Whenever I end up with a new vehicle I ultimately end up sticking in a new GPS/Receiver combination for better sound quality and a better GPS.
I am quite at home tearing into a dashboard as I was licensed to install CB radios in my teens as well as being the local go-to guy for 8-track stereo upgrades in the 70’s. I have spent a portion of my life laying upside down in a puddle on the car floor peering up into the mess of wires and brackets trying to keep things from dropping on my face. If you remember my post on my Datsun 280ZXT, I laid in that same position while welding in a clutch pedal bracket while getting very little welding slag on my face. I did make a note that the next time I convert a car from an automatic to a manual to do so while things are still disassembled.
Swapping out a factory radio usually involves choosing whether to hack into the existing factory wiring wire-by-wire, or my preference, getting a cable harness that mates with the factory plug and making an adapter out of it by splicing it to the connector that comes with the new radio.
Usually I still have to hunt down a few signals such as reverse indicator, parking brake indicator, vehicle speed sensor and the like. In my last vehicle the Vehicle Speed Sensor (VSS) wire was supposed to be in the factory harness, but driving experience showed it must not be as the GPS would show me driving 30 feet to the right of the highway. That and the calibration screen on the GPS verified that it was not receiving speed pulses.
In the years since the launch of the original Raspberry Pi we have seen the little British ARM-based board become one of the more popular single board computers in the hobbyist, maker, and hacker communities. It has retained that position despite the best efforts of other manufacturers, and we have seen a succession of competitor boards directly copying it by imitating its form factor. None of them have made a significant dent in the sales figures enjoyed by the Pi, yet they continue to appear on a regular basis.
We recently brought you news of the latest challenger in this arena, in the form of the Asus Tinker Board. This is a board that has made us sit up and take notice because unlike previous players this time we have a product from a giant of the industry. Most of us are likely to own at least one Asus product, indeed there is a good chance that you might be reading this on an Asus computer or monitor. Asus have made some very high quality hardware in their time, so perhaps this product will inherit some of that heritage. Thus it was with a sense of expectation that we ordered one of the first batch of Tinker Boards, and waited eagerly for the postman.
A member of the Asus Marketing team read this review and contacted Hackaday with some updated information. According to our discussion, the Tinker Board has not officially launched. This explains a lot about the current state of the Tinker Board. As Jenny mentions in her review below, the software support for the board is not yet in place, and as comments on this review have mentioned, you can’t source it in the US and most other markets. An internal slide deck was leaked on SlideShare shortly after CES (which explains our earlier coverage), followed by one retailer in the UK market selling the boards ahead of Asus’ launch date (which is how we got our hands on this unit).
Asus tells us that they are aiming for an end of February launch date, perhaps as soon as the 26th for the United States, UK, and Taiwan. Other markets might have some variation, all of this contingent on agreements with and getting stock to regional distributors. With the launch will come the final OS Distribution (TinkerOS based on Debian), schematics, mechanical block diagrams, etc. Asus tells Hackaday it is a top priority to deliver hardware video acceleration for the Rockchip on the Tinker Board. The Board Support Package which hooks the feature into Linux is not yet finished but will come either on launch day or soon after. This is the end of the update, please enjoy Jenny List’s full review below.
Last week we covered the past and current state of artificial intelligence — what modern AI looks like, the differences between weak and strong AI, AGI, and some of the philosophical ideas about what constitutes consciousness. Weak AI is already all around us, in the form of software dedicated to performing specific tasks intelligently. Strong AI is the ultimate goal, and a true strong AI would resemble what most of us have grown familiar with through popular fiction.
Artificial General Intelligence (AGI) is a modern goal many AI researchers are currently devoting their careers to in an effort to bridge that gap. While AGI wouldn’t necessarily possess any kind of consciousness, it would be able to handle any data-related task put before it. Of course, as humans, it’s in our nature to try to forecast the future, and that’s what we’ll be talking about in this article. What are some of our best guesses about what we can expect from AI in the future (near and far)? What possible ethical and practical concerns are there if a conscious AI were to be created? In this speculative future, should an AI have rights, or should it be feared?
Over the last decade or so the definition of what a ‘small satellite’ is has ballooned beyond the original cubesat design specification to satellites of 50 or 100 kg. Today a ‘smallsat’ is defined far more around the cost, and sometimes the technologies used, than the size and shape of the box that goes into orbit.
There are now more than fifty companies working on launch vehicles dedicated to lifting these small satellites into orbit, and while nobody really expects all of those to survive the next few years, it’s going to be an interesting time in the launcher market. Because I have a sneaking suspicion that Jeff Bezos’ statement that “there’s not that much interesting about cubesats” may well turn out to be the twenty first century’s “nobody needs more than 640kb,” and it’s possible that everybody is wrong about how many of the launcher companies will survive in the long term.
The history of aviation is a fascinating one, spanning more than two thousand years starting from kites and tower jumping. Many hackers are also aviation fans, and the name of Alberto Santos Dumont may be familiar, but if not, here we talk about his role and accomplishments in the field. Santos Dumont is one of the few aviation pioneers that made contributions in both balloons, airships and heavier-than-air aircraft.
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.)