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Orange Pi Development Boards

Obniz ESP32 Board is Programmable in JavaScript from the Cloud (Crowdfunding)

December 21st, 2017 No comments

ESP32 WiFi / Bluetooth boards are now commonly supported by the Arduino IDE, and alternative firmwares are also available to program them using JavaScript (e.g. Espruino), or MicroPython. But if are familiar with JavaScript / Node.js, and don’t want to flash your own firmware, Obniz board could be an option.

The board exposes 12 I/Os programmable from the company’s Cloud service, and features a OLED display used to show your program information, or a QR code to easily program the board from your smartphone’s browse once a WiFi connection has been setup.

Obniz hardware specifications:

  • Wireless Module – ESP-WROOM-ESP32 based on  ESP32 dual core 802.11 b/n/g WiFi + Bluetooth LE WiSoC
  • Display – 128×64 OLED display
  • I/Os
    • 12x I/O pins each configurable as GPIO, ADC, UART, SPI or I2C (no specialized pin, each can handle those functions)
    • Up to 1A drive per I/O to control motors
    • 3.3 or 5V selectable for each I/O
    • Short protection
  • Power Supply – 5V via micro USB port

You’d program the board directly inside your web browser using JavsScript in Obniz cloud, and the company also provide a parts library in JavaScript. A REST or Websocket APIs are also provided, so you could control or program the board with curl, Switch or Node.js.

Tokyo based CambrianRobotics has launched the solution on Kickstarter with the goal of raising 1.5 million JPY (~$13,200 US). A super early bird pledge of ~$26 should get your Obniz board in March 2018. If you’ve miss all early bird rewards, the required pledge amount rises to about $42. Other rewards include a robot kit, basic & ultra kits. Shipping adds about $4.40 to Japan, and up to $17.60 to the rest of the world.

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

November 30th, 2017 4 comments

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.

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AWS Deeplens specifications:

  • 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 learning models using Amazon infrastructure. Performance wise, the camera can infer 14 images/second on AlexNet, and 5 images/second on ResNet 50 for batch size of 1.

Six projects samples are currently available: object detection, hot dog not hot dog, cat and dog,  activity detection, and face detection. Read that blog post to see how to get started.

But if you want to make your own project, a typical workflow would be as follows:

  • Train a deep learning model using Amazon SageMaker
  • Optimize the trained model to run on the AWS DeepLens edge device
  • Develop an AWS Lambda function to load the model and use to run inference on the video stream
  • Deploy the AWS Lambda function to the AWS DeepLens device using AWS Greengrass
  • Wire the edge AWS Lambda function to the cloud to send commands and receive inference output

This steps are explained in details on Amazon blog.

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Intel also published a press release explaining how they are involved in the project:

DeepLens uses Intel-optimized deep learning software tools and libraries (including the Intel Compute Library for Deep Neural Networks, Intel clDNN) to run real-time computer vision models directly on the device for reduced cost and real-time responsiveness.

Developers can start designing and creating AI and machine learning products in a matter of minutes using the preconfigured frameworks already on the device. Apache MXNet is supported today, and Tensorflow and Caffe2 will be supported in 2018’s first quarter.

AWS DeepLens can be pre-ordered today for $249 by US customers only (or those using a forwarding service) with shipping expected on April 14, 2018. Visit the product page on AWS for more details.

Bolt IoT Platform Combines ESP8266, Mobile Apps, Cloud, and Machine Learning (Crowdfunding)

November 22nd, 2017 4 comments

There are plenty of hardware to implemented IoT projects now, but in many cases a full integration to get data from sensors to the cloud requires going though a long list of instructions. Bolt IoT, an Indian and US based startup, has taken up the task to simplify IoT projects with their IoT platform comprised of ESP8266 Bolt WiFi module, a cloud service with machine learning capabilities, and mobile apps for Android and iOS.

Bolt IoT module hardware specifications:

  • Wireless Module – A.I Thinker ESP12 module based on ESP8266 WiSoC
  • Connectivity – 802.11 b/g/n WiFi secured by WPA2
  • USB – 1x micro USB for power and programming
  • Expansion – 4-pin female header and 7-pin female header with 5 digital I/Os, 1x analog I/O, and UART
  • Misc – Cloud connection LED

The hardware is not the most interesting part of Bolt IoT, since it offers similar functionalities as other ESP8266 boards. But what may make the project worthwhile is built-in support for the company’s cloud service (lifetime access to backers) that simplifies node and data management, as well as Bolt IoT mobile app to control the board with your smartphone (Android or iOS)

Some other noticeable features of the Bolt IoT cloud platform include:

  • Remote configuration of the pins on Bolt WiFi module from the dashboard
  • Built-in code editor, and code deployment to all your Bolt based IoT devices with a single click.
  • Data Visualization
  • Machine learning for future data prediction and anomaly detection with just a few clicks.
  • Notifications over SMS and E-Mail.
  • Integration with systems like IFTTT and Zapier
  • Integration with smart home devices like Alexa and Google Home

The whole ecosystem supposedly allows developers to work 10 times faster, and use 80% less code than other methods.  The company will also provide an API that let you manage notifications, select third party visualization tools, and control devices from your own app.

The company launched their platform on Kickstarter at the beginning of November, and they’ve now surpassed their $10,000 funding target, having raised close to $30,000 from about 700 backers. Bolt IoT module with lifetime access to Bolt Cloud requires a $12 pledge, but they also have kits with Arduino baseboard and sensors starting with a $37 Starter Kit to the $650 Legendary kit with multiple Bolt board, and a very long list of modules. For some reasons that I may have missed all kits also include $10 credit with DigitalOcean VPS provider. Bolt Cloud will be free to all backers for life, but after the KS campaign Bolt IoT will charge a fee for commercial projects, and potentially for hobbyist projects too. Shipping adds $5 to $100 depending on the selected reward, and delivery is scheduled for February 2018.

Arduino Create Adds Support for Linux Development Boards (based on Intel processors for now)

November 7th, 2017 No comments

Most people are used to program Arduino compatible boards with the Arduino IDE that they’ve installed in their Windows/Linux/Mac OS computer, and manage everything locally. But Arduino introduced Arduino Create last year, which includes Arduino Web Editor allowing you to perform the same tasks in your web browser, and save your files in the cloud.

The company has now added Linux support to Arduino Create so that users can now program their Linux devices as if they were regular Arduino boards, and easily deploy IoT applications with integrated cloud services. The initial release has been sponsored by Intel, and currently supports X86/X86_64 boards, but other hardware architectures will be supported in the coming month.

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In the meantime, AAEON UP2 board is the best platform to get started, as a complete getting started guide is available for the platform. But other mini PCs such as Intel NUC, Dell Wyse, Gigabyte GB-BXT are also supported, and you’ll find more generic instructions to get started.

Multiple Arduino programs can run simultaneously on a Linux devices and can communicate with each other thanks to MQTT based Arduino Connector. There are a currently three projects based on UP Squared board on the Project Hub, and if you need help, a dedicated forum has been launched.

Intel provided a few more details about the initiative in their announcement, highlighting the following points:

  • Reduce set up time with native integration of UP Squared Grove Development Kit with Arduino Create
  • Pre-installed custom Ubuntu Server 16.04 OS on the UP Squared Grove Development Kit
  • Simple getting started experience in Arduino Create for Intel based IoT platforms running Ubuntu on Intel Atom, Intel Core, or Intel Xeon processors.
  • Integrated libraries and SDKs such as UPM sensor libraries supporting over 400+ sensors, OpenCV, Intel Math Kernel Library, Amazon Web Services (AWS), Microsoft Azure, etc…
  • Supports the ability to run multiple sketches / programs at the same time
  • Export your sketch to a CMake project providing an easy development bridge to Intel System Studio 2018
  • Integrates mraa, the hardware abstraction layer by Intel, into the Arduino core libraries enabling support for all Intel platforms

Voladd Cloud-Connected Linux 3D Printer is Powered by BeagleBone Black Board (Crowdfunding)

October 24th, 2017 No comments

So far, all of the 3D printers that have been reviewed on this blog require you to design or download a 3D object on your computer, and print it from an SD card. But thanks to OctoPrint software and cheap ARM Linux developments boards, it has become possible to add a Linux computer with webcam to remotely start and control the 3D printer for a few dozens dollars. Karl has even released an OctoPrint image for Orange Pi Lite board.

Voladd 3D printer already embeds a Linux board, namely the BeagleBone Black running Debian, which allows the 3D printer to be easy to use since no software  installation is required. You can start printing by selecting an object in a web browser or an app in your smartphone, and they’ve also taken steps to eliminate/reduce maintenance tasks, such as the inclusion of a filament cartridge.

Voladd 3D printer specifications:

  • Internal computer – BeagleBone Black based on TI AM335x ARM Cortex A8 processor
  • Connectivity – Ethernet, 802.11 b/g/n WiFi and NFC; MQTT protocol supported
  • Print area – 175 x 125 x 150 mm (xyz)
  • Printing plate – Surface treated, removable, and adjustable with 3 rollers; optional glass platform
  • Print head – 0.2 (coming next year), 0.4 or 0.6 mm
  • Noise attenuated fans
  • Misc – On/off button, switch dial for cartridge, LEDs for connectivity status, general status, server interaction and head heating.
  • Power Supply – 100-240V @ 50/60 Hz
  • Dimensions – 29 x 40 x 29 cm (xyz)
  • Weight – 4.5 kg
  • Certifications – CE, EAC

Voladd 3D printer will ship with a Cartridge with 420 grams of white filament, the printing base, , a quick start guide and warranty. Voladd Cartridges, made of biodegradable, recyclable, and plant-based PLA bioplastic, come in 7 possible colors (20 colors planned for next year), and it appears you can’t just buy filament from anywhere for a refill. So if I understand correctly, you’ll be tied to the company for both the cloud service and filament. But if it really works as advertised: no assembly, no manual calibration, no jamming, no cleaning, etc…,just select an object to print online, it could be a good option for people that just want something that works…

The company also explains the 3D printer will save you money in the long run, it’s good for the environment (no factory, no transportation, biodegradable materials..), secure (AES/TLS), sharable with friends, and Voladd Cloud also include support for the creation of simple objects like personalized signs.

They’ve also provided a tablet comparing Voladd to more typical and harder to use 3D printers.

The 3D printer has already surpassed its 25,000 Euros funding target on Kickstarter. Pledges start at 499 Euros for a “super early bird” rewards include the printer, a white PLA cartridge, and access to Voladd Cloud platform. Shipping adds 25 to 50 Euros if you live in the “Western World”, but for any other countries it goes up to 350 Euros, which means it could costs close to 1000 Euros once local taxes are included. Delivery is scheduled for December 10, 2017. More details may also be available on Voladd website.

Via LinuxGizmos

Google Cloud IoT Core Enters Public Beta, Various Devkits Available

September 29th, 2017 No comments

Back in May, I wrote about Allwinner R18 based Banana Pi BPI-M64 Board with Google Cloud IoT Core support, as Google unveils the new cloud service during Google I/O. However, at the time it was only available to selected partners, and Google has recently launched the public beta making their IoT device management platform available to all.

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I first learned about this through an ARM community blog post announcing availability of the ARM-based IoT Kit for Cloud IoT Core on Adafruit using Raspberry Pi 3 board,  a breadboard, and various modules that can be managed through Google services.

But that are plenty of other IoT kits or boards for Google Cloud IoT Core including:

You’ll find purchase links and documentation for each board on Google Cloud IoT Core’s IoT Kit page. Sample code specific to the RPI3 kit can also be found on Github.

Google Cloud IoT Core Architecture / Features Overview

Google IoT Core is free to use for up to 250 MB/month with no limit on the number of devices, and if you exceed this limit pricing per MB depends on data usage:

  • 250MB to 250 GB – $0.0045 per MB
  • 250GB to 5 TB – $0.0020 per MB
  • Over 5 TB – $0.00045 per MB

AutoPi is a 4G & GPS OBD-II Dongle Based on Raspberry Pi Zero W Board (Crowdfunding)

September 1st, 2017 8 comments

We’ve previously cover Macchina M2 OBD-II dongle based on an Arduino compatible MCU, and with 4G LTE support for the maker market, and iWave Systems OBD-II dongle with 4G LTE and LTE running Linux on NXP i.MX6 for the B2B market, but so far I had not seen an hackable OBD-II dongle running Linux for the maker market. AutoPi dongle fills that void as it is based on Raspberry Pi Zero W board, runs Raspbian with Autopi software (AutoPi Core), supports 4G LTE, GPS, etc,.. and connects to your car’s OBD-II socket.

AutoPi dongle specifications:

  • SoC – Broadcom BCM2835 ARN11 Core processor @ up to 1 GHz
  • System Memory – 512MB LPDDR2 SRAM
  • Storage – 8GB micro SD card
  • Cellular Connectivity
    • 4G Cat 1 modem with 3G/EDGE fallback working worldwide (but region locked)
    • 4G bands – Region specific
    • 3G fallback (WCDMA) – B1, B2, B4, B5, B8
    • EDGE fallback – B3, B8; quad band
    • micro SIM card slot
  • GNSS – Integrated GPS + A-GPS
  • Wireless Connectivity – 802.11 b/g/n WiFi, Bluetooth 4.1 LE
  • USB – 2x USB 2.0 ports
  • Video – mini HDMI output up to 1080p60
  • Audio – Built-in speakers
  • Car Interface
    • STN-2120 OBD-II, SW-CAN, MS-CAN to UART Interpreter IC
    • Supported Protocols: ISO 15765-4, ISO 14230-4, ISO 9141-2, SAE J1850 VPW, SAE J1850 PWM, SW-CAN, MS-CAN, ISO 15765, ISO 11898 (raw), K-Line, L-Line
  • Expansion – 18x unused GPIO pins
  • Sensors – 3-axis accelerometer
  • Power Supply – Via OBD-2 interface; built-in power management to avoid draining the car’s battery
  • Dimensions – 90 x 45 x 25 mm

The dongle comes pre-assembled with an OBD extension/relocation cable, a case with all electronics including RPi0 W, a micro SD card with AutoPi Core, and some Velcro strips.

Setup is pretty easy with 5 steps:

  1. Insert your micro SD card
  2. Insert the dongle into your vehicle’s OBD-II port
  3. Connect to AutoPi WiFi access point
  4. Configure the device with APN string and AutoPi key
  5. Connect to AutoPi cloud

The cloud platform allows you to remotely monitor your car, and the customizable dashboard gives access to an history of trips, car data, OBD commands, IFTTT, custom Python code development, terminal access, and a REST API is also available to develop your own web app.

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A lot of different features are possible thanks to AutoPi dongle and cloud platform, such as voice commands to control windows and aircon in your car, theft detection and tracking, remote start, crash detection with SMS alerts, auto lock/unlock from a smart device, and so on. All is supposed to be done securely, but the company did not provide that many details about that critical part for a such system.

AutoPi’s developers  have launched a Kickstarter campaign aiming to raise at least DKK 475,000 (~$76,000). If you live in Europe, you can pledge ~205 Euros to get an AutoPi from the batch to be delivered in January 2018, others can pledge ~$261 to get a sample from the second batch in March 2018. Note the software will improve overtime, and while all models will be upgradeable, AutoPi dongle with the fully implemented firmware and software will be delivered in the third batch and beyond starting from May 2018. Shipping adds ~$9.60 to Europe, and ~$14.4 to the rest of the world. You may want to visit AutoPi.io website for many more details about the solution.

Qualcomm Provides Details about 64-bit ARM Falkor CPU Cores used in Centriq 2400 Server-on-Chip

August 21st, 2017 8 comments

Qualcomm officially announced they started sampling Centriq 2400 SoC with 48 ARMv8 cores for datacenters & cloud workloads using a 10nm process, but at the time the company did not provide that many details about the solution or the customization made to the CPU cores.

Qualcomm has now announced that Falkor is the custom CPU design in Centriq 2400 SoC with the key features listed by the company including:

  • Fully custom core design – Designed specifically for the cloud datacenter server market, with a 64-bit only micro-architecture based on ARMv8 (Aarch64).
  • Scalable building block The Falkor core duplex includes two custom Falkor CPUs, a shared L2 cache and a shared bus interface to the Qualcomm System Bus (QSB) ring interconnect.
  • Designed for performance, optimized for power
    • 4-issue, 8-dispatch heterogeneous pipeline designed to optimize performance per unit of power, with variable length pipelines that are tuned per function to maximize throughput and minimize idle hardware.
    • power management techniques: independent p-state control for each of the CPUs and L2, with entry to and exit from low-power states controlled by hardware state machines, and hardware state retention for power-collapsed sleep states with ultra-fast recovery.
  • Performance under memory-intensive workloads Falkor is designed to fulfill the demand for larger instruction footprints using an innovative split instruction cache comprised of a single-cycle, low-power 24KB L0 I-cache complementing its 64KB L1 I-cache. The core also supports a 32KB L1 D-cache with a 3-cycle load-use latency. The L1 D-cache is augmented by a sophisticated multi-level hardware prefetch engine that dynamically adapts to system conditions.
  • Datacenter features
    • ARM Execution Levels (EL0-EL3) and TrustZone secure execution environment.
    • ARMv8 instruction extensions to accelerate cryptographic transform and secure hash operations such as AES, SHA1, and SHA2-256
    • RAS mechanisms needed to keep a datacenter running, such as fault isolation, reporting, and handling techniques.
  • System on a chip – The 48 Falkor CPUs are brought together in a fully-integrated SoC with high-bandwidth and low-latency ring interconnect, large L3 cache and multiple memory controllers. It also includes an on-die hardware-based immutable root of trust that authenticates firmware before the first line of firmware is ever executed

Centriq 2400 SoC is scheduled to start shipping later this year. You’ll find an in-depth overview of Falkor micro-architecture, and more slides on Anandtech.