Multiple object tracking using evolutionary MCMC-based particle algorithms

Algorithms are presented for detection and tracking of multiple clusters of co-ordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the filtering density of the clusters' state. The filters' tracking efficiency is enhanced by incorporating various sampling improvement strategies into the basic Metropolis-Hastings scheme. Thus, an evolutionary stage consisting of two primary steps is introduced: 1) producing a population of different chain realizations, and 2) exchanging genetic material between samples in this population. The performance of the resulting evolutionary filtering algorithms is demonstrated in two different settings. In the first, both group and target properties are estimated whereas in the second, which consists of a very large number of targets, only the clustering structure is maintained. © 2009 IFAC.

SuperJunction IGBTS: An evolutionary step of silicon power devices with high impact potential

15 years ago the vertical SuperJunction (SJ) concept conceived for SJ power MOSFETs was the last, major breakthrough in the field of silicon power devices. Today, the SuperJunction MOSFET technologies have reached a mature stage characterized by gradual performance improvements. SuperJunction Insulated Gate Bipolar Transistors (SJ IGBTs) could interrupt this stagnation holding promise to revitalize voltage classes from 600 up to 1200 V. Such SJ IGBTs surpass by a very significant margin their SJ MOSFET counterparts both in terms of power handling capability, on-state and turn-off losses, all at the same time. On the higher end of the voltage class, SJ IGBTs would top the performance of 1.2 kV IGBTs by a similar margin. © 2012 IEEE.