Creuzer

I’ve been giving myself a crash course refresher on light over the last new months. It started when I picked up a used Laser Cutter and wanted to figure out how it cuts with light. What I’ve written here is my understanding of things. I may be wrong, if so, please let me know in the comments.

My simplification of a CO2 laser is that it’s a poorly designed Neon light that gets way too hot and produces a heat ray that we can manipulate with mirrors to vaporize things. Magnifying lens on a sunny day style. I fear if I ever find an ant in my laser cutter whatever project I was working on will be a total loss as I will be chasing the ant with my laser beam. https://en.wikipedia.org/wiki/Carbon_dioxide_laser

So I now have this really sharp cut thing that I can’t see the blade on. A CO2 laser beam is invisible. If you see a red dot on a laser cutter, that is a separate cat-toy style red laser put in place so we can guess about where the actual invisible laser will do it’s thing.

My laser cutter’s light is 9.4-10.6 micron wavelength. This is the same wavelength that Humans glow in the dark. Well, everything room temperature glows at this light frequency or ‘color’.

Deep Infrared

A thermal camera can see light in this ‘Deep Infrared’ zone. I have a Seek Thermal camera that plugs into my cell phone which allows me to see effectively heat. I can walk around and find things that are plugged in that are doing a bad job of being off, and give off heat because they are still on. I can tell the temperature of anything, just by looking at it with my cell phone. I can also look for things that are supposed to help keep me warm and are failing at their job like doors and windows.

Except nothing is ever that simple. Materials have a property called ‘emissivity’. This is how well they emit light at a certain wavelength. Things that emit light well, tend to absorb light well. Things that don’t emit well tend to be reflective in nature.

Humans have a pretty high emissivity about .98 (with 1 being perfectly emissive and 0 being perfectly not-emissive) so we need clothes to stay warm as we would glow all of our heat away without them. But because we are highly emissive, we can absorb heat well too, so this is why you can feel heat being given off by things like hot pans, light bulbs, and turtle heat lamps, and sitting in a sunny spot.

Things such as shiny metals have a low emissivity, so they tend to reflect heat like a mirror. This is how camping space blankets work. The thermal ‘glow’ that us humans have gets reflected back at us. We absorb a lot of this reflected heat, so space blankets feel warm to us. Because, you know, science and stuff.

But, what this means, is that my fancy thermal camera can’t take accurate temperatures of shiny metal things. What I am really taking the temperature of is the things reflected on these ‘heat mirrors’. To do a good job using thermal imaging for temperature reading the more expensive equipment has material tables that you can assign to spots that have a lookup to a emissivity table so it can calculate the proper temperature based on what it sees.

I am going to carry some electrical tape which has a pretty high emissivity number around .96 and just stick that on things I want a proper temperature of. Because, you know, lazy and stuff.

Materials have some pretty funny ideas about what is ‘clear’ and what is ‘opaque’ at wavelengths other than what we can see with our eyes. Thin plastic bags that we can’t see through are transparent to deep infrared. Stick your hand in a bag, you can see your fingers as clearly as if the bag wasn’t there. Windows, glasses, things that we see through all the time are as black as night to thermal. “Low E” windows are not only black to thermal, they are reflective as well, so you can see your heat reflection in a ‘good’ modern window. CLICK. OH, that’s why “Low E” windows are better, they reflect heat. I get it now.

Near Infrared

Another portion of light, called Near Infrared, has some interesting properties as well. First off, things that aren’t metallic (reflective) are rather transparent. Things look kinda like jello at these wavelengths, the light can see into them a ways. A couple of centimeters often times.

The Near Infrared has another interesting ability. Oils, fats, sugars, alcohols, and proteins absorb certain frequencies of light – they have colors (for lack of a better word) in this range. Click here to Geek Out on Near Infrared. This means that a camera that uses Near Infrared is very useful around the house. We can look at something, and judging by it’s ‘color’ in Near Infrared, we can make a good guess as to what is made of, or at least major components of it. We can’t see near infrared, so we tend not to manipulate the colors in that range.

There is a gadget that takes advantage of these useful properties. The SCiO which is a Near Infrared ‘scanner’.

This little device is even cooler than the thermal camera. It’s small, and can tell you the interesting bits about your food like how many calories and of what type (fats, carbs, protein) are in it. You don’t have to guess at a restaurant if you are tracking your diet anymore.

This doesn’t work like a camera, it is instead a spectrograph. It doesn’t take a picture of stuff, it instead looks at all the colors that are present like how a prism works. You scan something with a SCiO and it breaks apart the intensities/brightness of different wavelengths of light (those would be the colors if this was visible light) and looks up what it sees against a database of stuff that it knows about and when it finds a match, tells you what it is looking at.
If we were to present only pure substances it would be able to tell us what things are easily. However, we don’t have much of anything that is truly pure. Table salt, sugar, baking soda, for the most part tap water are about all I can think of commonly around the house. Most of the stuff we interact with is made up of a variety of things.

This is where we get clever with the SCiO. Instead of needing to extract out the stuff into individual bits (imaging taking a baked cake and separate it back out to it’s flour, sugar, eggs, milk, water, etc) we just capture what a thing looks like in different bits of Near Infrared light and correlate it to things we’ve told the SCiO what they are previously.

The thing that makes this work is that we LIMIT THE DOMAIN of what things are so the SCiO has a chance at making a reasonable guess. For example, there are a lot of things that are Red. Lego, fancy cars, strawberries, some apples, etc. If we showed you a particular shade of red, and asked you what was that color, you can come up with a lot of wrong answers that are that exact shade of red. But if we said we have a berry that is this particular color, you would be able to tell very easily what it is most of the time. Especially if you can look back against other color samples and compare what you have now with what you have seen in the past.

So for the SCiO to work well, we need to train it. We get together a bunch of things that we want to tell apart if it’s not properly labeled. We then teach the SCiO this thing is X, that thing is Y. We can than ask SCiO what is this stuff, it’s something that belongs to this group that we trained it on.

An example could be clear liquids. Clean water, vodka, strong vodka, watered down vodka, rubbing alcohol, denatured alcohol, clear soda, vinegar. These things all look similar to our eyes. They will all look different from each other in Near Infrared. We can train the SCiO about all of these clear liquids, and when we find a glass of one and we don’t know what it is, we can check with the SCiO.

Amazing!

There are some ways in which the SCiO can fail.

• Shiny mirror like surfaces tend to reflect all light, regardless of the wavelength. Metals for example. We also see this in Visible light as well as Deep Infrared.
• Things that are black – they absorb light – tend to absorb all frequencies of light including Near Infrared. Things that have been colored black will likely be black to the SCiO as well, and it can’t get a good read on them.
• Only the major components of something can be read by a SCiO. If there isn’t enough of something to make a strong ‘color’ influence, it simply can’t be read. A SCiO can ‘see’ stuff that is more than 1% or so of the overall item.

Understanding how a fancy new tool works ‘under the hood’ helps me manage my expectations of what the tool can and can’t do well. I can ‘hack around the edges’ of it’s capabilities because I understand what the edges of capabilities are and why they exist.