I had to solve a problem. The problem was that I did not wanted to have the exact same session and screen shared across different work places/locations simultaneously. From looking at the same screen from a different floor to have the option to just walk over to the lab-desk solder some circuits together and have the very same documents opened already and set on the screens over there.
One option was to use a tablet or notebook and carry it around. But this would not solve the need to have the screen content displayed on several screens simultaneously.
Also I did not want to rely on the computing power of a notebook / tablet alone. Of course those would get more powerful over time. But each step would mean I would have to purchase a new one.
Then in a move of desperation I remembered the “old days” when ThinClients used to be the new-kid in town. And then I tried something:
It turns out: Nothing really. Docker is well prepared to host desktop environments. With a bit of tweaking and TigerVNC Xvnc I was able to pre-configure the most current Ubuntu to start my preferred Mate desktop environment in a container and expose it through VNC.
So I took one of those RaspberryPis, booted up the Raspbian Desktop lite and connected to the dockers VNC port. It all worked just like that.
The screenshot above holds an additional information for you. I wanted sound! Video works smooth up to a certain size of the moving video – after all those RaspberryPis only come with sub Gbit/s wired networking. But to get sound working I had to add some additional steps.
First on the RaspberryPI that you want to output the sound to the speakers you need to install and set-up pulseaudio + paprefs. When you configure it to accept audio over the network you can then configure the client to do so.
In the docker container a simple command would then redirect all audio to the network:
pax11publish -e -S thinclient
Just replace “thinclient” with the ip or hostname of your RaspberryPI. After a restart Chrome started to play audio across the network through the speakers of the ThinClient.
Now all my screens got those RaspberryPIs attached to them and with Docker I can even run as many desktop environments in parallel as I wish. And because VNC does not care about how many connections there are made to one session it means that I can have all workplaces across the house connected to the same screen seeing the same content at the same time.
And yes: The UI and overall feel is silky smooth. And since VNC adapts to some extend to the available bandwidth by changing the quality of the image even across the internet the VNC sessions are very much useable. Given that there’s only 1 port for video and 1 port for audio it’s even possible to tunnel those sessions across to anywhere you might need them.
In 2017 Texas Instruments had released a line of cheap industry grade LED projectors meant to be used in production lines and alike:
DLP® LightCrafter Display 2000 is an easy-to-use, plug-and-play evaluation platform for a wide array of ultra-mobile and ultra-portable display applications in consumer, wearables, industrial, medical, and Internet of Things (IoT) markets. The evaluation module (EVM) features the DLP2000 chipset comprised of the DLP2000.2 nHD DMD, DLPC2607 display controller and DLPA1000 PMIC/LED driver. This EVM comes equipped with a production ready optical engine and processor interface supporting 8/16/24-bit RGB parallel video interface in a small-form factor.
After I had learned about the existence of those small projectors I had to get a couple and try for myself. There would be so many immediate and potential applications in our house.
After having them delivered I did the first trial with just a breadboard and the Raspberry Pi 3.
The projector module has a native resolution of 640×360 – so not exactly high-pixel-density. And of course if the image is projected bigger the screen-door effect is quite noticeable. Also it’s not the brightest of images depending on the size. For the usual use-cases the brightness is definitely sufficient.
too low brightness for large projection size – no daylight projection
low resolution is an issue for text and web content – it is not so much of an issue for moving pictures as you might think. Video playback is well usable.
flimsy optics that you need to set focus manually – works but there is no automatic focus or alike.
very low powered – 2.5A/5V USB power supply is sufficient for Pi Zero + Projector on full brightness (30 lumen)
low brightness is not always bad – one of our specific use cases requires an as dim as possible image with fine grain control of thr brightness which this projector has.
extremely small footprint / size allows to integrate this device into places you would not have thought of.
almost fully silent operation – the only moving part that makes a sound is the color wheel inside the DLP module. You have to put your ear right onto it to hear anything.
passive cooling sufficient – even at full brightness an added heat sink is enough to dissipate the heat generated by the LED.
So what are these use cases that require such a projector you ask?
Night status display:
For the last 20+ years I am used to sleep with a “night playlist” running. So far a LED TV was used at the lowest brightness possible. Still it was pretty bright. The projector module allows to dim the brightness down to almost “moon brightness” and also allows to adjust the color balance towards the reds. This means: the perfect night projection is possible! And the power consumption is extremely low. A well watchable lowest brightness red-shifted image also means much lower temperatures on the projector module – it’s crazy how low powered, low temperature.
Season Window Projection:
Because the projector is small, low-powered and bright enough for back-lit projection we tried and succeeded with a Halloween window projection scene the last season.
It really looks funky from the outside – funky enough to have several people stop in front of the house and point fingers. All that while power consumption was really
House overall status projections:
When projecting information is that cheap and power efficient it really shines when used to display overall status information like house-alarm status, general switch maps, locations of family members and so on. I’ve left those to your imagination as these kind of status displays are more or less giving away a lot of personal information that isn’t well suited for the internet.
This is Leela. She is a 7 year old lilac white British short hair cat that lives with us. Leela had a sister who used to live with us as well but she developed a heart condition and passed away last year. Witnessing how quickly such conditions develop and evaluate we thought that we can do something to monitor Leelas health a bit to just have some sort of pre-alert if something is changing.
Kid in a Candystore
As this Internet of Things is becoming a real thing these days I found myself in a candy store when I’ve encountered that there are a couple of really really cheap options to get a small PCB with input/output connectors into my house WiFi network.
One of the main actors of this story is the so called ESP8266. A very small and affordable system-on-a-chip that allows you to run small code portions and connect itself to a wireless network. Even better it comes with several inputs that can be used to do all sorts of wonderful things.
And so it happened that we needed to know the weight of our cat. She seemed to get a bit chubby over time and having a point of reference weight would help to get her back in shape. If you every tried to weigh a cat you know that it’s much easier said than done.
The alternative was quickly brought up: Build a WiFi-connected scale to weigh her litter box every time she is using it. And since I’ve recently bought an evaluation ESP8266 I just had to figure out how to build a scale. Looking around the house I’ve found a broken human scale (electronics fried). Maybe it could be salvaged as a part donor?
A day later I’ve done all the reading on that there is a thing called “load-cell”. Those load cells can be bought in different shapes and sizes and – when connected to a small ADC they deliver – well – a weight value.
I cracked the human scale open and tried to see what was broken. It luckily turned out to have completely fried electronics but the load-cells where good to go.
Look at this load cell:
That brought down the part list of this project to:
an ESP8266 – an Adafruit Huzzah in my case
a HX711 ADC board to amplify and prepare the signal from the load-cells
a human scale with just enough space in the original case to fit the new electronics into and connect everything.
The HX711 board was the only thing I had to order hardware wise – delivered the next day and it was a matter of soldering things together and throwing in a small Arduino IDE sketch.
My soldering and wiring skills are really sub-par. But it worked from the get-go. I was able to set-up a small Arduino sketch and get measurements from the load-cells that seemed reasonable.
Now the hardware was all done – almost too easy. The software would be the important part now. In order to create something flexible I needed to make an important decision: How would the scale tell the world about it’s findings?
Two basic options: PULL or PUSH?
Pull would mean that the ESP8266 would offer a webservice or at least web-server that exposes the measurements in one way or the other. It would mean that a client needs to poll for a new number in regular intervals.
Push would mean that the ESP8266 would connect to a server somewhere and whenever there’s a meaningful measurement done it would send that out to the server. With this option there would be another decision of which technology to use to push the data out.
Now a bit of history: At that time I was just about to re-implement the whole house home automation system I was using for the last 6 years with some more modern/interoperable technologies. For that project I’ve made the decision to have all events (actors and sensors) as well as some additional information being channeled into MQTT topics.
“MQTT1 (formerly MQ Telemetry Transport) is an ISO standard (ISO/IEC PRF 20922) publish-subscribe-based “lightweight” messaging protocol for use on top of the TCP/IP protocol. It is designed for connections with remote locations where a “small code footprint” is required or the network bandwidth is limited. The publish-subscribe messaging pattern requires a message broker. Thebroker is responsible for distributing messages to interested clients based on the topic of a message. Andy Stanford-Clark and Arlen Nipper of Cirrus Link Solutions authored the first version of the protocol in 1999.”
Something build for oil-pipelines can’t be wrong for your house – can it?
So MQTT uses the notation of a “topic” to sub-address different entities within it’s network. Think of a topic as just a simple address like “house/litterbox/weight”. And with that topic MQTT allows you to set a value as well.
The alternative to MQTT would have been things like WebSockets to push events out to clients. The decision for the home-automation was done towards MQTT and so far it seems to have been the right call. More and more products and projects available are also focussing on using MQTT as their main message transport.
For the home automation I had already set-up a demo MQTT broker in the house – and so naturally the first call for the litterbox project was to utilize that.
The folks of Adafruit provide the MQTT library with their hardware and within minutes the scale started to send it’s measurements into the “house/litterbox/weight” topic of the house MQTT broker.
Some tweaking and hacking later the litterbox was put together and the actual litterbox set on-top.
Since Adafruit offers platform to also send MQTT messages towards and create neat little dashboards I have set-up a little demo dashboard that shows a selection of data being pushed from the house MQTT broker to the Adafruit.io MQTT broker.
These are the raw values which are sent into the weight topic:
So the implementation done and used now is very simple. On start-up the ESP8622 initialises and resets the weight to 0. It’ll then do frequent weight measurements at the rate it’s configured in the source code. Those weight measurements are being monitored for certain criteria: If there’s a sudden increase it is assumed that “the cat entered the litterbox”. The weight is then monitored and averaged over time. When there’s a sudden drop of weight below a threshold that last “high” measurement is taken as the actual cat weight and sent out to a /weight topic on MQTT. The regular measurements are sent separately to also a configurable MQTT topic.
And off course with a bit of logic this would be the calculated weight topic:
Of course it is not enough to just send data into MQTT topics and be done with it. Of course you want things like logging and data storage. Eventually we also wanted to get some sort of notification when states change or a measurement was taken.
MQTT, the cloud and self-hosted
Since MQTT is enabling a lot of scenarios to implement such actions I am going to touch just the two we are using for our house.
We wanted to get a push notification to our phones whenever a weight measurement was taken – essentially whenever the cat has done something in the litterbox. The easiest solution: Set-Up a recipe on If This Than That (IFTTT) and use PushOver to send out push notifications to whatever device we want.
To log and monitor in some sort of a dashboard the easiest solution seemed to be Adafruits offer. Of course hosted inside our house a combination of InfluxDB to store, Telegraf to gather and insert into InfluxDB and Chronograf to render nice graphs was the best choice.
Since most of the above can be done in the cloud (as of: outside the house with MQTT being the channel out) or inside the house with everything self-hosted. Some additional articles will cover these topics on this blog later.
There’s lots of opportunity to add more logic but as far as our experiments and requirements go we are happy with the results so far – we now regularly get a weight and the added information of how often the cat is using her litterbox. Especially for some medical conditions this is quite interesting and important information to have.
I am a frequent podcast live-stream listener. And being that I am enjoying the awesome service called xenim streaming network.
Any Podcast producer can join the xsn and with that can live-stream his own Podcast while recording. It’s CDN is based on voluntarily provided resources and pretty rock-solid as far as my experience with it goes.
Since I am a frequent user of this – and I’ve got that gorgeous SONOS hardware scattered around my house – I thought I need to have that service integrated into my SONOS set.
The SONOS system knows the concept of “Music Services”. There are quite a lot of them but xsn is missing. But SONOS is awesome and they got an API!
Unfortunately the API documentation is hidden behind a NDA wall so that would be a no-go. What’s not hidden is what the SONOS controllers have to discuss with all the existing services. Most of the time these do not use HTTPS so we’re free to listen to the chatters. I did just that and was able, for the sake of interoperability, to reverse engineer the SONOS SMAPI as far as it is necessary to make my little xsn Music Service work.
Step 1:Start your SONOS Controller Application and find out the IP address of your SONOS.
Click on “About My Sonos System” and check the IP address written next to the “Associated ZP”.
Step 2: Add the xsn Music Service.
By opening a browser window and browsing to: http://<your-associated-zp-ip>:1400/customsd.htm
When you’re there – fill out the fields as below. The SID is either 255, or if you used that previously, something between 240-253. The service name is “xenim streaming network”. The Endpoint URL and Secure Endpoint URL both are http://xsn.schrankmonster.de/xsn
Set the Polling interval to 30 seconds. Click on the Anonymous Authentication SOAP header policy and you’re good to go. Click on “send” to finish.
Step 3: Add the new Music Service to your SONOS Controller.
Click on “Add Music Services” and click through until you see “xenim streaming network”. Add the service and you’re set!
p.s.: It’s normal that the service icon is a question mark.
After setting up Boblight on two TVs in the house – one with 50 and one with 100 LEDs – I’ve used it for the last 5 months on a daily basis almost.
First of all now every screen that does not come with “added color-context” on the wall seems off. It feels like something is missing. Second of all it has made watching movies in a dark room much more enjoyable.
The only concerning factor of the past months was that the RaspberryPi does not come with a lot of computational horse-power and thus it has been operating at it’s limits all the time. With 95-99% CPU usage there’s not a lot of headroom for unexpected bitrate spikes and what-have-you.
So from time to time the Pis where struggling. With 10% CPU usage for the 50 LEDs and 19% CPU usage for the 100 LEDs set-up there was just not enough CPU power for some movies or TV streams in Full-HD.
So since even overclocking only slightly improved the problem of Boblight using up the precious CPU cycles for a fancy light-show I started looking around for alternatives.
“Hyperion is an opensource ‘AmbiLight’ implementation controlled using the RaspBerry Pi running Raspbmc. The main features of Hyperion are:
Low CPU load. For a led string of 50 leds the CPU usage will typically be below 1.5% on a non-overclocked Pi.
Json interface which allows easy integration into scripts.
A command line utility allows easy testing and configuration of the color transforms (Transformation settings are not preserved over a restart at the moment…).
Priority channels are not coupled to a specific led data provider which means that a provider can post led data and leave without the need to maintain a connection to Hyperion. This is ideal for a remote application (like our Android app).
HyperCon. A tool which helps generate a Hyperion configuration file.
XBMC-checker which checks the playing status of XBMC and decides whether or not to capture the screen.
Black border detector.
A scriptable effect engine.
Generic software architecture to support new devices and new algorithms easily.
Especially the Low CPU load did raise interest in my side.
Setting Hyperion up is easy if you just follow the very straight-forward Installation Guide. On Raspbmc the set-up took me 2 minutes at most.
If you got everything set-up on the Pi you need to generate a configuration file. It’s a nice JSON formatted config file that you do not need to create on your own – Hyperion has a nice configuration tool. Hypercon:
So after 2 more minutes the whole thing was set-up and running. Another 15 minutes of tweaking here and there and Hyperion replaced Boblight entirely.
What have I found so far?
Hyperions network interfaces are much more controllable than those from Boblight. You can use remote clients like on iPhone / Android to set colors and/or patterns.
It’s got effects for screen-saving / mood-lighting!
It really just uses a lot less CPU resources. Instead of 19% CPU usage for 100 LEDs it’s down to 3-4%. That’s what I call a major improvement
The processing filters that you can add really add value. Smoothing everything so that you do not get bright flashed when content flashes on-screen is easy to do and really helps with the experience.
All in all Hyperion is a recommended replacement for boblight. I would not want to switch back.
Airplay allows you to conveniently play music and videos over the air from your iOS or Mac OS X devices on remote speakers.
Since we just recently “migrated” almost all audio equipment in the house to SONOS multi-room audio we were missing a bit the convenience of just pushing a button on the iPad or iPhones to stream audio from those devices inside the household.
To retrofit the Airplay functionality there are two options I know of:
1: Get Airplay compatible hardware and connect it to a SONOS Input.
You have to get Airplay hardware (like the Airport Express/Extreme,…) and attach it physically to one of the inputs of your SONOS Set-Up. Typically you will need a SONOS Play:5 which has an analog input jack.
2: Set-Up a RaspberryPi with NodeJS + AirSonos as a software-only solution
You will need a stock RaspberryPi online in your home network. Of course this can run on virtually any other device or hardware that can run NodeJS. For the Pi setting it up is a fairly straight-forward process:
You start with a vanilla Raspbian Image. Update everything with:
sudo apt-get update
sudo apt-get upgrade
Then install NodeJS according to this short tutorial. To set-up the AirSonos software you will need to install additional avahi software. Especially this was needed for my install:
“The internet of things” is a buzzword used more and more. It means that things around you are connected to the (inter)network and therefore can talk to each other and, when combined, offer fantastic new opportunities.
So NodeRed is a NodeJS based toolset that allows you to create so called “flows” (see picture above). Those flows determine what reacts and happens when things happen. Fantastic, told you!
I tried a couple of times to compile NodeJS on the RaspberryPi and failed miserably. It not only takes ages to compile NodeJS on the Pi. After the successful compile and install run most of the time running it just results in an error message “Illegal Instruction” or “Ungültiger Maschinencode”.
Now there’s a pretty easy way to do that on your own. Run these commands: