Narrow Results

In this paper, a parallel algorithm for all nearest smallers problem without using doubly logarithmic tree is described. It is shown that using only $O(loglogn)$ time routines for merging and prefix minima, we can easily get an $O(loglogn)$ time parallel algorithm for the all Nearest Smallers problem.

The RR and RT time intervals extracted from the electrocardiogram measure respectively the duration of cardiac cycle and repolarization. The series of these intervals recorded during the exercise test are characterized by two trends: A decreasing one during the stress phase and an increasing one during the recovery, separated by a global minimum. We model these series as a sum of a deterministic trend and random fluctuations, and estimate the trend using methods of curve extraction: Running mean, polynomial fit, multi scale wavelet decomposition. We estimate the minimum location from the trend. Data analysis performed on a group of 20 healthy subjects provides evidence that the minimum of the RR series precedes the minimum of the RT series, with a time delay of about 19 seconds.