Compact double-side liquid-impingement-cooled integrated power electronic module

This paper presents a compact integrated power electronic module (IPEM) which seeks to overcome the volumetric power density limitations of conventional packaging technologies. A key innovation has been the development of a substrate sandwich structure which permits double side cooling of the embedded dies whilst controlling the mechanical stresses both within the module and at the heat exchanger interface. A 3-phase inverter module has been developed, integrating the sandwich structures with high efficiency impingement coolers, delink capacitance and gate drive units. Full details of the IPEM construction and electrical evaluation are given in the paper. © 2007 IEEE.

Demonstration of a compact InGaAsP active crosspoint switch operating at 1.5 μm using novel reflective y-coupler components

An extremely compact active optoelectronic crosspoint switch, having overall dimensions of 400 μm×200 μm, is reported. The device provides unity facet-to-facet gain for both bar and cross state operation for TE or TM input signals.

On-line learning of mutually orthogonal subspaces for face recognition by image sets.

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.