  We explain why aliasing can be detected in a generic temporally-sampled
stationary signal process. We then define a concept of stationarity that makes
sense for single waveforms. (This is done without assuming that the waveform is
a sample path of some underlying stochastic process.) We show how to use this
concept to detect aliasing in sampled waveforms. The constraint that must be
satisfied to make detection of aliasing possible is shown to be fairly
unrestrictive. We use simple harmonic signals to elucidate the method. We then
demonstrate that the method works for continuous-spectrum
signals---specifically, for time series from the Lorenz and Rossler systems.
Finally we explain how the method might permit the recovery of additional
information about Fourier components outside the Nyquist band.
