Careful, the walls have ears. Or more specifically, the smart speaker on the table has ears, as does the phone in your pocket, the fitness band on your wrist, possibly the TV, the fridge, the toaster, and maybe even the toilet. Oh, and your car is listening to you too. Probably.
How does one fight this profusion of listening devices? Perhaps this wearable smart device audio jammer will do the trick. The idea is that the MEMS microphones that surround us are all vulnerable to jamming by ultrasonic waves, due to the fact that they have a non-linear response to ultrasonic signals. The upshot of that is when a MEMS hears ultrasound, it creates a broadband signal in the audible part of the spectrum. That creates a staticky noise that effectively drowns out any other sounds the microphone might be picking up.
By why a wearable? Granted, [Yuxin Chin] and colleagues from the University of Chicago have perhaps stretched the definition of that term a tad with their prototype, but it turns out that moving the jammer around does a better job of blocking sounds than a static jammer does. The bracelet jammer is studded with ultrasonic transducers that emit overlapping fields and result in zones of constructive and destructive interference; the wearer’s movements vary the location of the dead spots that result, improving jamming efficacy. Their paper (PDF link) goes into deeper detail, and a GitHub repository has everything you need to roll your own.
We saw something a bit like this before, but that build used white noise for masking, and was affixed to the smart speaker. We’re intrigued by a wearable, especially since they’ve shown it to be effective under clothing. And the effect of ultrasound on MEMS microphones is really interesting.
The modern hacker wields a number of tools that operate on the principle of heating things up to extremely high temperatures, so a smoke alarm is really a must-have piece of equipment. But in an era where it seems everything is getting smarter, some might wonder if even our safety gear could benefit from joining the Internet of Things. Interested in taking a crack at improving the classic smoke alarm, [Vivek Gupta] grabbed a NodeMCU and started writing some code.
Now before you jump down to the comments and start smashing that keyboard, let’s make our position on this abundantly clear. Do not try to build your own smoke alarm. Seriously. It takes a special kind of fool to trust their home and potentially their life to a $5 development board and some Arduino source code they copied and pasted from the Internet. That said, as a purely academic exercise it’s certainly worth examining how modern Internet-enabled microcontrollers can be used to add useful features to even the most mundane of household devices.
In this case, [Vivek] is experimenting with the idea of a smoke alarm that can be silenced through your home automation system in the event of a false alarm. He’s using Google Assistant and IFTTT, but the code could be adapted to whatever method you’re using internally to get all your gadgets on the same virtual page. On the hardware side of things, the test system is simply a NodeMCU connected to a buzzer and a MQ2 gas sensor.
So how does it work? If the detector goes off while [Vivek] is cooking, he can tell Google Assistant that he’s cooking and it’s a false alarm. That silences the buzzer, but not before the system responds with a message questioning his skills in the kitchen. It’s a simple quality of life improvement and it’s certainly not hard to imagine how the idea could be expanded upon to notify you of a possible situation even when you’re out of the home.
The device is built around Google’s AIY Voice Kit, which consists of a Raspberry Pi with some additional hardware and software to enable it to process voice queries. [Liz] combined this with a Raspberry Pi camera and the Google Cloud Vision API. This allows WhatIsThat to respond to users asking questions by taking a photo, and then identifying what it sees in the frame.
It may seem like a frivolous project to those with working vision, but there is serious potential for this technology in the accessibility space. The device can not only describe things like animals or other objects, it can also read text aloud and even identify logos. The ability of the software to go beyond is impressive – a video demonstration shows the AI correctly identifying a Boston Terrier, and attributing a quote to Albert Einstein.
Artificial intelligence has made a huge difference to the viability of voice recognition – because it’s one thing to understand the words, and another to understand what they mean when strung together. Video after the break.
Standing desks are great conversation starters in the office – whether you like it or not. How do you know someone’s got a standing desk? Don’t worry, they’ll tell you. Standing desks have their benefits, but for maximum flexibility, many people choose a desk that can raise and lower depending on their needs. [Wassim] had just such a desk, but found pushing the buttons too 20th century for his tastes. Naturally, Google Assistant integration was the key here.
[Wassim] started out intending to capture and then spoof the desk controller’s signals to the motors, before realising it was likely easier to simply spoof button presses instead. This was achieved through a handful of NPN transistors and an Onion Omega2+ microcontroller board. Then it was a simple case of coding the controller to press the various buttons in response to HTTP requests received over WiFi. Google Assistant integration was then handled with IFTTT, though [Wassim] also discusses the possibility of implementing the full Smart Home API.
What exactly qualifies as comfort food is very much in the palate of the comfortee. Grilled cheese may not work for everyone under every circumstance, but we’ll risk a bet that the gooey delicacy is pretty close to universal, especially when you’re under the weather.
But if you’re too sick to grill up your own and don’t have anyone to do it for you, this grilled cheese sandwich-making robot might be the perfect kitchen accessory. Dubbed “The Cheeseborg” and built as a semester project by [Taylor Tabb], [Mitchell Riek], and [Evan Hill] at Carnegie-Mellon University, the bot takes a few shortcuts that might rankle the grilled cheese purist. Chief among these is the use of a sandwich press rather than a plain griddle. We understand that this greatly simplifies the flipping problem, but to us the flipping, especially the final high arcing double backflip onto the sandwich plate, is all part of the experience. Yes, a fair number of sandwiches end up going to the dog that way, but that’s beside the point.
As realized, Cheeseborg feeds bread and cheese from stacks using a vacuum arm, sprays the grill with butter, and uses a motorized arm to push the uncooked sandwich into the press. At the peak of grilled perfection, the press opens and ejects the sandwich to a waiting plate. As an added bonus, the whole thing is Google Assistant enabled so you can beseech Cheeseborg to fix you a sandwich from your sick bed. See it in action below.
In the near future of the Smart Home, you will be able to control anything with your voice. Assuming that everything supports the Smart Home standard you chose, that is. If you have a device that supports one of the other standards, you’ll end up uselessly yelling at it. Unless you use gBridge. As the name suggests, gBridge is a bridge between Google Assistant devices and the rest of the smart home universe. It’s an open source project that is available as a Docker image can be run on a low power device in the home, or on a hosted service.
Fundamentally, gBridge is a Google Assistant to MQTT translator. Message Queuing Telemetry Transport (MQTT) is the messaging protocol that many smart home devices use, as it runs over TCP and doesn’t take much power to implement. We’ve covered how to bash around in MQTT and do much of this yourself here, but gBridge looks to be somewhat easier to use. It’s just come out of beta test, and it looks like it might be a good way to get into Smart Home hacking.
There are, of course, plenty of other ways of doing this, such as IFFFT, but [Peter Kappelt], the brains behind gBridge, claims that it is more flexible, as it offers support for the whole Google Assistant vocabulary, so you can do things like put devices into groups or do more conditional control (such as if the light level in the hallway rises above a certain amount, start recording with a camera) with non-Google devices. [Peter] is also looking to run gBridge as a hosted service, where he does the behind the scenes stuff to update servers, etc, in return for a small fee.
Depending on who you talk to, Google Assistant is either a tool capable of quickly and clearly answering audio queries in natural langauge, or a noisier and less useful version of Wolfram Alpha. [William Franzin] decided it would be particularly cool to make the service available over ham radio – and that’s exactly what he did.
[William] got the idea for this project after first playing with the Internet Radio Linking Project, a system which uses VoIP technologies to link radio networks over the internet. Already having an IRLP node, it seemed only natural to make it into a gateway to the wider internet through integration with Google Assistant. Early work involved activating the assistant via DTMF tones, but [William] didn’t stop there – through the use of Picovoice, it became possible to use the system with the custom wakeword “Bumblebee”.
[William]’s project could prove particularly useful for when he’s out of cell coverage, but needs a little information like a weather report or a piece of trivia to settle an argument round the campfire. Additionally, it’s even possible to control the IRLP node through voice commands, too.