Pattern Matrix PCBs arrived & first tests

After triple checking the schematics and design files and ordering 80 PCBs (50 sensors and 30 i2c boards) there was an anxious wait for them to arrive and do some initial tests to find out if there were any mistakes. We now have enough boards to make two new pattern matrix devices, one 4X4 and one 5X5 – the plan is to evaluate the design and refine it for future builds.




The picture below shows the first test boards populated and plugged it into the Pi – it’s much neater than the lego and breadboard prototype! The good news is that it seems to work so far, the only problem I’ve had is with the hall effect sensors, the pads are a tiny bit too close together for my skills. After a couple of tricky situations fixed with a de-soldering pump, I think I’ve come up with a strategy that works. I can bend the outer pins away from each other and solder the central pin first – then bend them back to finish the outer ones and being very careful not to bridge the pads.


The blue jumpers on the square i2c boards allow you to program the device channel that the two expanders use – these could alternatively be hard soldered, but it’s good to have the option to reuse the parts or reconfigure a pattern matrix so we can add different sensors etc.


For reference, the KiCad 3D viewer models look pretty close.



Viruscraft: building a ‘reasonably accurate’ genetic game world simulation

The concept for the viruscraft game is to have a realtime genetic model or simulation of the host evolution which is adapting to the properties of a virus you are building (either on screen or via a tangible interface as part of an exhibit). This model needs to be realistic, but only up to a point – it can be more of a caricature of biology than a research model would need to be, as our intentions are educational rather than biological research.

Using our previous species prototype as a starting point, we have a network of connected locations that can be inhabited by organisms. These organisms can jump to neighbouring locations and be infected by others in the same place at the same time. Now we need to figure out how different species of these organisms could emerge over time that evolve immunity to a virus – so we can build up a family tree (phylogeny) similar to the ones we created for the egglab game but that is responding to the viruses that you create in realtime as you play. The evolution itself also has to happen fast enough that you can see effects of your actions ‘quickly enough’, but we’ll worry more about that later.

For a job like this we need to move back from fancy visualisations and graphics and try to get some fundamental aspects working, using standard tools like graphviz to understand what is going on to save time.

The first thing to do is to add a fixed length genetic string to each individual organism, this is currently 40 elements long and is made from biologically based A,T,C and G nucleotide symbols. We chose these so we can use biological analysis tools to test the system as we go along just like any other genetic process (more on that below). The organisms can also reproduce by spawning copies of themselves. When they do this they introduce random errors in the genetic code of their offspring which represents mutation.

Previously we were using a ‘SIRS’ model for virus infection (susceptible -> infected -> recovered -> susceptible), based on 4 global parameters that determined the probability of jumping from one state to the next. Using the genetics, the probability of infection is now different for every individual based on:

1. Is a virus infected individual in the current location?

2. If so, use our genetic code to determine the probability of catching it. Currently we use the ratio of A’s to T’s in the genetic string as a totally arbitrary place-holder ‘fitness function’, the lower the number the better. AAAAAAT is bad (fitness: 6) while TTTTTTA is good (fitness: 0.1666) – so we would expect the A’s to disappear over time and the T’s increase in the genetic strings. This number also determines the probability of dying from the disease and (inversely) the probability of gaining immunity to it.

3. A very small ‘background infection’ probability which overrides this, so the virus is always present at a low level and can’t die out.

The next thing we need is a life cycle for the organism – this needs to include the possibility of death and the disease model is now a ‘SIR’ one, as once recovered, individuals cannot go back to being susceptible again.


All the other non virus related probabilities in the simulation (spawning offspring, moving location, natural death) are currently globally set – to make sure we are seeing evolution based only on disease related behaviour for now.

This model as it is could form the foundation of a world level visualisation – seeing organisms running around from place to place catching and spreading your virus and evolving resistance to it. However this is only half the story we want to tell in the game, as it doesn’t include our time based ‘phylogenetic’ family tree view. For this, we still need to figure out how to group individuals into species so we can fully visualise the effects of your virus on the evolution of all the populations as a whole.

First we need to decide exactly what a species is – which turns out to be quite an arbitrary concept. The rather course approach that seems to work here is to say that two organisms represent two distinct species if more than a quarter of their genes are different between them.

We can now check each organism as it’s born – and compare its genome against a ‘blueprint’ one that represents the species that it’s parent belongs to. If it’s similar enough we add it to its parents species, if it’s too different we create a new species for it. This new species will have a copy of its genome as the ‘blueprint’ to compare all its descendants with. This should mean we can build up a set of related species over time.

If we run the simulation for 5000 time steps we can generate a phylogenetic family tree at the end, using the branch points between species to connect them. We are hiding species with only 1 member to make it simpler, and the population is started off with 12 unique individuals. Only one of which (species 10) is successful – all the later species are descendants of that one:


The numbers here are the ID, fitness and size of population for each species. The colours are an indication of population size. The fitness seems to increase towards the right (as the number drops) – which is what we’d expect if new species are emerging that cope better with the virus. You can imagine changing the virus will cause all this to shift dramatically. The “game mechanic” for viruscraft will all be about tinkering with the virus in different ways that changes the underlying fitness function of the host, and thus the evolution of the populations.

As we used standard biological symbols for our genetic code, we can also convert each species into an entry in a FASTA format text file. These are used by researchers to determine population structure from limited information contained in genetic samples:

> 1 0.75 6
> 3 0.46153846153846156 5
> 5 0.6153846153846154 171

In the FASTA file in the example above, the numbers after the ‘>’ are just used as identifiers and are the same as the tree above. The second line is the blueprint genome for the species (its first individual). We can now visualise these with one of many online tools for biological analysis:


This analysis is attempting to rebuild the first tree in a way, but it doesn’t have as much information to go on as it’s only looking at similarity of the genetic code. Also 40 bases is not really enough to do this accurately with such a high mutation rate – but I think it’s a good practice to keep information in such a way that it can be analysed like this.

PCB design for pattern matrix 2

This is the pattern matrix 2 tangible sensor schematic, which is fairly simple – just 4 hall effect sensors and a capacitor to smooth out any noise on the power supply.


We need to make hundreds of these for the Penelope Project, and we can save some costs by using the built in pull up resistors in the MCP32017 to get a decent signal from the sensors. The difficulty with this PCB is arranging the sensors so they align with the magnets in the tangible programming block in the optimum manner. From tests with the prototype Lego rig, this isn’t actually too critical – but it’s set up so the lead length can be tweaked a bit at soldering time.


This took me about 20 variations to finally get right, but the circuit is just about simple enough that it can be made single sided – this is good because the top side will be partly exposed, while the lower side with all the copper traces can be protected. It’s good practice to have large areas of copper left connected to ground, partly as it’s a common connection needed all over the board, partly for stability but also it reduces the amount of chemicals required to etch the circuit – as only the parts around the traces need to be removed.


The i2c expander board is a little more complicated. The design is made to be modular so we can stack up any number of these connected to the Raspberry Pi for different arrangements of sensors. Each board can deal with 8 sensor locations (each comprising 4 individual hall effect sensors). Their job is to convert the digital signals from each sensor into serial data (using the i2c protocol) so the Raspberry Pi can read them all just using 2 wires, plus 2 for power.


Each board can be configured to a separate i2c device address to tell it apart from the others using jumper connectors. This one had to be 2 sided, but I managed it without any ‘vias’ (holes to pass traces from one side of the board to another). I also added a power indicator LED as a last minute addition.


I’ve been learning the open source Kicad software to design these, which is now used by CERN for building the LHC, so it’s pretty fully featured! The idea is that you draw the schematic first, link each component with a physical ‘footprint’, then switch to the PCB design stage. Other software I’ve used in the past tries to route everything in one go for you (and can come up with some pretty strange and messy results). Kicad works in a semi-automatic manner – you need to draw each trace by hand, but it routes them around components and other traces, and suggests the shortest path for you. This is quite a lot better than a fully automatic approach as you have more control over the end result, and easily end up with a decent placement of all the parts.


This project is of course open hardware, and can be found on github here.

Pattern Matrix 2 haptic experiments

One of the potential future additions to our tangible programming hardware is haptic feedback, using sound/vibration to provide a extra channel of information through your fingers when programming with tangible blocks. We wanted to test this before designing the PCB hardware in case we could add it to the system simply – this was initiated by watching a reverse engineering video on youtube (I think by bigclivedotcom) where a technique was mentioned for sneakily reusing input lines (of which we have hundreds) as outputs when they are not being read, by using diodes to separate the signals.


Piezo transducers are often used as cheap speakers, and they can also provide some touch feedback by using lower frequencies. We tested one to see if it interfered with the magnetic fields, and the diaphragm doesn’t seem to be ferrous metal (I’m not actually sure what it is made from) so it can be placed right over the sensors with no effect. A bit more Lego to attach it to the prototype sensor unit, and to see we can feel it through the tangible block when it’s touching the sensor.

The first attempt consisted of simply plugging a speaker into the i/o line from the MCP23017 port expander, switching between input and output on the Pi and adding a diode to prevent the output voltage getting to the sensor.


The problem is all the circuits we’re using run on 3.3V, but the piezo speaker I’m using is rated up to 20V – so it’s just not strong enough to feel it. The Pi also provides 5V pins, so a second attempt to use a simple driver circuit (single transistor common emitter amplifier) to boost the voltage:


You can hear this version in the video above – it’s playing a sound using a frequency crudely determined by the 4 bit value the block orientation represents whenever it changes. This is louder (loud enough at least to make a recording) but still not enough to feel easily, even when you put the piezo between the sensor and the block. So it seems the best way to get this to work properly will be via a separate circuit, not something we can slot into the existing input hardware. Another advantage of doing it separately is that we can treat it more like a multichannel audio setup with a dedicated processor that is not interrupted by the sensor reading. One solution I saw was to use the same H bridges as used to drive motors from microcontrollers as these take much higher voltages – this will be something to try later on.