Archive

Posts Tagged ‘python’

Getting Started with MicroPython on ESP32 – Hello World, GPIO, and WiFi

October 16th, 2017 12 comments

I’ve been playing with several ESP32 boards over the months, and tried several firmware images. I started with a tutorial for Arduino Core on ESP32, a few month later I tested ESP32 JavaScript programming with Espruino on ESPino32 board, and recently Espressif Systems sent me ESP32 PICO core development board powered by their ESP32-PICO-D4 SiP, and while I took some pretty photos, I had not used it so far.

So I decided to go with yet another firmware, and this time, I played with MicroPython on ESP32, and will report my experience with basic commands, controlling GPIOs, and WiFi in this getting started post.

Flashing Micropython Firmware to ESP32 Board

Source code is available on Github, as a fork of MicroPython repo as ESP32 support has not been upstreamed yet. We could built the firmware from source, but there’s also a pre-built binary which you can download on MicroPython website.

I’ll be using Ubuntu 16.04 for the instructions, which should be pretty similar for other Linux distributions, especially the ones based on Debian, and if you’re using Windows 10, you should be able to follow the same instructions after installing Windows Subsystem for Linux with Ubuntu on your computer.

Let’s open a terminal, to download the firmware (October 14):

If you have not done so already, install the latest version of esptool:

Now connect the board via a micro USB to USB cable to your computer. The log should like like:

In my case, the device is ttyUSB0, but it may vary depending on the board used. We can now erase the flash, and copy the firmware to the board:

If the last step is successfull,  the output should be similar to the one below:

As a side note, version 2.1 of esptool does not know about ESP32-PICO-D4, but it can still detect an ESP32 device, and the update went through normally.

Hello World Sample / Boot Log with MicroPython

We can test the firmware, by connecting to the board using minicom, screen, putty, or whatever software you feel most comfortable with. I went with minicom, setup a connection to /dev/ttyUSB0 device with 115200 bps baudrate. I immediately tested the print function, and made an hard reset to check out the boot log:

The reset command will first generate some errors message, before rebooting the board:

We can type help function to get some more help:

I also often refered to MicroPython 1.9.2 documentation to write this quick start guide.

LED Blink Sample with MicroPython

The easiest way to test GPIOs is to connect an LED, since the board does not have user LED, only the power lED. I connected a 5V LED to pin 21 via a transistor to make the 3.3V to 5V conversion.

Controlling the LED in the command line interface is easy. Import the machine library, set the pin to output, and change the pin level as needed:

Success! But what about doing a proper blink sample? MicroPython developers’ official PyBoard would show as a USB mass storage drive in you computer, where can copy Python files like boot.py and main.py files, but in the case of ESP32 PICO core, it appears the only option is to use the serial console for programming, as we can’t simply copy files to the board from the host computer.

I  found a solution on Techtutorialsx – which also has plenty of articles about MicroPython on ESP32/ESP8266. We need ampy script that can be install from our Linux terminal:

However, the first time I tried it I got an error:

I installed files module, but the error remained. So instead I installed it for Python 3:

I then created blink.py on my computer to blink the LED every 500 ms:

Before uploading the file to the board, you can try to run it as follow:

If you have plenty of errors here, that’s probably because your code is incorrect. Since I’m not very familiar with Python, it happened to me a couple of times, until I got the code right, and the LED was blinking as expected.

Now that we’ve made sure the code works, we can now copy our sample to the board…

… reconnect to the serial console, and verify the file is there:

To run the program type the following:

The LED should blink again. You can interrupt the program with Ctrl+C, and if you want to soft reset the board, press Ctrl+D.

In order to automatically start the blink program at each boot, rename blink.py to main.py, delete blink.py, and copy main.py instead:

Power cycle the board, and the LED should start blinking almost immediately.

ESP32 WiFi with MicroPython (Station and AP modes)

We’ve got GPIOs working, but one of the most important feature of ESP32 is obvisouly WiFi. I’ll start by configuring the board in station mode. First import the network library, set the board to station mode, and scan access points:

The latter should return a list of access points with ssid, bssid, channel, RSSI, authmode, and hidden status as explained here.

I can then connect the board to one of the access points with:

The log above with IP address should give  a clue, but you can check connection status with the following function:

and use ifconfig to get the IP info:

Switching to AP mode is easy with the three commands below configuring the board with ESP32-PICO-CNX SSID:

At this stage I can see ESP32-PICO-CNX on my phone, but it’s an open connection. We can change that with authmode option that can take 5 values:

  • 0 – open
  • 1 – WEP
  • 2 – WPA-PSK
  • 3 – WPA2-PSK
  • 4 – WPA/WPA2-PSK

I’ll use WPA2-PSK and define the password with the config function.

Working as planned…

ESP32 Web Server with Micropython

Many ESP32 project will require a web interface for monitoring or configuration. Let’s first setup the board as an access point using the command we’ve used above:

Now create webserver.py file based on Python code found here that’s supposed to return the status of some GPIO pins in an HTML table:

Copy the file to the board:

Start the serial console again, import/run the python sample we’ve copied, and connect to the board (in my case http://192.168.4.1):

 

It works as expected, but we wrote the HTML code inside the Python file, and you need to handle socket programming by yourself. To further simply the task, some MicroPython web servers such as MicroWebSrv, and Picoweb are available.

MicroWebSrv (Not working yet for me)

I tried to install MicroWebSrv first, but never managed to make it work. I still reproduce the step I followed in case somebody finds out what I did wrong. I got the code, and copied files from the Linux terminal:

We can check the files are where they are supposed to be:

Go into the terminal (aka REPL console) to start a basic example, after setting up a connection:

I could connect to the server, but I would always get 404 error.

PicoWeb

So instead I switched to picoweb, adapting the instructions here and there. It’s very easy to install.  First make sure you have a working Internet connection in your board (i.e. set station mode), and install the web server with upip:

That’s the output if everything goes according to plans:

Now let’s go back to the host computer to create an html document, for example index.html:

as well as picowebtest.py sample file that will request the HTML page from the board, and return it to the client.

You’ll need to change “192.168.0.108” by the IP address of your board.

Let’s copy both files to the board…

… go back to the serial console, connect in station mode, and run the sample:

Type or copy/paste the URL in the last line into a web browser, and you should get the output below.

ESP32 Bluetooth with MicroPython

There’s no Bluetooth support in the official MicroPython documentation, because it’s work in progress, and for the most adventurous MrSulry released an alpha version  a few days ago. The Bluetooth API is also in flux, but the basic code to enable Bluetooth should look like:

I’ll update that section once Bluetooth makes it to the stable release, and/or when I’m sure the API is frozen.

Other ESP32 (Micro)Python Resources

I’ve just covered a few things that can be done with MicroPyhon on ESP32, and beside the official documentation, you can also check the various MicroPython ESP32 tutoral on techtutorialsx blog. Loboris also made another MicroPython ESP32 firmware that supports pSRAM as MicroPython may use a lot of RAM. If you’re interested in Python for ESP32, but Zerynth is another option for Python on ESP32 that works with an IDE/GUI available for Windows, Linux and MAC OS X. [Update: Yet other options are Pumbaa a port of MicroPython running on top of Simba, and Pycom version of MicroPython]

Dragonwally is a Stereoscopic Computer Vision Mezzanine for 96Boards CE Boards

October 11th, 2017 No comments

Hardware based on 96Boards specifications may not have the number of sales as Raspberry Pi or Orange Pi boards, but there’s heavily used by Linaro member and other developer working on bleeding edge software. More and more companies are designing boards compliant with the standard, and several new mezzanine expansion boards such as Secure96, were showcased at Linaro Connect SFO 2017, and are yet to be show up on 96Boards Mezzanine page.

Another 96Boards mezzanine expansion board in development is Dragonwally, designed for stereoscopic computer vision, currently used with DragonBoard 410c board, and targetting applications such as object recognition,  people counting, access control, or driver identification and safety.

DragonWally DW0 board specifications:

  • MIPI DSI interface with high speed connector
  • 2x 5MP cameras
  • 1x USB port
  • 96Boards CE compliant

The two Brazilian developers working on the project interfaced it with DragonBoard 410c running Linaro Debian, and using OpenCV and Python for computer vision development. To demonstrate the capability of the board, they added a touchscreen display for a demo leveraging Amazon Rekognition API for face recognition and camera distance estimation.

DragonWally board does not seem available yet, nor the source code for the demo above. If you’d like more information, visit DragonWally website, or join 96Boards OpenHours #74 tomorrow.

Hologram Unveils Nova 3G USB Dongle and Python SDK; 200 Raspberry Pi Zero W Kits Given Away to Developers

October 6th, 2017 No comments

This summer I discovered Hologram global cellular IoT SIM card, and since they provided free developer samples with 2MB of monthly data includes, I decided to get one to try it out. I received it a few weeks later, and to my surprise it worked, despite my country of residence having some strict requirements with regards to SIM card registration. The SIM card uses roaming, but with a low fixed worldwide pricing, and does not come with a phone number by default, so maybe that’s why I did not have to register.

The company is now back with Nova, an open source hardware cellular modem certified by OSHWA (ID #US000077). It’s basically 2G/3G USB dongle that’s controlled by Hologram Python SDK, specifically suited to Debian systems like Raspberry Pi 3 or BeagleBone Black. Hackster.io is also involved in the launch with a worldwide contest offering 200 free kits comprised of Nova 3G USB dongle and Raspberry Pi Zero W board for the best project ideas leveraging cellular IoT.

Nova will eventually come in three versions

  • 3G (in production now) – Ublox Sara-U201 module;  Global 3G/2G GSM;  GPRS/GSM/UMTS/HSPA: 850, 900, 1800, 1900 MHz;
  • Cat-M1 (November 2017) – Ublox Sara-R404M module; USA LTE Cat M-1; FDD: 13 (Verizon)
  • Cat-M1/NB1 (Q1 2018) – Ublox Sara-R410M-02B module; Global LTE Cat M-1+ NB; FDD: 1,2,3,4,5,8,12,13,17,18,19,20,25,26,28

All should have the same other interfaces and other specs:

 

  • u.FL Antenna Connector
  • Nano SIM card holder
  • UART GPIO Pads
  • USB Serial
  • Network Status LED; Power LED
  • Fully end certified (FCC, PTCRB, CE, and AT&T)
  • Dimensions – 46mm x 19mm x 6mm (Plugged in PCB);  71mm x 23mm x 9mm (w/ case)
  • Weight – 8 grams

The hardware kit includes the dongle, Hologram global IoT SIM card, a transparent enclosure, 2 Quad-band flexible u.FL antennas, and access to Hologram Developer Tools for modem and data management.

 

The dongle can be controlled using Hologram client tool, or Hologram Python SDK requiring ppp and Python 2.7 packages, and will allow you to send SMS, setup data connection, and more. Any SIM card should work, and it’s not tied to Hologram SIM card. While the company claims OSHWA certifications, the number US000077 is not present (empty line) in the OSHWA certification list yet, and so far, they’ve only released the PDF schematics. However, Python SDK is fully open source and released under an MIT license on Github.

More details can be found in the product page, and Nova 3G kit can be purchased now for $49.

But as mentioned in the introduction, if you have a great project idea, you could also get the kit for free, and possibly another “grand prize” (Apple Watch Series 3)once the project is completed. The contest is opened worldwide (except to US sanctioned countries) with the following timeline:

  • Submit your proposal by October 27, 2017
  • Best project ideas will be selected, and be sent their kit within around 14 days
  • Build and submit your project to Hackster.io by January 5, 2018
  • 8 Grand Prize winners will be announced on January 8, 2018 for four categories: gateway, asset tracking, remote controlling, and remote monitoring.

There are already 135 participants. Good luck!

 

Using GPIOs on NanoPi NEO 2 Board with BakeBit Starter Kit

May 21st, 2017 10 comments

NanoPi NEO 2 is a tiny 64-bit ARM development board powered by Allwinner H5 processor. FriendlyELEC sent me a couple of NEO 2 samples together with their BakeBit Start Kit with a NanoHat and various modules via GPIOs, analog input or I2C. I’ve already tested both Armbian with Linux 4.11 and Ubuntu Core Qt with Linux 3.10, and ran a few benchmarks on NanoPi NEO 2. You would normally prefer to use the Armbian image with Linux mainline since it provided better performance, but at the time I was told GPIO support was not there.

Configuring NanoPi NEO 2 board with BakeBit library

So this week-end, when I decided to test GPIO support and BakeBit Starter Kit, I decided to follow this advice, especially nanopi-neo2-ubuntu-core-qte-sd4g-20170329.img.zip image is still the recommended one in the Wiki. So I went with that image.

I’ll use Python examples from Bakebit library, but if you prefer something similar to WiringPi, you may consider using WiringNP library directly instead of using Bakebit. Since NanoHat Hub comes with header with digital I/O (including 2 PWM), analog input, I2C and UART interfaces, I’ll make sure I try samples for all interfaces I have hardware for. FriendlyELEC did not include a module with a UART interface, so I’ll skip that one.

I followed instructions in BakeBit wiki from a terminal which you can access from the serial console or SSH. First, we need to retrieve the source code:

Then we can start the installation:

The last line will install the following dependencies:

  • python2.7           python2.7
  • python-pip         alternative Python package installer
  • git                        fast, scalable, distributed revision control system
  • libi2c-dev           userspace I2C programming library development files
  • python-serial     pyserial – module encapsulating access for the serial port
  • i2c-tools              This Python module allows SMBus access through the I2C /dv
  • python-smbus   Python bindings for Linux SMBus access through i2c-dev
  • minicom             friendly menu driven serial communication program
  • psutil                   a cross-platform process and system utilities module for n
  • WiringNP           a GPIO access library for NanoPi NEO

This will take a while, and after it’s done, the board will automatically reboot.

We can check if everything is properly running, but try out one of the Python scripts:

hmm, python-smbus was supposed to be installed via the installation script. Let’s try to install it manually:

Running the command again with verbose option shows the download URL is not valid:

So I went to https://pypi.python.org/simple/ looking for another python-smbus library in case the name has changed, and I finally installed the pysmbus:

I could go further, but the I2C bus was not detected:

So maybe the driver needs to be loaded. But running sudo modprobe i2c_sunxi it does nothing, and I could notice the .ko file is missing from the image…

So let’s try to build the source code for the board following the Wiki intructions:

We also need to install required build packages…

… download gcc-linaro-aarch64.tar.xz toolchain, and copy it to lichee/brandy/toolchain directory (do not extract it, it will be done by the build script).

Now we can try to build the kernel for NanoPi NEO 2 (and other Allwinner H5 boards).

and it failed with more errors possible related to CROSS_COMPILE flag. There must be a better solution… FriendlyELEC guys might not work on Saturday afternoon, and while I did contact them, I decided to try one of their more recent images with Linux 4.11 available here.

Let’s pick nanopi-neo2_ubuntu-core-xenial_4.11.0_20170518.img.zip since it has a similar name, and is much newer (released 3 days ago). I repeated the installation procedure above, and …

Success! Albeit after 4 to 5 hours of work… Let’s connect hardware to ind out whether it actually works, and not just runs.

Analog Input and Digital Output – Sound Sensor Demo

The simplest demo would be to use the LED module, but let’s do something more fun with the Sound Sensor demo I found in BakerBit Starter Kit printed user’s manual, and which will allow us to use both digital output with the LED module connected to D5 header, and analog input with the Sound sensor module connected to A0 header. Just remember the long LED pin is the positive one.

You can run the code as follows:

I changed the source a bit including the detection threshold, and timing to make it more responsive:

The LED will turn on each time the the sound level (actually analog voltage) is above 1.46V.

PWM and Analog Input – Servo and Rotary Angle Sensor Demo

We can test PWM output using the Servo module connected to D5 header, and control it using the rotary angle sensor module connected the A0 analog input header .

Click to Enlarge

The sample for the demo runs fine, and use the potentiometer is detected:

However, the servo is not moving at all. Raspberry Pi relies on rpi-config to enable things like I2C and other I/Os, and I noticed npi-config in the Wiki for NEO 2. So I ran it, and sure enough PWM was disabled.

So I enabled it, and answered Yes when I was asked to reboot. The only problem is that it would not boot anymore, with the system blocked at:

So maybe something went wrong during the process, so I re-flashed the Ubuntu image, reinstalled BakeBit, and re-enabled PWM0. But before rebooting, I checked the boot directory, and noticed boot.cmd, boot.scr, and the device tree file (sun50i-h5-nanopi-neo2.dtb) had been modified. The DTB looks fine, as I could decode it, and find the pwm section:

Let’s reboot the board. Exact same problem with the boot stuck at “Starting kernel…”. So there’s something wrong with the way npi-config modifies one or more of the files. With hindsight, I should have made a backup of those three files before enabling PWM the second time… I’ll give up on PWM for now, and ask FriendlyELEC to look into it.

I2C and Analog Input – OLED UI controlled with Joystick

The final test I’ll use the I2C OLED display module connected to one of the I2C headers, together with the analog joystick module connected to A0 header.

Click to Enlarge

Let’s run the sample for the demo:

It works, but there’s a bit of a lag, and the sample may have to be improved to better detect various states. I’ll show what I mean in the video below.

The bad parts are that documentation is not up-to-date, enabling PWM will crash the image, and while the Python sample do demonstrate IO capabilities, they should probably be improved to be more responsive. The good part is that we’re getting there, the hardware kit is a really nice, and I think the documentation and software should become much better in June, as FriendlyELEC has shown to be responsive to the community issues.

What? Python sucks? You can use C language with GPIOs too

If Python is not your favorite language, FriendlyELEC also provided some C languages samples in the C directory:

As we’ve seen above, Bakebit library appears to rely on WiringNP, and you’d normally be able to list the GPIOs as follows:

The utility is not too happy about seeing an Allwinner H5 board. But maybe the library in the board is not up-to-date, so I have built it from source:

and run the gpio sample again:

Excellent! It’s not quite a work-out-of-box experience, but NanoPi NEO 2 can be used with (most) GPIOs.

My adventures with NanoPi NEO 2 board are not quite done, as I still have to play with NanoHat PCM5102A audio add-on board, which I may end up combining with a USB microphone to play with Google Assistant SDK, and I’m expecting NanoPi NAS Kit v1.2 shortly. I’ll also update this post once PWM is working.

Top Programming Languages & Operating Systems for the Internet of Things

May 19th, 2017 3 comments

The Eclipse foundation has recently done its IoT Developer Survey answered by 713 developers, where they asked  IoT programming languages, cloud platforms, IoT operating systems, messaging protocols (MQTT, HTTP), IoT hardware architectures and more.  The results have now been published. So let’s have a look at some of the slides, especially with regards to programming languages and operating systems bearing in mind that IoT is a general terms that may apply to sensors, gateways and the cloud, so the survey correctly separated languages for different segments of the IoT ecosystem.

Click to Enlarge

C and C++ are still the preferred languages for constrained devices, and developers are normally using more than one language as the total is well over 100%.

Click to Enlarge

IoT gateways are more powerful and resourceful (memory/storage) hardware, so it’s no surprise higher level languages like Java and Python join C and C++, with Java being the most used language with 40.8% of respondents.

Click to Enlarge

When it comes to the cloud with virtually unlimited resources, and no need to interface with hardware in most cases, higher level languages like Java, JavaScript, Node.js, and Python take the lead.

Click to Enlarge

When it comes to operating systems in constrained IoT devices, Linux takes the lead with 44.1%, in front of bare metal (27.6%) and FreeRTOS (15.0 %). Windows is also there in fourth place probably with a mix of Windows IoT core, Windows Embedded, and WinCE.

Click to Enlarge

Linux is the king of IoT gateways with 66.9% of respondent using it far ahead of Windows in second place with 20.5%. They have no chart for the cloud, probably because users just don’t run their own Cloud servers, but relies on providers. They did ask specifically about the Linux distributions used for IoT projects, and the results are a bit surprising with Raspbian taking the lead with 45.5%, with Ubuntu Core following closely at 44.4%.

Click to Enlarge

Maybe Raspbian has been used during the prototyping phase or for evaluation, as most developers (84%) have been using cheap development boards like Arduino, BeagleBone or Raspberry Pi. 20% also claim to have deployed such boards in IoT solutions.

Click to Enlarge

That’s only a few slides of the survey results, and you’ll find more details about Intel/ARM hardware share, messaging & industrial protocols, cloud solutions, wireless connectivity, and more in the slides below.

Via Ubuntu Insights

GPU Accelerated Object Recognition on Raspberry Pi 3 & Raspberry Pi Zero

April 30th, 2017 6 comments

You’ve probably already seen one or more object recognition demos, where a system equipped with a camera detects the type of object using deep learning algorithms either locally or in the cloud. It’s for example used in autonomous cars to detect pedestrian, pets, other cars and so on. Kochi Nakamura and his team have developed software based on GoogleNet deep neural network with a a 1000-class image classification model running on Raspberry Pi Zero and Raspberry Pi 3 and leveraging the VideoCore IV GPU found in Broadcom BCM283x processor in order to detect objects faster than with the CPU, more exactly about 3 times faster than using the four Cortex A53 cores in RPi 3.

They just connected a battery, a display, and the official Raspberry Pi camera to the Raspberry Pi boards to be able to recognize various objects and animals.

The first demo is with Raspberry Pi Zero.

and the second demo is on the Raspberry Pi 3 board using a better display.

Source code? Not yet, but he is thinking about it, and when/if it is released it will probably be found on his github account, where there is already py-videocore Python library for GPGPU on Raspberry Pi, which was very likely used in the demos above. They may also have used TensorFlow image recognition tutorials as a starting point, and/or instructions to install Tensorflow on Raspberry Pi.

If you are interested in Deep Learning, there’s a good list of resources with links to research papers, software framework & applications, tutorials, etc… on Github’s .

$30 BakeBit Starter Kit Adds Sensors & Buttons to Your NanoPi NEO & NEO Air Boards

January 20th, 2017 1 comment

FriendlyElec (previously FriendlyARM) launched NanoPi NEO and then NanoPi NEO Air board as respectively Ethernet and WiFi/Bluetooth connected boards for IoT applications. But so far, there was no ecosystem around the board, you had to use your own sensor modules, and write your own software to control them. This has now changed with the launch a BakeBit Starter Kit with twelve sensor modules, a NanoHat Hub add-on board designed for NanoPi boards, as well as BakeBit Library to control the hardware.

NanoPi NEO with NanoHat and Two Modules

The NanoHat Hub plugs into the two NanoPi NEO headers and provide 12 headers with 3x I2C interfaces, 3x analog interfaces, 2x UART interfaces, and 4x digital interfaces among which D3 and D5 support PWM, compatible with SeeedStudio Grove modules. You then have a choice of 12 modules to connect to the NanoHat Hub:

  • OLED Module
  • Ultrasonic Module
  • Green LED Module
  • Red LED Module
  • LED Bar Module
  • Rotary Angle Sensor Module
  • Joystick Module
  • Sound Sensor Module
  • Button Module
  • Light Sensor Module
  • Buzzer Module
  • Servo Module

BakeBit Starter Kit – Click to Enlarge

But now that you have your hardware setup with multiple module, you still need to program the thing, and that’s where BitBake library, based on Grove Pi, comes into play, as it allows you to program the module easily with Python programming. More details can be found in the Wiki for BakeBit NanoHat and modules.

BakeBit Starter Kit is now sold for $29.99 (promotion), but if you already have Grove modules, you could also simply purchase NanoHat Hub for $12.99. Bear in mind that Chinese New Year is around the corner, so any order passed after January 24th and beyond, will be processed after the holidays around February 6th. [Update: The company has also released a $9.99 NanoHat PCM5102A audio board for NanoPi Boards]

39 Euros FiPy Board Supports Sigfox, LoRa, LTE Cat M1/NB1, Bluetooth 4.2, and WiFi (Crowdfunding)

November 24th, 2016 1 comment

Long range LPWAN solutions have just started to hit the market, and there are so many standards such as Sigfox and LoRa that it’s difficult to know who will eventually be the winner, or if different standards will co-exist over the long term, and in a general sense it might not be so easy to decide which one is best suited to your project without experimenting first. Pycom has a solution to this problem, as they’ve made a board similar to LoPy with WiFi, Bluetooth, and LoRa, but instead included 5 long and short range IoT protocols: Sigfox, LoRa, LTE Cat M1 & Cat NB1, Bluetooth, and WiFi.

pycom-fipy-boardPycom FiPy board specifications:

  • SoC – Espressif ESP32 dual core Tensilica L108 processors @ up to 160 MHz with BT 4.2 and WiFi
  • System Memory – 4MB RAM
  • Storage – 8MB flash memory
  • Connectivity
    • WiFi 802.11 b/g/n @ 16 Mbps up to 1 km range & Bluetooth 4.2 with common u.FL antenna connector and chip antenna
    • LoRa and Sigfox transceiver
      • common u.FL antenna connector, RF switch
      • Lora
        • 868 MHz (Europe) at +14dBm maximum
        • 915 MHz (North and South America, Australia and New Zealand) at +20dBm maximum
        • Node range up to 40 km, nano-gateway range up to 22 km (max 100 nodes).
        • Power Consumption – 10mA Rx, 28mA Tx
      • Sigfox
        • Maximum Tx power – +14dBm (Europe), +22dBm (America), +22dBm (Australia and New Zealand)
        • Node range up to 50km
        • Operating Frequencies
          • RCZ1 – 868MHz (Europe)
          • RCZ2 – 902MHz (US, Canada and Mexico)
          • RCZ3 – (Japan and Korea)
          • RCZ4 – 920 – 922MHz (ANZ, Latin America and S-E Asia)
        • Power Consumption
          • Sigfox (Europe) – 17mA in Rx mode, 47mA in Tx mode and 0.5uA in standby
          • Sigfox (Australia, New Zealand and South America) – 24mA in Rx mode, 257 mA in Tx mode and 0.5uA in standby
    • Cellular LTE CAT M1/NB1 transceiver
      • u.FL antenna connector and nano SIM socket
      • Operating frequencies – 34 bands supported from 699 to 2690MHz
      • 3GPP Release 13 LTE Advanced Pro
      • Peak power estimations – Tx current = 420mA peak @ 1.5Watt Rx current = 330mA peak @ 1.2Watt
  • Expansion – 2x 14 pin headers with UART, 2x SPI, 2x I2C, I2S, SDIO, 8x 12-bit ADC, 2x 8-bit DACs, up to 16 PWMs, up to 22 GPIOs
  • Misc – WS2812 RGB LED, reset switch, 32 KHz RTC (in SoC)
  • Dimensions – 55 x 20 x 3.5 mm
  • Temperature Range – -40 to 85 degrees Celsius
  • Certifications – CE, FCC,  Sigfox network certification, LoRa Alliance certification, LTE-M CAT M1/NB1 cellular –  global networks

fipy-lte-cat-module-sim-card

FiPy name is most probably derived from Five IoT protocols, and microPython support. As the board is compatible with WiPy, LoPy and SiPy you can use the usual Pymakr IDE and Pymate Mobile app to write your program and control the board. The company has also introduced two new add-on boards:

  • PySense board with an ambient light sensor, a barometric pressure sensor, a humidity sensor, a 3-axis 12-bit accelerometer, and a temperature sensor, as well as a micro SD card, a micro USB port, and a LiPo battery charger
  • PyTrack board with a GNSS + Glonass GPS and a 3-axis accelerometer, as well as a micro SD card, a micro USB port, and a LiPo battery charger. This can be very useful to track moving assets such as cars or bicycles.
sigfox-lora-wifi-bluetooth-board-lte

FiPy and PyTrack

The project has just launched on Kickstarter as already surpassed its 25,000 Euros funding target. Most early bird rewards are gone, but you can pledge 39 Euros for FiPy board,  59 Euros (Early bird) for PySense Kit, 65 Euros (Early bird) for PyTrack kit, optionally adding 7 Euros for a Sigfox/Lora antenna, and 7 Euros more for an LTE-M cellular antenna. Shipping adds 8 to 25 Euros depending on the selected rewards, and delivery is scheduled for April 2017. Just a warning for users who are not based in the US or Europe: please make sure you comply with your country regulations, especially in terms of frequency used, as such nodes will have multiple kilometers range, and you may not want to break the law, and possibly get a visit from your local police or military…