Bayesian classification of acoustical waveforms under environmental variability

We present in this paper a new multivariate probabilistic approach to Acoustic Pulse Recognition (APR) for tangible interface applications. This model uses Principle Component Analysis (PCA) in a probabilistic framework to classify tapping pulses with a high degree of variability. It was found that this model, achieves a higher robustness to pulse variability than simpler template matching methods, specifically when allowed to train on data containing high variability. © 2011 IEEE.

Bayesian Active Learning for Classification and Preference Learning

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.

Operation of brushless doubly-fed machine for drive applications

This paper presents dynamic and steady-state performance of the Brushless Doubly-Fed Machine (BDFM) operating as a variable speed drive. A simple closed-loop control system is used which only requires a speed feedback. The controller is capable of stabilising the machine when changes in speed and torque are applied. The machine starts in cascade mode and then makes a transition to the synchronous mode to reach the desired speed. This will allow a uni-directional converter to be used. The experiments included in this paper were carried out on a 180 frame size BDFM.

Stable operation of the Brushless Doubly-Fed Machine (BDFM)

A simple method of controlling the Brushless Doubly-Fed Machine (BDFM) is presented. The controller comprises two Proportional-Integral (PI) modules and requires only the rotor speed feedback. The machine model and the control system are developed in MATLAB. Both simulation and experimental results are presented. The performance of the system is presented in the motoring and generating operations. The experimental tests included in this paper were carried out on a 180 frame size BDFM with a nested-loop rotor. © 2007 IEEE.

Synchronous operation control of the Brushless Doubly-Fed Machine

The paper presents a novel vector control structure for the Brushless Doubly-Fed Machine (BDFM) which is derived based on the machine synchronous operation. In fact, the synchronous operation of the BDFM provides an efficient approach for determining the required reference angle in the machine vector control structure. The utilization of such reference angle makes the vector control structure presented in this paper different from and, in fact, more effective than the existing rotor flux and stator flux orientation schemes proposed for the machine. The results of implementing the vector control scheme in simulations confirm the effectiveness of the proposed approach for the BDFM control. © 2010 IEEE.

Construction of nonparametric Bayesian models from parametric Bayes equations

We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite graphs or infinite permutations. The problem has generated much interest in machine learning, where it is treated heuristically, but has not been studied in full generality in non-parametric Bayesian statistics, which tends to focus on models over probability distributions. Our approach applies a standard tool of stochastic process theory, the construction of stochastic processes from their finite-dimensional marginal distributions. The main contribution of the paper is a generalization of the classic Kolmogorov extension theorem to conditional probabilities. This extension allows a rigorous construction of nonparametric Bayesian models from systems of finite-dimensional, parametric Bayes equations. Using this approach, we show (i) how existence of a conjugate posterior for the nonparametric model can be guaranteed by choosing conjugate finite-dimensional models in the construction, (ii) how the mapping to the posterior parameters of the nonparametric model can be explicitly determined, and (iii) that the construction of conjugate models in essence requires the finite-dimensional models to be in the exponential family. As an application of our constructive framework, we derive a model on infinite permutations, the nonparametric Bayesian analogue of a model recently proposed for the analysis of rank data.

Diffraction based phase compensation method for phase-only liquid crystal on silicon devices in operation.

A method to measure the optical response across the surface of a phase-only liquid crystal on silicon device using binary phase gratings is described together with a procedure to compensate its spatial optical phase variation. As a result, the residual power between zero and the minima of the first diffraction order for a binary grating can be reduced by more than 10 dB, from -15.98 dB to -26.29 dB. This phase compensation method is also shown to be useful in nonbinary cases. A reduction in the worst crosstalk by 5.32 dB can be achieved when quantized blazed gratings are used.