Normally closed microgrippers based on diamond-like carbon/Ni bimorph and diamond-like carbon/metal/polymer trimorph structures

Multi-finger, normally-closed microgrippers made from a bilayer of a metal and diamond-like carbon (DLC) or a trilayer of a polymer, metal and DLC have been analysed, simulated and fabricated. Temperatures of ∼700 K are necessary to open Ni/DLC bimorph structures. Microgrippers made from an SU8/DLC bilayer or SU8/Al/DLC trilayer have also been fabricated, and fully closed microcages with diameters of ∑40 μm have been obtained. Using SU8 reduces the opening temperature of these devices to only ∼400 K.

All-optical switching in a vertical coupler space switch employing photocarrier-induced nonlinearity

A novel compact integrated nonlinear optical switch is demonstrated. Using a high-power picosecond pulse of 5-ps pulsewidth and 250-MHz repetition rate, all-optical switching with a contrast ratio of 23 dB has been achieved using an in-fiber input power < 14 dBm (100 pJ/pulse). The switch speed depends on the carrier sweep-out time, which can be reduced to the 10 ps range by either applying a reverse bias or by introduction of carrier recombination centers in the active layer.

A comparison of losses in small (<1 kW) drives using sine and space vector pulse width modulation schemes

this paper quantifies effects of using three different pulse width modulation (PWM) schemes on the losses in the inverter and induction motor of a 1 kW drive. Direct measurements of losses have been made with a calorimeter. Results show that for the inverter, discontinuous PWM excitation reduces losses by up to 15% compared to sine and symmetrical space vector PWM methods. However, at a low modulation index the greater harmonic content with discontinuous PWM increased motor losses by nearly 20%. This study demonstrates the importance of careful choice of modulation scheme to achieve high overall drive efficiency. © 2005 IEEE.

A new distance for scale-invariant 3D shape recognition and registration

This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a real and challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2011 IEEE.

State-space inference and learning with Gaussian processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors.