to the satellite - but with machine learning we can track much more: what devices hear, see, or feel. In this blog post we'll take you through the process of deploying a bird sound classification project using an Arduino Nano 33 BLE Sense board and a Lacuna Space LS200 development kit. The inferencing results are then sent to a TTN application.

Note: Access to the Lacuna Space program and dev kit is closed group at the moment. Get in touch with Lacuna Space for hardware and software access. The technical details to configure your Arduino sketch and TTN application are available in our GitHub repository.

 

Our bird sound model classifies house sparrow and rose-ringed parakeet species with a 92% accuracy. You can clone our public project or make your own classification model following our different tutorials such as Recognize sounds from audio or Continuous Motion Recognition.

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Once you have trained your model, head to the Deployment section, select the Arduino library and Build it.

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Import the library within the Arduino IDE, and open the microphone continuous example sketch. We made a few modifications to this example sketch to interact with the LS200 dev kit: we added a new UART link and we transmit classification results only if the prediction score is above 0.8.

Connect with the Lacuna Space dashboard by following the instructions on our application’s GitHub ReadMe. By using a web tracker you can determine when the next good time a Lacuna Space satellite will be flying in your location, then you can receive the signal through your The Things Network application and view the inferencing results on the bird call classification:

    {
       "housesparrow": "0.91406",
       "redringedparakeet": "0.05078",
       "noise": "0.03125",
       "satellite": true,
   }

No Lacuna Space development kit yet? No problem! You can already start building and verifying your ML models on the Arduino Nano 33 BLE Sense or one of our other development kits, test it out with your local LoRaWAN network (by pairing it with a LoRa radio or LoRa module) and switch over to the Lacuna satellites when you get your kit.

Originally posted on the Edge Impulse blog by Aurelien Lequertier - Lead User Success Engineer at Edge Impulse, Jenny Plunkett - User Success Engineer at Edge Impulse, & Raul James - Embedded Software Engineer at Edge Impulse

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I recently joined the Embedded Online Conference thinking I was going to gain new insights on embedded and IoT techniques. But I was pleasantly surprised to see a huge variety of sessions with a focus on modern software development practices. It is becoming more and more important to gain familiarity with a more modern software approach, even when you’re programming a constrained microcontroller or an FPGA.

Historically, there has been a large separation between application developers and those writing code for constrained embedded devices. But, things are now changing. The embedded world intersecting with the world of IoT, data science, and ML, and the deeper co-operation between software and hardware communities is driving innovation. The Embedded Online Conference, artfully organised by Jacob Beningo, represented exactly this cross-section, projecting light on some of the most interesting areas in the embedded world - machine learning on microcontrollers, using test-driven development to reduce bugs and programming an FPGA in Python are all things that a few years ago, had little to do with the IoT and embedded industry.

This blog is the first part of a series discussing these new and exciting changes in the embedded industry. In this article, we will focus on machine learning techniques for low-power and cost-sensitive IoT and embedded Arm-based devices.

Think like a machine learning developer

Considered for many year's an academic dead end of limited practical use, machine learning has gained a lot of renewed traction in recent years and it has now become one of the most interesting trends in the IoT space. TinyML is the buzzword of the moment. And this was a hot topic at the Embedded Online Conference. However, for embedded developers, this buzzword can sometimes add an element of uncertainty.

The thought of developing IoT applications with the addition of machine learning can seem quite daunting. During Pete Warden’s session about the past, present and future of embedded ML, he described the embedded and machine learning worlds to be very fragmented; there are so many hardware variants, RTOS’s, toolchains and sensors meaning the ability to compile and run a simple ‘hello world’ program can take developers a long time. In the new world of machine learning, there’s a constant churn of new models, which often use different types of mathematical operations. Plus, exporting ML models to a development board or other targets is often more difficult than it should be.

Despite some of these challenges, change is coming. Machine learning on constrained IoT and embedded devices is being made easier by new development platforms, models that work out-of-the-box with these platforms, plus the expertise and increased resources from organisations like Arm and communities like tinyML. Here are a few must-watch talks to help in your embedded ML development: 

  • New to the tinyML space is Edge Impulse, a start-up that provides a solution for collecting device data, building a model based around it and deploying it to make sense of the data directly on the device. CTO at Edge Impulse, Jan Jongboom talks about how to use a traditional signal processing pipeline to detect anomalies with a machine learning model to detect different gestures. All of this has now been made even easier by the announced collaboration with Arduino, which simplifies even further the journey to train a neural network and deploy it on your device.
  • Arm recently announced new machine learning IP that not only has the capabilities to deliver a huge uplift in performance for low-power ML applications, but will also help solve many issues developers are facing today in terms of fragmented toolchains. The new Cortex-M55 processor and Ethos-U55 microNPU will be supported by a unified development flow for DSP and ML workloads, integrating optimizations for machine learning frameworks. Watch this talk to learn how to get started writing optimized code for these new processors.
  • An early adopter implementing object detection with ML on a Cortex-M is the OpenMV camera - a low-cost module for machine vision algorithms. During the conference, embedded software engineer, Lorenzo Rizzello walks you through how to get started with ML models and deploying them to the OpenMV camera to detect objects and the environment around the device.

Putting these machine learning technologies in the hands of embedded developers opens up new opportunities. I’m excited to see and hear what will come of all this amazing work and how it will improve development standards and transform embedded devices of the future.

If you missed the conference and would like to catch the talks mentioned above*, visit www.embeddedonlineconference.com

*This blog only features a small collection of all the amazing speakers and talks delivered at the Conference!

Part 2 of my review can be viewed by clicking here

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