A new, fast response CO2 sensor

The inability of emissions reduction methods to meet upcoming legislation without an unacceptable increase in vehicle cost is a major problem of automobile manufacturer. This work aims to develop a cost-effective reduction of automobile emissions. A prototype CO2 sensor with 5 msec response time was built and bench tested, then used on an engine. The sensor design was based on standard emissions measurement technology using non-dispersive IR absorption. An improved sensor has now been completed with significant improvements in terms of signal to noise ratio and long-term stability. The improved sensor will be used to measure CO2 concentrations on three different engines. The results will then be used to validate engine and catalyst models and to propose control strategies aimed at reducing overall emissions. A brief description of the sensor itself was presented. Original is an abstract.

Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

An evaluation of volumetric interest points

This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of depth sensors and the diminishing returns to be had from appearance alone have seen a surge in shape-based methods. In this work we investigate the performance of several detectors of interest points in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application. © 2011 IEEE.