Friday, May 29, 2020

Raspberry Pi 4 with 8GB of RAM...

Released yesterday, there is now a Raspberry Pi 4 with 8Gb of RAM, for $75 in the US!
(See the Raspberry Pi blog).
And it comes along with a beta-64 bit OS, named Raspi OS, that targets Raspberry Pis 3 and higher.

I tried it (on a Raspberry Pi 4, with 4 Gb of RAM), it works fine, and fast!
This beta version does not come with Java installed, but a simple sudo apt-get install default-jdk installs it (JDK 11) in a couple of minutes.
I cloned a repo ( and built it without any problem or error.

Now, I might wait a bit longer to get a new Raspberry Pi. As it is now, it should be able to support 16 Gb of RM, I'll wait a bit, and see...

Anyway, that makes yet another good reason NOT to get a Mac.
Steve Jobs vs Eben Upton..., I vote for Eben Upton, biiiiiig time.

Friday, May 22, 2020

Using OpenCV to downgrade an image

This is a bunch of comments on the code visible on github. The goal here is to downgrade a color image to display it on a led matrix, like a small OLED screen here, an SSD1306. We will use OpenCV, in Java. Here are the steps we follow:
  • We start from the colored image
  • We turn it to gray
  • We thresh it
  • We resize it (smaller)
  • We store it in a file, custom format
  • We can then display the image on the led matrix (oled screen here)
Original Grayed
Threshed Resized
The level of details of the final display is obtained during the threshold part.
See in
    // threshold
    Mat threshed = new Mat();
            150, // 127,
Tweaking the thresh parameter (150 above) leads to different results.
The final result is stored in a binary file (image.dat).
The matrix used here is 128x64 pixels big. The file will contain 64 lines of 2 longs.
A Java long has 64 bits, 2 longs make 128 bits, that's all we need to encode one line of 128 leds on the screen.
See the code in for details.

Adios Papou

Thursday, January 09, 2020

dAISy AIS HAT for the Raspberry Pi

Just received the dAISy HAT from Wegmatt, it just works!
Whoever can click can do it.

And this was the opportunity to keep working on the AISParser, and I have also added a custom TCP Forwarder to the Multiplexer, along with an AIS filter on the regular TCP Forwarder.

This way, you can forward NMEA data on one port, and AIS data on another one. This is not necessary, but it can be nice to have.

Here is an example of a yaml driving the Multiplexer:

# MUX definition.
name: "With a GPS and AIS"
  with.http.server: true
  http.port: 9999
  init.cache: true
  - type: serial
    # GPS
    port: /dev/ttyUSB0
    baudrate: 4800
    verbose: false
  - type: serial
    # AIS
    port: /dev/ttyS0
    baudrate: 38400
    verbose: false
  - type: tcp
    port: 7002
  - type: tcp
    subclass: nmea.forwarders.AISTCPServer
    port: 7003
  - cls: nmea.computers.AISManager

And OpenCPN is happy in both cases.

See more details here.

Friday, August 30, 2019

Autonomous Raspberry Pi

Solar powered, with a wireless keyboard and touchpad.

Solar powered, with a wireless keyboard and touchpad.

The solar panel

Closed. The keyboard can also fit in the box.

Sunday, April 21, 2019

San Juan Islands, WA

Data logging in San Juan Islands, Washington:

Apr 19-21, San Juan Island

Apr 22, San Juan Island to Orcas Island

Apr 23, Hiking in Orcas Island

Apr 24, Hiking in Orcas Island, Mountain Lake

Apr-25, Back ashore

Data logging was done as explained here, here and here.

Tuesday, March 12, 2019

Easy Low Pass Filter

Instead of implementing a buffer and smooth it, there is a much easier and efficient way to do it. Here is a simple JavaScript implementation. First you define your accumulator function:
 function lowPass(alpha, value, acc) {
   return (value * alpha) + (acc * (1 - alpha));
Then you need to define your APLHA coefficient:
 const ALPHA = 0.015;
Then you can invoke the accumulator with the aplha coefficient on the data to smooth:
 let filteredGustArray = [];
 let acc = 0; => {
   acc = lowPass(ALPHA, dp.gust, acc);
This produces an array containing the smoothed data. Here is a representation of what it looks like, along with the data to smooth (raw data in red, smoothed data in blue):
The demo data are available here. Just run the script with nodejs like
 $ node max.gust.js both > data.csv
This will produce a csv file you can then import into any spreadsheet program, to see the figure above.