Ultraviolet Imaging with Low Cost Smartphone Sensors: Development and Application of a Raspberry Pi-Based UV Camera
<p>A schematic (<b>Centre</b>) of the Bayer filter array and its positioning on the photodetector array. Also shown are microscope images of the PiCam sensor pre- (<b>Left</b>) and post- (<b>Right</b>) Bayer removal process.</p> "> Figure 2
<p>A profile image of the Raspberry Pi Camera Module (<b>Right</b>), and the modified system with custom built optics (<b>Left</b>). The custom design is bolted to the camera board using the pre-existing mount holes.</p> "> Figure 3
<p>Plots of average pixel signal (in digital number; DN) vs. shutter speed (ms) for a cropped region (800 × 600 pixels) of clear-sky images taken at 310 nm: (<b>A</b>) the 10-bit RAW image output shows a linear increase in DN with respect to shutter speed; (<b>B</b>) the 8-bit standard output JPEG image shows a non-linear response in all three of the red-green-blue (RGB) channels (Red-channel DNs are plotted here) in line with gamma correction. An error bar is inserted on one data point per exposure time, indicating the standard deviation of the pixel intensities in the cropped region; the bar heights are approximately the same for all points of equivalent shutter speed, and just one bar is displayed for clarity.</p> "> Figure 4
<p>(<b>A</b>) A cropped image of the Drax smokestack taken at 310 nm with a shutter speed of 300 ms. The initial image pixels are binned to generate a pixel resolution of 648 × 486, to reduce noise; dark image subtraction and mask corrections have been applied. (<b>B</b>) As in (A) but at 330 nm with a shutter speed of 40 ms. (<b>C</b>) The resulting calibrated SO<sub>2</sub> image of Drax power station stack and plume showing the clear capacity of the system to resolve the plume emissions. (<b>D</b>) A cross-section of (C) showing gas concentrations along the row delineated by the red line. The background noise level can be clearly observed between pixels 300 to 350.</p> "> Figure 5
<p>Time series of SO<sub>2</sub> flux from Drax power station for a 15 min acquisition period.</p> ">
Abstract
:1. Introduction
2. UV Camera Development
3. Measurements of Power Station Sulphur Dioxide Emissions
4. Discussion and Concluding Remarks
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Wilkes, T.C.; McGonigle, A.J.S.; Pering, T.D.; Taggart, A.J.; White, B.S.; Bryant, R.G.; Willmott, J.R. Ultraviolet Imaging with Low Cost Smartphone Sensors: Development and Application of a Raspberry Pi-Based UV Camera. Sensors 2016, 16, 1649. https://doi.org/10.3390/s16101649
Wilkes TC, McGonigle AJS, Pering TD, Taggart AJ, White BS, Bryant RG, Willmott JR. Ultraviolet Imaging with Low Cost Smartphone Sensors: Development and Application of a Raspberry Pi-Based UV Camera. Sensors. 2016; 16(10):1649. https://doi.org/10.3390/s16101649
Chicago/Turabian StyleWilkes, Thomas C., Andrew J. S. McGonigle, Tom D. Pering, Angus J. Taggart, Benjamin S. White, Robert G. Bryant, and Jon R. Willmott. 2016. "Ultraviolet Imaging with Low Cost Smartphone Sensors: Development and Application of a Raspberry Pi-Based UV Camera" Sensors 16, no. 10: 1649. https://doi.org/10.3390/s16101649
APA StyleWilkes, T. C., McGonigle, A. J. S., Pering, T. D., Taggart, A. J., White, B. S., Bryant, R. G., & Willmott, J. R. (2016). Ultraviolet Imaging with Low Cost Smartphone Sensors: Development and Application of a Raspberry Pi-Based UV Camera. Sensors, 16(10), 1649. https://doi.org/10.3390/s16101649