137. Signal Processing Techniques
Pulling information from your data
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This collection of posts and tutorials covers a variety of tricks, hacks, and methods for handling signals. Regardless of whether the signal is one dimensional (like audio), two dimensional (like an image), three dimensional (like video or point clouds), or higher, we usually have to massage it a bit to pull out the information that we care about. Signal processing techniques are useful for minimizing noise, exaggerating the aspects of the data we care about, and conditioning the signal for use in a particular algorithm. Here are some common ones.
PreviewHow to normalize a signal by min and max
PreviewHow to normalize a signal by mean and variance
StartRate of change
PreviewHow to turn an image into numbers
PreviewHow to convert an RGB image to grayscale
PreviewHow convolution works in one dimensional arrays
I love solving puzzles and building things. Machine learning lets me do both. I got started by studying robotics and human rehabilitation at MIT (MS '99, PhD '02), moved on to machine vision and machine learning at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, and cloud data science at Microsoft. At Facebook I worked to get internet and electrical power to those in the world who don't have it, using deep learning and satellite imagery and to do a better job identifying topics reliably in unstructured text. Now at iRobot I work to help robots get better and better at doing their jobs. In my spare time I like to rock climb, write robot learning algorithms, and go on walks with my wife and our dog, Reign of Terror.