sbts-aru VS sbts-install

Unfortunately a Raspberry Pi is a bit ill suited for production environments. Id recommend an RTC module. Otherwise this might be helpful: https://github.com/hcfman/sbts-aru

If you install my sbts-aru project

https://github.com/hcfman/sbts-aru

It will shrink your partitions, add news and install one of these and set up a sub micro second system clock and an audio recorder suitable for sound localization with a single install command.

I recommend primo EM272 microphone capsules for use with https://github.com/hcfman/sbts-aru. They are high quality, very sensitive with high signal to noise ratio, lauded for nature recording use cases. They can be bought assembled for around 65 euros in the Netherlands. However these capsules are often found in much more expensive equipment.

My sound localizing Raspberry Pi installs a resilient base system as part of its install.

https://github.com/hcfman/sbts-aru

https://hackaday.com/2023/12/30/localizing-fireworks-launche...

With one command it for all Pi’s for both Raspbian and bookworm it:

* Shrinks the file system (Gee, how does it do that with just one disk ? ;-) )

I have a sound localization project that can help with that

https://github.com/hcfman/sbts-aru

You need to be able to hear the sound from three or more recorders. And normally localization is better within the polygon of microphones but there’s an area of better localizability extending outside of a vertex.

Interesting history!

And while we are in the subject of sound localizing may I take the liberty of introducing my new raspberry pi sound localizing audio recorder.

https://github.com/hcfman/sbts-aru

It installs with one command on all Raspberry Pi versions and synchronizes the system time to less than 1 microsecond of error with a cheap GPS.

With three of these I’ve been able to sound localize the explosions from illegal fireworks to a specific car park from more than 3km away with lots of houses in between.

When I got to the car park I could smell the sulphur from the fireworks.

This will even run on a Raspberry Pi zero running of a battery with a 6 euro neo 7m gps and a 6 euro usb mic.

Very nice tool!

So grab a spare Raspberry Pi, a GPS, a cheap USB sound card and a mic and get recording with this Pi based Acoustic Recording Unit

https://github.com/hcfman/sbts-aru

And while you are at it, install 3x or more and localize where the birds are.

Sound localizing. I was intensely passionate about it when I saw the possibility to do it well on a Raspberry Pi. There were quite a few more problems than so expected which is why it took five months (of weekends) to complete it well.

But I’m super happy with the result and have a bunch of geeks with recording nodes setup a long distance apart. Localizing large explosions show that it’s possible to localize to a carpets even when some of the nodes are almost 5 away.

For those interested, here is the project:

https://github.com/hcfman/sbts-aru

Back in 2011 I made a commercial product that ran on the earliest plug computers from global scale technologies. I only sold 20 of 'em and every single one of them was being returned with SD card corruption problems. I had to quickly pivot to keeping the rootfs read-only. I've been a fan ever since.

Incidentally, that early commercial product was a home security product with a very small amount of home automation. I released this into open source with a new name in 2021 and now runs on the Jetson series SBCs (https://github.com/hcfman/sbts-install). Except then including high end YOLO models as triggers.

Because it was intended to be a standalone product it supported https with a GUI wrapper around all of the certificate operations. This still exists in my open source version, making it easy to use self signed certificates for intra-device rest calls.

But I've kept and expanded upon the multi-partition memory overlayFS approach and the installation of this system first asks you to install the sbts-base system, which installs the multi-partition memory overlayFS so that other's can use this as their own base systems.

Is anyone else using the StalkedByTheState security system? It runs on Jetson SBCs and works with the full sized yolov6 and yolov7 models.

Super proactive detection and response

https://github.com/hcfman/sbts-install

I use StalkedByTheState (https://github.com/hcfman/sbts-install) with 15 cameras all being evaluated with an NVIDIA GPU with large model yolov6 and matches double checked with large yolov7. Practically never get a false positive in a complex environment and never get a miss. The port to the Orin series still needs to be completed though.

Sweet! If you are prepared to spend more for more GPU compute power from Jetson SBCs you could use larger models. I’m busy adding the

https://www.deepfaune.cnrs.fr/

model to my security system software

https://github.com/hcfman/sbts-install

It’s almost there but still have to finish the install scripts for the Orin series.

The deep fauna model can be used for all of France’s fauna which includes foxes and badgers (and wolves, bears, lynxes and more of course).

Nice that you should say that :) I also wrote a home automation system.

Well... actually, it's a kinda burglar alarm except it's focus is alerting way ahead of actually getting inside. I added home automation bits (minimal) just so one can elicit a real world response, but I do use it for minimal home automation (Lights, heating control and letting the chickens in and out).

I have yet to announce on on hacker news and so because I still have to write a bunch of documentation. It uses the latest state of the art computer vision triggering, currently yolov7 and I'm about to release support for yolov6. It's multimodel, so you can double check it's result with more than one model, even for specific parts of an image.

The architecture diagam and installation instructions are here:

https://github.com/hcfman/sbts-install

I actually, started writing this in 2008 and most of the current state in terms of the user interface were finished in 2012 but I only released it into open source last year really. In 2019 I add support for yolov3 and have been adding models even since.

It's written in Java and installs in just two commands on nvidia jetsons at the moment (Not in containers). The inference framework is a simple websocket based wrapper around other models so it's easy to add new models and that's written in python.

Well I finish the new release to introduce yolov6 and support for the seeed studios platforms I'll make an announcement on hacker news, note I still have a lot of work to do on documentation.