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Everyday DSP for Programmers: Edge Detection

Up until now we've been learning about the fundamental concepts and basic tools of digital signal processing (DSP). Now we're going to use those tools to solve a common problem in DSP: edge detection. When you think of edge detection, you probably immediately think of image processing and finding edges in photographs or a video signal. It's true that there are plenty of edge detection applications in image processing, especially with some of the new technologies being developed for self-driving cars, facial recognition, and automatic photo processing, but edge detection is useful in a much wider range of applications than that.

Anywhere you need to monitor a signal for a change of state is a likely candidate for edge detection. That would include the feedback systems in robotic motion, industrial automation, touch screens and other touch inputs, pressure sensors in car airbag safety systems, accelerometers in any toy or gadget that senses motion, and the list goes on. Sometimes, the relevant signal in these systems varies within a well-defined range, and the edge is easy to detect by setting a fixed threshold. If the signal crosses the threshold, the system detects an edge and does what it needs to do.

Other times edge detection is not so simple. If the signal drifts around over long periods of time, or the threshold for the occurrence of an edge doesn't necessarily happen at the same value every time, it's difficult or impossible to set a fixed threshold for detecting an edge. In cases like this, we need to do some further processing to build up a signal that we can assign a fixed threshold to for detecting edges. That's the kind of problem we'll attempt to solve here, detecting edges in a signal where the edges tend to happen anywhere within its range. Let's start by generating a representative signal that would be somewhat difficult to process.

Everyday DSP for Programmers: Signal Variance

After covering averaging in the last two posts, the natural next thing to look at is how much the signal varies around the average. This property of a signal is called signal variance, and there are a couple different ways to calculate it, depending on how the average has been calculated. Let's see how variance can be calculated and what it tells us about a signal.

Everyday DSP for Programmers: Step Response of Averaging

Last week we took a look at different kinds of averaging, and used them to analyze historical gas prices. Looking at a complex signal like gas prices gives us a nice comparison of the behaviors of the various averaging methods, but that only gives us an idea of what averaging does for one specific signal. What if we want to understand what the different averaging methods do in a more general way?

One way to analyze the different methods is by applying them to the fundamental signals. The output that results from applying an averaging function to one of the fundamental signals is called the response of the function. If the signal is the DC signal, it's called the DC response. If the signal is the step function, it's called the step response, and so on. We'll look at the step response in more detail, but first, let's briefly discuss the responses of the different averaging functions to each of the fundamental signals.

Everyday DSP for Programmers: Averaging

After finishing up the introductory concepts of DSP last week, we've now moved firmly into the digital, sampled domain, and we're going to start taking a look at the programming algorithms that are commonly used in DSP. Fundamentally, digital signal processing has a lot in common with statistics. When we start analyzing signals, some of the first tools we look at using are based in statistical analysis: maximum and minimum values, averages, and variances.

Maximums and minimums mostly come into play when bounding and normalizing signals. If you want to find the amplitude of a signal, then you'll scan through the samples to find the extreme values. It's fairly straightforward, and there's no need to delve into them until they're needed.

Averages are another story, though. Not all averages are created equal, and different types of averages are more useful in different situations. We'll take a look at five types of averages in this post, including the full, block, moving, exponential, and filter averages. To ground the discussion a bit, let's apply these different averages to a real-world dataset. Gas prices are something that most people watch pretty closely, and it happens to be a series with some interesting characteristics, so let's go with that. I'll use the weekly U.S. regular reformulated retail gasoline prices from the last twenty years, made available online through the website.

Everyday DSP for Programmers: Sampling Theory
