Most video game manufacturers aren’t too keen on homebrew games, or people trying to get more utility out of a video game system than it was designed to have. While some effort is made to keep people from slapping a modchip on an Xbox or from running an emulator for a Playstation, it’s almost completely impossible to stop some of the hardware hacking that is common on older cartridge-based games. The only limit is usually the cost of an EPROM programmer, but [Robson] has that covered now with his Arduino-based SNES EPROM programmer.
Normally this type of hack involves finding any cartridge for the SNES at the lowest possible value, burning an EPROM with the game that you really want, and then swapping the new programmed memory with the one in the worthless cartridge. Even though most programmers are pricey, it’s actually not that difficult to write bits to this type of memory. [Robson] runs us through all of the steps to get an Arduino set up to program these types of memory, and then puts it all together into a Super Nintendo where it looks exactly like the real thing.
If you don’t have an SNES lying around, it’s possible to perform a similar end-around on a Sega Genesis as well. And, if you’re more youthful than those of us that grew up in the 16-bit era, there’s a pretty decent homebrew community that has sprung up around the Nintendo DS and 3DS, too.
Evolution is one clever fellow. Next time you’re strolling about outdoors, pick up a pine cone and take a look at the layout of the bract scales. You’ll find an unmistakable geometric structure. In fact, this same structure can be seen in the petals of a rose, the seeds of a sunflower and even the cochlea bone in your inner ear. Look closely enough, and you’ll find this spiraling structure everywhere. It’s based on a series of integers called the Fibonacci sequence. Leonardo Bonacci discovered the sequence while trying to figure out how many rabbits he could make starting with just two. It’s quite simple — add the right most integer to the previous one to get the next one in the sequence. Starting from zero, this would give you 0-1-1-2-3-5-8-13-21 and so on. If one was to look at this sequence in the form of geometric shapes, they can create square tiles whose sides are the length of the value in the sequence. If you connect the diagonal corners of these tiles with an infinite curve, you end up with the spiral that you saw in the pine cone and other natural objects.
So how did mother nature discover this geometric structure? Surely it does not know math. How then can it come up with intricate and sophisticated structures? It turns out that this Fibonacci spiral is the most efficient way of squeezing the most amount of stuff in the least amount of space. And if one takes natural selection seriously, this makes perfect sense. Eons of trial and error to make the most copies of itself has stumbled upon a mathematical principle that permeates life on earth.
The homo sapiens brain is the product of this same evolutionary process, and has been evolving for an estimated 7 million years. It would be foolish to think that this same type of efficiency natural selection has stumbled across would not be present in the current homo sapiens brain. I want to impress upon you this idea of efficiency. Natural selection discovered the Fibonacci sequence solely because it is the most efficient way to do a particular task. If the brain has a task of storing information, it is perfectly reasonable that millions of years of evolution has honed it so that it does this in the most efficient way possible as well. In this article, we shall explore this idea of efficiency in data storage, and leave you to ponder its applications in the computer sciences.
I was buying a new laptop the other day and had to make a choice between 4GB of memory and 8. I can remember how big a deal it was when a TRS-80 went from 4K (that’s .000004 GB, if you are counting) to 48K. Today just about all RAM (at least in PCs) is dynamic–it relies on tiny capacitors to hold a charge. The downside to that is that the RAM is unavailable sometimes while the capacitors get refreshed. The upside is you can inexpensively pack lots of bits into a small area. All of the common memory you plug into a PC motherboard–DDR, DDR2, SDRAM, RDRAM, and so on–are types of dynamic memory.
The other kind of common RAM you see is static. This is more or less an array of flip flops. They don’t require refreshing, but a static RAM cell is much larger than an equivalent bit of dynamic memory, so static memory is much less dense than dynamic. Static RAM lives in your PC, too, as cache memory where speed is important.
For now, at least, these two types of RAM technology dominate the market for fast random access read/write memory. Sure, there are a few new technologies that could gain wider usage. There’s also things like flash memory that are useful, but can’t displace regular RAM because of speed, durability, or complex write cycles. However, computers didn’t always use static and dynamic RAM. In fact, they are relatively newcomers to the scene. What did early computers use for fast read/write storage?
[Brek] needed to store 64 bits of data from his GPS to serve as a last-known-position function. This memory must be non-volatile, sticking around when the GPS and power are off. Solutions like using a backup battery or employing a $0.25 EEPROM chip were obviously too pedestrian. [Brek] wanted to store his 64 bits in style and that means hand-wired core memory.
OK, we’re pretty sure that the solution came first, and then [Brek] found a fitting problem that could be solved, but you gotta give him props for a project well executed and well documented.
Since 1998 we’ve been privileged to partake in an arcade game known as Dance Dance Revolution, but before that, way back in the 70’s, was the Simon game. It’s essentially a memory game that asks the player to remember a series of lights and sounds. [Uberdam] decided to get the best of both worlds and mixed the two together creating this giant foot controlled Simon game. (English translation.)
The wood platform that serves as the base of the project was fitted with four capacitive sensors, each one representing a “color” on the Simon game. When a player stomps on a color, a capacitive sensor sends a signal to a relay which in turn notifies the Raspberry Pi brain of the input. The Pi also takes care of showing the player the sequence of colored squares that must be stepped on, and keeps track of a player’s progress on a projector.
This is a pretty good way of showing how a small, tiny computer like the Raspberry Pi can have applications in niche environments while also being a pretty fun game. We all remember Simon as being frustrating, and we can only imagine how jumping around on a wooden box would make it even more exciting. Now, who can build a robot that can beat this version of Simon?
Heat up that iron, you’re going to want to try this one: [Hugatry] is adding hardware to his laptop by tapping into the i2c lines on the memory module. We love this because the penalty for borking memory during the soldering process is much lower than when soldering directly to a motherboard!
Until we watched the video after the break we hadn’t realized that memory modules usually have an i2c EEPROM on them. This is actually a standard called Serial Presence Detect which allows the BIOS to poll the memory and configure automatically. It seems ironic that we knew the Raspberry Pi HAT standard uses this same trick but didn’t know it was on computer memory as well.
Hardware-wise this provides an easy method of soldering your own equipment to the bus. From there it becomes a software hack. Linux, of course, makes this quite easy and that is demonstrated by [Hugatry] with an LM75 temperature sensors. We would like to hear from our Windows and OSX using readers on how the i2c bus can be accessed within those OS’s.
The hack that pulls this off is a simple one compared to bike hacks we’ve previously covered. Gears on the head tube make this possible. It was built by his welder friends who challenged him to ride it. He couldn’t at first; determined to overwrite his brain’s memory of bike riding, he practiced until he finally succeeded. It took him eight months. When it was time to ride an old-fashioned bike, it only took him about twenty minutes to “un-learn” the Backwards Brain Bike. [Destin’s] biking illustrates neuroplasticity, memory, and learning in a fun way (fun for us; no doubt frustrating for him).
As a testament to the sponge-like brains of youth, [Destin’s] son learned to ride the Backwards Brain Bike in only two weeks.