Low-cost, scalable optical packet switching networks with multi-wavelength labels

We propose a self-forwarding packet-switched optical network with bit-parallel multi-wavelength labels. We experimentally demonstrate transmission of variable-length optical packets over 80 km of fiber and switching over a 1×4 multistage switch with two stages. © 2007 Optical Society of America.

Video tracking using dual-treewavelet polar matching and particle filtering

In this paper, we describe a video tracking application using the dual-tree polar matching algorithm. The models are specified in a probabilistic setting, and a particle ilter is used to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to track a simulated video moving target in an urban environment with complete and partial occlusions. © The Institution of Engineering and Technology.

Fourier analysis and gabor filtering for texture analysis and local reconstruction of general shapes

Since the pioneering work of Gibson in 1950, Shape- From-Texture has been considered by researchers as a hard problem, mainly due to restrictive assumptions which often limit its applicability. We assume a very general stochastic homogeneity and perspective camera model, for both deterministic and stochastic textures. A multi-scale distortion is efficiently estimated with a previously presented method based on Fourier analysis and Gabor filters. The novel 3D reconstruction method that we propose applies to general shapes, and includes non-developable and extensive surfaces. Our algorithm is accurate, robust and compares favorably to the present state of the art of Shape-From- Texture. Results show its application to non-invasively study shape changes with laid-on textures, while rendering and retexturing of cloth is suggested for future work. © 2009 IEEE.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. ©2010 IEEE.

Reprint of: Flow filtering and the physical sources of aerodynamic sound

The physical sources of sound are expressed in terms of the non-radiating part of the flow. The non-radiating part of the flow can be obtained from convolution filtering, as we demonstrate numerically by using an axi-symmetric jet satisfying the Navier-Stokes equations. Based on the frequency spectrum of the source, we show that the sound sources exhibit more physical behaviour than sound sources based on acoustic analogies. To validate the sources of sound, one needs to let them radiate within the non-radiating flow field. However, our results suggest that the traditional Euler operator linearized about the time-averaged part of the flow should be sufficient to compute the sound field. © 2010 Published by Elsevier Ltd.