Switched reluctance motor drive without direct rotor position sensing

A closed-loop control technique based on monitoring phase current risetime for switched reluctance (SR) motors without direct rotor-position sensors has been studied and implemented successfully. In this technique the variation in incremental phase inductance in a SR motor is used to detect rotor position. A control circuit for current-waveform-based rotor position detection has been implemented using hard-wire digital circuits. Torque-speed and system-efficiency characteristics resulting from the application of the method to a 4-kW, four-phase SR motor with an IGBT drive are presented.

Radio over fibre enabled sensing and communications system for in building

This paper describes work on radio over fiber distributed antenna systems for improving the quality of radio coverage for in-building applications. The DAS network has also been shown to provide improved detection for Gen 2 UHF RFID tags. Using pre-distortion to reduce the problem of the RFID second harmonic, a simple heterogeneous sensing and communications system is demonstrated. © 2011 NOrthumbria University.

Multiview stereo via volumetric Graph-Cuts and occlusion robust photo-consistency.

This paper presents a volumetric formulation for the multi-view stereo problem which is amenable to a computationally tractable global optimisation using Graph-cuts. Our approach is to seek the optimal partitioning of 3D space into two regions labelled as "object" and "empty" under a cost functional consisting of the following two terms: (1) A term that forces the boundary between the two regions to pass through photo-consistent locations and (2) a ballooning term that inflates the "object" region. To take account of the effect of occlusion on the first term we use an occlusion robust photo-consistency metric based on Normalised Cross Correlation, which does not assume any geometric knowledge about the reconstructed object. The globally optimal 3D partitioning can be obtained as the minimum cut solution of a weighted graph.

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.