+\label{sec:usecases}
+As an example of a common hardware design where the use of higher-order
+functions leads to a very natural description is a FIR filter, which is
+basically the dot-product of two vectors:
+
+\begin{equation}
+y_t = \sum\nolimits_{i = 0}^{n - 1} {x_{t - i} \cdot h_i }
+\end{equation}
+
+A FIR filter multiplies fixed constants ($h$) with the current
+and a few previous input samples ($x$). Each of these multiplications
+are summed, to produce the result at time $t$. The equation of a FIR
+filter is indeed equivalent to the equation of the dot-product, which is
+shown below:
+
+\begin{equation}
+\mathbf{x}\bullet\mathbf{y} = \sum\nolimits_{i = 0}^{n - 1} {x_i \cdot y_i }
+\end{equation}
+
+We can easily and directly implement the equation for the dot-product
+using higher-order functions:
+
+\begin{code}
+xs *+* ys = foldl1 (+) (zipWith (*) xs hs)
+\end{code}
+
+The \hs{zipWith} function is very similar to the \hs{map} function seen
+earlier: It takes a function, two vectors, and then applies the function to
+each of the elements in the two vectors pairwise (\emph{e.g.}, \hs{zipWith (*)
+[1, 2] [3, 4]} becomes \hs{[1 * 3, 2 * 4]} $\equiv$ \hs{[3,8]}).
+
+The \hs{foldl1} function takes a function, a single vector, and applies
+the function to the first two elements of the vector. It then applies the
+function to the result of the first application and the next element from
+the vector. This continues until the end of the vector is reached. The
+result of the \hs{foldl1} function is the result of the last application.
+As you can see, the \hs{zipWith (*)} function is just pairwise
+multiplication and the \hs{foldl1 (+)} function is just summation.
+
+Returning to the actual FIR filter, we will slightly change the