Was working with wio terminal with sgp30 and sht40 to detect smoke and fire this afternoon to do the exact same thing, store the data in csv format and upload it to edge impulse. I didn't know that it need to be normalize to get a better result. Thanks shawn for another lesson you share
This guide was so cool! Thanks! I was intimidated to work with Edge Impulse but now I'll give it a try. A tip for the Wio Terminal: #include "TFT_eSPI.h" TFT_eSPI tft; TFT_eSprite spr = TFT_eSprite(&tft); and use spr. instead of tft. to avoid the LCD flickering when it updates
Given N independent functions, f_n(x_1,...,x_N), (sensor measurements) of N variables (temp, humidity, x-concentration,...), it is always possible to derive an orthonormal basis for the variables since df_n = sum_m partial {f_n/x_m} dx_m. We merely need to measure that matrix of partials and to invert that matrix at each data point. Often closed form approximations to the inverted matrix entries is close enough. The details of this matrix WILL be sensitive to the particular sensors used... fit the function parameters to your data. It is wise to repeat this several times and to average the parameters over these samples.
Great tutorial! Thanks a lot! Shawn,Thanks a lot! @ShawnHymel, do you think that 2 models in cascade would help to improve spirit result? I mean, a first one classifying only tea, coffee and spirit and a second one having spirit as “input’ and with vodka, run and whisky as labels?
I was thinking something like this too. Identify that it is a spirit first and then distinguish the differences then. Also I wonder if you could even drop the Ethanol sensor at that point since they would all be highly correlated there.
Was working with wio terminal with sgp30 and sht40 to detect smoke and fire this afternoon to do the exact same thing, store the data in csv format and upload it to edge impulse. I didn't know that it need to be normalize to get a better result. Thanks shawn for another lesson you share
Amazing, the best class of my life. 42 minutes, is much more interesting than 1-year os school. For more content like this one. thousands of likes.
This guide was so cool! Thanks! I was intimidated to work with Edge Impulse but now I'll give it a try.
A tip for the Wio Terminal:
#include "TFT_eSPI.h"
TFT_eSPI tft;
TFT_eSprite spr = TFT_eSprite(&tft);
and use spr. instead of tft. to avoid the LCD flickering when it updates
Ah! Good to know. I was wondering how to fix that. Thank you!
Given N independent functions, f_n(x_1,...,x_N), (sensor measurements) of N variables (temp, humidity, x-concentration,...), it is always possible to derive an orthonormal basis for the variables since df_n = sum_m partial {f_n/x_m} dx_m. We merely need to measure that matrix of partials and to invert that matrix at each data point. Often closed form approximations to the inverted matrix entries is close enough. The details of this matrix WILL be sensitive to the particular sensors used... fit the function parameters to your data. It is wise to repeat this several times and to average the parameters over these samples.
Very detailed video, thank you once again for another excellent and informational video!
Great tutorial! Thanks a lot! Shawn,Thanks a lot! @ShawnHymel, do you think that 2 models in cascade would help to improve spirit result? I mean, a first one classifying only tea, coffee and spirit and a second one having spirit as “input’ and with vodka, run and whisky as labels?
I was thinking something like this too. Identify that it is a spirit first and then distinguish the differences then. Also I wonder if you could even drop the Ethanol sensor at that point since they would all be highly correlated there.
at 10:12 it is showing incorrect syntax for me
Tried this many times but always getting an anomaly score way too high and dont know what it could possibly happening. Any Ideas?
Thanks Shawn, another great video!
Great project! Can I translate this to vietnamese and share it to our community ?
But what about bad smells like good gone bad, gas leaks etc?
Seriously, I can't smell and I need this
Would you mind sending me 3 Research Papers correlated with this project?
COOL
more data !!!
Can it detect farts?
A fart detector automatic air freshener system is now possible 👍🏻😁