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<div class="content-title top"><a id="overview">Overview</a></div> | <div class="content-title top"><a id="overview">Overview</a></div> | ||
− | <p>In the process of creating our OxyPonics system, we realized we needed to make a cheap, flexible, camera system capable of quantifying and localizing our optical signals. To keep costs low, we built our system out of a ten dollar camera and a Raspberry Pi. This allows us to properly image and track fluorescence to a reasonable degree without the need for expensive lab equipment like plate readers or spectrofluorometers. Using OpenCV, an open-source package for computer vision, we created software to track the fluorescent signal in our frame, efficiently eliminate noise, and record and send the measured intensity to a web server which tracks the readings. While our optics are designed for rxRFP, our software works for any wavelength and is capable of cheaply identifying and quantifying localized fluorescence in a wide variety of environments, providing a powerful tool for labs on a budget. Our software can be accessed <a class="link" href="https://github.com/Cornell-iGEM/iGEM-Detection"> | + | <p>In the process of creating our OxyPonics system, we realized we needed to make a cheap, flexible, camera system capable of quantifying and localizing our optical signals. To keep costs low, we built our system out of a ten dollar camera and a Raspberry Pi. This allows us to properly image and track fluorescence to a reasonable degree without the need for expensive lab equipment like plate readers or spectrofluorometers. Using OpenCV, an open-source package for computer vision, we created software to track the fluorescent signal in our frame, efficiently eliminate noise, and record and send the measured intensity to a web server which tracks the readings. While our optics are designed for rxRFP, our software works for any wavelength and is capable of cheaply identifying and quantifying localized fluorescence in a wide variety of environments, providing a powerful tool for labs on a budget. Our software can be accessed on our GitHub <a class="link" href="https://github.com/Cornell-iGEM/iGEM-Detection">here.</a> |
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<div class="content-title"><a id="detection">Detection</a></div> | <div class="content-title"><a id="detection">Detection</a></div> | ||
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<div class="content-title"><a id="signal">Signal Processing</a></div> | <div class="content-title"><a id="signal">Signal Processing</a></div> | ||
− | <p>We implemented a rudimentary Kalman filter in order to process our data. The base code came from <a class="link" | + | <p>We implemented a rudimentary Kalman filter in order to process our data. The base code came from <a class="link" href="http://scipy-cookbook.readthedocs.io/items/KalmanFiltering.html">here</a>, but was modified to use our actual measurements and incorporate our model. It was also generalized to work for a vector state variable. |
</p> | </p> | ||
<div class="content-title"><a id="storage">Storage</a></div> | <div class="content-title"><a id="storage">Storage</a></div> |
Latest revision as of 23:36, 30 October 2017
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