Roll Your Own Differentiation Filters
Introduction
There are many times in digital signal processing that it is necessary to obtain estimates of the derivative of some signal or process from discretely sampled values. Such numerical derivatives are useful in applications such as edge detection, rate of change estimation and optimization, and can even be used as part of quick and efficient dc-blocking filters.
Suppose then, that we want to approximate the derivative of a function at a set of points $\{x_i:i=1,...,N\}$ from...
Roll Your Own Differentiation Filters
IntroductionThere are many times in digital signal processing that it is necessary to obtain estimates of the derivative of some signal or process from discretely sampled values. Such numerical derivatives are useful in applications such as edge detection, rate of change estimation and optimization, and can even be used as part of quick and efficient dc-blocking filters.
Suppose then, that we want to approximate the derivative of a function at a set of points $\{x_i:i=1,...,N\}$ from...
Roll Your Own Differentiation Filters
IntroductionThere are many times in digital signal processing that it is necessary to obtain estimates of the derivative of some signal or process from discretely sampled values. Such numerical derivatives are useful in applications such as edge detection, rate of change estimation and optimization, and can even be used as part of quick and efficient dc-blocking filters.
Suppose then, that we want to approximate the derivative of a function at a set of points $\{x_i:i=1,...,N\}$ from...
