Difference between revisions of "PSD Sensor"
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==Development Log== | ==Development Log== | ||
===04 July 2022 (The 1st Experiment)=== | ===04 July 2022 (The 1st Experiment)=== | ||
| + | [[File:Turb sensor lab experiment setup 1.jpg|thumb|Camera Turbidity Sensor Lab Experiment Setup]] | ||
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'''Camera:''' [https://www.arducam.com/ov2640/ OmniVision OV2640 Camera], 640*480, JPG output, default camera settings (automatic exposure, gain, and white balance), lens not changed. The camera is connected to a [https://www.arducam.com/product/b0051b0011-arducam-f-shield-v2-camera-module-shield-with-ov2640-for-arduino-uno-mega2560-due/ ArduCAM Arduino Shield] which is compatible with the Arduino Mega board. The board is set up using ArduCAM's [https://github.com/ArduCAM/Arduino/tree/master/ArduCAM Arduino Library]. When taking images, the Arduino Mega is connected directly to the PC, and the images are sent and saved on the PC directly, for now. | '''Camera:''' [https://www.arducam.com/ov2640/ OmniVision OV2640 Camera], 640*480, JPG output, default camera settings (automatic exposure, gain, and white balance), lens not changed. The camera is connected to a [https://www.arducam.com/product/b0051b0011-arducam-f-shield-v2-camera-module-shield-with-ov2640-for-arduino-uno-mega2560-due/ ArduCAM Arduino Shield] which is compatible with the Arduino Mega board. The board is set up using ArduCAM's [https://github.com/ArduCAM/Arduino/tree/master/ArduCAM Arduino Library]. When taking images, the Arduino Mega is connected directly to the PC, and the images are sent and saved on the PC directly, for now. | ||
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'''Results:''' | '''Results:''' | ||
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| + | [[File:20220708 Experiment result 1.png|400px]] | ||
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| + | As shown in the plot, there is a linear relationship between the log(NTU) and average grayscale, with the squared-R = 0.8726. However, this result is not good at all, as the grayscale values almost plateau when the turbidity values go above 100 NTU. This may be attributed to the strength of the LED. To be more specific, the light intensity of the LED is too strong, hence when the turbidity reaches a certain value (more than 100 in this case), the particles in the water have already scattered too much light that the camera is not able to handle. Therefore, in future experiments, the LED intensity should be lowered. Another issue of this experiment is that the silts are so heavy that they settle down very quickly, thus the actual turbidity of the solution may also change quickly. | ||
Revision as of 06:12, 18 August 2022
Introduction
This page will present the development of the Camera Turbidity and PSD Sensor. More introductions to come...
Development Log
04 July 2022 (The 1st Experiment)
Camera: OmniVision OV2640 Camera, 640*480, JPG output, default camera settings (automatic exposure, gain, and white balance), lens not changed. The camera is connected to a ArduCAM Arduino Shield which is compatible with the Arduino Mega board. The board is set up using ArduCAM's Arduino Library. When taking images, the Arduino Mega is connected directly to the PC, and the images are sent and saved on the PC directly, for now.
Turbidity Solution: The tap water is mixed with natural silts to create the synthetic solution. The turbidity value ranges from 0 to 639 NTU in this experiment.
Methods: First, put the synthetic turbidity solution into the 3d-printed black container, after measuring the actual turbidity (in the unit of NTU) using the Thermo Fisher turbidity meter. Then, light up the LED (from the top hole of the container lid) and take images (from the side window) of the solution at a 90-degree angle. This light scattering method is a common method used for turbidity measurement. Generally, when the water is more turbid, the increased particles in the water scatter more light beams in directions other than the straight direction, hence the light intensity received from the side directions (such as 90 degrees) will increase. The RGB (red, green, and blue) values of each pixel of the images are obtained using python script and converted into a single value - grayscale (0 - 255, completely black to completely white) using the Luma formula (Y = 0.299R + 0.587G + 0.114B). The average grayscale value of all pixels of each photo is calculated. Finally, plot the relationship between the average grayscale value of each image and its corresponding solution turbidity.
Results:
As shown in the plot, there is a linear relationship between the log(NTU) and average grayscale, with the squared-R = 0.8726. However, this result is not good at all, as the grayscale values almost plateau when the turbidity values go above 100 NTU. This may be attributed to the strength of the LED. To be more specific, the light intensity of the LED is too strong, hence when the turbidity reaches a certain value (more than 100 in this case), the particles in the water have already scattered too much light that the camera is not able to handle. Therefore, in future experiments, the LED intensity should be lowered. Another issue of this experiment is that the silts are so heavy that they settle down very quickly, thus the actual turbidity of the solution may also change quickly.
