Analytic model for turn off in the silicon-on-insulator LIGBT

Lateral insulated gate bipolar transistors (LIGBTs) in silicon-on-insulator (SOI) show a unique turn off characteristic when compared to junction-isolated RESURF LIGBTs or vertical IGBTs. The turn off characteristic shows an extended `terrace' where, after the initial fast transient characteristic of IGBTs due to the loss of the electron current, the current stays almost at the same value for an extended period of time, before suddenly dropping to zero. In this paper, we show that this terrace arises because there is a value of LIGBT current during switch off where the rate of expansion of the depletion region with respect to the anode current is infinite. Once this level of anode current is approached, the depletion region starts to expand very rapidly, and is only stopped when it reaches the n-type buffer layer surrounding the anode. Once this happens, the current rapidly drops to zero. A quasi-static analytic model is derived to explain this behaviour. The analytically modelled turn off characteristic agrees well with that found by numerical simulation.

Nonlinear analysis of cardiac rhythm fluctuations using DFA method

An approach by which the detrented fluctuation analysis (DFA) method can be used to help diagnose heart failure was demonstrated. DFA was applied to patients suffering from congestive heart failure (CHF) to check correlations between DFA indices and CHF, and determine a correlation between DFA indices and mortality, with a particular attention to the residue parameter, which is a measure of the departure of the DFA from its power law approximation. DFA parameters proved to be useful as a complement to the physiological parameters weber and FE to sort out the patients into three prognostic group.

Detecting bipedal motion from correlated probabilistic trajectories

This paper is about detecting bipedal motion in video sequences by using point trajectories in a framework of classification. Given a number of point trajectories, we find a subset of points which are arising from feet in bipedal motion by analysing their spatio-temporal correlation in a pairwise fashion. To this end, we introduce probabilistic trajectories as our new features which associate each point over a sufficiently long time period in the presence of noise. They are extracted from directed acyclic graphs whose edges represent temporal point correspondences and are weighted with their matching probability in terms of appearance and location. The benefit of the new representation is that it practically tolerates inherent ambiguity for example due to occlusions. We then learn the correlation between the motion of two feet using the probabilistic trajectories in a decision forest classifier. The effectiveness of the algorithm is demonstrated in experiments on image sequences captured with a static camera, and extensions to deal with a moving camera are discussed. © 2013 Elsevier B.V. All rights reserved.