For those who choose to let their cats live a more or less free-range life, there are usually two choices. One, you can adopt the role of servant and run for the door whenever the cat wants to get back inside from their latest bird-murdering jaunt. Or two, install a cat door and let them come and go as they please, sometimes with a “present” for you in their mouth. Heads you win, tails you lose.
There’s another way, though: just let the cat ask to be let back in. That’s the approach that [Tennis Smith] took with this machine-learning kitty doorbell. It’s based on a Raspberry Pi 4, which lives inside the house, and a USB microphone that’s outside the front door. The Pi uses Tensorflow Lite to classify the sounds it picks up outside, and when one of those sounds fits the model of a cat’s meow, a message is dispatched to AWS Lambda. From there a text message is sent to alert [Tennis] that the cat is ready to come back in.
There’s a ton of useful information included in the repo for this project, including step-by-step instructions for getting Amazon Web Services working on the Pi. If you’re a dog person, fear not: changing from meows to barks is as simple as tweaking a single line of code. And if you’d rather not be at the beck and call of a cat but still want to avoid the evidence of a prey event on your carpet, machine learning can help with that too.
[via Tom’s Hardware]
Biohacking projects are not new to Hackaday and it’s certainly a genre that really piques our interest. Our latest biohacking device comes courtesy of [Manivannan] who brings his flavor of a wearable biosensor with some security elements built-in through AWS.
The hardware is composed of some impressive components we have seen. He has an AD8232 electrocardiogram front end, the MAX30102 integrated pulse oximeter IC for determining blood oxygen and heart rate, and the ever-popular LM35 for measuring body temperature. Either of these chips would be perfect for your next DIY biosensor project though you might try the MAX30205 body temperature sensor given its 0.1-degree Celsius accuracy. However, what really piqued our interest was the use of Microchip’s AVR-IoT WA Development Board. Now we’ve talked about this board before and also mentioned you could probably do all the same things with an ESP-device, but perhaps now we get to see the board a bit more in action.
[Manivannan] walks the reader through the board’s setup and everything looks to be pretty straightforward. He ultimately rigged together a very primitive dashboard for viewing all his vitals in real-time, demonstrating how you could put together your own patient dashboard for remote monitoring of vitals or other sensor signals. He emphasizes that all this is powered through AWS, giving him some added security layers that are critical for protecting his data from unwanted viewers.
Though [Manivannan’s] security implementation doesn’t rise to the standard of medical devices, maybe it will serve as a case study in the growing open-source medical device movement.
Continue reading “A DIY Biometric Device With Some Security Considerations” →
Having a shared coffee maker in the workplace is both a blessing and a curse. It’s nice to have constant access to coffee, but it can be frustrating to find the coffee pot emptied right as you walk in to the break room. To solve this problem in their office, [Vitort] and co. built an IOT solution that notifies everyone of the current coffee status on a Slack channel.
This project wasn’t built just as a convenience for the office, either. It makes extensive use of AWS SNS, the simple notification system from Amazon Web Services because they wanted to learn to use this technology specifically. Besides the notification system, the device itself is based on a NodeMCU/ESP8266, communicating over WiFi, and is a simple push-button design which coffee drinkers push when a fresh pot is made, and then push again when the coffee is empty.
While relatively straightforward, this project is a good one to look at if you’ve been interested in AWS at all, especially the simple notification system. It’s a pretty versatile tool, and all of the code used in the project is available on the project page for your reading pleasure. If you’re more interested in the coffee aspect of this project, we have a special coffee maker for you too.
Back in January when we announced the Train All the Things contest, we weren’t sure what kind of entries we’d see. Machine learning is a huge and rapidly evolving field, after all, and the traditional barriers that computationally intensive processes face have been falling just as rapidly. Constraints are fading away, and we want you to explore this wild new world and show us what you come up with.
Where Do You Run Your Algorithms?
To give your effort a little structure, we’ve come up with four broad categories:
- Machine Learning on the Edge
- Edge computing, where systems reach out to cloud resources but run locally, is all the rage. It allows you to leverage the power of
other people’s computers the cloud for training a model, which is then executed locally. Edge computing is a great way to keep your data local.
- Machine Learning on the Gateway
- Pi’s, old routers, what-have-yous – we’ve all got a bunch of devices laying around that bridge space between your local world and the cloud. What can you come up with that takes advantage of this unique computing environment?
- Machine Learning in the Cloud
- Forget about subtle — this category unleashes the power of the cloud for your application. Whether it’s Google, Azure, or AWS, show us what you can do with all that raw horsepower at your disposal.
- Artificial Intelligence Blinky
- Everyone’s “hardware ‘Hello, world'” is blinking an LED, and this is the machine learning version of that. We want you to use a simple microprocessor to run a machine learning algorithm. Amaze us with what you can make an Arduino do.
These Hackers Trained Their Projects, You Should Too!
We’re a little more than a month into the contest. We’ve seen some interesting entries bit of course we’re hungry for more! Here are a few that have caught our eye so far:
- Intelligent Bat Detector – [Tegwyn☠Twmffat] has bats in his… backyard, so he built this Jetson Nano-powered device to capture their calls and classify them by species. It’s a fascinating adventure at the intersection of biology and machine learning.
- Blackjack Robot – RAIN MAN 2.0 is [Evan Juras]’ cure for the casino adage of “The house always wins.” We wouldn’t try taking the Raspberry Pi card counter to Vegas, but it’s a great example of what YOLO can do.
- AI-enabled Glasses – AI meets AR in ShAIdes, [Nick Bild]’s sunglasses equipped with a camera and Nano to provide a user interface to the world. Wave your hand over a lamp and it turns off. Brilliant!
You’ve got till noon Pacific time on April 7, 2020 to get your entry in, and four winners from each of the four categories will be awarded a $100 Tindie gift card, courtesy of our sponsor Digi-Key. It’s time to ramp up your machine learning efforts and get a project entered! We’d love to see more examples of straight cloud AI applications, and the AI blinky category remains wide open at this point. Get in there and give machine learning a try!
Some people love Amazon, while others think it has become too big and invasive. But you have to admit, they build gigantic and apparently reliable systems. Interestingly, they recently released a library of white papers from their senior staff called the Builder’s Library.
According to their blog post:
The Amazon Builders’ Library is a collection of living articles that take readers under the hood of how Amazon architects, releases, and operates the software underpinning Amazon.com and AWS. The Builders’ Library articles are written by Amazon’s senior technical leaders and engineers, covering topics across architecture, software delivery, and operations. For example, readers can see how Amazon automates software delivery to achieve over 150 million deployments a year or how Amazon’s engineers implement principles such as shuffle sharding to build resilient systems that are highly available and fault tolerant.
The Amazon Builders’ Library will continue to be updated with new content going forward.
Continue reading “Behind Amazon’s Doors Is A Library” →
If there’s one thing any cat will work for, it’s food. Usually, this just consists of meowing and/or standing on your chest until you give up the goods. [DynamicallyInvokable] has a beautiful cat, Emma, who meows loudly for food at obscene hours of the morning. As she ages, it’s getting harder and more important to control her weight. Clearly, it was time to build the ultimate automatic cat feeder—one that allows him to get lazy while at the same time getting smart about Emma’s weight.
After a year and a half of work, the feeder is complete. Not only does it deliver the goods several times a day, it sends a heap of data to the cloud about Emma’s eating habits. There’s a scale built into the platform, and another in the food bowl. Together, they provide metrics galore that get automatically uploaded to AWS. Everything is controlled with an ESP32 Arduino, including a rainbow of WS2812s that chases its tail around the base of the feeder. The faster it goes, the closer it is to feeding time.
The best part about this unique feeder is that nearly every piece is 3D printed, including the gears. Be sure to check out the build gallery, where you can watch it come together piece by piece. Oh, and claw your way past the break to see Emma get fed.
Emma doesn’t have to worry about sharing her food. If she did, maybe [DynamicallyInvokable] could use facial recognition to meet the needs of multiple cats.
Continue reading “Cat Feeder Adds Metrics To Meow Mix” →
The first step to reducing the energy consumption of your home is figuring out how much you actually use in the first place. After all, you need a baseline to compare against when you start making changes. But fiddling around with high voltage is something a lot of hackers will go out of their way to avoid. Luckily, as [Xavier Decuyper] explains, you can build a very robust DIY energy monitoring system without having to modify your AC wiring.
In the video after the break, [Xavier] goes over the theory of how it all works, but the short version is that you just need to use a Current Transformer (CT) sensor. These little devices clamp over an AC wire and detect how much current is passing through it via induction. In his case, he used a YHDC SCT-013-030 sensor that can measure up to 30 amps and costs about $12 USD. It outputs a voltage between 0 and 1 volts, which makes it extremely easy to read using the ADC of your favorite microcontroller.
Once you’ve got the CT sensor connected to your microcontroller, the rest really just depends on how far you want to take the software side of things. You could just log the current consumption to a plain text file if that’s your style, but [Xavier] wanted to challenge himself to develop a energy monitoring system that rivaled commercial offerings so he took the data and ran with it.
A good chunk of his write-up explains how the used Amazon Web Services (AWS) to process and ultimately display all the data he collects with his ESP32 energy monitor. Every 30 seconds, the hardware reports the current consumption to AWS through MQTT. The readings are stored in a database, and [Xavier] uses GraphQL and Dygraphs to generate visualizations. He even used Ionic to develop a cross-platform mobile application so he can fawn over his professional looking charts and graphs on the go.
We’ve already seen how carefully monitoring energy consumption can uncover some surprising trends, so if you want to go green and don’t have an optically coupled electricity meter, the CT sensor method might be just what you need.
Continue reading “Building A Safe ESP32 Home Energy Monitor” →