zeldone

Interesting. Basically obsidian for your agent? or different? Then why the black hole as image? XD

The difference is that you have a spare board.... a webcam lying around somewhere, and you put them together to run some CCTV. That's all :)

Updated. You have a gif now that shows a preview of the interface.

I was able to keep everything running without a reboot for more than 3 weeks.

thanks!

Correct! But I have to be honest... I do not like the night vision since with a consumer webcam is not working very well... I will test with a cheap camera that support by default night vision... I will keep you posted!

Correct! But I have to be honest... I do not like the night vision since with a consumer webcam is not working very well... I will test with a cheap camera that support by default night vision... I will keep you posted!

Motion is a great software and it overlaps with CheapSecurity. However this software pretend to be: 1. a single-camera-per-instance CCTV with a web dashboard and Telegram bot (motion is a full-featured NVR/motion daemon: multi-camera, masks, timelapse, SQL logging, external triggers, etc...) 2. One independent process per camera; multi-camera is done by running multiple instances. (motion is multi-camera support in one process) 3. Small Flask dashboard with live stream, settings toggles, and bulk actions on recordings. (motion is functional stream/status pages) 4. Specifically optimized for low-power ARM boards like the Odroid XU4 5. Last but not least small Python/Flask/OpenCV codebase, easy to hack on...

That's it

Exploring datasets with missing variables in the context of multivariate analysis requires careful consideration to ensure the integrity and reliability of your results.

libscientific, compared with the most diffused ML packages (scikit-learn and so on), do not impute or exclude features with missing variables. Thanks to the NIPALS algorithm implemented in the most common and used multivariate algorithms (Principal Component Analysis, Partial Least-Squares, and Consensus Principal Component Analysis), skip the missing values, avoiding erroneous conclusions due to data alteration.

Is multivariate analysis a valid tool to consider in 2024, the AI era, LLM, GPT-4? The answer is Yes. Multivariate analysis is relevant and essential in various disciplines, including statistics, data science, economics, psychology, biology, chemistry, and general data science. Multivariate analysis helps organize data to uncover actionable insights for current problems and complex relations between data.