Face recognition from video

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence "reillumination" algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate (over 99.7%), significantly outperforming state-of-the-art commercial software and methods from the literature. © Springer-Verlag Berlin Heidelberg 2006.

A parametrically excited vibration energy harvester

In the arena of vibration energy harvesting, the key technical challenges continue to be low power density and narrow operational frequency bandwidth. While the convention has relied upon the activation of the fundamental mode of resonance through direct excitation, this article explores a new paradigm through the employment of parametric resonance. Unlike the former, oscillatory amplitude growth is not limited due to linear damping. Therefore, the power output can potentially build up to higher levels. Additionally, it is the onset of non-linearity that eventually limits parametric resonance; hence, this approach can also potentially broaden the operating frequency range. Theoretical prediction and numerical modelling have suggested an order higher in oscillatory amplitude growth. An experimental macro-sized electromagnetic prototype (practical volume of ∼1800 cm3) when driven into parametric resonance, has demonstrated around 50% increase in half power band and an order of magnitude higher peak power density normalised against input acceleration squared (293 μW cm-3 m-2 s4 with 171.5 mW at 0.57 m s-2) in contrast to the same prototype directly driven at fundamental resonance (36.5 μW cm-3 m-2 s4 with 27.75 mW at 0.65 m s-2). This figure suggests promising potentials while comparing with current state-of-the-art macro-sized counterparts, such as Perpetuum's PMG-17 (119 μW cm-3 m-2 s4). © The Author(s) 2013.

A trapped field of 17.6 T in melt-processed, bulk Gd-Ba-Cu-O reinforced with shrink-fit steel

The ability of large-grain (RE)Ba2Cu3O7-δ ((RE)BCO; RE = rare earth) bulk superconductors to trap magnetic fields is determined by their critical current. With high trapped fields, however, bulk samples are subject to a relatively large Lorentz force, and their performance is limited primarily by their tensile strength. Consequently, sample reinforcement is the key to performance improvement in these technologically important materials. In this work, we report a trapped field of 17.6 T, the largest reported to date, in a stack of two silver-doped GdBCO superconducting bulk samples, each 25 mm in diameter, fabricated by top-seeded melt growth and reinforced with shrink-fit stainless steel. This sample preparation technique has the advantage of being relatively straightforward and inexpensive to implement, and offers the prospect of easy access to portable, high magnetic fields without any requirement for a sustaining current source. © 2014 IOP Publishing Ltd.