som News - Page 58 of 78 - CNX Software

August 1, 2017 by Jean-Luc Aufranc (CNXSoft) - 1 Comment

Realtek RTL8195AM Ameba WiFi + NFC Module Sells for $9 Shipped

Last year, Realtek Ameba IoT SoCs and development kits launched with boards such as Ameba Arduino, and later, the family got some buzz thanks to $2 RTL8710AF modules like Pine64 Padi IoT stamp, which looked competitive priced against ESP8266 SoC, and featuring an ARM Cortex-M3 core. Back to 2017, ESP8266 appears to still be the preferred platform for makers, and the community around Reatek Ameba processor is relatively small, but maybe the solutions are being integrated into commercial products rather than hobbyists project. Today, as I browsed the web, I noticed that are also some Realtek RTL8195AM module with WiFi, and NFC starting with an “Realtek Ameba-RTL8195AM WiFi & NFC Module” I first found on DFrobot for $15 per unit, but after spending a bit more time searching, I ended finding what looks like the same model for $8.99 including shipping on IC Station. RTL8195AM module (MJIOT-AMB-02) specifications: SoC – […]

July 31, 2017 by Jean-Luc Aufranc (CNXSoft) - No Comments

Acme Systems Introduces RoadRunner Microchip SAMA5D21 Cortex-A5 SoM and Berta D2 Evaluation Board

Acme Systems has been designing compact and relatively inexpensive systems-on-module based on Atmel (now Microchip) processor such as their Aria G25 or Arietta G25 modules. The company has now launched RoadRunner system-on-module (SoM) powered by Microchip SAMA5D21 Cortex A5 processor, as well as the corresponding Berta D2 development kit to get started with the SoM. RoadRunner (D2) SoM specifications: SoC – Microchip SAMA5D27 ARM Cortex A5 processor @ 500MHz System Memory – 256 MB DDR3L RAM Storage – 16 MByte QSPI flash memory (optionally none, 128 MB or 256 MB) Board to Board Connectors – 2x 100-pin connectors pitch 0.4 mm with all the CPU signals Power Supply – 3.3V DC Dimensions – 40 x 30 x 3.5mm Weight- 5g Temperature Range – -40° to +85°C RoHS compliant The module is said to be “fully supported inside the Linux Kernel main stream” with “all the kernel drivers are fully open […]

July 24, 2017November 21, 2018 by Jean-Luc Aufranc (CNXSoft) - 5 Comments

Variscite DART-6UL SoM, an Alternative to Intel Edison Module

Intel recently announced it will discontinue manufacturing and selling all SKUs of the Intel® Edison compute modules and developer kits. The initial version of Edison was released in the beginning of 2014, with a second version being released by the end of 2014. It was intended for the IoT market, with dimensions of 35.5x25x3.9mm. The Edison features an Intel Atom processor, consisting of two Atom Silvermont cores running at 500MHz. It includes a fixed configuration of 1GB integrated RAM, and 4GB eMMC flash on-board. Dual-band (2.4GHz and 5GHz) Wi-Fi, Bluetooth 4.0 and USB controllers complete the package. According to Intel’s announcement, the last shipment of Edison family boards is planned for December 2017. This announcement will have a critical impact on companies that already integrated the Edison board in their products, as well as the many companies that engaged in the development process of integrating the Edison board into their products. While some of these companies are rushing to place […]

July 21, 2017July 21, 2017 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

Onion Omega2S and Omega2S+ Linux WiFi Modules Launched for OEMs

Onion Omega2 and Omega2+ are tiny WiFi IoT development boards powered by Mediatek MT7688 MIPS processor running LEDE – OpenWrt fork – that sold for just $5 and up in Kickstarter, but are now selling for $7.50 and $9 respectively. The board also support various add-on boards, and a great for evaluation and various projects. But they may not be ideal for people who want to integrate the technology into their products, and that’s why the company have just launched Omega2S and Omega2S+ with about the same specifications, but in a package more suitable to be integrated into products for mass production. Onion Omega2S/2S+ specifications: WiSoC – Mediatek MT7688 MIPS processor @ 580 MHz System Memory / Storage Omega2S – 64MB DDR2 / 16MB flash Omega2S+ – 128MB DDR2 / 32 MB flash Connectivity – 802.11 b/g/n WiFi with u.FL antenna connector Baseboard Interface – Half holes (aka castellated holes) […]

June 29, 2017 by Jean-Luc Aufranc (CNXSoft) - No Comments

Canonical Releases Ubuntu Core 16 for Raspberry Pi 3 Compute Module

Now that Canonical has refocused its development efforts on Cloud and IoT, Ubuntu Core has become even more important for the company, which has just released Ubuntu Core 16 for the Raspberry Pi 3 Compute Module, which is better suited for industrial projects than Raspberry Pi boards, for example thanks to the more resilient built-in storage of CM3 module. Ubuntu Core was already supported on Raspberry Pi 2 & 3, Intel Joule, DragonBoard 410c, Intel NUC, and Samsung Artik boards, as well as KVM to run Ubuntu Core in a virtual environment. One of the advantages of running Ubuntu Core is the availability of snaps and branded app stores, making it easy to provide updates, and promote app for the platform. Screenly is one commercial project that will take advantage of Ubuntu Core on CM3 module for their digital signage applications. You’ll find instructions to get started with Ubuntu Core […]

June 2, 2017 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

Compulab’s Miniature “Bend & Fold” UCM-iMX7 System-on-Module Could Fit into a Watch

Many companies are still releasing NXP – soon to be Qualcomm – i.MX 6/7 system-on-modules, but I don’t cover all of them, since we have already many to choose from. But Compulab’s latest UCM-iMX7 SoM differentiates itself by it size, using the company’s “Ultra-compact Multilevel Module” (UCMM) technology, to pack NXP i.MX7 processor, 2GB RAM, 64 GB eMMC flash, and a wireless module into a 30 x 27 x 8 mm volume that could potentially fit into something as small as a watch. The picture above clearly shows how UCMM technology works with the module comprised of two rigid PCBs and one flexible layer routing signals between the two allowing to bend and fold the rigid parts together to form a vertical stack. The principle could be extended to more PCBs and flexible layers, sof for example you could have four PCBs with three flexible layers in future / custom […]

May 31, 2017June 1, 2017 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

Boardcon Introduces Rockchip RK3399 PICO3399 CPU Module and EM3399 Baseboard

There’s a limited number of boards based on Rockchip RK3399 processor, with the easiest to work with (for non-Chinese readers) probably being Firefly-RK3399. Shenzhen Xunlong is working on their own Orange Pi RK3399 board, 9Tripod released their X3399 SoM and devkit, Boardcon has also launched their own RK3399 SoM (system-on-module) and baseboard solution with respectively PICO3399 CPU module and EM3399 board. PICO3399 SoM specifications: SoC – Rockchip RK3399 hexa core processor with a dual ARM Cortex-A72 core cluster @ up to 2.0 GHz, quad ARM Cortex-A53 cluster, and ARM Mali-T860MP4 GPU System Memory – 4GB LPDDR3 Storage – 8GB eMMC flash 314-pin edge connector with 2x USB2.0 Host, 2x USB3.0 or 2x Type-C, UART, MIPI, GbE, HDMI in&out, Audio, I2C, I2S, PCI-E, SD/MMC/SDIO, GPIO, eDP.. Power Supply – 5V Dimensions – 82 x 50mm (8 layers) The company provides support for Android6.0.1 and Debian for the module. If the info […]

May 24, 2017May 24, 2017 by Jean-Luc Aufranc (CNXSoft) - No Comments

Getting Started with OpenCV for Tegra on NVIDIA Tegra K1, CPU vs GPU Computer Vision Comparison

This is a guest post by Leonardo Graboski Veiga, Field Application Engineer, Toradex Brasil Introduction Computer vision (CV) is everywhere – from cars to surveillance and production lines, the need for efficient, low power consumption yet powerful embedded systems is nowadays one of the bleeding edge scenarios of technology development. Since this is a very computationally intensive task, running computer vision algorithms in an embedded system CPU might not be enough for some applications. Developers and scientists have noticed that the use of dedicated hardware, such as co-processors and GPUs – the latter traditionally employed for graphics rendering – can greatly improve CV algorithms performance. In the embedded scenario, things usually are not as simple as they look. Embedded GPUs tend to be different from desktop GPUs, thus requiring many workarounds to get extra performance from them. A good example of a drawback from embedded GPUs is that they are […]