open source

Posted on by Debashis Das - 1 Comment on IcyBlue Feather V2 board features Lattice Semi iCE5LP4K FPGA in Adafruit Feather form factor

IcyBlue Feather V2 board features Lattice Semi iCE5LP4K FPGA in Adafruit Feather form factor

IcyBlue Feather V2 FPGA Bo

The IcyBlue Feather V2 from Oak Development Technologies is a powerful and compact dev board that combines the Lattice Semi iCE5LP4K FPGA with the Adafruit Feather form factor. This unique combination allows this FPGA board to be compatible with the Adafruit FeatherWings ecosystem, providing functionalities such as additional GPIOs, displays, connectivity modules, and more. This new board features a USB-C port for powering and programming the FPGA. Additionally, it features two hardware I2C and SPI blocks that do not consume FPGA resources while operating. The board also includes 22 accessible GPIOs, a bright RGB LED for status indication, and two user-programmable LEDs. Previously, we have discussed many similar tiny FPGA-based development boards, such as the Lattice Semi MachXO2 FPGA, tinyVision.ai Pico-Ice board, Silicon Witchery S1, and ULX3S Education Board. Feel free to explore these if you are looking for similar options. IcyBlue Feather V2 Specification FPGA – Lattice Semi iCE40 […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 1 Comment on LattePanda Mu is an x86 Compute Module based on Intel Processor N100 CPU

LattePanda Mu is an x86 Compute Module based on Intel Processor N100 CPU

LattePanda Mu x86 Compute Module N100

The LattePanda Mu is a compute module/system-on-module based on the popular Intel Processor N100 quad-core Alder Lake-N processor that can run Windows or Linux and aims to provide a more powerful solution than the Raspberry Pi 5 and upcoming Raspberry Pi CM5 (Compute Module 5). The LattePanda Mu does not follow any SoM standard and instead comes in a custom 69.6 x 60mm form factor using a 260-pin SO-DIMM edge connector. The module is equipped with 8GB RAM and 64GB eMMC flash by default, and the interfaces exposed through the edge connector (PCIe, USB, Ethernet, HDMI…) make it suitable for a range of applications such as IoT, robotics, digital signage, and edge computing through custom carrier boards. LattePanda Mu specifications: SoC – Intel Processor N100 quad-core Alder Lake-N processor @ up to 3.4 GHz (Turbo) with 6MB cache, 24EU Intel HD graphics @ 750 MHz; TDP: 6W System Memory – […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 6 Comments on Kodi 21.0 Omega released with FFmpeg 6, LG webOS support, and more

Kodi 21.0 Omega released with FFmpeg 6, LG webOS support, and more

Kodi 21.0 Omega

Kodi 21.0 “Omega” has just been released with the latest version of the open-source media center adding 3,750 commits since the release of Kodi 20.0 “Nexus” on January 15th, 2023 that notably added AV1 hardware decoding in Android and x86. The new Kodi 21.0 version updates FFmpeg to version 6, adds native support for LG webOS televisions after some reverse-engineering, and implements new features such as Dolby Vision on-the-fly profile conversion in Android, native windowing in macOS that does not rely on the SDL library, and an in-game player viewer to view which game port each player’s controller is currently connected to. Most of the changes were not user-facing and instead, were under-the-hood improvements to the stability, performance, and security of Kodi. You’ll find more changes in the Kodi 21.0 Alpha/Beta/RC pre-release announcements and the complete list of changes on GitHub.  That also means Kodi v22 “P*” development has started. […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 9 Comments on Google’s Jpegli open-source library can compress high quality images 359 more than traditional JPEG codecs

Google’s Jpegli open-source library can compress high quality images 35% more than traditional JPEG codecs

Jpegli ELO score vs other JPEG libraries

Google has released the Jpegli open-source library for advanced JPEG coding that maintains backward compatibility while delivering an up to 35% compression ratio improvement at high-quality compression settings. Google Research has been working on improving the compression of data (Brotli), audio (e.g. Lyra V2), and images with a project such as WebP for many years in order to speed up the web and make it consume less bandwidth for dollar savings and lower carbon emissions.  Jpegli is their latest project and aims to improve the compression ratio of legacy JPEG files on systems were modern compression such as WebP may not be available or desirable. Jpegli highlights: Support both an encoder and decoder complying with the original JPEG standard (8-bit) and offering API/ABI compatibility with libjpeg-turbo and MozJPEG. Focus on high-quality results with up to 35% better compression ratio. Just as fast as libjpeg-turbo and MozJPEG. Support for 10+ bits […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 11 Comments on Avaota A1 open-source hardware SBC is powered by Allwinner T527 octa-core Cortex-A55 SoC

Avaota A1 open-source hardware SBC is powered by Allwinner T527 octa-core Cortex-A55 SoC

Avaota A1 open-source hardware Allwinner T527 SBC

We’ve recently covered MYiR Tech MYD-LT527 industrial development board based on Allwinner T527 octa-core Cortex-A55 AI SoC and noted Orange Pi is working on one that should even get mainline Linux support. The Avaoto A1 offers another Allwinner T527 hardware option with an SBC design that’s fully open-source. The board is equipped with up to 4GB RAM, 128GB eMMC flash, HDMI and DisplayPort video outputs, two gigabit Ethernet ports, a WiFi 6 and Bluetooth 5.4 module, a few USB ports, a 3.5mm audio jack and the usual 40-pin GPIO header for expansion. Avaota A1 specifications: SoC – Allwinner T527 (or Allwinner A527 with Avaota A1C board, not sure what the differences are between the two) CPU Octa-core Arm Cortex-A55 processor with four cores @ 1.80 GHz and four cores @ 1.42GHz XuanTie E906 RISC-V core up to 200 MHz GPU – Arm Mali-G57 MC1 GPU with support for OpenGL ES […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 4 Comments on BeagleY-AI SBC features TI AM67A vision processor with 4 TOPS AI accelerators

BeagleY-AI SBC features TI AM67A vision processor with 4 TOPS AI accelerators

Texas Instruments AM67A single board computer

The BeagleBoard.org Foundation’s BeagleY-AI is an open-source hardware, credit card-sized SBC powered by a Texas Instruments AM67A quad-core Cortex-A53 vision processor with various programmable blocks capable of delivering up to 4 TOPS for AI algorithms. The board ships with 4GB RAM, relies on a microSD card slot for storage and OS booting, implements gigabit Ethernet, WiFi 6, and Bluetooth 5.4 connectivity, and can drive up to three displays via micro HDMI, OLDI (LVDS), and MIPI DSI interfaces. The BeagleY-AI also comes with two MIPI CSI camera interfaces, four USB 3.0 ports, a USB Type-C port, and a 40-pin GPIO header for expansion. We can also see a 16-pin PCIe FPC connector that looks somewhat similar to the 20-pin PCIe connector on the Raspberry Pi 5 but officially supports PCIe Gen3 x1. BeagleY-AI specifications: SoC – Texas Instruments AM67A (J722S) “vision processor” CPU Quad-core 64-bit Arm Cortex-A53 processor at 1.4GHz Arm […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 2 Comments on LoLRa project – Transmitting LoRa packets without radio using CH32V003, ESP8266, or ESP32-S2 MCU

LoLRa project – Transmitting LoRa packets without radio using CH32V003, ESP8266, or ESP32-S2 MCU

LolRa

The LoLRa project is a firmware-only LoRa transmission open-source project that works without a Semtech radio and instead relies on an I2S or SPI interface (so not exactly bit-banging) to transmit data with microcontrollers such as WCH CH32V003, or Espressif Systems ESP8266 and ESP32-S2 microcontrollers. LoRa is a proprietary protocol by Semtech, but people have been trying to reverse-engineer the LoRa PHY for years, and this culminated with a LoRa GNU Radio SDR implementation last year. But CNLohr found out you don’t even need a radio to send LoRa packets and you can instead use SPI or I2S interfaces from general-purpose microcontrollers to send packets that can be decoded by commercial off-the-shelf LoRa gateways and other chips. The current implementation is designed for the  ITU Region 2 (aka The Americas) targeting the 902-928MHz frequency band, but the code could be changed for Region 1 (EU, Russia, Africa) to target 863-870MHz […]

Posted on by Tomisin Olujinmi - 6 Comments on Arduino Nano Matter board specifications and price announced

Arduino Nano Matter board specifications and price announced

arduino nano matter board

The Arduino Nano Matter is the product of a collaboration between Arduino and Silicon Labs. The Nano Matter board was announced in January and is powered by SiLabs’ MGM240S chip. It offers multiple wireless connectivity options such as Matter, OpenThread, and Bluetooth Low Energy. Support for the Matter standard is the Nano Matter board’s key offering. Matter is an open-source, connectivity protocol that lets smart home devices from different manufacturers interoperate seamlessly. The 45mm x 18mm board leverages dual-mode connectivity, with IEEE 802.15.4 (Thread) for mesh networking and Bluetooth Low Energy for short-range communication. It is targeted at the Internet of Things, home automation, professional automation, environmental monitoring, and climate control applications. Prospective industrial applications include machine-to-machine interoperability, machine status monitoring, and worker status optimization. Arduino Nano Matter specs: MPU – SiLabs MGM240SD22VNA MCU core – 32-bit Arm Cortex-M33 with DSP (digital signal processing) instruction and FPU (floating-point unit) @ […]

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