ConvolveByRiemannSum assumes that x(t) and h(t) have finite support,

and that the user provides samples of these functions in two vectors,

x and h. These vectors contain samples of x, sampled at time points

given in vector tx = tx_min:Delta_t:tx_max and samples of h sampled

at time points in vector th = th_min:Delta_t:th_max, respectively.

The functions x and h are assumed to be zero outside tx_min and tx_max

and outside th_min and th_max, respectively.

Note that while tx_min, tx_max, th_min, and th_max may differ, the time

sample vectors must share the same time-interval, Delta_t.-ConvolveByRiemannSum assumes that x(t) and h(t) have finite support,

　and that the user provides samples of these functions in two vectors,

　x and h. These vectors contain samples of x, sampled at time points

　given in vector tx = tx_min:Delta_t:tx_max and samples of h sampled

　at time points in vector th = th_min:Delta_t:th_max, respectively.

　The functions x and h are assumed to be zero outside tx_min and tx_max

　and outside th_min and th_max, respectively.

　Note that while tx_min, tx_max, th_min, and th_max may differ, the time

　sample vectors must share the same time-interval, Delta_t.