The ongoing COVID-19 pandemic has drawn attention to how clean our indoor environments are, and by measuring the CO2 levels within a room, infection risks can be approximated since more CO2 is correlated with poor ventilation. Software engineer Swapnil Verma had the idea to use computer vision to count the number of occupants within a space and attempt to gauge the concentration of the gas accordingly.
The hardware powering this project is an Arduino Portenta H7 combined with a Vision Shield add-on that allows the board to capture images. From here, Verma used a subset of the PIROPO dataset, which contains recordings of indoor rooms and ran the YOLOv5-based auto labeling utility within Edge Impulse to draw bounding boxes around people. Once labeled, a FOMO model was trained with a respectable F1 score of 91.6%.
Testing the system was done by observing how well the Portenta H7, running the object detection model from Edge Impulse, did at tracking a person moving throughout a room. Even though the model only takes an input of 240x240px image data, it still performed admirably in this task. For the last step of estimating CO2 levels, Verma’s code simply takes the number of people detected in the frame and multiplies it by a constant. For more details, you can read his post here.
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.
TinyML is the faster, real-time, most power-efficient, and most privacy friendly form of edge analytics. Image credit: Carbon Robotics.
Building TinyML Applications
The process of developing and deploying TinyML applications entails:
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.
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.
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.
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.
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.
Deploying the Binary to a Microcontroller, which makes the microcontroller able to analyse data and derive real-time insights.
Building a Google Assistant using tinyML. Image credit: Arduino.
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.
Portenta Cat. M1/NB IoT GNSS Shield: Connectivity and positioning for your boards
Arduino Team — January 31st, 2022
Despite how powerful and high-performance we make our boards, we know some of you always want more – especially in the fast-evolving Industry 4.0! Enter the Portenta Cat. M1/NB IoT GNSS Shield, a new product we developed in partnership with aerospace, defense, transportation and security multinational Thales.
It’s what you need to unleash a world of new opportunities for edge computing. By leveraging a Cinterion TX62 wireless module built for highly efficient, low-power IoT applications, the Portenta Cat. M1/NB IoT GNSS Shield delivers optimized bandwidth and performance, while adding global connectivity and positioning capabilities to Portenta and MKR boards.
It is the ideal solution for the development of positioning, tracking and remote monitoring applications in industrial settings, including agriculture, public utilities and smart cities.
With the new Portenta Cat. M1/NB IoT GNSS Shield, you can:
Easily track assets – across the city or worldwide – from personal valuables to entire fleets of vehicles, thanks to the GNSS feature and a choice between GPS, GLONASS, Galileo or BeiDou positioning.
Add cellular capabilities to any Portenta boards connected to local sensors, leveraging the Cat.M1/NB IoT GNSS Shield’s connectivity features.
Get real-time insight from sensors located worldwide relaying geotagged data.
Much more. We’re excited to discover what the Arduino community, clients and partners will be able to do with the extended features and top performance provided by this shield.
Key benefits
Change connectivity capabilities without changing the board
Add NB-IoT, CAT.M1 and positioning to any Portenta or MKR board
Possibility to create a small multiprotocol router (WiFi – BT + NB-IoT/CAT.M1)
Low-power module
What can the Portenta Cat. M1/NB IoT GNSS Shield do for you?
Here are just a few examples:
If you work in agriculture, you can create your own solution for gas detection, optical sensing, machinery alarm systems and even biological bug traps.
If you develop smart city solutions, you could use the Portenta Cat. M1/NB IoT GNSS Shield for a new, intelligent parking system or street lighting, connecting data and automated actions for a truly optimized use of resources and enhanced user experience.
The computational power of the Portenta H7 combined with the Portenta Cat. M1/NB IoT GNSS Shield greatly reduces the communication bandwidth requirements in IoT applications. The Portenta Cat. M1/NB IoT GNSS Shield is specifically designed for edge ML applications, enabling low-power, long-distance communications over NBIoT and CAT.M1 networks.
So, are you ready to take your projects to the next level? Add a Portenta Cat. M1/NB IoT GNSS Shield to your Portenta H7 or MKR board.
The Portenta Cat. M1/NB IoT GNSS Shield is available for €73/$87.60 USD.For more information and full tech specs, please visit the Arduino Pro website.
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 incredible 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.
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 incredible 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.
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 incredible 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.
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 incredible 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.
At the basis of each weather forecast is data — and a lot of it. And although the vast majority of atmospheric data collection is fully automated, determining cloud volumes and types are still done manually. This problem is what inspired Swapnil Verma to create a project that utilizes machine learning to categorize six different classes of clouds.
The hardware for this system consists of an Arduino Portenta H7 due to its powerful processor and array of connectivity features, along with a Portenta Vision Shield for the camera. Both of these boards were mounted to a custom base on top of a tripod and powered by a battery bank over USB-C.
The MicroPython software installed on the Portenta H7 relies on the OpenMV library for capturing images from the Vision Shield and performing a small amount of processing on them. From there, Verma trained an image classification model on nearly 2,100 images of various labeled cloud types — clear sky, patterned cloud, thin white cloud, thick white cloud, thick dark cloud, and veil cloud — using Edge Impulse and deployed it back to the board. As the Portenta runs, it collects an image, classifies it locally, and then sends the result via MQTT to client devices, which lets them read the incoming data remotely. Verma even included a mode that takes images at a slow rate and sleeps in between to save battery power.
To read more about the Verma’s cloud classifier project, you can visit its writeup here on Hackster.io and watch the demo below.
The fantastical world of wizards and magic is one that can be explored by reading a book, and what better way to represent this than building your very own interactive diorama within a reading corner? Well, that is exactly what Andy of element14 Presents created when he combined a small display, computer vision, and LED lights into a fun bookshelf adornment, which would accompany readers on their journeys.
To begin, Andy had to figure out how to get a computer vision system into a space that is no larger than a shoebox, and for this task, he settled on using the Portenta H7 board plus its Vision Shield to gather images and classify them. His attempts to integrate a string of NeoPixels and an ePaper display module with MicroPython were unsuccessful, so this required a switch to only using C with TensorFlow Lite and some custom functions to take the framebuffers from the camera and determine if a face is present.
The diorama models themselves were fashioned from cardboard model railway kits that included houses and a few streetlights. Finally, the LEDs were added both behind the houses and inside of each lamppost that allows them to flicker and light up when a person is watching the display. The ePaper module switches between various stills such as a wanted poster and the element14 logo.
To see more about how this diorama was constructed, check out Andy’s video below!
We launched the powerful Portenta H7 last year. The more targeted Portenta H7 Lite just a few weeks ago. And we’re back (already!), with another new product that fills the gap between the previous two versions.
It’s known as Portenta H7 Lite Connected, but we like to call it “the best of both worlds.”
The Portenta H7 Lite Connected is powerful, with integrated wireless connectivity, yet remains cost-optimized. You could think of it as the H7 with only one secure element and no high-resolution video interface. Or if you prefer, the H7 Lite with the ability to connect.
Adding this third iteration to the Portenta family and Arduino Pro ecosystem is our way of saying we want to be at our your side. No matter what smart project you are working on. You can always count on Arduino’s reliability, versatility, and ease of implementation; picking the product that best suits your needs.
Portenta H7 Lite (left), H7 (center) and H7 Lite Connected (right)
Here’s where the Portenta H7 Lite Connected fits into the Arduino ecosystem.
If you need excellent computational power to deploy AI on the edge.
You’re building a solution that will interact with other robotics systems, and therefore does not require a high-resolution video interface.
Prototyping an idea that you know will need to connect to WiFi but is still not at the stage where you want to invest in a fully-fledged Portenta H7.
Get the exact features you need with three options to choose from in the Portenta H7 family. And remember, the 80-pin high-density connectors at the bottom of every Portenta mean you can simply upgrade to a different version at any time.
We all interact with the sewer system at multiple points throughout the day, and having it fail can lead to catastrophic results. Every year in the United States alone, an estimated 23,000 to 75,000 sewer pipe failures are reported, which means billions of gallons of untreated and hazardous waste is released into the environment. But rather than having a person constantly inspect the system on location, Huy Mai came up with a way to use computer vision in conjunction with embedded machine learning to automatically detect when a defect has occurred.
The device, which Huy calls TinySewer, is comprised of an Arduino Portena H7 and Portenta Vision Shield – LoRa. This combination allows for data to be streamed wirelessly to a cloud service for later review. Huy designed TinySewer to be a low-cost, add-on module that can be easily integrated into existing robotic systems or sewer inspection tools.
TinySewer also implements a model trained with Edge Impulse that can detect four common types of pipe problems: cracks, root intrusion, obstructions, and displacement. With this feature, a fault in a pipe can be reported to a central IoT hub in nearly real-time.
Finally, the Portena H7 can enter a sleep mode where it saves enough power to run off a single 5V, 2.4A battery for an extended period.
To read more about the TinySewer project, you can visit Huy’s write-up here. A demo of it in action can be seen below.
We’re proud to announce the latest addition to our industrial range, the new Portenta H7 Lite. Designed for developers who want the computational power of Arduino Pro’s Portenta H7 flagship, but don’t need the video output, additional security features or connectivity.
The Portenta H7 Lite is a streamlined, tightly targeted solution for AI applications and low-latency control projects. Perfect for everything from high-end industrial machinery to laboratory equipment and mission-critical devices.
The Portenta H7 Lite offers top performance, reliability and versatility. A cost-effective solution when you don’t need all the features of the Portenta H7.
The Portenta H7 Lite is a perfect fit for lots of applications.
Crowded radio environments or any situations where you need to have an Ethernet connection, or you don’t need to connect to the Internet.
Robotics controller systems and other machine operations that don’t require the H7’s high-resolution video interface.
Any projects where security is important, but not critical as the Portenta H7 Lite features one secure element, rather than two.
For your next Industry 4.0 IoT project, all you have to do is choose which Portenta board suits your needs best. You’ve got a great new choice, and we have you covered! Maximize the performance and power balance of your smart solutions with Portenta H7 and H7 Lite.
Python support for three of the hottest Arduino boards out there is now yours. Through our partnership with OpenMV, the Nano RP2040 Connect, Nano 33 BLE and Nano 33 BLE Sense can now be programmed with the popular MicroPython language. Which means you get OpenMV’s powerful computer vision and machine learning capabilities thrown in.
OpenMV IDE and MicroPython Editor
While you can’t use Python directly with the Arduino IDE, you can use the OpenMV editor, and its version of MicroPython. From the editor, you can install MicroPython and load your scripts directly to the supported Arduino boards.
MicroPython is a great implementation of the full Python programming language, designed to run on microcontrollers. There’s extensive documentation all across the web, which is another huge advantage of learning and using Python for your Arduino projects.
There are so many reasons to get excited about MicroPython for these new Arduino boards. To name a few…
OpenMV’s machine learning and computer vision tools.
Great for computer science education.
Easy for web developers and coders to switch from other platforms to Arduino.
Huge number of MicroPython libraries, tutorials, guides and support online.
Simple to upgrade hardware as project demands increase (eg, upgrade from a Nano RP2040 Connect to a Portenta H7).
There are also lots of Arduino + Python projects that have been posted over the years. Now you can add the Nano devices to those projects and expand on them with their new MicroPython capabilities.
Get Started with Python on Arduino
To help you get cracking, we’ve put together a few guides for each of the supported Arduino boards. The Portanta H7 already supports MicroPython, but we’ve included it below for the sake of completion.
If it’s the first time you’ve used Python on your Arduino board, you’ll need to follow a few steps to get everything working together. Depending on which board you’re using, you might need to update the bootloader to make it compatible with OpenMV. Then you can connect to the board to upload the latest firmware and make it compatible with the editor.
There are guides to take you through the process for each board, and it’s not a complex task. Once completed, your boards will be ready to program them using MicroPython.
These simple tutorials will get you moving quickly.
Furthermore, you can find a few examples of MicroPython scripts you can upload and run on the various boards, too. It’s a great way to test the Python waters with your Arduino boards, and pick up a couple of hints and tips on using the language.
If you’ve got any resources, hints or tips of your own when it comes to learning or using Python, please do share them with the community! We want to hear all about your experiences, and any projects you build using Arduino and Python together.
We’ll keep you updated as we add more documentation and tutorials for MicroPython over on Arduino Docs, so keep an eye out for those.
We’re excited to announce that Arduino has partnered with Altium and the IPC Education Foundation (IPCEF) to launch a student electronics design challenge to engage, educate, and enhance PCB design capabilities while developing STEM solutions to environmental challenges.
The Innovation for Environmental Change 2021 International Student Design Competition (#PCBeTheChange) encourages student teams to help address common environmental concerns using Altium’s educational tools with Arduino hardware. Teams from high schools and colleges will be using Altium’s Upverter Modular PCB design software and the Portenta H7 to create a prototype design that will improve the environment in each team’s respective local area. The students will be challenged to tackle one or more environmental concerns, such as air pollution, water quality and solar energy capture.
“At Arduino, we believe that it is very crucial to empower scientists of the future to address common challenges of our time using technology. We’re delighted to partner with Altium LLC and the IPC Education Foundation in the Innovation for Environmental Change 2021 International Student Design Competition; this competition really aligns with our goal of creating the next generation of STEM solutions.” — Lotte Nørregaard Andersen, Head of Arduino Education
Participating teams can enter the design challenge while harnessing Altium’s Upverter Education training modules plus the Upverter Modular tool. Altium features multiple educational initiatives designed to support high school STEM teachers and students, along with programs to support college students and industry professionals.
Teams will be eligible to win cash prizes for each category: high school and college: $1,500 (1st place), $750 (2nd place) and $500 (3rd place), free access to IPC APEX EXPO in San Diego, California from January 25th-27th, 2022 as well as virtual access to AltiumLive 2022 CONNECT, co-located alongside the IPC APEX EXPO at the San Diego Convention Center. Designs will be featured on display at the IPC Design Booth; awards will be presented at the IPC APEX EXPO STEM Outreach Event.
Registration for #PCBeTheChange is now open and runs through Friday, October 1st. Team designs must be received online by Friday, November 19th. Competition winners will be announced on Friday, December 17th, followed by virtual presentations for the first place and runner-up entrants.
Enhance your Arduino development with fast and easy debugging from Segger
Arduino Team — August 12th, 2021
Arduino has partnered with Segger to further support developers in creating their own embedded systems, implementing compatibility of Segger debugging solutions with Portenta boards.
Debuggers are the scalpel that allows a developer to dissect any application code running on embedded hardware. This versatile tool helps the programmer to halt programs at specific points, inspect values stored in memory units, modify CPU registers and enter test data to narrow down on buggy pieces of code. This tool comes in handy when you want to locate malfunctioning code and fix faulty program execution.
J-Link debug probes are the most popular choice for optimizing the debugging and flash programming experience. Among the key benefits are:
Record-breaking flashloaders, up to 3MB/s RAM download speed.
Unlimited Flash Breakpoints feature allows the user to set an unlimited number of breakpoints when debugging in flash memory.
Wide range of CPUs and architectures supported; in fact, everything from single 8051 to mass market Cortex-M to high-end cores like Cortex-A (32- & 64-bit).
Direct interface with SPI flashes, without the need of a CPU between J-Link and the SPI flash.
Supported by major IDEs.
In the meantime, we’re working to make the Arduino IDE 2.0 compatible with Segger debugger solutions.
To quickly get started, check out our new tutorials on the Portenta Breakout and MKR boards. You’ll learn how to debug your Arduino sketch by connecting Portenta Breakout to the Segger J-link device and using the Ozone debugger and performance analyzer.
Ozone is Segger’s full-featured graphical debugger for embedded systems. Thanks to features such as trace, code profiling and code coverage analysis, it’s also an extremely powerful performance analyzer. Ozone supports the debugging of any embedded application on C/C++ source and assembly level. It can load applications built with any toolchain/IDE and even debug the target’s resident application without any source. Ozone includes all well-known debug controls and information windows, while making use of the best performance of J-Link debug probes. The user interface is highly intuitive, yet fully configurable. Each window can be moved, re-sized and docked to fit every developer’s needs.
There are four different J-Link models already available on the Arduino Store:
J-LINK BASE COMPACT: USB powered JTAG debug probe supports a large number of CPU cores. Based on a 32-bit RISC CPU, it can communicate at high speed with supported target CPUs.
J-LINK PLUS COMPACT: With this compact version of the J-Link PLUS, users have an unlimited number of flash breakpoints. Mounts securely and unobtrusively into development and end user equipment
J-LINK EDU: Reserved for educational purposes, the J-LINK EDU offers the same functionalities as the J-Link BASE. It’s been designed to allow students, educational facilities and hobbyists access to top of the line debug probe technology.
J-LINK EDU MINI: The smallest J-Link debugger, intended for non-commercial use.
To connect the Portenta boards with J-Link debuggers, there are two adapters available: Segger’s 50-Mil 10-Pin Patch Adapter and J-Link 19-pin Cortex-M Adapter. The 50-Mil 10-Pin Patch Adapter converts the standard 20 pin 0.1″ connector to the standard 10-pin 0.05″ Cortex-M connector. This allows custom connections/wiring between the 20-pin and 10-pin side.
The 19-Pin Cortex-M Adapter allows JTAG, SWD, and SWO connections between J-Link and Cortex-M based target hardware systems. It adapts from the 20-pin 0.1” JTAG connector to a 19-pin 0.05” Samtec FTSH connector as defined by Arm.
For more information and tech specs, please check out the Segger items in the store.
Arduino Pro is introducing a powerful new member of the Portenta product family, the Portenta Machine Control. It’s a fully-centralized, low-power, industrial control unit able to drive equipment and machinery. Plus, you can program it using the Arduino framework or other embedded development platforms.
Thanks to its computing power, the Portenta Machine Control enables a wide range of predictive maintenance and AI use cases. It enables the collection of real-time data from the factory floor, while supporting remote control of equipment, including from the cloud.
Key benefits include:
Shorter time-to-market
Enhance existing products
Add connectivity for monitoring, as well as control
Each I/O pin can be configured, so you can tailor it to your needs
Make equipment smarter, as well as AI-ready
Provide security and robustness from the ground up
Open new business model opportunities (such as servitization)
Interact with your equipment with advanced human-machine interfaces (HMI)
Modular design for adaptation, expansion and upgrades
Business as a Service
The Portenta Machine Control allows companies to enable new business-as-a-service models. You can monitor customer usage of equipment for predictive maintenance while gathering valuable production data.
The device enables industry standard soft-PLC control. Because of this, it’s able to connect to a range of external sensors and actuators. For example, the following options are all available.
Isolated digital I/O, 4-20mA compatible analog I/O
Three configurable temperature channels
Dedicated I2C connector.
Multiple choices are available for network connectivity, including USB, Ethernet and WiFi and BLE. Furthermore, it offers impressive compatibility through industry specific protocols such as RS485. All I/O are protected by resettable fuses, but on-board power management ensures maximum reliability in harsh environments.
The Portenta Machine Control core runs an Arduino Portenta H7 microcontroller board. This is a highly reliable design operating at industrial temperature ranges (-40 °C to +85 °C). Firstly, it boasts a dual-core architecture that doesn’t require any external cooling. Secondly, thanks to this versatile processor, you can also connect external human-machine interfaces. These include displays, touch panels, keyboards, joysticks and mice to enable on-site configuration of state machines and direct manipulation of processes.
The Portenta Machine Control’s design addresses a large variety of use cases. It’s possible to configure a selection of the I/O pins in software. Because of this, it stands out as a powerful computer to unify and optimize production where one single type of hardware can serve all your needs.
Additional Portenta Machine Control Features
Furthermore, it offers these other outstanding features.
Industrial performance leveraging the power of Arduino Portenta boards
DIN rail compatible housing
Push-in terminals for fast connection
Compact size (170 x 90x 50 mm)
Reliable design, operating at industrial temperature rates (-40 °C to +85 °C) with a dual-core architecture and no external cooling
Embedded RTC (real time clock), for perfect synchronization of processes
Leverage embedded connectivity without any external equipment
CE, FCC and RoHS certified
The Portenta Machine Control can be used in multiple industries, across a wide range of machine types. For example, labelling machines, form and seal machines, cartoning machines, gluing machines, electric ovens, industrial washers and dryers, mixers and more.
As a result, adding the Portenta Machine Control to your existing processes mean you become the owner of your own solutions in the market of machines.
The Portenta Machine Control is now available for €279/$335.
We are pleased to announce the launch of the new Arduino Portenta Breakout, designed for developing hardware projects, testing, and debugging on Portenta family boards.
The Portenta Breakout exploits all the capabilities of the input and outputs, making all high density connectors’ signals individually accessible.
The Portenta Breakout reduces development time for industrial grade solution automation based on the Portenta line. Designed to help the hardware engineers and makers who want to develop a proprietary device for Portenta family boards or interfacing external devices to the Portenta family boards (e.g. the Portenta H7). It is now quick and easy to connect and test external hardware components and devices in the lab using all the high density connectors’ signals of the Portenta individually.
Rapid development for machine vision
Connectivity to the OpenMV Global Shutter Camera Module is provided on the Portenta Breakout, allowing for rapid development of machine vision applications alongside the Portenta family.
Test external hardware and devices
The Portenta Breakout enables easy debugging through the JTAG connector and allows for inspection of the bus lines through the breakout pins. In addition to the breakout pins, the Portenta Breakout features Ethernet, USB and SD sockets, a coin cell, a power button, an external power supply, an OpenMV camera socket, and configurable boot selection modes.
Features include:
Power ON button
Boot mode DIP switch
Connectors
USBA
RJ45 GBit Ethernet
MicroSD card
OpenMV shutter module
MIPI 20T JTAG with trace capability
Power
CR2032 RTC lithium battery backup
External power terminal block
I/O
Break out all Portenta high density connector signals
Male/female HD connectors for interposing breakout between Portenta and shield to debug signals
Beyond use in the development lab, the Portenta Breakout can act as a first point of entry for educating technicians in industrial grade control and embedded systems.
The new Portenta Breakout is now available on the Arduino Store.
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.
“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,” saysAndrea Richetta, Arduino Pro BU leader.
The Ethernet version of the Arduino Portenta Vision Shield is now available for 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 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 operating 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.
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