The rumor mill has recently been buzzing about Nintendo’s plans to introduce a new version of their extremely popular Switch console in time for the holidays. A faster CPU, more RAM, and an improved OLED display are all pretty much a given, as you’d expect for a mid-generation refresh. Those upgraded specifications will almost certainly come with an inflated price tag as well, but given the incredible demand for the current Switch, a $50 or even $100 bump is unlikely to dissuade many prospective buyers.
But according to a report from Bloomberg, the new Switch might have a bit more going on under the hood than you’d expect from the technologically conservative Nintendo. Their sources claim the new system will utilize an NVIDIA chipset capable of Deep Learning Super Sampling (DLSS), a feature which is currently only available on high-end GeForce RTX 20 and GeForce RTX 30 series GPUs. The technology, which has already been employed by several notable PC games over the last few years, uses machine learning to upscale rendered images in real-time. So rather than tasking the GPU with producing a native 4K image, the engine can render the game at a lower resolution and have DLSS make up the difference.
The implications of this technology, especially on computationally limited devices, is immense. For the Switch, which doubles as a battery powered handheld when removed from its dock, the use of DLSS could allow it to produce visuals similar to the far larger and more expensive Xbox and PlayStation systems it’s in competition with. If Nintendo and NVIDIA can prove DLSS to be viable on something as small as the Switch, we’ll likely see the technology come to future smartphones and tablets to make up for their relatively limited GPUs.
But why stop there? If artificial intelligence systems like DLSS can scale up a video game, it stands to reason the same techniques could be applied to other forms of content. Rather than saturating your Internet connection with a 16K video stream, will TVs of the future simply make the best of what they have using a machine learning algorithm trained on popular shows and movies?
Why doesn’t this kind of stuff ever happen to us? One lucky day back in high school, [Dave Sieg] stumbled upon a room full of new equipment and a guy standing there scratching his head. [Dave]’s curiosity about this fledgling television studio was rewarded when that guy asked [Dave] if he wanted to help set it up. From that point on, [Dave] had the video bug. The rest is analog television history.
Today, [Dave] is the proud owner and maintainer of two Scanimate machines — the first R&D prototype, and the last one of only eight ever produced. The Scanimate is essentially an analog synthesizer for video signals, and they made it possible to move words and pictures around on a screen much more easily than ever before. Any animated logo or graphics seen on TV from the mid-1970s to the mid-80s was likely done with one of these huge machines, and we would jump quite high at the chance to fiddle with one of them.
Analog television signals were continuously variable, and much like an analog music synthesizer, the changes imposed on the signal are immediately discernible. In the first video below, [Dave] introduces the Scanimate and plays around with the Viceland logo a bit.
Stick around for the second and third videos where he superimposes the Scanimate’s output on to the video he’s making, all the while twiddling knobs to add oscillators and thoroughly explaining what’s going on. If you’ve ever played around with Lissajous patterns on an oscilloscope, you’ll really have a feel for what’s happening here. In the fourth video, [Dave] dives deeper and dissects the analog circuits that make up this fantastic piece of equipment.
There was a time when making a machine to identify objects in a camera was difficult, even without trying to do it in real time. But now, you can do it with a Jetson Nano board for under $60. How well does it work? Watch [Murtaza’s] video below and see what you think.
The first few minutes of the video piqued our interest, and good thing, too, because the 50 lines of code get a 50-plus minute video! It is worth watching, though, because there’s a lot of good information about how to apply this technique in your own projects.
For its next trick, speech for each scene is processed by combining subtitle information with the audio track of the video. The audio is analyzed for emotion to determine the appropriate speech bubble type and size of the subtitle text. Frames are even analyzed to establish which person is speaking for proper placement of the bubbles. It can then create layouts of the keyframes, determining panel sizes for each page based on the region-of-interest analysis.
The process is completed by stylizing the keyframes with flat color through quantization, for that classic cel shading look, and then populating the layouts with each frame and word balloon.
The team conducted a study with 40 users, pitting their results against previous techniques which require more human intervention and still besting them in every measure. Like any great superhero, the team still sees room for improvement. In the future, they would like to improve the accuracy of keyframe selection and propose using a neural network to do so.
For American readers of a certain age, Local on the 8s likely holds a special spot in your heart. The program, once a staple of The Weather Channel, would provide viewers with a text and eventually graphical depiction of their local forecast set to some of the greatest smooth jazz ever heard outside of an elevator. In the days before smartphones, or even regular Internet access for that matter, these broadcasts were a critical part of planning your day in the 1980s through to the early 2000s.
Up until recently the technical details behind these iconic weather reports were largely unknown, but thanks to the Herculean efforts of [techknight], the fascinating engineering that went into the WeatherSTAR 4000 machines that pumped out current conditions and Shakin’ The Shack from CATV distribution centers all over the US for decades is now being documented and preserved. The process of reversing the hardware and software has actually been going on for the last couple of years, but all those juicy details are now finally going to be available on the project’s Hackaday.IO page.
It all started around Christmas of 2018, when an eBay alert [techknight] had configured for the WeatherSTAR 4000 finally fired off. His offer was accepted, and soon he had the physical manifestation of Local on the 8s in his own hands. He’d reasoned that getting the Motorola MC68010 machine working would be like poking around in a retrocomputer, but it didn’t take long for him to realize he’d gotten himself into a much larger project than he could ever have imagined.
The trick of a volumetric display is the ability to add a third dimension for positioning pixels. Here [Sean] delivered that ability with a stack up of ten screens to add a depth element. This is not such an easy trick. These small OLED displays are all over the place but they share a common element: a dark background over which the pixels appear. [Sean] has gotten his hands on some transparent OLED panels and with some Duck-Duck-Go-Fu we think it’s probably a Crystalfontz 128×56 display. Why is it we don’t see more of these? Anyone know if it’s possible to remove the backing from other OLED displays to get here. (Let us know in the comments.)
The rest of the built is fairly straight-forward with a Feather M4 board driving the ten screens via SPI, and an MPU-6050 IMU for motion input. The form factor lends an aesthetic of an augmented reality device and the production approach for the video puts this in a Bladerunner or Johnny Mnemonic universe. Kudos for expanding the awesome of the build with an implied backstory!
Audio and video synthesizers have been around for decades, and are pretty much only limited by one’s willingness to spend money on them. That is, unless you can develop your own FPGA-supercharged synthesizer to really get a leg up on the consumer-grade components. Of course, as [Julian] found out in this four-year project, you tend to pay for it anyway in time spent working on your projects.
[Julian] has actually decided to stop working on the project and open-source it to anyone who wants to continue on. He has already finished the PCB layout on a gargantuan 8-layer print, done all of the routing and parts selection, and really only needed to finish testing it to complete the project. It’s powered by the Xilinx Zynq and is packed with features too: HDMI, DDR3 ram, USB, a handful of sensors, and an Arduino Uno-style header to make interfacing and programming a breeze.
While we’re sympathetic with setting aside a project that we’ve worked so hard on, with most of the work done on this one it should be pretty easy to pick up and adapt for anyone interested in carrying the torch. If you were hoping to wet your whistle with something with fewer PCB layers, though, we’ve seen some interesting (but slightly simpler) video synthesizers made out of other unique hardware as well.