Smart Child Seat Aims to Prevent Tragedy

For most of us, a memory lapse is as harmless as forgetting to bring the garbage to the curb, or maybe as expensive as leaving a cell phone and cup of coffee on the roof of the car before driving off. But when the toddler sleeping peacefully in the car seat slips your mind in the parking lot, the results can be deadly.

We have no doubt that child detection systems will soon be standard equipment on cars, like backup cameras and trunk-escape levers are now. Not willing to wait, [ayavilevich] came up with his own car occupancy sensor for child seats (Update: We originally linked to the Instructable but [ayavilevich] wrote in and mentioned this is actual Hackaday Prize entry and he’s looking for more people to get involved in the project).

Dubbed Fochica, for “Forgotten Child in Car Alert,” the system is clearly a proof of concept right now, but it has potential. The Arduino Uno senses Junior’s presence in the car seat with a homebrew capacitive sensor under the padding of the seat and a magnetic reed switch in the chest harness buckle. An Android app on a smartphone pairs with a BLE module to get the sensors’ status, and when the phone goes out of Bluetooth range while the seat is occupied, the app sounds an alarm. Simple, but effective.

We like how well [ayavilevich] thought this through. Systems like this are best left uncomplicated, so any improvements he makes should probably concentrate on engineering a reliable, fieldable device. Another hack we’ve presented in the kid-safety space is fast stairwell lights for a visually impaired girl, which might provide some ideas.

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An Interactive Oasis At Burning Man

An oasis in the desert is the quintessential image of salvation for the wearied wayfarer. At Burning Man 2016, Grove — ten biofeedback tree sculptures — provided a similar, interactive respite from the festival. Each tree has over two thousand LEDs, dozens of feet of steel tube, two Teensy boards used by the custom breath sensors to create festival magic.

Grove works like this: at your approach — detected by dual IR sensors — a mechanical flower blooms, meant to prompt investigation. As you lean close, the breath sensors in the daffodil-like flower detect whether you’re inhaling or exhaling, translating the input into a dazzling pulse of LED light that snakes its way down the tree’s trunk and up to the bright, 3W LEDs on the tips of the branches.

Debugging and last minute soldering in the desert fixed a few issues, before setup — no project is without its hiccups. The entire grove was powered by solar-charged, deep-cycle batteries meant to least from sunset to sunrise — or close enough if somebody forgot to hook the batteries up to charge.

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Ask Hackaday: How Does This Air Particle Sensor Work?

The hardware coming out of [Dr. Peter Jansen]’s lab is the craziest stuff you can imagine. He’s built a CT scanner out of plywood, and an MRI machine out of many, many turns of enamel wire. Perhaps his best-known build is his Tricorder – a real, all-sensing device with permission from the estate of [Gene Roddenberry] to use the name. [Peter]’s tricorder was one of the finalists for the first Hackaday Prize, but that doesn’t mean he’s stopped working on it. Sensors are always getting better, and by sometime in the 23rd century, he’ll be able to fit a neutrino detector inside a tiny hand-held device.

One of the new sensors [Peter] is working with is the MAX30105 air particle sensor. The marketing materials for this chip say it’s designed for smoke detectors and fire alarms, but this is really one of the smallest dust and particle sensors on the market. If you want a handheld device that detects dust, this should be the chip you’re looking at.

Unfortunately, Maxim is being very, very tight-lipped about how this particle sensor works. There is a way to get access to raw particle counts and the underlying algorithms, and Maxim is more than willing to sell those algorithms through a third-party distributor. That’s simply not how we do things around here, so [Peter] is looking for someone with a fancy particle sensor to collect a few hours of data so he can build a driver for this chip.

Here’s what we know about the MAX30105 air particle sensor. There are three LEDs inside this chip (red, IR, and green), and an optical sensor underneath a piece of glass. The chip drives the LEDs, light reflects off smoke particles, and enters the optical sensor. From there, magic algorithms turn this into a number corresponding to a particle count. [Peter]’s hackaday.io log for this project has tons of data, math, and statistics on the data that comes out of this sensor. He’s also built a test rig to compare this sensor with other particle sensors (the DSM501A and Sharp sensors). The data from the Maxim sensor looks good, but it’s not good enough for a Tricorder. This is where you, o reader of Hackaday, come in.

[Peter] is looking for someone with access to a fancy particle sensor to collect a few hours worth of data with this Maxim sensor in a test rig. Once that’s done, a few statistical tests should be enough to verify the work done so far and build a driver for this sensor. Then, [Peter] will be able to play around with this sensor and hopefully make a very cheap but very accurate air particle sensor that should be hanging on the wall of your shop.

Quadruped Robot Can Crawl Under Cars and Jump-Kick-Open Doors

The wheel is a revolutionary invention — as they say — but going back to basics sometimes opens new pathways. Robots that traverse terrain on legs are on the rise, most notably the Boston Dynamics Big Dog series of robots — and [Ghost Robotics]’ Minitaur quadruped aims to keep pace.

One of [Ghost Robotics] founders, [Gavin Knneally] states that co-ordination is one of the main problems to overcome when developing quadruped robots; being designed to clamber across especially harsh terrain, Minitaur’s staccato steps carry it up steep hills, stairs, across ice, and more. Its legs also allow it to adjust its height — the video shows it trot up to a car, hunker down, then begin to waddle underneath with ease.

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The Right Circuit Turns Doppler Module into a Sensor

Can you buy a working radar module for $12? As it turns out, you can. But can you make it output useful information? According to [Mathieu], the answer is also yes, but only if you ignore the datasheet circuit and build this amplification circuit for your dirt cheap Doppler module.

The module in question is a CDM324 24-GHz board that’s currently listing for $12 on Amazon. It’s the K-band cousin of the X-band HB100 used by [Mathieu] in a project we covered a few years back, but thanks to the shorter wavelength the module is much smaller — just an inch square. [Mathieu] discovered that the new module suffered from the same misleading amplifier circuit in the datasheet. After making some adjustments, a two-stage amp was designed and executed on a board that piggybacks on the module with a 3D-printed bracket.

Frequency output is proportional to the velocity of the detected object; the maximum speed for the sensor is only 14.5 mph (22.7 km/h), so don’t expect to be tracking anything too fast. Nevertheless, this could be a handy sensor, and it’s definitely a solid lesson in design. Still, if your tastes run more toward using this module on the 1.25-cm ham band, have a look at this HB100-based 3-cm band radio.

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Using Backscatter Radio for a Soil Sensor Network

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.

We really like the principles [Spyros] leveraged here, and the simplicity of the system. We can’t help but wonder what sort of synergies there are between this project and the 2015 Hackaday Prize-winning Vinduino project.

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The Megapixel Race and its Clear Winner

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?

The Technology

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.

cmos_image_sensor_mechanism_illustration
CMOS Image Sensor Mechanism Illustration, By User:たまなるたみ – drawing created myself, GPL, https://commons.wikimedia.org/w/index.php?curid=371238. Note that each pixel has its own amplifier.

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.

cockfield-minco
A sunrise picture taken with an entry-level DSLR at 1600 ISO. At this sensitivity, noise in the clouds can be seen in the form of random fluctuations of some pixels. This effect would be mitigated by a camera with a larger sensor, a lower sensor sensitivity with a longer shutter speed (which would blur the turbine blades) or a scene with more light. Photo  © 2016 by Bryan Cockfield

 

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.

Gigapixels!

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.

design_of_the_lsst_camera
LSST Image Sensor, By Todd Mason, Mason Productions Inc. / LSST Corporation – https://www.lsst.org/sites/default/files/photogallery/Camera_CU-full.jpg, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=52230238

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.)