In order to help his friend prepare for a talk at DEFCON this weekend, [Craig] built an IR photodiode amplifier circuit. The circuit extended the detection range of the hack from a few inches to a few feet. We’re suckers for some well-designed analog circuitry, and if you are too, be sure to check out the video embedded below.
[Artem Litvinovich] wanted to see by heat vision like in the Predator movies. He not only succeeded but went on to see in color, medium-wave IR, short-wave IR, and ultraviolet using a very unique approach since his effort began back in 2009.
He started with a box based on the basic pinhole camera concept. In the box is a physical X-Y digitizer moving a photodiode to collect the thousands of points needed to create a picture. First all he got, due to the high signal amplification, was the 60 cycle hum that permeates our lives. A Faraday cage around the box helped but metal foil around the sensor and amplifier finally eliminated the noise. Now he had pictures in the near infrared (NIR). Continue reading “Using Missile Tech to See Like Predator”
[cpldcpu] just can’t leave the mysteries of candles alone. We’ve covered his explorations of candle flicker LEDs before, but this time he’s set his sensors on the real thing. [cpldcpu] hooked a photodiode to his oscilloscope, pointed it at a candle flame, and recorded the result.
The first interesting observation was the candle slowly changed brightness, whether it was interacted with or not. Next he measured the effect when the flame was disturbed by small gusts of air. This produced a bright flicker with an oscillation at 5Hz before returning to steady state, which as [stygiansonic] mentioned in a the Hacker News comment, is a known phenomenon used in flame detectors. Neat! There’s even an equation:
Under normal gravity conditions, the flames have a well defined oscillation frequency which is inversely proportional to the square root of the burner diameter, D, and to a good approximation can be written as f » 1.5/D½, with D given in meters.
[cpldcpu] then compiled his measurements into a series of graphs and ultimately an animated gif comparing the candle steady state, a real candle’s flicker, and the flicker he recorded from a candle flickr LED. It’s surprising how different the fake is from the real thing. You can look at his measurements and code at his github.
[via Hacker News]
Chances are pretty good you’ve had a glowing probe clipped to your fingertip or earlobe in some clinic or doctor’s office. If you have, then you’re familiar with pulse oximetry, a cheap and non-invasive test that’s intended to measure how much oxygen your blood is carrying, with the bonus of an accurate count of your pulse rate. You can run down to the local drug store or big box and get a fingertip pulse oximeter for about $25USD, but if you want to learn more about photoplethysmography (PPG), [Rajendra Bhatt]’s open-source pulse oximeter might be a better choice.
PPG is based on the fact that oxygenated and deoxygenated hemoglobin have different optical characteristics. A simple probe with an LED floods your fingertip with IR light, and a photodiode reads the amount of light reflected by the hemoglobin. [Rajendra]’s Easy Pulse Plugin receives and amplifies the signal from the probe and sends it to a header, suitable for Arduino consumption. What you do with the signal from there is up to you – light an LED in time with your heartbeat, plot oxygen saturation as a function of time, or drive a display to show the current pulse and saturation.
We’ve seen some pretty slick DIY pulse oximeters before, and some with a decidedly home-brew feel, but this seems like a good balance between sophisticated design and open source hackability. And don’t forget that IR LEDs can be used for other non-invasive diagnostics too.
The 2015 Hackaday Prize is sponsored by:
[Jordan] managed to cobble together his own version of a low resolution digital camera using just a few components. The image generated is pretty low resolution and is only in grey scale, but it’s pretty impressive what can be done with some basic hardware.
The heart of the camera is the image sensor. Most consumer digital cameras have tons of tiny receptors all jammed into the sensor. This allows for a larger resolution image, capturing more detail in a smaller space. Unfortunately this also usually means a higher price tag. [Jordan’s] sensor includes just a single pixel. The sensor is really just an infrared photodiode inside of a tube. The diode is connected to an analog input pin on an Arduino. The sensor can be pointed at an object, and the Arduino can sense the brightness of that one point.
In order to compile an actual image, [Jordan] needs to obtain readings of multiple points. Most cameras do this using the large array of pixels. Since [Jordan’s] camera only has a single pixel, he has to move it around and take each reading one at a time. To accomplish this, the Arduino is hooked up to two servo motors. This allows the sensor to be aimed horizontally and vertically. The Arduino slowly scans the sensor in a grid, taking readings along the way. A Processing application then takes each reading and compiles the final image.
Since this camera compiles an image so slowly, it sometimes has a problem with varying brightness. [Jordan] noticed this issue when clouds would pass over while he was taking an image. To fix this problem, he added an ambient light sensor. The Arduino can detect the amount of overall ambient light and then adjust each reading to compensate. He says it’s not perfect but the results are still an improvement. Maybe next time he can try it in color.
[Artlav] wanted to build a digital camera, but CCDs are expensive and don’t respond well to all wavelengths of light. No problem, then, because with a photodiode, a few stepper motors, the obligatory Arduino, and a cardboard box, it’s pretty easy to make one from scratch.
The camera’s design is based on a camera obscura – a big box with a pinhole in one side. This is all a camera really needs as far as optics go, but then there’s the issue of digitizing the faint image projected onto the rear of the camera. That’s fine, just build a cartesian robot inside the box and throw a photodiode in there.
There are a few considerations when choosing a photodiode for a digital camera. Larger photodieodes have higher noise but lower resolution. [Artlav] has been experimenting with a few diodes, but his options are limited by export control restrictions.
Even with the right photodiode, amplifying the tiny amount of current – picoamps in some cases – is hard. The circuit is extremely sensitive to EMI, and it’s inside a box with stepper motors pulled from the scrap bin. It’s amazing this thing works at all.
Still, [Artlav] was able to get some very high resolution images across a huge range of wavelengths. He’s even getting a few images in mid-wave infrared, turning this homebrew digital camera into the slowest thermal imaging camera we’ve ever seen.
There are many ways to detect a heartbeat electronically. One of the simpler ways is to take [Orlando’s] approach. He’s built a finger-mounted pulse detector using a few simple components and an Arduino.
This circuit uses a method known as photoplethysmography. As blood is pumped through your body, the volume of blood in your extremities increases and decreases with each heartbeat. This method uses a light source and a detector to determine changes in the amount of blood in your extremities. In this case, [Orlando] is using the finger.
[Orlando] built a finger cuff containing an infrared LED and a photodiode. These components reside on opposite sides of the finger. The IR LED shines light through the finger while the photodiode detects it on the other side. The photodiode detects changes in the amount of light as blood pumps in and out of the finger.
The sensor is hooked up to an op amp circuit in order to convert the varying current into a varying voltage. The signal is then filtered and amplified. An Arduino detects the voltage changes and transmits the information to a computer via serial. [Orlando] has written both a LabVIEW program as well as a Processing program to plot the data as a waveform. If you’d rather ditch the PC altogether, you might want to check out this standalone heartbeat sensor instead.