The Hive at Kew

Art meets bees, science and electronics

by Paul Curzon, Queen Mary University of London

(from the archive)

a boy lying in the middle of the Hive at Kew Gardens.

Combine an understanding of science, with electronics skills and the creativity of an artist and you can get inspiring, memorable and fascinating experiences. That is what the Hive, an art instillation at Kew Gardens in London, does. It is a massive sculpture linked to a subtle sound and light experience, surrounded by a wildflower meadow, but based on the work of scientists studying bees.

The Hive is a giant aluminium structure that represents a bee hive. Once inside you see it is covered with LED lights that flicker on and off apparently randomly. They aren’t random though, they are controlled by a real bee hive elsewhere in the gardens. Each pulse of a light represents bees communicating in that real hive where the artist Wolfgang Buttress placed accelerometers. These are simple sensors like those in phones or a BBC micro:bit that sense movement. The sensitive ones in the bee hive pick up vibrations caused by bees communicating with each other The signals generated are used to control lights in the sculpture.

A new way to communicate

This is where the science comes in. The work was inspired by Martin Bencsik’s team at Nottingham Trent University who in 2011 discovered a new kind of communication between bees using vibrations. Before bees are about to swarm, where a large part of the colony split off to create a new hive, they make a specific kind of vibration, as they prepare to leave. The scientists discovered this using the set up copied by Wolfgang Buttress, using accelerometers in bee hives to help them understand bee behaviour. Monitoring hives like this could help scientists understand the current decline of bees, not least because large numbers of bees die when they swarm to search for a new nest.

Hear the vibrations through your teeth

Good vibrations

The Kew Hive has one last experience to surprise you. You can hear vibrations too. In the base of the Hive you can listen to the soundtrack through your teeth. Cover your ears and place a small coffee stirrer style stick between your teeth, and put the other end of the stick in to a slot. Suddenly you can hear the sounds of the bees and music. Vibrations are passing down the stick, through your teeth and bones of your jawbone to be picked up in a different way by your ears.

A clever use of simple electronics has taught scientists something new and created an amazing work of art.


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A hoverfly on a leaf

EPSRC supports this blog through research grant EP/W033615/1, and through EP/K040251/2 held by Professor Ursula Martin. 

Hoverflies: comin’ to get ya

by Peter W McOwan and Paul Curzon, Queen Mary University of London

(from the archive)

A hoverfly on a blade of grass

By understanding the way hoverflies mate, computer scientists found a way to sneak up on humans, giving a way to make games harder.

When hoverflies get the hots for each other they make some interesting moves. Biologists had noticed that as one hoverfly moves towards a second to try and mate, the approaching fly doesn’t go in a straight line. It makes a strange curved flight. Peter and his student Andrew Anderson thought this was an interesting observation and started to look at why it might be. They came up with a cunning idea. The hoverfly was trying to sneak up on its prospective mate unseen.

The route the approaching fly takes matches the movements of the prospective mate in such a way that, to the mate, the fly in the distance looks like it’s far away and ‘probably’ stationary.

Tracking the motion of a hoverfly and its sightlines

How does it do this? Imagine you are walking across a field with a single tree in it, and a friend is trying to sneak up on you. Your friend starts at the tree and moves in such a way that they are always in direct line of sight between your current position and the tree. As they move towards you they are always silhouetted against the tree. Their motion towards you is mimicking the stationary tree’s apparent motion as you walk past it… and that’s just what the hoverfly does when approaching a mate. It’s a stealth technique called ‘active motion camouflage’.

By building a computer model of the mating flies, the team were able to show that this complex behaviour can actually be done with only a small amount of ‘brain power’. They went on to show that humans are also fooled by active motion camouflage. They did this by creating a computer game where you had to dodge missiles. Some of those missiles used active motion camouflage. The missiles using the fly trick were the most difficult to spot.

It just goes to show: there is such a thing as a useful computer bug.


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EPSRC supports this blog through research grant EP/W033615/1, and through EP/K040251/2 held by Professor Ursula Martin. 

Ant Art

by Paul Curzon, Queen Mary University of London

(from the archive)

The close up head of an ant staring at you
Image by Virvoreanu Laurentiu from Pixabay 

There are many ways Artificial Intelligences might create art. Breeding a colony of virtual ants is one of the most creative.

Photogrowth from the University of Coimbra does exactly that. The basic idea is to take an image and paint an abstract version of it. Normally you would paint with brush strokes. In ant paintings you paint with the trails of hundreds of ants as they crawl over the picture, depositing ink rather than the normal chemical trails ants use to guide other ants to food. The colours in the original image act as food for the ants, which absorb energy from its bright parts. They then use up energy as they move around. They die if they don’t find enough food, but reproduce if they have lots. The results are highly novel swirl-filled pictures.

The program uses vector graphics rather than pixel-based approaches. In pixel graphics, an image is divided into a grid of squares and each allocated a colour. That means when you zoom in to an area, you just see larger squares, not more detail. With vector graphics, the exact position of the line followed is recorded. That line is just mapped on to the particular grid of the display when you view it. The more pixels in the display, the more detailed the trail is drawn. That means you can zoom in to the pictures and just see ever more detail of the ant trails that make them up.

You become a breeder of a species of ant

that produce trails, and so images,

you will find pleasing

Because the virtual ants wander around at random, each time you run the program you will get a different image. However, there are lots of ways to control how ants can move around their world. Exploring the possibilities by hand would only ever uncover a small fraction of the possibilities. Photogrowth therefore uses a genetic algorithm. Rather than set all the options of ant behaviour for each image, you help design a fitness function for the algorithm. You do this by adjusting the importance of different aspects like the thickness of trail left and the extent the ants will try and cover the whole canvas. In effect you become a breeder of a species of ant that produce trails, and so images, you will find pleasing. Once you’ve chosen the fitness function, the program evolves a colony of ants based on it, and they then paint you a picture with their trails.

The result is a painting painted by ants bred purely to create images that please you.


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EPSRC supports this blog through research grant EP/W033615/1, and through EP/K040251/2 held by Professor Ursula Martin. 

Knitters and Coders: separated at birth?

People often say that computers are all around us, but you could still escape your phone and iPod and go out to the park, far away from the nearest circuit board if you wanted to. It’s a lot more difficult to get away from the clutches of computation itself though. For one thing, you’d have to leave your clothes at home. Queen Mary Electronic Engineer Karen Shoop tells us about the code hidden in knitting, and what might happen when computers learn to read it.

Boy with wool hat and jumper on snowy day

If you’re wearing something knitted look closely at it (if it’s a sunny day then put this article away till it gets colder). Notice how the two sides don’t look the same: some parts look like a raised ‘v’ and others like a wave pattern. These are made by the two knitting stitches: knit and purl. With knit you stick the needle through and then behind the knitting; with purl you stick the needle in the other direction, starting behind the knitting and then pointing at the knitter. Expert knitters know that there’s more to knitting than just these two stitches, but we’ll stick to knit and purl. As these stitches are combined, the wool is transformed from a series of waves or ‘v’s into a range of patterns: stretchy stripes (ribs), raised speckles (moss), knots and ropes (cable). It all depends on the number of purls and knits, how they are placed next to each other and how often things are repeated.

Knitters get very proficient at reading knitting patterns, which are just varying combinations of k (knits) and p (purls). So the simplest pattern of all, knitting a square, would look something like:

’30k (30 knit stitches), finish the line, then repeat this 20 times’.

A rib would look like: ‘5k, 5p, then repeat this [a certain number of times], then repeat the line [another number of times]’

To a computer scientist or electronic engineer all this looks rather like computer code or, to be precise, like the way of describing a pattern as a computer program.

How your jumper is like coding

So look again at your knitted hat/jumper/cardi and follow the pattern, seeing how it changes horizontally and vertically. Just as knitters give instructions for this in their knitting pattern, coders do the same when writing computer programs. Specifically programmers use things called regular expressions. They are just a standard way to describe patterns. For example a regular expression might be used to describe what an email address should look like (specifying rules such as that it has one ‘@’ character in the middle of other characters, no full-stops/periods immediately before the @ and so on), what a phone number looks like (digits/numbers, no letters, possibly brackets or hyphens) and now what a knitting pattern looks like (lots of ks and ps). Regular expressions use a special notation to precisely describe what must be included, what might possibly be included, what cannot be, and how many times things should be repeated. If you were going to teach a computer how to read knitting patterns, a regular expression would be just what you need.

Knitting a regular expression

Let’s look at how to write a knitting pattern as a regular expression. Let’s take moss or seed stitch as our example. It repeats a “knit one purl one” pattern for one line. The next line then repeats a “purl one knit one” pattern, so that every knit stitch has a purl beneath it and vice versa. These two lines are repeated for as long as is necessary. How might we write that both concisely and precisely so there is no room for doubt?

In knitting notation (assuming an even number of stitches) it looks like: Row 1: *k1, p1; rep from * Rows 2: *p1, k1; rep from * or Row 1: (K1, P1) rep to end Row 2: (P1, K1) rep to end Repeat these 2 rows for length desired.

piles of woollen clothes

All this is fine … if it’s being read by a human, but to write experimental knitting software the knitting notation we have to use a notation a computer can easily follow: regular expressions fit the bill. Computers do not understand the words we used in our explanation above: words like ‘row’, ‘repeat’, ‘rep’, ‘to’, ‘from’, ‘end’, ‘length’ and ‘desired’, for example. We could either write a program that makes sense of what it all means for the computer, or we could just write knitting patterns for computers in a language they can already do something with: regular expressions. If we wanted to convert from human knitting patterns to regular expressions we would then write a program called a compiler (see Smart translation) that just did the translation.

In a regular expression to give a series of actions we just name them. So kp is the regular expression for one knit stitch followed immediately by one purl. The knitting pattern would then say repeat or rep. In a regular expression we group actions that need to be repeated inside curved brackets, resulting in (kp). To say how many times we need to repeat, curly brackets are used, so kp repeated 10 times looks like this: (kp){10}.

Since the word ‘row’ is not a standard coding word we then use a special character, written, \n, to indicate that a new line (=row) has to start. The full regular expression for the row is then (kp){10}\n. Since the first line was made of kps the following line must be pks, or (pk){10}\n

These two lines have to be repeated a certain number of times themselves, say 20, so they are in turn wrapped up in yet more brackets, producing: ((kp){10}\n(pk){10}\n){20}.

If we want to provide a more general pattern, not fixing the number of kps in a row or the number of rows, the 10 and 20 can be replaced with what are called variables – x and y. They can each stand for any number, so the final regular expression is:

((kp){x}\n(pk){x}\n){y}

How would you describe a rib as a regular expression (remember, that’s the pattern that looks like stretchy stripes)? The regular expression would be ((kp){x}\n){y}.

Regular expressions end up saying exactly the same thing as the standard knitting patterns, but more precisely so that they cannot be misunderstood. Describing knitting patterns in computer code is only the start, though. We can use this to write code that makes new patterns, to find established ones or to alter patterns, like you’d need to do if you were using thicker wool, for example. An undergraduate student at Queen Mary, Hailun Li, who likes knitting, used her knowledge to write an experimental knitting application that lets users enter their own combination of ps and ks and find out what their pattern looks like. She took her hobby and saw how it related to computing.

Look at your woolly jumper again…it’s full of computation!

– Karen Shoop, Queen Mary University of London, Summer 2014