With almost 8 billion souls to feed and a changing climate to deal with, there’s never been a better time to field a meaningful “Internet of Agriculture.” But the expansive fields that make industrial-scale agriculture feasible work against the deployment of sensors and actuators because of a lack of infrastructure to power and connect everything. So a low-power radio network for soil moisture sensors is certainly a welcome development.
We can think of a lot of ways that sensors could be powered in the field. Solar comes to mind, since good exposure to the sun is usually a prerequisite for any cropland. But in practice, solar has issues, the prime one being that the plants need the sun more, and will quickly shade out low-profile soil-based sensors.
That’s why [Spyros Daskalakis] eschewed PV for his capacitive soil moisture sensors in favor of a backscatter technique very similar to that used in both the Great Seal Bug and mundane RFID tags alike. The soil sensor switches half of an etched PCB bowtie antenna in and out of a circuit at a frequency proportional to soil moisture. A carrier signal from a separate transmitter is reflected off the alternately loaded and unloaded antenna, picking up subcarriers with a frequency proportional to soil moisture. [Spyros] explains more about the sensor design and his technique for handling multiple sensors in his paper.
Like any Moore’s Law-inspired race, the megapixel race in digital cameras in the late 1990s and into the 2000s was a harsh battleground for every manufacturer. With the development of the smartphone, it became a war on two fronts, with Samsung eventually cramming twenty megapixels into a handheld. Although no clear winner of consumer-grade cameras was ever announced (and Samsung ended up reducing their flagship phone’s cameras to sixteen megapixels for reasons we’ll discuss) it seems as though this race is over, fizzling out into a void where even marketing and advertising groups don’t readily venture. What happened?
A brief overview of Moore’s Law predicts that transistor density on a given computer chip should double about every two years. A digital camera’s sensor is remarkably similar, using the same silicon to form charge-coupled devices or CMOS sensors (the same CMOS technology used in some RAM and other digital logic technology) to detect photons that hit it. It’s not too far of a leap to realize how Moore’s Law would apply to the number of photo detectors on a digital camera’s image sensor. Like transistor density, however, there’s also a limit to how many photo detectors will fit in a given area before undesirable effects start to appear.
Image sensors have come a long way since video camera tubes. In the ’70s, the charge-coupled device (CCD) replaced the cathode ray tube as the dominant video capturing technology. A CCD works by arranging capacitors into an array and biasing them with a small voltage. When a photon hits one of the capacitors, it is converted into an electrical charge which can then be stored as digital information. While there are still specialty CCD sensors for some niche applications, most image sensors are now of the CMOS variety. CMOS uses photodiodes, rather than capacitors, along with a few other transistors for every pixel. CMOS sensors perform better than CCD sensors because each pixel has an amplifier which results in more accurate capturing of data. They are also faster, scale more readily, use fewer components in general, and use less power than a comparably sized CCD. Despite all of these advantages, however, there are still many limitations to modern sensors when more and more of them get packed onto a single piece of silicon.
While transistor density tends to be limited by quantum effects, image sensor density is limited by what is effectively a “noisy” picture. Noise can be introduced in an image as a result of thermal fluctuations within the material, so if the voltage threshold for a single pixel is so low that it falsely registers a photon when it shouldn’t, the image quality will be greatly reduced. This is more noticeable in CCD sensors (one effect is called “blooming“) but similar defects can happen in CMOS sensors as well. There are a few ways to solve these problems, though.
First, the voltage threshold can be raised so that random thermal fluctuations don’t rise above the threshold to trigger the pixels. In a DSLR, this typically means changing the ISO setting of a camera, where a lower ISO setting means more light is required to trigger a pixel, but that random fluctuations are less likely to happen. From a camera designer’s point-of-view, however, a higher voltage generally implies greater power consumption and some speed considerations, so there are some tradeoffs to make in this area.
Another reason that thermal fluctuations cause noise in image sensors is that the pixels themselves are so close together that they influence their neighbors. The answer here seems obvious: simply increase the area of the sensor, make the pixels of the sensor bigger, or both. This is a good solution if you have unlimited area, but in something like a cell phone this isn’t practical. This gets to the core of the reason that most modern cell phones seem to be practically limited somewhere in the sixteen-to-twenty megapixel range. If the pixels are made too small to increase megapixel count, the noise will start to ruin the images. If the pixels are too big, the picture will have a low resolution.
There are some non-technological ways of increasing megapixel count for an image as well. For example, a panoramic image will have a megapixel count much higher than that of the camera that took the picture simply because each part of the panorama has the full mexapixel count. It’s also possible to reduce noise in a single frame of any picture by using lenses that collect more light (lenses with a lower f-number) which allows the photographer to use a lower ISO setting to reduce the camera’s sensitivity.
Of course, if you have unlimited area you can make image sensors of virtually any size. There are some extremely large, expensive cameras called gigapixel cameras that can take pictures of unimaginable detail. Their size and cost is a limiting factor for consumer devices, though, and as such are generally used for specialty purposes only. The largest image sensor ever built has a surface of almost five square meters and is the size of a car. The camera will be put to use in 2019 in the Large Synoptic Survey Telescope in South America where it will capture images of the night sky with its 8.4 meter primary mirror. If this was part of the megapixel race in consumer goods, it would certainly be the winner.
With all of this being said, it becomes obvious that there are many more considerations in a digital camera than just the megapixel count. With so many facets of a camera such as physical sensor size, lenses, camera settings, post-processing capabilities, filters, etc., the megapixel number was essentially an easy way for marketers to advertise the claimed superiority of their products until the practical limits of image sensors was reached. Beyond a certain limit, more megapixels doesn’t automatically translate into a better picture. As already mentioned, however, the megapixel count can be important, but there are so many ways to make up for a lower megapixel count if you have to. For example, images with high dynamic range are becoming the norm even in cell phones, which also helps eliminate the need for a flash. Whatever you decide, though, if you want to start taking great pictures don’t worry about specs; just go out and take some photographs!
(Title image: VISTA gigapixel mosaic of the central parts of the Milky Way, produced by European Southern Observatory (ESO) and released under Creative Commons Attribution 4.0 International License. This is a scaled version of the original 108,500 x 81,500, 9-gigapixel image.)
With interest and accessibility to both wearable tech and virtual reality approaching an all-time high, three students from Cornell University — [Daryl Sew, Emma Wang, and Zachary Zimmerman] — seek to turn your body into the perfect controller.
That is the end goal, at least. Their prototype consists of three Kionix tri-axis accelerometer, gyroscope and magnetometer sensors (at the hand, elbow, and shoulder) to trace the arm’s movement. Relying on a PC to do most of the computational heavy lifting, a PIC32 in a t-shirt canister — hey, it’s a prototype! — receives data from the three joint positions, transmitting them to said PC via serial, which renders a useable 3D model in a virtual environment. After a brief calibration, the setup tracks the arm movement with only a little drift in readings over a few minutes.
The best thing about owning a 3D printer or CNC router may not just be what you can additively or subtractively create with it. With a little imagination you can turn your machine into a 3D scanner, and using capacitive sensors to image items turns out to be an interesting project.
[Nelson]’s scanner idea came from fiddling with some capacitive sensors at work, and with a high-resolution capacitance-to-digital sensor chip in hand, he set about building a scan head for his printer. In differential mode, the FDC2212 sensor chip uses an external LC tank circuit with two plain sensor plates set close to each other. The sensor plates form an air-dielectric variable capacitor, and the presence of an object can be detected with high sensitivity. [Nelson]’s custom sensor board and controller ride on a 3D-printed bracket and scan over the target on the printer bed. Initial results were fuzzy, but after compensating for room temperature variations and doing a little filtering on the raw data, the scans were… still pretty fuzzy. But there’s an image there, and it’s something to work with.
When you want to play around with a new technology, do you jump straight to production machinery? Nope. Nothing beats a simplified model as proof of concept. And the only thing better than a good proof of concept is an amusing proof of concept. In that spirit [Eric Tsai], alias [electronichamsters], built the world’s most complicated electronic gingerbread house this Christmas, because a home-automated gingerbread house is still simpler than a home-automated home.
Yeah, there are blinky lights and it’s all controlled by his smartphone. That’s just the basics. The crux of the demo, however, is the Bluetooth-to-MQTT gateway that he built along the way. A Raspberry Pi with a BTLE radio receives local data from BTLE sensors and pushes them off to an MQTT server, where they can in principle be read from anywhere in the world. If you’ve tried to network battery-powered ESP8266 nodes, you know that battery life is the Achilles heel. Swapping over to BTLE for the radio layer makes a lot of sense.
Everyone’s favorite viscoelastic non-Newtonian fluid has a new use, besides bouncing, stretching, and getting caught in your kid’s hair. Yes, it’s Silly Putty, and when mixed with graphene it turns out to make a dandy force sensor.
To be clear, [Jonathan Coleman] and his colleagues at Trinity College in Dublin aren’t buying the familiar plastic eggs from the local toy store for their experiments. They’re making they’re own silicone polymers, but their methods (listed in this paywalled article from the journal Science) are actually easy to replicate. They just mix silicone oil, or polydimethylsiloxane (PDMS), with boric acid, and apply a little heat. The boron compound cross-links the PDMS and makes a substance very similar to the bouncy putty. The lab also synthesizes its own graphene by sonicating graphite in a solvent and isolating the graphene with centrifugation and filtration; that might be a little hard for the home gamer to accomplish, but we’ve covered a DIY synthesis before, so it should be possible.
With the raw materials in hand, it’s a simple matter of mixing and kneading, and you’ve got a flexible, stretchable sensor. [Coleman] et al report using sensors fashioned from the mixture to detect the pulse in the carotid artery and even watch the footsteps of a spider. It looks like fun stuff to play with, and we can see tons of applications for flexible, inert strain sensors like these.
In Texas — at least around Houston — we don’t have basements. We do, however, have bilges. Both of these are subject to taking on water when no one is paying attention. A friend of mine asked me what I thought of an Instructable that showed how to make a water sensor using a few discrete components. The circuit would probably work — it relied on the conductivity of most water to supply enough current to a bipolar transistor’s base to turn it on.
It is easy to overthink something like this, so I told my friend he should go with something a little more old-fashioned. I don’t know the origin of it, but it is older than I am. You can make a perfectly good water detector with things you probably already have around the house. My point isn’t that you should (or shouldn’t) construct a homemade water sensor. My point is that you don’t always need to go to the high-tech solution.