  I describe a new method for smoothing a one-dimensional curve in Euclidian
space with an arbitrary number of dimensions. The basic idea is borrowed from
renormalization group theory which previously was applied to biological
macromolecules. There are two crucial differences from other smoothing methods
which make the algorithm unique: data compression and recursive implementation.
One of the simplest forms of the method that is described in this article has
only one free parameter - the number of iterative steps. This means that
hardware implementation should be relatively easy because each loop is simple
and strictly defined. The method could be beneficially applied to pattern
recognition and data compression in future studies.
