Automated Tracking of Drosophila Specimens.

The fruit fly Drosophila Melanogaster has become a model organism in the study of neurobiology and behavior patterns. The analysis of the way the fly moves and its behavior is of great scientific interest for research on aspects such as drug tolerance, aggression or ageing in humans. In this article, a procedure for detecting, identifying and tracking numerous specimens of Drosophila by means of computer vision-based sensing systems is presented. This procedure allows dynamic information about each specimen to be collected at each moment, and then for its behavior to be quantitatively characterized. The proposed algorithm operates in three main steps: a pre-processing step, a detection and segmentation step, and tracking shape. The pre-processing and segmentation steps allow some limits of the image acquisition system and some visual artifacts (such as shadows and reflections) to be dealt with. The improvements introduced in the tracking step allow the problems corresponding to identity loss and swaps, caused by the interaction between individual flies, to be solved efficiently. Thus, a robust method that compares favorably to other existing methods is obtained.

An algorithm for phase-space detection of the P characteristic points.

A new algorithm, based on embedding phase space, to detect the P-wave characteristic points of an ECG signal is reported in this paper. The multi-lead ECG is transformed into points of an embedding phase space where similar ECG morphologies are converted into phase space points that are close using some distance measure. The algorithm is robust with respect to the type of selected characteristic points (onset, peak and end), morphology changes, baseline oscillations and high frequency noise. The performance of the algorithm has been successfully validated using both simulated and real ECG signals.