Electrical Grid Demystified: How Energy Gets Where Its Needed

Even if you’re reading this on a piece of paper that was hand-delivered to you in the Siberian wilderness, somewhere someone had to use energy to run a printer and also had to somehow get all of this information from the energy-consuming information superhighway. While we rely on the electric grid for a lot of our daily energy needs like these, it’s often unclear exactly how the energy from nuclear fuel rods, fossil fuels, or wind and solar gets turned into electrons that somehow get into the things that need those electrons. We covered a little bit about the history of the electric grid and how it came to be in the first of this series of posts, but how exactly does energy get delivered to us over the grid? Continue reading “Electrical Grid Demystified: How Energy Gets Where Its Needed”

Brake Light Blinker Does It with Three Fives

Sometimes you use a Raspberry Pi when you really could have gotten by with an Arudino. Sometimes you use an Arduino when maybe an ATtiny45 would have been better. And sometimes, like [Bill]’s motorcycle tail light project, you use exactly the right tool for the job: a 555 timer.

One of the keys of motorcycle safety is visibility. People are often looking for other cars and often “miss” seeing motorcyclists for this reason. Headlight and tail light modulators (circuits that flash your lights continuously) are popular for this reason. Bill decided to roll out his own rather than buy a pre-made tail light flasher so he grabbed a trusty 555 timer and started soldering. His circuit flashes the tail light a specific number of times and then leaves it on (as long as one of the brake levers is depressed) which will definitely help alert other drivers to his presence.

[Bill] mentions that he likes the 555 timer because it’s simple and bulletproof, which is exactly what you’d need on something that will be attached to a motorcycle a be responsible for alerting drivers before they slam into you from behind.

We’d tend to agree with this assessment of the 555; we’ve featured entire 555 circuit contests before. His project also has all of the tools you’ll need to build your own, including the files to have your own PCB made. If you’d like inspiration for ways to improve motorcycle safety in other ways, though, we can suggest a pretty good starting point as well.

Bus Pirate Commandeers I2C

The Bus Pirate is one of our favorite tool for quick-and-dirty debugging in the microcontroller world. Essentially it makes it easy to communicate with a wide variety of different chips via a serial terminal regardless of the type of bus that the microcontroller uses. Although it was intended as a time-saving prototyping device, there are a lot of real-world applications where a Bus Pirate can be employed full-time, as [Scott] shows us with his Bus Pirate data logger.

[Scott] needed to constantly measure temperature, and the parts he had on hand included an LM75A breakout board that has a temperature sensor on board. These boards communicate with I2C, so it was relatively straightforward to gather data from the serial terminal. From there, [Scott] uses a Python script to automate the process of gathering the data. The process he uses to set everything up using a Raspberry Pi is available on the project site, including the code that he used in the project.

[Scott] has already used this device for a variety of different projects around his house and it has already proven incredibly useful. If you don’t already have a Bus Pirate lying around there are a few other ways to gather temperature data, but if you have an extra one around or you were thinking about purchasing one, then [Scott]’s project is a great illustration of the versatility of this device.

Printer Vulnerabilites Almost as Bad as IoT

Recently ZDNet and Gizmodo published articles outlining a critical flaw in a large array of personal printers. While the number of printers with this flaw is staggering, the ramifications are even more impressive. Ultimately, any of these printers could have documents sent to them stolen even if the document was only intended to be printed as a hard copy.

Luckily the people responsible for this discovery are white-hat in nature, and the release of this information has been made public so the responsible parties can fix the security flaws. Whether or not the “responsible party” is the manufacturer of the printer, though, is still somewhat unclear because part of the exploit takes advantage of a standard that is part of almost all consumer-grade printers. The standard itself may need to be patched.

Right now, however, it doesn’t seem clear exactly how deep the rabbit hole goes. We all remember the DDoS attack that was caused by Internet of Things devices that were poorly secured, and it seems feasible that networked printers could take some part in a similar botnet if a dedicated user really needed them. At the very least, however, your printed documents might not be secure at all, and you may be seeing a patch for your printer’s firmware in the near future.

 

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.)

Reprogramming Bluetooth Headphones for Great Justice

Like a lot of mass-produced consumer goods, it turns out that the internal workings of Bluetooth headphones are the same across a lot of different brands. One common Bluetooth module is the CSR8645, which [lorf] realized was fairly common and (more importantly) fairly easy to modify. [lorf] was able to put together a toolkit to reprogram this Bluetooth module in almost all of these headphones.

This tip comes to us from [Tigox] who has already made good use of [lorf]’s software. Using the toolkit, he was able to reprogram his own Bluetooth headphones over a USB link to his computer. After downloading and running [lorf]’s program, he was able to modify the name of the device and, more importantly, was able to adjust the behavior of the microphone’s gain which allowed him to have a much more pleasant user experience.

Additionally, the new toolkit makes it possible to flash custom ROMs to CSR Bluetooth modules. This opens up all kinds of possibilities, including the potential to use a set of inexpensive headphones for purposes other than listening to music. The button presses and microphones can be re-purposed for virtually any task imaginable. Of course, you may be able to find cheaper Bluetooth devices to repurpose, but if you just need to adjust your headphones’ settings then this hack will be more useful.

[Featured and Thumbnail Image Source by JLab Audio LLC – jlabaudio.com, CC BY-SA 4.0]

Ping Pong Ball-Juggling Robot

There aren’t too many sports named for the sound that is produced during the game. Even though it’s properly referred to as “table tennis” by serious practitioners, ping pong is probably the most obvious. To that end, [Nekojiru] built a ping pong ball juggling robot that used those very acoustics to pinpoint the location of the ball in relation to the robot. Not satisfied with his efforts there, he moved onto a visual solution and built a new juggling rig that uses computer vision instead of sound to keep a ping pong ball aloft.

The main controller is a Raspberry Pi 2 with a Pi camera module attached. After some mishaps with the planned IR vision system, [Nekojiru] decided to use green light to illuminate the ball. He notes that OpenCV probably wouldn’t have worked for him because it’s not fast enough for the 90 fps that’s required to bounce the ping pong ball. After looking at the incoming data from this system, an algorithm extracts 3D information about the ball and directs the paddle to strike the ball in a particular way.

If you’ve ever wanted to get into real-time object tracking, this is a great project to look over. The control system is well polished and the robot itself looks almost professionally made. Maybe it’s possible to build something similar to test [Nekojiru]’s hypothesis that OpenCV isn’t fast enough for this. If you want to get started in that realm of object tracking, there are some great projects that make use of that piece of software as well.