The Banana Pi BPI-CanMV-K230D-Zero is a compact and low-power single-board computer built around the Kendryte K230D dual-core XuanTie C908 RISC-V chip with an integrated third-generation Knowledge Process Unit (KPU) for AI computation. It follows the form factor of the Raspberry Pi Zero or Raspberry Pi Zero 2W board and targets IoT and ML applications. The SBC comes with 128MB of LPDDR4 RAM and uses a microSD card slot for storage. Additional features of this board include dual MIPI-CSI camera inputs for 4K video, a 40-pin GPIO header for I2C, UART, SPI, PWM, and more. Wireless features include 2.4GHz WiFi, USB 2.0 with OTG, and microphone support. These features make this SBC suitable for applications such as AI tasks such as image, video, and audio processing. Banana Pi BPI-CanMV-K230D-Zero Specifications SoC – Kendryte K230D CPU CPU1 – 64-bit RISC-V processor @ 1.6GHz with RVV 1.0 support CPU2 – 64-bit RISC-V processor […]
Orange Pi 4A low-cost octa-core SBC is powered by Allwinner T527 Cortex-A55 AI SoC with a 2TOPS NPU
Orange Pi 4A is a new low-cost credit card-size single board computer (SBC) powered by an Allwinner T527 octa-core Cortex-A55 processor with a 2TOP NPU and offered with either 2GB or 4GB RAM. The board also comes with multiple storage options: a 128 or 256Mbit SPI NOR flash for the bootloader, an eMMC socket for up to 128GB modules, an M.2 socket for NVMe SSDs, and a microSD card slot. It’s also equipped with four USB 2.0 ports, a gigabit Ethernet port, three display interfaces (HDMI, MIPI DSI, eDP), two camera interfaces, and a 40-pin “Raspberry Pi” header. The Orange Pi 4A is somewhat equivalent to an octa-core Raspberry Pi 3/4 with some extra features. Orange Pi 4A specifications: SoC – Allwinner T527 CPU Octa-core Arm Cortex-A55 @ up to 1.8GHz (four cores) and up to 1.42 GHz (four cores) XuanTie E906 RISC-V core @ 200MHz GPU – Arm Mali-G57 […]
Giveaway Week 2024 winners announced!
We’re now ready to announce the winners of CNX Software’s Giveaway Week 2024. We offered some of the review samples we tested (and some we did not test) in the last year, and for the fourth year running, RAKwireless also gave away two IoT development kits shipped directly to winners. This year’s prizes also included a RISC-V motherboard, a 3D depth camera, a few Arm development boards, two touchscreen displays, and an Alder Lake-N mini PC/router. All those products can be seen in the photo, minus some accessories. You’ll find more than seven devices because we organized the third Giveaway Week on CNX Software Thailand simultaneously with four prizes. We had seven winners on CNX Software: Jupiter RISC-V mini-ITX motherboard – François-Denis, Canada Orbbec Femto mega 3D depth and 4K RGB camera – Reifu, Japan RAKwireless Blues.ONE LoRaWAN, LTE-M, and NB-IoT devkit – OldCrow, Portugal Mixtile Core 3588E development kit […]
Forlinx launches NXP i.MX 95 SoM and development board with 10GbE, CAN Bus, RS485, and more
Forlinx FET-MX95xx-C is a system-on-module (SoM) based on NXP i.MX 95 SoC with up to six Cortex-A55 cores, an Arm Cortex-M7 real-time core clocked at 800 MHz, an Arm Cortex-M33 “safety” core clocked at 333 MHz, and equipped with 8GB LPDDR4x and 64GB eMMC flash. The company also provides the feature-rich OK-MX95xx-C development board based on the i.MX 95 module with a wide range of interfaces such as dual GbE, a 10GbE SFP+ cage, terminal blocks with RS485 and CAN Bus interface, three USB Type-A ports, two PCIe slots, and more. Forlinx FET-MX95xx-C system-on-module Specifications: SoC – NXP i.MX 9596 CPU 6x Arm Cortex-A55 application cores clocked at 1.8 GHz (industrial) with 32K I-cache and D-cache, 64KB L2 cache, and 512KB L3 cache Arm Cortex-M7 real-time core clocked at 800 MHz Arm Cortex-M33 safety core clocked at 333 MHz GPU – Arm Mali-G310 V2 GPU for 2D/3D acceleration with support […]
Giveaway Week 2024 – Orbbec Femto mega 3D depth and 4K RGB camera
The second prize of Giveaway Week 2024 is the Orbbec Femto Mega 3D depth and 4K RGB camera powered by an NVIDIA Jetson Nano module and featuring Microsoft ToF technology found in Hololens and Azure Kinect DevKit. The camera connects to Windows or Linux host computers through USB or Ethernet and is supported by the Orbbec SDK with the NVIDIA Jetson Nano running depth vision algorithms to convert raw data to precise depth images. I first reviewed the Orbbec Femto Mega using the Orbbec Viewer for a quick test connected to an Ubuntu laptop (as shown above) before switching to a more complex demo using the Orbbec SDK for body tracking in Windows 11. Although it was satisfying once it worked, I struggled quite a lot to run the body tracking demo in Windows 11, so there’s a learning curve, and after you have this working, you’d still need to […]
Orbbec Gemini 335Lg 3D depth and RGB camera features MX6800 ASIC, GMSL2/FAKRA connector for multi-device sync on NVIDIA Jetson Platforms
The Orbbec Gemini 335Lg is a 3D Depth and RGB camera in the Gemini 330 series, built with a GMSL2/FAKRA connector to support the connectivity needs of autonomous mobile robots (AMRs) and robotic arms in demanding environments. As an enhancement of the Gemini 335L, the 335Lg features a GMSL2 serializer and FAKRA-Z connector ensuring reliable performance in industrial applications requiring high mobility and precision. The Gemini 335Lg integrates with the Orbbec SDK, enabling flexible platform support across deserialization chips, carrier boards, and computing boxes, including NVIDIA’s Jetson AGX Orin and AGX Xavier. The device can operate in both USB and GMSL (MIPI) modes, which can be toggled via a switch next to the 8-pin sync port, with GMSL as the default. The GMSL2/FAKRA connection provides high-quality streaming with synchronized multi-device capability, enhancing adaptability for complex setups. Previously, we covered several 3D cameras from Orbbec, including the Orbbec Femto Mega 3D […]
OpenUC2 10x is an ESP32-S3 portable microscope with AI-powered real-time image analysis
Seeed Studio has recently launched the OpenUC2 10x AI portable microscope built around the XIAO ESP32-S3 Sense module. Designed for educational, environmental research, health monitoring, and prototyping applications this microscope features an OV2640 camera with a 10x magnification with precise motorized focusing, high-resolution imaging, and real-time TinyML processing for image handling. The microscope is modular and open-source making it easy to customize and expand its features using 3D-printed parts, motorized stages, and additional sensors. It supports Wi-Fi connectivity with a durable body, uses USB-C for power and swappable objectives make it usable in various applications. Previously we have written about similar portable microscopes like the ioLight microscope and the KoPa W5 Wi-Fi Microscope, and Jean-Luc also tested a cheap USB microscope to read part number of components. Feel free to check those out if you are looking for a cheap microscope. OpenUC2 10x specifications: Wireless MCU – Espressif Systems ESP32-S3 CPU […]
Waveshare ESP32-S3 ETH board provides Ethernet and camera connectors, supports Raspberry Pi Pico HATs
Waveshare has recently launched the ESP32-S3-ETH development board with an Ethernet RJ45 jack, a camera interface, and compatibility with Raspberry Pi Pico HAT expansion boards. This board includes a microSD card interface and supports OV2640 and OV5640 camera modules. Additionally, it offers an optional Power over Ethernet (PoE) module, making it ideal for applications such as smart home projects, AI-enhanced computer vision, and image acquisition. Previously, we have written about LILYGO T-ETH-Lite, an ESP32-S3 board with Ethernet and optional PoE support. We have also written about LuckFox Pico Pro and Pico Max, Rockchip RV1106-powered development boards with 10/100M Ethernet and camera support. The ESP32-S3-ETH board is like a combination of those two, where you get an ESP32-S3 microcontroller, Ethernet (with optional PoE), and a camera interface. ESP32-S3 ETH development board specifications: Wireless module ESP32-S3R8 MCU – ESP32-S3 dual-core LX7 microprocessor @ up to 240 MHz with Vector extension for machine learning Memory – 8MB PSRAM Storage […]