Raspberry Pi CM3+ based Iono Pi Max industrial controller comes with an impressive number of I/Os

As the Raspberry Pi Compute Module 4 is only expected for next year, companies are still launching products based on Raspberry Pi Compute Module 3+ (CM3+), and Sfera Labs has just introduced Iono Pi Max industrial computer powered by a Raspberry Pi CM3+ system-on-module. Housed in a DIN rail enclosure, the Linux controller offers Fast Ethernet, three USB ports, isolated CAN and serial boards, some analog I/Os, a relay, as well as a real-time clock (RTC), integrated UPS, and more. Iono Pi Max specifications: SoM compatibility Iono Pi Max 3+ – Already fitted with Raspberry Pi Compute Module 3+ (Lite, 8GB, 16GB, or 32GB eMMC version) Iono Pi Max Solo – Barebone model compatible with Raspberry Pi Compute Module 3, CM3 Lite, CM3+ 8GB/16GB32GB, and CM3+ Lite MCU – Microchip SAME54 Cortex-M4F microcontroller with 1 MB flash, 256 KB RAM connected to the Raspberry Pi module via I2C, USB and CAN. It can also take control of all serial ports, […]

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Microchip SAMD21 Machine Learning Evaluation Kits Work with Cartesiam, Edge Impulse and Motion Gestures Solutions

While it all started in the cloud Artificial Intelligence is now moving at the very edge is ultra-low power nodes, and Microchip has launched two SAMD21 Arm Cortex-M0+ machine learning evaluation kits that now work with AI/ML solutions from Cartesiam, Edge Impulse, and Motion Gestures. Bot machine learning evaluation kits come with SAMD21G18 Arm Cortex-M0+  32-bit MCU, an on-board debugger (nEDBG), an ATECC608A CryptoAuthentication secure element, ATWINC1510 Wi-Fi network controller, as well as Microchip MCP9808 high accuracy temperature sensor and a light sensor. But EV45Y33A development kit is equipped with an add-on board featuring Bosch’s BMI160 low-power Inertial Measurement Unit (IMU), while EV18H79A features an add-on board with TDK InvenSense ICM-42688-P  6-axis MEMS. The photo above makes it clear both SAMD21 machine learning evaluation kits rely on the same baseboard with a MikroBus socket connected to either 6DOF IMU 2click or 6DOF IMU 14 click add-on board from MikroElektronika. Both boards are similar, but the latter was just released this […]

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JPEGDEC is a Faster JPEG Arduino Library Designed for 32-bit MCUs

In order to ensure software compatibility, Arduino libraries are supposed to work on various types of hardware from 8-bit microcontrollers with a limited amount of memory to more powerful 32-bit chips like STM32 Arm Cortex-M MCU or ESP32 dual-core Tensilica WiSoC that can access a larger amount of RAM. This is all good, but in some cases, this may affect performance. Larry Bank noticed this when looking for a JPEG viewers for Arduino and only found ones which sacrificed speed to work on MCUs with very little RAM. So he started to work on JPEDDEC JPEG Arduino library optimized for speed and compatible with any MCU with at least 20K of RAM. Optimizations go beyond just loading more data into memory, as Larry explains in a blog post, the library also performs the removal of stuffed bytes, optimize the Huffman decode and DCT parts, and more. Some of the key features of the library include: Support for any MCU with […]

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ODYSSEY-X86J4105 SBC Review with Ubuntu 20.04 – Raspberry Pi & Arduino Headers Tested

We’ve already reviewed ODYSSEY-X86J4105 SBC with Windows 10. When combined with Re_Computer enclosure it’s a typical Intel Gemini Lake mini PC but with a twist: Arduino and Raspberry Pi header. The latter works fine in Windows, but at the time, the Raspberry Pi header does not. So Linux is our only option. I’ve now had time to install and test Ubuntu 20.04 on ODYSSEY-X86J4105 single board computer. I did not install Ubuntu on the internal eMMC flash where Windows 10 resides, but instead on a 128GB M.2 SATA SSD. I’ll first run some usual command to check system information, then run benchmarks, and check whether all features are working before focusing the review on the Arduino and Raspberry Pi headers. ODYSSEY-X86J4105 Ubuntu 20.04 System Info We can check some of the information from the system in the terminal: An Intel Celeron J4105 processor with 8GB RAM and the rootfs on a 127GB partition looks good to me. Features Testing Most […]

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Keyboard FeatherWing Brings Keyboard & Display to Adafruit Feather Boards

Solder Party is a brand new Swedish startup founded by Arturo182 who previously designed several maker boards including the tiny Serpente R2 CircuitPython prototyping board, and its first product, named Keyboard FeatherWing, brings a QWERTY keyboard and 2.6” color LCD with resistive touch screen to any boards compatible with Adafruit Feather for factor. That means you can build a portable device that runs Linux via the  Giant Board, features an FPGA (Orange Crab), or a wide range of connectivity options including WiFi, Bluetooth LE, LoRa, etc… depending on your chosen board. The solution actually brings a bit more than just a keyboard and color, as shown by the list of Keyboard FeatherWing key features: Display – 2.6” 320×240 16-bit color LCD with resistive touch screen (SPI) Storage – MicroSD card slot (SPI) User Inputs controlled by a Microchip SAMD20 MCU QWERTY keyboard (I2C) 5-way button 4 soft tactile buttons Expansion Stemma QT/Qwiic I2C connector Dual-row Adafruit Feather compatible female headers […]

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HeliOS is a Tiny Embedded OS Designed for Arduino Boards

Mannie Peterson (aka FellFromTree) has developed an embedded operating system called HeliOS that’s designed specifically for 8-bit and 32-bit Arduino boards, and can easily be used from the Arduino IDE. HeliOS is said to have only 21 function calls and implements cooperative and event-driven multitasking, task notification/messaging, timers, and memory management. It’s a non-preemptive multitasking kernel so you won’t have to deal with mutexes. The developer explains how scheduling works with HeliOS: HeliOS uses a run-time balanced strategy which ensures tasks with shorter run-times are prioritized over tasks with longer run-times. This ensures all running tasks receive approximately equal total run-time without using context switching. The other multitasking option available in HeliOS is event driven multitasking, which uses the wait/notify and timer interfaces. Mixing cooperative and event driven tasks in HeliOS is not a problem. To give it a try, simply open the Arduino IDE Library Manager, search for “HeliOS” and click install it. There will be some example including […]

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SAMD21 Lite is a Stamp-sized, MikroBus Compatible Cortex-M0+ MCU Board

If you’re a fan of tiny microcontroller boards, you’ll be pleased with BOKRA SAMD21 Lite board powered by Microchip SAMD21 Arm Cortex-M0+ MCU, exposing I/Os in a way compatible with MikroBus socket, and adding a Grove connector for good measure. SAMD21 Lite board specifications: MCU – Microchip ATSAMD21G18A Arm Cortex-M0+ microcontroller @ 48 MHz with 256KB flash memory, 32KB RAM Expansion I/Os MicroBus socket with GPIO, SPI, I2C, USART, PWM, DAC, +5V, +3.3V, GND I2C Grove connector 10-pin I/O header with GPIO, USB 2.0, VIN, GND Misc – Reset button, red power LED, 2x user LED (yellow/green), J1/J2 jumper to enable/disable I2C pull-ups Debugging – 4-pin SWD header Power Supply 5V input via VIN MIC5528500 voltage regulator Output current – 500 mA Dimensions -28.6 x 25.4 mm Despite its tiny size, SAMD21 Lite exposes all expect five I/Os from the microcontroller. The Microbus headers and the I2C Grove connector allow it to support hundreds of add-on boards from MicroElektronika or […]

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Edge Impulse Enables Machine Learning on Cortex-M Embedded Devices

Artificial intelligence used to happen almost exclusively in the cloud, but this introduces delays (latency) for the users and higher costs for the provider, so it’s now very common to have on-device AI on mobile phones or other systems powered by application processors. But recently there’s been a push to bring machine learning capabilities to even lower-end embedded systems powered by microcontrollers, as we’ve seen with GAP8 RISC-V IoT processor or Arm Cortex-M55 core and the Ethos-U55 micro NPU for Cortex-M microcontrollers, as well as Tensorflow Lite. Edge Impulse is another solution that aims to ease deployment of machine learning applications on Cortex-M embedded devices (aka Embedded ML or TinyML) by collecting real-world sensor data, training ML models on this data in the cloud, and then deploying the model back to the embedded device. The company collaborated with Arduino and announced support for the Arduino Nano 33 BLE Sense and other 32-bit Arduino boards last May. The solution supports motion […]

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