Sorting networks using L/sub p/ mean comparators for signal processing applications

Abstract

Digital implementations of sorting networks that rely on a digital signal processor core are not as efficient as their analog counterparts. This paper builds on the comparators for which efficient analog implementations exist that employ operational amplifiers. From a statistical point of view, comparators are based on nonlinear means. Their probability density function and the first-and second-order moments are derived for independent uniformly distributed inputs. comparators provide estimates of the minimum and maximum of their inputs. A proper approach to compensate for the estimation errors is proposed. Applications of the comparators in odd-even transposition networks, median approximation networks, and min/max networks are presented.