Planet Arduino

Wevolver’s previous article about the Arduino Pro ecosystem outlined how embedded sensors play a key role in transforming machines and automation devices to Cyber Physical Production Systems (CPPS). Using CPPS systems, manufacturers and automation solution providers capture data from the shop floor and use it for optimizations in areas like production schedules, process control, and quality management. These optimizations leverage advanced data Internet of Things (IoT) analytics over manufacturing datasets, which is the reason why data are the new oil.

Deployment Options for IoT Analytics: From Cloud Analytics to TinyML

IoT analytics entail statistical data processing and employ Machine Learning (ML) functions, including Deep Learning (DL) techniques i.e., ML based on deep neural networks. Many manufacturing enterprises deploy IoT analytics in the cloud. Cloud IoT analytics use the vast amounts of cloud data to train accurate DL models. Accuracy is important for many industrial use cases like Remaining Useful Life calculation in predictive maintenance. Nevertheless, it is also possible to execute analytics at the edge of the network. Edge analytics are deployed within embedded devices or edge computing clusters at the factory’s Local Area Network (LAN). They are appropriate for real-time use cases that demand low latency such as real-time detection of defects. Edge analytics are more power-efficient than cloud analytics. Moreover, they offer increased data protection as data stays within the LAN.

During the last couple of years, industrial organizations use TinyML to execute ML models within CPU and memory-constrained devices. TinyML is faster, real-time, more power-efficient, and more privacy-friendly than any other form of edge analytics. Therefore, it provides benefits for many Industry 4.0 use cases.

Building TinyML Applications

The process of developing and deploying TinyML applications entails:

1. Getting or Producing a Dataset, which is used for training the TinyML model. In this direction, data from sensors or production logs can be used.
2. Train an ML or DL Model, using standard tools and libraries like Jupyter Notebooks and Python packages like TensorFlow and NumPy. The work entails Exploratory Data Analysis steps towards understanding the data, identifying proper ML models, and preparing the data for training them.
3. Evaluate the Model’s Performance, using the trained model predictions and calculating various error metrics Depending on the achieved performance, the TinyML engineer may have to improve the model and avoid overfitting on the data. Different models must be tested to find the best one.
4. Make the Model Appropriate to Run on an Embedded Device, using tools like TensorFlow Lite which provides a “converter” library that turns a model into a space-efficient format. TensorFlow Lite provides also an “interpreter” library that runs the converted model using the most efficient operations for a given device. In this step, a C/C++ sketch is produced to enable on device deployment.
5. On-device Inference and Binary Development, which involves the C/C++ and embedded systems development part and produces a binary application for on-device inference.
6. Deploying the Binary to a Microcontroller, which makes the microcontroller able to analyse data and derive real-time insights.

Leveraging AutoML for Faster Development with Arduino Pro

Nowadays, Automatic Machine Learning (AutoML) tools are used to develop TinyML on various boards, including Arduino boards. Emerging platforms such as Edge Impulse, Qeexo and SensiML, among others, provide AutoML tools and developers’ resources for embedded ML development. Arduino is collaborating with such platforms as part of their strategy to make complex technologies open and simple to use by anyone.

Within these platforms, users collect real-world sensor data, train ML models on the cloud, and ultimately deploy the model back to an Arduino device. It is also possible to integrate ML models with Arduino sketches based on simple function calls. AutoML pipelines ease the tasks of (re)developing and (re)deploying models to meet complex requirements.

The collaboration between Arduino and ML platforms enables thousands of developers to build applications that embed intelligence in smart devices such as applications that recognize spoken keywords, gestures, and animals. Implementing applications that control IoT devices via natural language or gestures is relatively straightforward for developers who are familiar with Arduino boards.

Arduino has recently introduced its new Arduino Pro ecosystem of industrial-grade products and services, which support the full development, production and operation lifecycle from Hardware and Firmware to Low Code, Clouds, and Mobile Apps. The Pro ecosystem empowers thousands of developers to jump into Industry 4.0 development and to employ advanced edge analytics.

Big opportunity at every scale

The Arduino ecosystem provides excellent support for TinyML, including boards that ease TinyML development, as well as relevant tools and documentation. For instance, the Arduino Nano 33 BLE Sense board is one of the most popular boards for TinyML. It comes with a well-known form factor and various embedded sensors. The latter include a 9-axis inertial sensor that makes the board ideal for wearable devices, as well as for humidity and temperature sensors. As another example, Arduino’s Portenta H7 board includes two asymmetric cores, which enables simultaneously runs of high level code such as protocol stacks, machine learning or even interpreted languages (e.g., MicroPython or JavaScript). Furthermore, the Arduino IDE (Integrated Development Environment) provides the means for customizing embedded ML pipelines and deploying them in Arduino boards.

In a Nutshell

ML and AI models need not always to run over powerful clouds and related High Performance Computing services. It is also possible to execute neural networks over tiny memory-limited devices like microcontrollers, which opens unprecedented opportunities for pervasive intelligence. The Arduino ecosystem offers developers the resources they need to ride the wave of Industry 4.0 and TinyML. Arduino boards and the IDE lower the barriers for thousands of developers to engage with IoT analytics for industrial intelligence.

Read the full article on Wevolver.

The post From Embedded Sensors to Advanced Intelligence: Driving Industry 4.0 Innovation with TinyML appeared first on Arduino Blog.

Projects don’t get much more ambitious than DIY GUY Chris’ Arduino-powered jet engine. We’ve been following the work he’s done building a custom carrier board for the Portanta H7, and now we get to see it in action.

Portenta Jet Engine

To be honest, just building a working DIY jet engine model is impressive enough. But the model Chris has created is so much more than that.

The 3D-printed model has a breakaway section that lets us see the engine in action. A superb educational tool that covers everything from design and control to operation. And it looks like so much fun to make and play with, too.

His latest project puts the custom built Portenta H7 “Throne” board to use. This is a breakout, or carrier board, that he developed to explore ways to use the Portenta H7’s high density connectors. In this application it’s driving a high powered a DC motor that runs his jet engine model.

It’s an elaborate build, with a lot of printed, moving parts. In many respects the application that the H7 is used for is pretty simple, at least on the surface. But what’s great about Chris’ latest project is that it’s an excellent example of how the Arduino board could be implemented in industrial applications.

His excellent (and very professional) breakout board — the Throne — is a further demonstration of this, showing how adaptable devices like the H7 are in combination with custom solutions. So it’s worth taking a look at Chris’ other videos about the Throne’s development, as well as his mightily impressive DIY jet engine.

The post DIY jet engine powered by a Portenta H7 appeared first on Arduino Blog.

We’re excited to announce the launch of the Arduino Portenta Vision Shield, a production-ready expansion for the powerful Arduino Portenta H7 that adds a low-power camera, two microphones, and connectivity — everything you need for the rapid creation of edge ML applications.

Always-on machine vision

The Portenta Vision Shield comes with an ultra-low-power Himax camera. The camera module autonomously detects motion while the Portenta H7 is in stand-by — only waking up the microcontroller when needed.

Voice and audio event recognition

The Portenta Vision Shield features two ultra-compact and omnidirectional MP34DT06JTR microphones, bringing voice recognition and audio event detection. Both the video and audio data can be stored on an SD card, and transmitted through Ethernet or LoRa® modules (plus option of the WiFi or BLE on the Portenta H7 module).

Additional LoRa® or Ethernet connectivity

The powerful Arduino Portenta H7 makes machine possible learning on-device — greatly reducing the communication bandwidth requirement in an IoT application. The LoRa® module option is specifically designed for edge ML applications, enabling low-power, long distance communication over LoRa® wireless protocol and LoRaWAN networks. 

The Ethernet version is perfect for all those wired applications that need high bandwidth data transfer speed. 

(N.B. The LoRa® and Ethernet connectivity options on the Portenta Vision Shield are in addition to the existing WiFi and BLE connectivity provided by the Portenta H7 module.)

Embedded computer vision made easy

In tandem with the launch of the Portenta Vision Shield Arduino has teamed up with OpenMV to make their IDE  fully compatible with the Portenta. The OpenMV IDE provides an easy way into computer vision using MicroPython as a programming paradigm. There are an abundance of AI/machine learning algorithms available straight ‘out of the box’ providing a user experience we are sure you will appreciate.

Download the free license to OpenMV for Arduino Editor and browse through the examples we have prepared for you to try out embedded machine vision with your new Portenta Vision Shield.

“Embedded machine learning will transform industries. The Portenta Vision Shield is now the fastest way to go from concept to deployment of low-power machine vision and audio applications — delivering certified, production-ready hardware with support from easy-to-use ML software frameworks,” says Andrea Richetta, Arduino Pro BU leader. 

The Ethernet version of the Arduino Portenta Vision Shield is now available in pre-order on the Arduino Store, while the LoRa® version will be in stock by the end of this year.

We’re kicking off this year’s CES with some big news.

Millions of users and thousands of companies across the world already use Arduino as an innovation platform, which is why we have drawn on this experience to enable enterprises to quickly and securely connect remote sensors to business logic within one simple IoT application development platform: a new solution for professionals in traditional sectors aspiring for digital transformation through IoT. 

Combining a low-code application development platform with modular hardware makes tangible results possible in just one day. This means companies can build, measure, and iterate without expensive consultants or lengthy integration projects.

Built on ARM Pelion technology, the latest generation of Arduino solutions brings users simplicity of integration and a scalable, secure, professionally supported service. 

“By combining the power and flexibility of our production ready IoT hardware with our secure, scalable and easy to integrate cloud services we are putting in the hands of our customers something really disruptive,” commented Arduino CEO Fabio Violante. “Among the millions of Arduino customers, we’ve even seen numerous businesses transform from traditional ‘one off’ selling to subscription-based service models, creating new IoT-based revenue streams with Arduino as the enabler. The availability of a huge community of developers with Arduino skills is also an important plus and gives them the confidence to invest in our technology”.  

But that’s not all. At CES 2020, we are also excited to announce the powerful, low-power new Arduino Portenta family. Designed for demanding industrial applications, AI edge processing and robotics, it features a new standard for open high-density interconnect to support advanced peripherals. The first member of the family is the Arduino Portenta H7 module – a dual-core Arm Cortex-M7 and Cortex-M4 running at 480MHz and 240MHz, respectively, with industrial temperature-range (-40 to 85°C) components. The Portenta H7 is capable of running Arduino code, Python and JavaScript, making it accessible to an even broader audience of developers.

The new Arduino Portenta H7 is now available for pre-order on the Arduino online store, with an estimated delivery date of late February 2020.

We’re kicking off this year’s CES with some big news.

Millions of users and thousands of companies across the world already use Arduino as an innovation platform, which is why we have drawn on this experience to enable enterprises to quickly and securely connect remote sensors to business logic within one simple IoT application development platform: a new solution for professionals in traditional sectors aspiring for digital transformation through IoT. 

Combining a low-code application development platform with modular hardware makes tangible results possible in just one day. This means companies can build, measure, and iterate without expensive consultants or lengthy integration projects.

Built on ARM Pelion technology, the latest generation of Arduino solutions brings users simplicity of integration and a scalable, secure, professionally supported service. 

“By combining the power and flexibility of our production ready IoT hardware with our secure, scalable and easy to integrate cloud services we are putting in the hands of our customers something really disruptive,” commented Arduino CEO Fabio Violante. “Among the millions of Arduino customers, we’ve even seen numerous businesses transform from traditional ‘one off’ selling to subscription-based service models, creating new IoT-based revenue streams with Arduino as the enabler. The availability of a huge community of developers with Arduino skills is also an important plus and gives them the confidence to invest in our technology”.  

But that’s not all. At CES 2020, we are also excited to announce the powerful, low-power new Arduino Portenta family. Designed for demanding industrial applications, AI edge processing and robotics, it features a new standard for open high-density interconnect to support advanced peripherals. The first member of the family is the Arduino Portenta H7 module – a dual-core Arm Cortex-M7 and Cortex-M4 running at 480MHz and 240MHz, respectively, with industrial temperature-range (-40 to 85°C) components. The Portenta H7 is capable of running Arduino code, Python and JavaScript, making it accessible to an even broader audience of developers.

The new Arduino Portenta H7 is now available for pre-order on the Arduino online store, with an estimated delivery date of late February 2020.