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Correcting Analogue signal due temperature and humidity variations.?

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I have attached my dataset as a reference, which I believe can be downloaded. I was hoping to have my data displayed as a result of my code on here, but I could not get it working, so I have attached images instead.
I have an analogue signal from the electrochemical sensor over a period of a few months. There is also temperature and humidity in the same Time Table.
I also have reference data. My sensor data does not need to be identical but uses the reference data to ensure that the general trend is met. This is more as confirmation that the sensor is working correctly.
The first plot shows Temperature(Blue) and Humidity(Red). The second plot my sensor(blue) and ref data(red).
The issue I am facing is that my sensor data needs to be corrected due to the environmental effects of temperature and humidity. Temperature correction is fairly simple, as you need to create some form of a lookup table for each temperature step.
But when it comes to humidity or change in humidity, that is where I have some challenges. As you can see, it creates some type of transients, negative and positive spikes. The negative spike is more often as humidity levels drop.
I have performed a simple process of creating NaN when the sensor is producing negative values and then using fillmissing() with a moving mean, but that does not solve the issue with the positive spikes or accurate results when spikes are much shorter and not negative.
When you look at the spikes, you can identify the area at the bottom to have some characteristic of what the signal should be, but the sudden drop or rise in amplitude needs to be reduced or corrected.
Has anyone else had similar issues, and what method did you use?

Answers (1)

Mathieu NOE
Mathieu NOE on 4 Jan 2023
I have not experienced a similar situation, but maybe my idea is worth a look
I wanted to find the local positive peaks we can find on both hum and sensor data
then I created a new signal based on mixing the raw and a smoothed version of it.
So when you are in the vicinity of the identied peaks , the new signal is mostly the smoothed version of the raw signal (and vice versa)
see the result in yellow : most of the time it's the same signal as the raw data , only at some locations we take instead the smoothed signal
now your local dips and peaks are smoothed out
t1 = refdata.Time;
Ref_data = refdata.Var1;
t2 = exampleData.Time;
Sensor_data = exampleData.Sensor;
Temp_data = exampleData.Temp;
Hum_data = exampleData.Hum;
%% zoom data on first 80 samples
ind = 1:80;
t2 = t2(ind);
Sensor_data = Sensor_data(ind);
Hum_data = Hum_data(ind);
% find common x indexes where Sensor and Hum data has local maxima
tf1 = islocalmax(Hum_data,'MinProminence',1,'ProminenceWindow',4);
tf2 = islocalmax(Sensor_data,'MinProminence',1,'ProminenceWindow',4);
locs = find(tf1&tf2);
Sensor_data_s = smoothdata(Sensor_data,'gaussian',13);
%% create mix ratio profile
% ratio is close to one at vicinity of locs x indexes , zero otherwise
% gaussian peak profile
x = (1:numel(t2))';
ratio = zeros(size(x));
a = 0.07;
for ck = 1:numel(locs)
ratio = ratio + exp(-a*(x - locs(ck)).^2);
% plot(x,ratio);
%% apply mix formula
Sensor_data_mix = (1-ratio).*Sensor_data + ratio.*Sensor_data_s;
legend('Sensor data (raw)','Sensor data (smoothed)','Sensor data (mix)');
legend('Hum data','Hum peaks selected');
Dharmesh Joshi
Dharmesh Joshi on 12 Jan 2023
Sorry for the delay,
It worked to a cetain degree. I am also expirmenting with Regresion Application.

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