NanoCOM-APL Apollo Lake COM Express Module is Designed for Machine Vision & Factory Automation

AAEON has just launched  NanoCOM-APL, a Type 10 COM Express module based on Intel Apollo Lake Atom, Celeron, or Pentium processor with up to 8 LPDDR4 memory, and on-board support for dual MIPI CSI interfaces. The module is designed for machine vision and factory automation, and also features Gigabit Ethernet, optional on-board eMMC flash, PCIe x4, ten USB interfaces, and more. NanoCOM-APL Apollo Lake COM Express Module There are currently three variants of NanoCOM-APL module with the following specifications specifications: SoC NANOCOM-APL-A10-0001 – Intel Pentium N4200 quad core Apollo Lake processor @ 1.10 / 2.50 GHz with Intel HD Graphics 505; 6W TDP NANOCOM-APL-A10-0002 – Intel Celeron N3350 dual core Apollo Lake processor @ 1.10 / 2.40 GHz with Intel HD Graphics 500; 6W TDP NANOCOM-APL-A10-0003 – Intel Atom x7-E3950 quad core Apollo Lake-I processor @ 1.60 / 2.00 GHz with Intel HD Graphics 505; 12W TDP System Memory – A10-0002: 2GB LPDDR4; A10-001 & A10-003: 4GB LPDDR4 Storage – …

AAEON Releases an Intelligent Vending Machine Development Kit based on UP Board

Vending-Machine-Reference-Design

UP board is a low cost development board powered by an Intel Atom x5-Z8350 Cherry Trail processor with 1GB to 4GB RAM, and 16 to 64GB eMMC flash that mostly follows Raspberry Pi 3 form factor. AAEON is now offering an “intelligent vending development kit” featuring UP board together with a vending machine controller (VMC) board, a motor for the machine’s internal mechanisms, a camera, a QR Code device, and all the necessary cables, as well as optional WiFi and Bluetooth modules. Some details about AIOT-MSSP01 Mini SSP Vending control board: “Intel Intelligent Vending Based” Motor Control – 24V or 12V, Supports GPIO, DC, PWM Type, Supports up to 160 DC Motors Support LCD and Keypad Feature I/Os Vending machine specific interfaces / standards 1x MDB (MultiDrop Bus) 1x DEX (Digital EXchange) 1x Protocol A (EXE) 1x 1-WIRE 4-channel ADC x 1 4-channel Relay GPIO for 12V & 5V by switch x 1 2x 16 Bit GPIO 1x 8 Bit …

Google AIY Projects Kits are Easier to Use in 2018 with Raspberry Pi Zero WH and All Accessories Included

Google launched two AIY Projects kits last year with a Voice Kit that took a Raspberry Pi 3 to create a smart speaker, and a Vision kit for hardware accelerated computer vision using a Raspberry Pi Zero W board. Google reports the kits are popular, especially for STEM education,  but educators/parents had to buy the Raspberry Pi boards and micro SD cards themselves, as well as flash the firmware to the cards. So the company decided to redesign both kits to work with the Raspberry Pi Zero WH (RPi Zero W with headers), and include it inside the box with cable and pre-provisioned SD card, so kids can get started faster with experimentation with having to setup the kits. So that means we now have AIY Projects Voice Kit v2 with RPi Zero WH and micro SD card with firmware, as well as  AIY Projects Vision Kit v1.1 with RPi Zero WH, a Raspberry Pi Camera v2, and a micro SD …

VIA SOM-9X20 Module Powers Smart Recognition and Smart Machine Vision Platforms

At the end of last year, VIA launched SOM-9X20 system-on-module powered by Qualcomm Snapdragon 820 processor coupled with 4GB PoP LPDDR4 RAM, 64 GB eMMC flash, as well as WiFi + Bluetooth module, and a GNSS/GPS receiver. The company introduced two software platform for the SoM and development kit at embedded world 2018: a Smart Machine Vision Platform for manufacturing management and control systems, and a Smart Recognition Platform for facial recognition, object detection, people counting & tracking, etc… VIA SOM-9X20 module specifications: SoC – Qualcomm Snapdragon 820 quad- core processor with 2x high-performance Kryo cores up to 2.15GHz, 2x low power Kryo cores up to 1.593GHz, and Adreno 530 GPU supporting OpenGL ES 3.1/ GEP, GL4.4, DX11.3/ 4, OpenCL 2.0, Renderscript-Next System Memory – 4 GB POP LPDDR4 RAM Storage – 64 GB eMMC 5.1/ UFS 2.0 flash On-module Connectivity Wi-Fi 802.11 a/ b/ g/ n/ ac + Bluetooth 4.1 (QCA6174A -1) with two antenna connectors GNSS/GPS RF receiver …

Arm’s Project Trillium Combines Machine Learning and Object Detection Processors with Neural Network Software

We’ve already seen Neural Processing Units (NPU) added to Arm processors such as Huawei Kirin 970 or Rockchip RK3399Pro in order to handle the tasks required by machine learning & artificial intelligence in a faster or more power efficient way. Arm has now announced their Project Trillium offering two A.I. processors, with one ML (Machine Learning) processor and one OD (Object Detection) processor, as well as open source Arm NN (Neural Network) software to leverage the ML processor, as well as Arm CPUs and GPUs. Arm ML processor key features and performance: Fixed function engine for the best performance & efficiency for current solutions Programmable layer engine for futureproofing the design Tuned for advance geometry implementations. On-board memory to reduce external memory traffic. Performance / Efficiency – 4.6 TOP/s with an efficiency of 3 TOPs/W for mobile devices and smart IP cameras Scalable design usable for lower requirements IoT (20 GOPS) and Mobile (2 to 5 TOPS) applications up to …

A Day at Chiang Mai Maker Party 4.0

The Chiang Mai Maker Party 4.0 is now taking place until December 9, and I went there today, as I was especially interested in the scheduled NB-IoT talk and workshop to find out what was the status about LPWA in Thailand. But there are many other activities planned, and if you happen to be in Chiang Main in the next few days, you may want to check out the schedule on the event page or Facebook. I’m going to go though what I’ve done today to give you a better idea about the event, or even the maker movement in Thailand. Booth and activity area should be the same over the 4 days, but the talks, open activity, and workshop will be different each day. Today, people could learn how to solder in the activity area. The even was not really big with manufacturers/sellers like ThaiEasyElec, INEX, or Gravitech closer to the entrance… … and slighter higher up in a …

$45 AIY Vision Kit Adds Accelerated Computer Vision to Raspberry Pi Zero W Board

AIY Projects is an initiative launched by Google that aims to bring do-it yourself artificial intelligence to the maker community by providing affordable development kits to get started with the technology. The first project was AIY Projects Voice Kit, that basically transformed Raspberry Pi 3 board into a Google Home device by adding the necessary hardware to support Google Assistant SDK, and an enclosure. The company has now launched another maker kit with AIY Project Vision Kit that adds a HAT board powered by Intel/Movidius Myriad 2 VPU to Raspberry Pi Zero W, in order to accelerate image & objects recognition using TensorFlow’s machine learning models. The kit includes the following items: Vision Bonnet accessory board powered by Myriad 2 VPU (MA2450) 2x 11mm plastic standoffs 24mm RGB arcade button and nut 1x Privacy LED 1x LED bezel 1x 1/4/20 flanged nut Lens, lens washer, and lens magnet 50 mil ribbon cable Pi0 camera flat flex cable MIPI flat flex …

AWS DeepLens is a $249 Deep Learning Video Camera for Developers

Amazon Web Services (AWS) has launched Deeplens, the “world’s first deep learning enabled video camera for developers”. Powered by an Intel Atom X5 processor with 8GB, and featuring a 4MP (1080p) camera, the fully programmable system runs Ubuntu 16.04, and is designed expand deep learning skills of developers, with Amazon providing tutorials, code, and pre-trained models. AWS Deeplens specifications: SoC – Intel Atom X5 Processor with Intel Gen9 HD graphics (106 GFLOPS of compute power) System Memory – 8GB RAM Storage – 16GB eMMC flash, micro SD slot Camera – 4MP (1080p) camera using MJPEG, H.264 encoding Video Output – micro HDMI port Audio – 3.5mm audio jack, and HDMI audio Connectivity – Dual band WiFi USB – 2x USB 2.0 ports Misc – Power button; camera, WiFi and power status LEDs; reset pinhole Power Supply – TBD Dimensions – 168 x 94 x 47 mm Weight – 296.5 grams The camera can not only do inference, but also train deep …