Nothing makes you appreciate your vision more than getting a little older and realizing that it used to be better and that it will probably get worse. But imagine how much more difficult it would be if you were totally blind. That was what happened to [Berna Gomez] when, at 42, she developed a medical condition that destroyed her optic nerves leaving her blind in a matter of days and ending her career as a science teacher. But thanks to science [Gomez] can now see, at least to some extent. She volunteered after 16 years to have a penny-sized device with 96 electrodes implanted in her visual cortex. The research is in the Journal of Clinical Investigation and while it is a crude first step, it shows lots of promise and uses some very novel techniques to overcome certain limitations.
The 96 electrodes were in a 10×10 grid with the four corner electrodes missing. The resolution, of course, is lacking, but the project turned to a glasses-mounted camera to acquire images and process them, reducing them to signals for the electrodes that may not directly map to the image.
The electrical signals emitted by the human body tell us a lot about what’s going on inside. But getting those signals inside your microcontroller is not straightforward: the voltages are too small for most ADCs, and the ever-present 50 or 60 Hz mains frequency makes it hard to discern subtle changes. Over at Upside Down Labs, [Deepak Kathri] developed a universal biosensor interface called the BioAmp EXG Pill to make all this a lot easier.
Its name refers to the fact that it can be used for several different bio-electrical sensing applications: ECG, EMG, EOG and EEG, which deal with signals coming from the heart, muscles, eyes and brain, respectively. To enable such flexibility, the board has connectors for two or three electrodes, as well as solder pads to mount resistors and capacitors to adjust the gain and bandwidth. An instrumentation amplifier increases the strength of the desired signal while rejecting noise and interference.
The form factor allows easy connection to electrodes on one side and a data acquisition system on the other. Measuring just 25.4 mm long and 10 mm wide, it should be easy to integrate into any type of biosensing gizmo you can come up with. [Deepak] has made several demo setups, showing him using the Pill with an Arduino to measure his heart rate, detect eye blinks, and even control a robot arm using his own arm muscles!
The EXG Pill is an evolution of an earlier EMG-only project. We’ve seen several great ECG and EEG projects before, but is the first time we’ve seen one amplifier that can do them all.
There’s little debate that Amazon’s Alexa ecosystem makes it easy to add voice control to your smart home, but not everyone is thrilled with how it works. The fact that all of your commands are bounced off of Amazon’s servers instead of staying internal to the network is an absolute no-go for the more privacy minded among us, and honestly, it’s hard to blame them. The whole thing is pretty creepy when you think about it.
Which is precisely why [André Hentschel] decided to look into replacing the firmware on his Amazon Echo with an open source alternative. The Linux-powered first generation Echo had been rooted years before thanks to the diagnostic port on the bottom of the device, and there were even a few firmware images floating around out there that he could poke around in. In theory, all he had to do was remove anything that called back to the Amazon servers and replace the proprietary bits with comparable free software libraries and tools.
Taping into the Echo’s debug port.
Of course, it ended up being a little trickier than that. The original Echo is running on a 2.6.x series Linux kernel, which even for a device released in 2014, is painfully outdated. With its similarly archaic version of glibc, newer Linux software would refuse to run. [André] found that building an up-to-date filesystem image for the Echo wasn’t a problem, but getting the niche device’s hardware working on a more modern kernel was another story.
He eventually got the microphone array working, but not the onboard digital signal processor (DSP). Without the DSP, the age of the Echo’s hardware really started to show, and it was clear the seven year old smart speaker would need some help to get the job done.
The solution [André] came up with is not unlike how the device worked originally: the Echo performs wake word detection locally, but then offloads the actual speech processing to a more powerful computer. Except in this case, the other computer is on the same network and not hidden away in Amazon’s cloud. The Porcupine project provides the wake word detection, speech samples are broken down into actionable intents with voice2json, and the responses are delivered by the venerable eSpeak speech synthesizer.
As you can see in the video below the overall experience is pretty similar to stock, complete with fancy LED ring action. In fact, since Porcupine allows for multiple wake words, you could even argue that the usability has been improved. While [André] says adding support for Mycroft would be a logical expansion, his immediate goal is to get everything documented and available on the project’s GitLab repository so others can start experimenting for themselves.
The 6502 was a revolutionary processor for its time. Offered at a small fraction of the cost of other processors available when it was released, it became adopted in such iconic computers at the Atari 2600, the Apple II, the NES, and the Commodore 64. For that reason it’s still extremely popular among retrocomputing enthusiasts who will often go to great lengths to restore these computers or build them from scratch. [jamesbowman] had an idea to build a 6502-based computer with the processor only, leaving the rest of the computer up to an FPGA.
He describes the system as a “brain in a vat” since a real 6502 is used as the “brain” and all other functions are passed off to the FPGA. In his build he uses an FPGA board with built-in graphics abilities, but the truly interesting part of this build is how the FPGA handles memory. If a particular value is placed on the data bus of the 6502, it loops forever through the entire memory and executes all of the instructions it finds. This saved a lot of time getting this system up and running, and he is able to demonstrate it by showing a waveform on the video output of the device.
Of course you can take an FPGA and emulate an entire computer based on a 6502, but using the actual silicon in a build like this really ensures that the user can learn and understand the hardware involved without some of the other tedium of doing things such as converting old video signals to HDMI for example. It’s a great take on retrocomputing that we expect to see more of in the future.
Our abilities to multitask, to quickly learn complex maneuvers, and to instantly recognize objects even as infants are just some of the ways that human brains make use of our billions of synapses. Biologically, our brain requires fluid-filled cavities, nerve fibers, and numerous other cells and connections in order to function. This isn’t the case with a new kind of brain recently announced by a team of MIT engineers in Nature Nanotechnology. Compared to the size of a typical human brain, this new “brain-on-a-chip” is able to fit on a piece of confetti.
When you take a look at the chip, it is more similar to tiny metal carving than to any neurological organ. The technology used to design the chip is based on memristors – silicon-based components that mimic the transmissions of synapses. A concatenation of “memory” and “resistor”, they exist as passive circuit elements that retain a relationship between the time integrals of current and voltage across an element. As resistance varies, tiny read charges are able to access a history of applied voltage. This can be accomplished by hysteresis and other non-linear properties of passive circuitry.
These properties can be best observed at nanoscale levels, where they aren’t dwarfed by other electronic and field effects. A tiny positive and negative electrode are separated by a “switching medium”, or space between the two electrodes. Voltage applied to one end causes ions to flow through the medium, forming a conduction channel to the other end. These ions make up the electrical signal transmitted through the circuit.
In order to fabricate these memristors, the researchers used alloys of silver for the positive electrode, and copper alongside silicon for the negative electrode. They sandwiched the two electrodes along an amorphous medium and patterned this on a silicon chip tens of thousands of times to create an array of memristors. To train the memristors, they ran the chips through visual tasks to store images and reproduce them until cleaner versions were produced. These new devices join a new category of research into neuromorphic computing – electronics that function similar to the way the brain’s neural architecture operates.
The opportunity for electronics that are capable of making instantaneous decisions without consulting other devices or the Internet spell the possibility of portable artificial intelligence systems. Though we already have software systems capable of simulating synaptic behavior, developing neuromorphic computing devices could vastly increase the capability of devices to do tasks once thought to belong solely to the human brain.
3D printing has opened up a world of possibilities in plastic, food, concrete, and other materials. Now, MIT engineers have found a way to add brain implants to the list. This technology has the potential to replace electrodes used for monitoring and implants that stimulate brain tissue in order to ease the effects of epilepsy, Parkinson’s disease, and severe depression.
Existing brain implants are rigid and abrade the grey matter, which creates scar tissue over time. This new material is soft and flexible, so it hugs the wrinkles and curves. It’s a conductive polymer that’s been thickened into a viscous, printable paste.
The team took a conductive liquid polymer (water plus nanofibers of a polystyrene sulfonate) and combined it with a solvent they made for a previous project to form a conductive, printable hydrogel.
In addition to printing out a sheet of micro blinky circuits, they tested out the material by printing a flexible electrode, which they implanted into a mouse. Amazingly, the electrode was able to detect the signal coming from a single neuron. They also printed arrays of electrodes topped with little wells for holding neurons so they can study the neurons’ signals using the electrode net underneath.
The Samsung PS-WTX500 subwoofer is designed to be used as part of a 5.1 channel home theater system, but not just any system. It contains the amplifiers for all the channels, but they’ll only function when the subwoofer is connected to the matching receiver. [Alejandro Zarate] figured there must be some way to unlock the system’s full functionality without being limited to the original receiver, he just needed to reverse engineer how the subwoofer worked.
All the wires tuck underneath the Arduino
The result is a fantastically well documented write-up that covers the whole process, starting with how [Alejandro] identified and researched the Pulsus PS9829B Digital Audio Processor (DAP). Documentation for this particular chip seems hard to come by, but he was able to find a similar chip from the same manufacturer that was close enough to put him on the right track. From there, he started studying the SPI communications between the DAP and the subwoofer’s S3P70F4 microcontroller.
After analyzing the communication between the two chips, [Alejandro] pulled the S3P70F4 off the board and wired an Arduino Pro Mini 328 in its place. The Arduino was quite a bit larger than the original microcontroller, but with some careful wiring, he manged a very professional looking installation. Short of coming up with a custom PCB adapter, we don’t think it could look much better.
With some relatively straightforward code and a listing of the captured byte sequences, the Arduino was able to power up the PS-WTX500’s amplifiers and handle the incoming audio signal as a stand-alone device.
