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 (https://github.com/OlivierLD/raspberry-coffee.git) 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.

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 OpenCVSwingColor2BW.java:

    // threshold
    Mat threshed = new Mat();
    Imgproc.threshold(gray,
            threshed,
            150, // 127,
            255,
            0);

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 OpenCVSwingColor2BW.java for details.

Adios Papou

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"
context:
  with.http.server: true
  http.port: 9999
  init.cache: true
channels:
  - type: serial
    # GPS
    port: /dev/ttyUSB0
    baudrate: 4800
    verbose: false
  - type: serial
    # AIS
    port: /dev/ttyS0
    baudrate: 38400
    verbose: false
forwarders:
  - type: tcp
    port: 7002
    properties: no.ais.properties
  - type: tcp
    subclass: nmea.forwarders.AISTCPServer
    port: 7003
computers:
  - cls: nmea.computers.AISManager
    properties: ais.mgr.properties

And OpenCPN is happy in both cases.

See more details here.

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.

Path detection with OpenCV and friends

That one is still in Python, Java will follow. See here.

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.

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;
 data.data.forEach(dp => {
   acc = lowPass(ALPHA, dp.gust, acc);
   filteredGustArray.push(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.

Smart TCP Watch, prototype.

TCP, no BlueTooth (and as a result, no Smart Phone) is required. The "watch" can connect directly to the network.
See a first prototype here.
And a short video here.

Thanksgiving Kayaking in Drakes Estero

Very cool!

The logging is done with a Raspberry Pi connected to a GPS, as explained here.

Smart watch..., who's smart, who's watching?

I got a Pebble watch a while back, for $100 it did everything I was expecting from it. I came with a cloud/web-based IDE, a really fine piece of work, it was possible to develop and debug applications running on the watch in JavaScript, that was really well done, smart and neat.
Then a while back, Pebble got acquired by Fitbit. And now, the Cloud IDE of Pebble is not available anymore. Than means I cannot develop new apps for my Pebble, even if it is still working just fine.
Why would I buy a new watch (twice as expensive), and re-write all my apps? This is quite frustrating...
In fact, there was something wrong from the beginning.
All those so-called smart watches need a Bluetooth cell-phone to connect to, and from there it will reach other data. Why not a TCP-based protocol from the watch, to bypass the phone? Power consumption? I doubt it.
This is not about accessibility or configuration either, I do it all the time for Raspberry Pis and similar boards, ssh and similar protocols have been here for this kind of remote access, for ages, and for good reasons.
Smart glasses, head-up displays (HUD), smart watches, all those devices are just displays, all they need is to connect to a data bus and display what they mean to (just like an NMEA bus).
I suspect some marketing bullshit behind the scene..., again.

If this kind of TCP watch does not show up soon, I'll build one. Bam!

Raspberry PI, PWM, servos, and PCA9685

The code mentioned below can be found in this git repo.
Pulse Width Modulation (PWM) is the technique used from a digital source to simulate an analog output.
For example, imagine that you want to dim an led from a digital device, to make it look like it is glowing. The digital device only has pins that can take 2 values: 0 or 3V3.
0 means that the led will be off, 3V3 means it will be on, at 100% of its brightness.
In short, it is on or off, and there is nothing in between.
But here is an idea to work around that issue:
To show it at 50% of its brightness, the idea is to turn it off 50% of the time, and on 50% of the time.
To show it at 25% of its brightness, it will be on 25% of the time, and off 75% of the time.
If the on-off cycles are short and fast enough, a human eye will no be able to see them, it will only have the illusion of the resulting brightness.
A human eye cannot make the distinction between images separated by less than one 10^th of a second. That is why the movies are shot at 24 images per second, so you cannot tell the difference between the frames.
This technique is call Persistence of Vision (POV).
The #1 parameter of PoV is the human retina. To have an idea of how much it is important, just put your cat in front of a TV, and see how much he/she reacts. To a cat, it might just be a fuzzy screen...

The early movies - like Charlie Chaplin's silent ones - were shot at 16 images per second, fast enough to induce POV. They were later projected by faster projectors - 24 frames per second. That is why the characters seem to move faster. They were originally moving normally.

Here are examples of PWM applied to POV:
At work, for real:

The PCA9685 is a servo driver PCB.
The Raspberry PI does not have analog pins, we need to use Pulse Width Modulation to simulate analog values, a servo is an analog device.
We use for that the method setPWM(channel, 0, pulse), that will eventually write to the registers of the device.
An instruction like setPWM(channel, 0, pulse) means:

• On channel channel (0 to 15 on the PCA9685)
• in each cycle, turn the power on between 0 and pulse.

pulse has a value between 0 and 4095, that is 4096 distinct values, 4096 is 2¹², the PCA9685 is a 12 bit device.

The frequency

The frequency is provided in Hertz (Hz). A frequency of 60 means 60 cycles per second.
At 60 Hz, a cycle will be 1 / 60 second, which is 0.01666666 second, or 16.66666 milli-second (ms).

The pulse

For each of the cycles set above by setting the frequency, we need to determine the int value, between 0 and 4095, corresponding to the pulse in milliseconds we want to simulate with PWM.
In the class i2c.servo.pwm.PCA9685.java, this is done in this method:

public static int getServoValueFromPulse(int freq, float targetPulse) {
  double pulseLength = 1_000_000; // 1s = 1,000,000 us per pulse. "us" is to be read "micro (mu) sec".
  pulseLength /= freq;  // 40..1000 Hz
  pulseLength /= 4_096; // 12 bits of resolution. 4096 = 2^12
  int pulse = (int) Math.round((targetPulse * 1_000) / pulseLength); // in millisec
  if (verbose) {
    System.out.println(String.format("%.04f \u00b5s per bit, pulse: %d", pulseLength, pulse));
  }
  return pulse;
}

The cycle length (in ms) obviously depends on the frequency.
The pulse required for the servo to work is emitted once per cycle.

Example

As an example, let us calculate for a 60 Hz frequency the pulse value to send to setPWM(channel, 0, pulse) for a 1.5 millisecond PWM:

• 1 cycle has a duration of 1 / 60 second, or 16.66666 milliseconds.
• each cycle is divided in 4096 slots, we can say that 4096 bits = 16.6666 ms.
• the solution is provided by a rule of three: value = 4096 * (pulse / 16.66666), which is 368.64, rounded to 369.

A comment about servos' compliance and reliability

Theoretically, servos follow those rules:

Pulse	Standard	Continuous
1.5 ms	0 °	Stop
2.0 ms	90 °	FullSpeed forward
1.0 ms	-90 °	FullSpeed backward

That happens not to be always true, some servos (like https://www.adafruit.com/product/169 or https://www.adafruit.com/product/155) have values going between 0.5 ms and 2.5 ms.
Before using them, servos should be calibrated. You can use the class i2c.samples.IntercativeServo.java can be used for that, you set the pulse values interactively, and you see what the servo is doing.

$> ./inter.servo
Connected to bus. OK.
Connected to device. OK.
freq (40-1000)  ? > 60
Setting PWM frequency to 60 Hz
Estimated pre-scale: 100.72526
Final pre-scale: 101.0
Servo Channel (0-15) : 1
Entry method: T for Ticks (0..4095), P for Pulse (in ms) > p
Enter 'quit' to exit.
Pulse in ms > 1.5
setServoPulse(1, 1.5)
4.0690 μs per bit, pulse:369
-------------------
Pulse in ms > 0.5
setServoPulse(1, 0.5)
4.0690 μs per bit, pulse:122
-------------------
Pulse in ms > 0.6
setServoPulse(1, 0.6)
4.0690 μs per bit, pulse:147
-------------------
Pulse in ms > 2.4
setServoPulse(1, 2.4)
4.0690 μs per bit, pulse:589
-------------------
Pulse in ms > 2.5
setServoPulse(1, 2.5)
4.0690 μs per bit, pulse:614
-------------------
... etc.

Once you have determined the appropriate min and max values, you also have the int values to feed the setPWM with.

---

Some links:

• https://github.com/OlivierLD/raspberry-pi4j-samples/blob/master/I2C.SPI/PWM.md
• http://raspberrypi.lediouris.net/servo/readme.html
• POV at work

---

Languages Comparison

For the fun: Same problem addressed in several languages, read the paper here.

It is about matrix and systems of equations resolution, curve smoothing, etc.

Done in C, Java, Processing, Scala, Kotlin, Python, JavaScript, Groovy, Go, Clojure (in progress), ...

Controlling invisible machines with emails, from Java

Here is the problem

You have your network at home, with several machines connected to it (laptops, tablets, Raspberry PIs, phones, etc). Your home network is a Local Area Network (aka LAN), the machines can see each other, but they cannot be seen from outside, from the Internet.
You may very well want to deal with those machines while away from home, to restart services, launch a new program, or even reboot.
In the configuration mentioned above, this is simple, you just cannot do it. And that is frustrating!
There is a way though. Those machines on your home LAN can send and receive emails...

Using JavaMail

JavaMail is a Java package that has been available for ever, it understands the email protocols (IMAP, POP3, SMTP, etc), and can be used to interact with email accounts programmatically.

An example

There is an example of such an interaction on this github repository.
The fastest way to get it running is to run the following commands (these are for Linux - and MacOS - on Windows, use the git shell):

$ git clone https://github.com/OlivierLD/raspberry-coffee.git
$ cd raspberry-coffee
$ cd common-utils
$ ../gradlew shadowJar
$ cp email.properties.sample email.properties
$ vi email.properties
$ # Here you modify your properties file to match your email account
$ java -cp ./build/libs/common-utils-1.0-all.jar email.examples.EmailWatcher -send:google -receive:google

The -send:google -receive:google depends on the settings in your email.properties.
Then, to the account mentioned in the email.properties, send a message like this:

Subject: execute
Content:
whoami
ifconfig
uname -a

Note: this example requires the content to be in plain/text.
Once the message is received by the EmailWatcher, it sends you an acknowledgement:

Then, the 3 commands are processed by the EmailWatcher, you would see in its console an output like that:

Start receiving.
Received:
whoami
ifconfig
uname -a

Operation: [execute], sent for processing...
pi
lo0: flags=8049 mtu 16384
 options=1203
 inet 127.0.0.1 netmask 0xff000000 
 inet6 ::1 prefixlen 128 
...

And finally, you receive an email like that:

... meaning that the commands you've sent have been executed.

You can also attach the script to execute to a blank email, with topic execute-script:
Attach a file like this:

#!/bin/bash
whoami
ifconfig
ps -ef | grep EmailWatcher

... and just wait for the result to come back to you:

Scripts execution returned: 
pi
eth0: flags=4099  mtu 1500
        ether a4:ba:db:c9:04:2e  txqueuelen 1000  (Ethernet)
        RX packets 0  bytes 0 (0.0 B)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 0  bytes 0 (0.0 B)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0
        device interrupt 18  

lo: flags=73  mtu 65536
        inet 127.0.0.1  netmask 255.0.0.0
        inet6 ::1  prefixlen 128  scopeid 0x10
        loop  txqueuelen 1  (Local Loopback)
        RX packets 9215  bytes 2022884 (1.9 MiB)
        RX errors 0  dropped 0  overruns 0  frame 0
        TX packets 9215  bytes 2022884 (1.9 MiB)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0

wlan0: flags=4163  mtu 1500
        inet 192.168.42.3  netmask 255.255.255.0  broadcast 192.168.42.255
        inet6 fe80::4038:1f53:b94f:ccc2  prefixlen 64  scopeid 0x20
        ether 78:e4:00:78:ad:8f  txqueuelen 1000  (Ethernet)
        RX packets 8848724  bytes 696021134 (663.7 MiB)
        RX errors 0  dropped 0  overruns 0  frame 18848059
        TX packets 6040965  bytes 795472510 (758.6 MiB)
        TX errors 0  dropped 0 overruns 0  carrier 0  collisions 0
        device interrupt 17  base 0xc000  

pi      12476 12472  1 16:39 pts/0    00:00:53 java -cp ./build/libs/RasPISamples-1.0-all.jar weatherstation.email.EmailWatcher -send:google -receive:google
pi      16204 16199  0 18:04 pts/0    00:00:00 grep EmailWatcher
>> sh ./attachments/2018-04-26_18-04-27/sample.sh returned status 0

Comments

This process is not synchronous, this could be a drawback... But still, it allows you to interact remotely with machines invisible from the Internet.

Having the command

java -cp ./build/libs/common-utils-1.0-all.jar email.examples.EmailWatcher -send:google -receive:google

fired when the machine boots will allow you make sure it is waiting for your emails as soon as the machine is up.

This EmailWatcher as it is also allows you to execute scripts, attached to the email. Look into the code for details ;)
It is even possible to ssh to another machine and execute a bunch of commands stored in a script... The command you send in the email's body would be like

ssh pi@192.148.42.13 bash -s < ~/nodepi.banner.sh

If a password is required, use sshpass:

sshpass -p 'secret-password' ssh pi@192.148.42.13 bash -s < ~/nodepi.sudo.sh

You can even sudo:

echo 'secret-password' | sudo -S privilegedCommand

This can be dangerous, hey? With great power come great responsibilities...

Head-Up Display (HUD)

The idea here is to display a screen on a transparent support - like a wind shield.
The data are displayed on the screen, reflected on the transparent support, and nothing is preventing you from seeing through it.

(Click the image to enlarge it)

Here is above an HTML page, tweaked by some CSS classes to mirror the data (as the page is reflected on the screen, the page content has to be displayed as in a mirror, and flipped upside down.). In this case, the page is rendered on Chromium in kiosk mode, running on a Raspberry PI with a touch screen attached to it.

CSS Classes:

    .mirror {
      display: block;
      -webkit-transform: matrix(-1, 0, 0, 1, 0, 0);
      -moz-transform: matrix(-1, 0, 0, 1, 0, 0);
      -o-transform: matrix(-1, 0, 0, 1, 0, 0);
      transform: matrix(-1, 0, 0, 1, 0, 0);
    }

    .upside-down {
      height: 100%;
      width: 100%;
      -moz-transform: rotate(180deg);
      -webkit-transform: rotate(180deg);
      -ms-transform: rotate(180deg);
      -o-transform: rotate(180deg);
      transform: rotate(180deg);
    }

    .mirror-upside-down {
      display: block;
      -webkit-transform: matrix(-1, 0, 0, 1, 0, 0) rotate(180deg);
      -moz-transform: matrix(-1, 0, 0, 1, 0, 0) rotate(180deg);
      -o-transform: matrix(-1, 0, 0, 1, 0, 0) rotate(180deg);
      transform: matrix(-1, 0, 0, 1, 0, 0) rotate(180deg);
    }

In the picture above, we use the class as follow:

<div id="the-div" class="mirror-upside-down big" style="padding: 0px; text-align: center;">
  <hr/>
  <table>
    <tr>
      <td colspan="2">GPS Data</td>
    </tr>
    <tr>
      <td>
        <span>Your position:</span>
        <br/>
        <span>N 37° 44.93'</span>
...

The page on the screen (not on the wind shield) would actually look like this:

---

GPS Data
Your position: N 37° 44.93' W 122°30.42'	Your Speed: 12.34 kts

You can also work around the perspective effect on the reflected page by tweaking the CSS classes:

    .mirror-upside-down {
      display: block;
      -webkit-transform: matrix(-1, 0, 0, 1, 0, 0) rotate(180deg) perspective(50em) rotateX(-40deg);
      -moz-transform: matrix(-1, 0, 0, 1, 0, 0) rotate(180deg) perspective(50em) rotateX(-40deg);
      -o-transform: matrix(-1, 0, 0, 1, 0, 0) rotate(180deg) perspective(50em) rotateX(-40deg);
      transform: matrix(-1, 0, 0, 1, 0, 0) rotate(180deg) perspective(50em) rotateX(-40deg);
    }

---

GPS Data
Your position: N 37° 44.93' W 122°30.42'	Your Speed: 12.34 kts

We call this the Star Wars effect. ;)

Possibilities are endless!
The full page is here.

Docker on the Raspberry PI

This post intends to illustrate how Docker can work around the "But it works on my machine!.." syndrome.

Let's say you have a nodejs project you want to share with others.
The application reads GPS data through a Serial port, and feeds a WebSocket server.
The data can then be visualized through a Web interface.

To enable everything, you need to:

1. Have a Raspberry PI
2. Flash its SD card and connect it to a network
3. Install build tools
4. Install git
5. Install NodeJS and npm
6. Clone the right git repository
7. Install all the required node modules
8. Drill down into the right directory
9. Start the node server with the right script
10. Access the Raspberry PI from another machine on the same network, and reach the right HTML page.

This is certainly not difficult, but there are many ways to do several mistakes at each step of the process!

Docker can take care of the steps 3 to 9. It will build the image, and then run it.
The image can also be pushed to a repository, so users would not have to build it.
Just to run it after downloading it.

The only pre-requisite would be to have installed Docker on the machine (the Raspberry PI here), as explained here.
Create a Dockerfile like this (available here):

 FROM resin/raspberrypi3-debian:latest

 LABEL maintainer="Olivier LeDiouris <olivier@lediouris.net>"

 RUN echo "alias ll='ls -lisah'" >> $HOME/.bashrc

 RUN apt-get update
 RUN apt-get install sysvbanner
 RUN apt-get install -y curl git build-essential
 RUN curl -sL https://deb.nodesource.com/setup_9.x | bash -
 RUN apt-get install -y nodejs
 RUN echo "banner Node-PI" >> $HOME/.bashrc
 RUN echo "git --version" >> $HOME/.bashrc
 RUN echo "echo -n 'node:' && node -v" >> $HOME/.bashrc
 RUN echo "echo -n 'npm:' && npm -v" >> $HOME/.bashrc

 RUN mkdir /workdir
 WORKDIR /workdir
 RUN git clone https://github.com/OlivierLD/node.pi.git
 WORKDIR /workdir/node.pi
 RUN npm install

 EXPOSE 9876
 CMD ["npm", "start"]

In this case, the full Docker image creation (named oliv-nodepi below) comes down to 1 line (the one in bold red):

 $ docker build -t oliv-nodepi .
Sending build context to Docker daemon  752.6kB
Step 1/20 : FROM resin/raspberrypi3-debian:latest
 ---> c542b8f7a388
Step 2/20 : MAINTAINER Olivier LeDiouris 
 ---> Using cache
 ---> b2ff0d7c489f
Step 3/20 : ADD nodepi.banner.sh /
 ---> 535733298dd1
Step 4/20 : RUN echo "alias ll='ls -lisah'" >> $HOME/.bashrc
 ---> Running in 09baf7261a55
Removing intermediate container 09baf7261a55
 ---> 71e1e4c95663
Step 5/20 : RUN apt-get update
 ---> Running in 5d817a941a14
Get:1 http://security.debian.org jessie/updates InRelease [94.4 kB]
Get:2 http://archive.raspbian.org jessie InRelease [14.9 kB]
Get:3 http://archive.raspberrypi.org jessie InRelease [22.9 kB]

...

npm notice created a lockfile as package-lock.json. You should commit this file.
added 166 packages in 81.166s
Removing intermediate container 13986530db28
 ---> 051eb94b8a3c
Step 19/20 : EXPOSE 9876
 ---> Running in 67b587845fe0
Removing intermediate container 67b587845fe0
 ---> 46973b7ba9ac
Step 20/20 : CMD ["npm", "start"]
 ---> Running in 153bf2ea02ad
Removing intermediate container 153bf2ea02ad
 ---> 6bf3d76d38ae
Successfully built 6bf3d76d38ae
Successfully tagged oliv-nodepi:latest
ed9a7d9042dddd3939b1788cf0e89d16f5273192a6456266507f072f90ce91bc
 $

Once the step above is completed, plug in your GPS, and run

 $ docker run -p 9876:9876 -t -i --privileged -v /dev/ttyUSB0:/dev/ttyUSB0 -d oliv-nodepi:latest

Then from a machine seeing the Raspberry PI on its network (it can be the Raspberry PI itself), reach http://raspi:9876/data/demos/gps.demo.wc.html in a browser.

This shows you the position the GPS has computed, and the satellites in sight.
You can also login to the image:

 $ docker run -it oliv-nodepi:latest /bin/bash

 #     #                                 ######    ###
 ##    #   ####   #####   ######         #     #    #
 # #   #  #    #  #    #  #              #     #    #
 #  #  #  #    #  #    #  #####   #####  ######     #
 #   # #  #    #  #    #  #              #          #
 #    ##  #    #  #    #  #              #          #
 #     #   ####   #####   ######         #         ###

 git version 2.1.4
 node:v9.11.1
 npm:5.6.0
 root@b9679d0d65a7:/workdir/node.pi#

... and do whatever you like.
The build operation needs to be done once.
There is no need to do it again as long as no change in the image is required.

Quick comment

So, with Docker, you do not deliver a software, you actually deliver an image (a virtual machine), on which a software is running.
This is indeed redefining the concept of portability that made Java and other JVM-aware languages so successful.
This may very well explain the rise of languages like Golang (aka Go).
It runs on my machine? Well, here is my machine! You can download and run it. Enjoy!

Java Weather Station

This is a SwitchDocLabs SDLWeather80422 Weather Station, installed on the roof.

It is connected to a Raspberry PI A+, all the software is written in Java, no Python, no Arduino-like code, no C++.

There is an optional nodeJS server that runs on the Raspberry PI too, to enable WebSockets.

Find the core code here, and the example implementation here.

MySQL, PHP, Web Interface

Web-Components interface, pings the server every second.

WebSocket Web Interface, updated in real-time.

---

Some papers by John Shovic turned out to be very useful, specially in understanding what this debounce aspect is all about.

See it live here.

It even comes with a pebble application.

Moving the Raspberry PI away from Swing

Rationale

This starts from a simple observation. A Raspberry PI can run on a boat, and consumes a very small amount of energy. It can do a lot of computations, logging, and multiplexing, among many others. It can run 24x7, without you noticing. It makes no noise, almost no light, and requires ridiculous amount of energy to run. Even a Raspberry PI Zero does this kind of job (for even less power), successfully.
One thing it is not good at is graphical UI. A graphical desktop is often too demanding on a small board like the Raspberry PI Zero. It becomes some times really slow, and cumbersome.
Running on it a program like OpenCPN seems absurd to me. Such a program runs fine on a bigger device, with several gigabytes of RAM available.
But, running a laptop 24x7 would be in many cases too demanding, specially on a sailboat, where everyone hates to run the engine ;)
I observed that at sea, I spend only a couple hours a day in front of the laptop, but it is often running by itself, doing some logging or calculations.
This is where it comes together, you could have a Raspberry PI Zero doing logging, multiplexing and what not, broadcasting require data on its own network (see the NMEA Multiplexer about that), then you would use a laptop whenever necessary, connecting on the Raspberry PI's network to get NMEA Data and more.
In addition, you can also use tablets and smart-phones, those devices know how to connect to a network, and have great rendering capabilities.
A problem is that writing a native application on those devices requires specific knowledge of the operating system, those skills are often redundant. iOS, Android, JavaFx, Swing all have UI rendering capabilities, but they're all totally different, and the learning curve for each of them is not always smooth.
A solution would be to write the UI part of the applications using HTML. Whatever OS runs on your laptop, tablet or smartphone (Windows, MacOS, iOS, Linux, Android, etc), you have a browser available, supporting HTML5 (if it does not, you should really upgrade it).
HTML5 and JavaScript have been gaining a lot of momentum in the recent years, new frameworks like jQuery, ionic, ReactJS, ...) appear every day, and provide really rich and nice UI.
My feeling would be to go down this route whenever possible, that would save a lot of efforts, and provide a pretty cool Graphical User Interface (GUI). I have written a lot of GUI in Swing. It would be now time to upgrade it. Re-writing them using JavaFX does not sound like the right choice. If I have to learn a new language to build a modern GUI, for now I'd rather use JavaScript and HTML5. This way, the same code runs whenever a browser exists... You have REST APIs available on the server (again, a Raspberry PI, even the Zero does the job well), and you use AJAX and Promises to get to them from the Web UI (WebSockets are also a realistic option, tested). The computation required to produce the payload returned by the REST services (often in json format) is easily supported by a Raspberry PI, and the complexity of the UI rendering is 100% taken care of by the browser, running on a more powerful device.

Implementation

To make sure all this is realistic, we have a REST implementation of a Tide Server, available here.
First, we have defined the REST Services, like

 /GET /tide-stations
 /GET /tide-stations/{station}
 /POST /tide-stations/{station}/wh?from=XXX&to=YYY
 /POST /tide-stations/{station}/wh/details?from=XXX&to=YYY

this is the easy part - and then an HTML5/JavaScript User Interface.

Harmonic coefficients are available for display

Period of time goes - in this UI - up to 1 month.

For one month, with harmonic coefficients, the volume of data transferred from the server is about 10Mb, it took about 14 seconds to get them.

In a most common case, it is around 25Kb.

This is running on a Raspberry PI, even a Raspberry PI Zero does the job without complaining.
There are a couple of challenges to address, JavaScript is not very well TimeZone equipped. But there are ways to get it to work.
That seems to be a viable approach.

Interestingly, even if we are trying here to address an energy problem - and not a budget one - a Raspberry PI Zero today cost just $10.

NMEA Multiplexer, OpenCPN, GPSd...

I have been working on an NMEA Multiplexer that can run on small boards, like the Raspberry PI Zero. The code is available on github, see the documentation in the README.md.

It allows to mix all kinds of NMEA Sources into a single (or multiple) stream(s). You can read from Serial Ports, Log file(s), TCP, WebSocket, Sensors (like BME280, HTU21DF, LSM303, etc), merge those data and rebroadcast them on Serial port, TCP, Log file, WebSocket, GPSd, etc. UDP is being worked on.

Data can also be computed and injected in the output stream, like True Wind, Current direction and speed, etc.

As a graphical desktop can be cumbersome on small boards, the Multiplexer comes with a tiny HTTP server that provides a Web UI and REST services to allow remote Admin.

It also comes with several demos and samples

This all works just fine with OpenCPN, SeaWi, that can take TCP streams as NMEA Data Input.
I was also wondering about GPSd. I had some mixed feelings about it. Mostly, I was asking myself "Why should I parse GPSd json objects if I can parse NMEA Sentences?", and could not find any satisfying reason. The topic is mentioned on the GPSd web site's FAQ pages, but nothing clear (to me) came up from that.
Interestingly, OpenCPN can also take GPSd streams as input. But there is a trick.
The first GPSd exchange begins with a ?WATCH request. It is followed by a JSON Object like this:

  ?WATCH={"enable":true,"json":true}

... and here is the trick, OpenCPN sends a

 ?WATCH={"enable":true,"nmea":true}

This nmea option is "poorly" documented, but very useful. Instead of sending JSON objects, GPSd spits out the raw NMEA sentences, as they were read. Then GPSd is just a regular TCP stream, and OpenCPN already knows how to parse the NMEA sentences it delivers. This way, GPSd is not limited to strictly GPS-related sentences. It can convey all NMEA sentences, Boat Speed related, Wind related, etc. This is what the GPSd forwarder that comes with the Multiplexer is doing.

Happy Streaming, happy new year!

Live Wallpaper, offline

The Live Wallpaper (in the navigation console) has been improved. Several bugs were fixed (like the one for the daylight), and you can now use it offline, like when no NMEA data are coming. This can be useful when you are not on the boat (like at home).

You can give a default position (in the preferences), and it will be used when no GPS Data is available.
New features:

• The tilt is based on the Sun declination
• You can have Moonlight and Sunlight
• The tide curve of the closest station is displayed (closest in a radius of 100 nautical miles)
• Sun and Moon positions are displayed (when the body is visible)

Code and (some) doc are available on GitHub.

IoT for dummies

Internet of things, what's the big deal?
We have a device, that can read sensors on one side, and connect to the Internet on the other, nothing revolutionary here. The challenge is to reach the device, from somewhere on the Internet, the device that reads the sensors does not necessary have a public IP address.
This is why the key component is the IoT server.
The device (the one with sensors) pushes data on the IoT server. The server can then be read from anywhere on the net.

On top of that, you can push data to the IoT device. For example, in a room:

• the device is connected to a temperature sensor, and pushes temperature data to the IoT server (and so, they can be read from a browser, smartphone...)
• the device is connected to a relay, data can be sent to the device (like from a browser, smartphone...), to drive the relay. If the relay is connected to a heater, you can manage the room temperature.

Typically, you can have a Raspberry PI, Arduino, or similar board, reading - at home - a temperature sensor to publish the air temperature onto the IoT server. You access those data from a browser (from your office, your smart-phone, whatever). Then you may decide to turn the heater on at home, the Raspberry PI (or its friends) has a relay that drives the heater.

Many server also use some push technology (like WebSocket or similar), so a client can be notified. A server like Adafruit IO provides this service for free, Particle also does it to some extend.

In addition, Adafruit IO provides a REST interface, very slick.
See an example of a REST client - in Java - on github. It shows how to read and write data on the server.

A real IoT application is also featured here in github, along with its Adafruit IO dashboard. It reads a BME280 (for the air temperature), and provides a toggle button (switch) that drives a relay on the Raspberry PI, to turn a heater on or off.