In the interesting field of IoT a lot of buzz is made around the predictive maintenance use cases. What is predictive maintenance?
The main promise of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures.
The key is “the right information in the right time”. By knowing which equipment needs maintenance, maintenance work can be better planned (spare parts, people, etc.) and what would have been “unplanned stops” are transformed to shorter and fewer “planned stops”, thus increasing plant availability. Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling.
So in simpler terms: If you can predict that something will break you can repair it before it breaks. This improvse reliability and save costs, even though you repaired something that did not yet need repairs. At least you would be able to reduce inconveniences by repairing/maintaining when it still is easy to be done rather than under stress.
You would probably agree with me that these are a very industry-specific use cases. It’s easy to understand when it is tied to an actual case that happened.
Let me tell you a case that happened here last week. It happened to Leela – a 10 year old white British short hair lady cat with gorgeous blue eyes:
Ever since her sister had developed a severe kidney issue we started to unobtrusively monitor their behavior and vital signs. Simple things like weight, food intake, water intake, movement, regularities (how often x/y/z).
When Leela now visits her litter box she is automatically weighed and it’s taken note that she actually used it.
A lot of data is aggregated on this and a lot of things are being done to that data to generate indications of issues and alerts.
This alerted us last weekend that there could be an issue with Leelas health as she was suddenly visiting the litter box a lot more often across the day.
We did not notice anything with Leela. She behaved as she would everyday, but the monitoring did detect something was not right.
What had happened?
On the morning of March 9th Leela already had been to the litter box above average. So much above average that it tripped the alerting system. You can see the faded read area in the top of the graph above showing the alert threshold. The red vertical line was drawn in by me because this was when we got alerted.
Now what? She behaved totally normal just that she went a lot more to the litter box. We where concerned as it matched her sisters behavior so we went through all the checklists with her on what the issue could be.
We monitored her closely and increased the water supplied as well as changed her food so she could fight a potential bladder infection (or worse).
By Monday she did still not behave different to a degree that anyone would have been suspicious. Nevertheless my wife took her to the vet. And of course a bladder infection was diagnosed after all tests run.
She got antibiotics and around Wednesday (13th March) she actually started to behave much like a sick cat would. By then she already was on day 3 of antibiotics and after just one day of presumable pain she was back to fully normal.
Interestingly all of this can be followed up with the monitoring. Even that she must have felt worse on the 13th.
With everything back to normal now it seems that this monitoring has really lead us to a case of “predictive cat maintenance”. We hopefully could prevent a lot of pain with acting quick. Which only was possible through the monitoring in place.
Health is a huge topic for the future of devices and gadgets. Everyone will casually start to have more and more devices in their daily lifes. Unfortunately most of those won’t be under your own control if you do not insist on being in control.
You do not have to build stuff yourself like I did. You only need to make the right purchase decisions according to things important to you. And one of these things on that checklist should be: “am I in full control of the data flow and data storage”.
If you are not. Do not buy!
By coincidence the idea of having the owner of the data in full control of the data itself is central to my current job at MindSphere. With all the buzz and whistles around the Industry IoT platform it all breaks down to keep the actual owner of the data in control and in charge. A story for another post!
Ever since we had changed our daily diet we started to weigh everything we eat or cook. Like everything.
Quickly we found that those kitchen scale you can cheaply buy are either not offering the convenience we are looking for or regularly running out of power and need battery replacements.
As we already have all sorts of home automation in place anyway the idea was born to integrate en ESP8266 into two of those cheap scales and – while ripping out most of their electronics – base the new scale functionality on the load cells already in the cheap scale.
So one afternoon in January 2018 I sat down and put all the parts together:
After the hardware portion I sat down and programmed the firmware of the ESP8266. The simple idea: It should connect to wifi and to the house MQTT broker.
It would then send it’s measures into a /raw topic as well as receive commands (tare, calibration) over a /cmd topic.
Now the next step was to get the display of the measured weights sorted. The idea for this: write a web application that would connect to the MQTT brokers websocket and receive the stream of measurements. It would then add some additional logic like a “tare” button in the web interface as well as a list of recent measurements that can be stored for later use.
An additional automation would be that if the tare button is pressed and the weight is bigger than 10g the weight would automatically be added to the measurements list in the web app – no matter which of the tare buttons where used. The tare button in the web app or the physical button on the actual scale. Very practical!
Here’s a short demo of the logic, the scale and the web app in a video:
GIST: I am looking for interested hackers who want to help me implement a neural network that improves the accuracy of bluetooth low energy based indoor location tracking.
I am currently applying the last finishing touched to a house wide bluetooth low energy based location tracking system. (All of which will be opensourced)
The system consists of 10+ ESP-32 Arduino compatible WiFi/Bluetooth system-on-a-chip. At least one per room of a house.
These modules are very low powered and have one task: They scan for BLE advertisements and send the mac and manufacturer data + the RSSI (signal strength) over WiFi into specific MQTT topics.
There is currently a server component that takes this data and calculates a probable location of a seen bluetooth low energy device (like the apple watch I am wearing…). It currently is using a calibration phase to level in on a minimum accuracy. And then simple calculation matrices to identify the most probable location.
This all is very nice but since I got interested in neural networks and KI development – and I think many others might as well – I am asking here for also interested parties to join the effort.
I do have an existing set-up as well as gigabytes of log data.
Did you know how dangerous Lithium-Polymer batteries can be? Well, if not treated well they literally burst in flames spontaneously.
So it’s quite important to follow a couple of guidelines to not burn down the house.
Since I am just about to start getting into the hobby of FPV quadcopter racing I’ve tried follow those guidelines and found that the smart house can help me tracking things.
Unfortunately there are not a lot of LiPo chargers available at reasonable price with computer interfaces to be monitored while charging/discharging the batteries. But there are a couple of workarounds I’ve found useful.
a proper case. I’ve got myself one of those “Bat-Safe” boxes that fit a couple of battery packs and help me store them safely. Even if one or many burst into flames the case is going to contain any heat and fire as good as possible and with the air / pressure filter it’ll hopefully get rid of most of the very nasty smoke (I hear). Cables go into it, so the actual charging process takes place with everything closed and latched.
the obvious smoke detector which is on it’s own connected to the overall fire alarm is mounted on top, like literally on top. It’ll send out the alert to all other smoke alarms in the house making them go beep as well as sending out high priority push notifications to everyone.
an actual camera is monitoring the box all the time calling on alerts if something is fishy (like making sound, smoke, movement of any sort). When charging is done the charger will beep – this is being caught by the cameras microphones and alerts are sent out.
the temperature inside the case is monitored all the time. The surrounding temperatures are usually pretty stable as this case is stored in my basement and as the charging goes on the temperatures inside the case will climb up and eventually level out and fall when charging / discharging is done. Now the system basically will look at the temperatures, decide wether it’s rising of falling and alerting appropriately.
There’s a couple more things to it, like keeping track of charging processes in a calendar as you can see in the flowchart behind all the above.
There are a lot of things that happen in the smart house that are connected somehow.
And the smart house knows about those events happening and might suggest, or even act upon the knowledge of them.
A simple example:
In our living room we’ve got a nice big aquarium which, depending on the time of the day and season, it is simulating it’s very own little dusk-till-dawn lightshow for the pleasure of the inhabitants.
Additionally the waterquality is improved by an air-pump generating nice bubbles and enriching the water with oxygen. But that comes a cost: When you are in the room those bubbles and the hissing sound of the inverter for the “sun” produces sounds that are distracting and disturbing to the otherwise quiet room.
Now the smart home comes to the rescue:
It detects that whenever someone is entering the room and staying for longer, or powering up the TV or listening to music. Also it will log that regularly when these things happen also the aquarium air and maybe lights are turned off. Moreso they are turned back on again when the person leaves.
These correlations are what the smart house is using to identify groups of switches, events and actions that are somehow tied together. It’ll prepare a report and will recommend actions which at the push of a button can become a routine task always being executed when certain characteristics are lining up.
And since the smart house is a machine, it can look for correlations in a lot more dimensions a human could: Date, Time, Location, Duration, Sensor and Actor values (power up TV, Temperature in room < 22, Calendar = November, Windows closed => turn on the heating).
Did you notice that most calendars and timers are missing an important feature. Some information that I personally find most interesting to have readily available.
It’s the information about how much time is left until the next appointment is coming up. Even smartwatches, which should should be jack-of-all-trades in regards of time and schedule, do not display the “time until the next event”.
Now I came across this shortcoming when I started to look for this information. No digital assistant can tell me right away how much time until a certain event is left.
But the connected house also is based upon open technologies, so one can add these kind of features easily ourselves. My major use cases for this are (a) focussed work, plan quick work-out breaks and of course making sure there’s enough time left to actually get enough sleep.
As you can see in the picture attached my watch will always show me the hours (or minutes) left until the next event. I use separate calendars for separate displays – so there’s actually one for when I plan to get up and do work-outs.
Having the hours left until something is supposed to happen at a glance – and of course being able to verbally ask through chat or voice in any room of the house how long until the next appointment gives peace of mind :-).
The Internet of Things might as well become your Internet of Money. Some feel the future to be with blockchain related things like BitCoin or Ethereum and they might be right. So long there’s also this huge field of personal finances that impacts our lives allday everyday.
And if you get to think about it money has a lot of touch points throughout all situations of our lifes and so it also impacts the smart home.
Lots of sources of information can be accessed today and can help to stay on top of the things going on as well as make concious decisions and plans for the future. To a large extend the information is even available in realtime.
– cost tracking and reporting
– alerting and goal setting
– consumption and resource management
– like fuel oil (get alerted on price changes, …)
– stock monitoring alerting
– and more advanced even automated trading
– bank account monitoring, in- and outbound transactions
– expectations and planning
After all this is about getting away from lock-in applications and freeing your personal financial data and have a all-over dashboard of transactions, plans and status.
Water! Fire! Whenever one of those are released uncontrolled inside the house it might mean danger to life and health.
Having a couple of fish and turtle tanks spread out in the house and in addition a server rack in the basement it’s important to know when there’s a leak of water at moments notice.
As the server-room also is housing some water pumps for a well you got all sorts of dangers mixed in one location: Water and Fire hazard.
To detect water leaks all tanks and the pumps for the well are equipped with water sensors which send out an alerting signal as soon as water is detected. This signal is picked up and pushed to MQTT topics and from there centrally consumed and reacted upon.
Of course the server rack is above the water level so at least there is time to send out alerts while even power is out for the rest of the house (all necessary network and uplink equipment on it’s own batteries).
For alerting when there is smoke or a fire, the same logic applies. But for this some loud-as-hell smoke detectors are used. The smoke detectors interconnect with each other and make up a mesh for alerting. If one goes off. All go off. One of them I’ve connected to it’s very own ESP8266 which sends a detected signal to another MQTT topic effectively alerting for the event of a fire.
In one of the pictures you can see what happened when the basement water detector did detect water while the pump was replaced.
A lot of things in a household have weight, and knowing it’s weight might be crucial to health and safety.
Some of those weight applications might tie into this:
– your own body weight over a longer timespan
– the weight of your pets, weighed automatically (like on a kitty litter box)
– the weight of food and ingredients for recipes as well as their caloric and nutrition values
– keeping track of fill-levels on the base of weights
All those things are easily done with connected devices measuring weights. Like the kitty-litter box at our house weighing our cat every time. Or the connected kitchen-scale sending it’s gram measurements into an internal MQTT topic which is then displayed and added more smarts through an App on the kitchen-ipad consuming that MQTT messages as well as allowing recipe-weigh-in functions.
It’s not only surveillance but pro-active use. There are beekeepers who monitor the weight of their bee hives to see what’s what. You can monitor all sorts of things in the garden to get more information about it’s wellbeing (any plants, really).
Weekend is laundry time! The smart house knows and sends out notifications when the washing machine or the laundry dryer are done with their job and can be cleared.
Of course this can all be extended with more sensory data, like power consumption measurements at the actual sockets to filter out specific devices much more accurate. But for simple notification-alerting it’s apparently sufficient to monitor just at the houses central power distribution rack.
On the sides this kind of monitoring and pattern-matching is also useful to identify devices going bad. Think of monitoring the power consumption of a fridge. When it’s compressor goes bad it’s going to consume an increasing amount of power over time. You would figure out the malfunction before it happens.
Same for all sorts of pumps (water, oil, aquarium,…).
All this monitoring and pattern matching the smart house does so it’s inhabitants don’t have to.
We love music. We love it playing loud across the house. And when we did that in the past we missed some things happening around.
Like that delivery guy ringing the front doorbell and us missing an important delivery.
This happened a lot. UNTIL we retrofitted a little PCB to our doorbell circuit to make the house aware of ringing doorbells.
Now everytime the doorbell rings a couple of things can take place.
– push notifications to all devices, screens, watches – that wakes you up even while doing workouts
– pause all audio and video playback in the house
– take a camera shot of who is in front of the door pushing the doorbell
And: It’s easy to wire up things whatever those may be in the future.
So how do you manage all these sensors and switches, and lights, and displays and speakers…
One way has proven to be very useful and that is by using a standard calendar.
Yes, the one you got right on your smartphone or desktop.
A calendar is a simple manifestation of events in time and thus it can be used to either protocol or schedule events.
So the smart house uses calendars to:
schedule on/off times for switches, alarms, whatever can be switched
notes down locations and can react upon locations on schedule or when members of the household arrive/leave those locations based on calendar events
reminds members in the house on upcoming events
protocols media playback (what song,…) for later search
lets members of the house set events through different means like voice, smartphone, …
So what am I using this calendar(s) for? Simple. It’s there to track travelling since I know when I was where by simply searching the calendar (screenshot). It’s easy to make out patterns and times of things happening since a calendar/timeline view feels natural. Setting on/off times and such is just a bliss if you can make it from your phone in an actual calendar rather than a tedious additional app or interface.
And of course: the house can only be smart about things when it has a way to gather and access that data. Reacting to it’s inhabitants upcoming and previous events adds several levels of smartness.
We all know it: After a long day of work you chilled out on your bean bag and fell asleep early. You gotta get up and into your bed upstairs. So usually light goes on, you go upstairs, into bed. And there you have it: You’re not sleepy anymore.
Partially this is caused by the light you turned on. If that light is bright enough and has the right color it will wake you up no matter what.
To fight this companies like Apple introduced things like “NightShift” into iPhones, iPads and Macs.
“Night Shift uses your computer’s clock and geolocation to determine when it’s sunset in your location. It then automatically shifts the colors in your display to the warmer end of the spectrum.”
Simple, eh?. Now why does your house not do that to prevent you being ripped out of sleepy state while tiptoeing upstairs?
Right! This is where the smart house will be smart.
Nowadays we’ve got all those funky LED bulbs that can be dimmed and even their colours set. Why none of those market offerings come with that simple feature is beyond me:
After sunset, when turned on, default dim to something warmer and not so bright in general.
I did implement and it’s called appropriately the “U-Boot light”. Whenever we roam around the upper floor at night time, the light that follows our steps (it’s smart enough to do that) will not go full-blast but light up dim with redish color to prevent wake-up-calls.
The smart part being that it will take into account:
– movement in the house
– sunset and dawn depending on the current geographic location of the house (more on that later, no it does not fly! (yet))
– it’ll turn on and off the light according to the path you’re walking using the various sensors around anyways
Now that you got your home entertainment reacting to you making a phone call (use case #1) as well as your current position in the played audiobook (use case #3) you might want to add some more location awareness to your house.
If your house is smart enough to know where you are, outside, inside, in what room, etc. – it might as well react on the spot.
So when you leave/enter the house:
– turn off music playing – pause it and resume when you come back
– shutdown unnecessary equipment to limit power consumption when not used and start-back up to the previous state (tvs, media centers, lights, heating) when back
– arm the cameras and motion sensors
– start to run bandwidth intense tasks when no people using resources inside the house (like backing up machines, running updates)
– let the roomba do it’s thing
– switch communication coming from the house into different states since it’s different for notifications, managing lists and spoken commands and so on.
There’s a lot of things that that benefit from location awareness.
Bonus points for outside house awareness and representing that like a “Weasly clock”…“xxx is currently at work”.
Bonus points combo breaker for using an open-source service like Miataru (http://miataru.com/#tabr3) for location tracking outside the house.
So you’re listening to this audio book for a while now, it’s quite long but really thrilling. In fact it’s too long for you to go through in one sitting. So you pause it and eventually listen to it on multiple devices.
We’ve got SONOS in our house and we’re using it extensively. Nice thing, all that connected goodness. It’s just short of some smart features. Like remembering where you paused and resuming a long audio book at the exact position you stopped the last time. Everytime you would play a different title it would reset the play-position and not remember where you where.
With some simple steps the house will know the state of all players it has. Not only SONOS but maybe also your VCR or Mediacenter (later use-case coming up!).
Putting together the strings and you get this:
Whenever there’s a title being played longer than 10 minutes and it’s paused or stopped the smart house will remember who, where and what has been played and the position you’ve been at.
Whenever that person then is resuming playback the house will know where to seek to. It’ll resume playback, on any system that is supported at that exact position.
Makes listening to these things just so much easier.
Bonus points for a mobile app that does the same thing but just on your phone. Park the car, go into the house, audiobook will continue playback, just now in the house instead of the car. The data is there, why not make use of it?
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.
Since I am frequently using the xenim streaming network service but I was missing out on the functionality to replay recent shows. With the wonderful functionality of Re-Live made available through ReliveBot I have now added this replay feature and I am using it a lot since.
Within the SONOS controller app it looks like this:
I was on a business trip the other day and the office space of that company was very very nice. So nice that they had all sorts of automation going on to help the people.
For example when you would run into a room where there’s no light the system would light up the room for you when it senses your presence. Very nice!
There was some lag between me entering the room, being detected and the light powering up. So while running into a dark room, knowing I would be detected and soon there would be light, I shouted “Computer! Light!” while running in.
That StarTrek reference brought an old idea back that it would be so nice to be able to control things through omnipresent speech recognition.
I am aware that there’s Siri, Cortana, Google Now. But those things are creepy because they involve external companies. If there are things listening to me all day every day, I want them to be within the premise of the house. I want to know exactly down to the data flow what is going on and sent where. I do not want to have this stuff leave the house at any times. Apart from that those services are working okayish but well…
Let alone the hardware. Usually the existing assistants are carried around in smart phones and such. Very nice if you want to touch things prior to talking to them. I don’t want to. And no, “Hey Siri!” or “OK Google” is not really what I mean. Those things are not sophisticated enough yet. I was using “Hey Siri!” for less than 24 hours. Because in the first night it seemed to have picked up something going on while I was sleeping which made it go full volume “How can I help!” on me. Yes, there’s no “don’t listen when I am sleeping” thing. Oh it does not know when I am sleeping. Well, you see: Why not?
Anyway. What I wish there was:
cheap hardware – a microphone(-array) possibly to put into every room. It either needs to have WiFi or LAN. Something that connects it to the network. A device that is carried around is not enough.
open source speech recognition – everything that is collected by the microphone is processed through an open source speech recognition tool. Full text dictation is a bonus, more importantly heavy-duty command recognition and simple interactions.
open source text to speech – to answer back, if wanted
And all that should be working on a basic level without internet access. Just like that.
The next time you stumble across a PDF file with security and not allowing you to print or copy/paste.
“QPDF is a command-line program that does structural, content-preserving transformations on PDF files. It could have been called something like pdf-to-pdf. It also provides many useful capabilities to developers of PDF-producing software or for people who just want to look at the innards of a PDF file to learn more about how they work.
QPDF is capable of creating linearized (also known as web-optimized) files and encrypted files. It is also capable of converting PDF files with object streams (also known as compressed objects) to files with no compressed objects or to generate object streams from files that don’t have them (or even those that already do). QPDF also supports a special mode designed to allow you to edit the content of PDF files in a text editor. For more details, please see the documentation links below.
QPDF includes support for merging and splitting PDFs through the ability to copy objects from one PDF file into another and to manipulate the list of pages in a PDF file. The QPDF library also makes it possible for you to create PDF files from scratch. In this mode, you are responsible for supplying all the contents of the file, while the QPDF library takes care off all the syntactical representation of the objects, creation of cross references tables and, if you use them, object streams, encryption, linearization, and other syntactic details.
QPDF is not a PDF content creation library, a PDF viewer, or a program capable of converting PDF into other formats. In particular, QPDF knows nothing about the semantics of PDF content streams. If you are looking for something that can do that, you should look elsewhere. However, once you have a valid PDF file, QPDF can be used to transform that file in ways perhaps your original PDF creation can’t handle. For example, programs generate simple PDF files but can’t password-protect them, web-optimize them, or perform other transformations of that type.”
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!
Since the SONOS system I’ve bought turned out to be highly hackable I’ve spent some quality-time this weekend fixing the worst downside I’ve found so far that the SONOS system had for me
I am listening to a lot of Podcasts and Audiobooks. And it turns out that those two Genre are not particularly good supported by SONOS. When you’re listening to a 4 hour podcast and you stop it to play a song in between (since you stretch the listening of that podcast to several days) the next time you start that 4 hour podcast the SONOS system did not remember the position that you stopped at the last time and restarts the podcast from the beginning.
If you did not remember where you left of the last time, you’re lost. The same goes for Audiobooks.
Now this is the first feature I am teaching my SONOS system. And I am opensourcing it so you can do it as well.
Now the Auto Bookmarker Tool will, with the help of the sonos-http-api, monitor your household and whenever something longer than 10 minutes is played and stopped it bookmarks the last played position. Whenever you restart that track it will then seek to the last known position automatically.