Sensitivity of wear rates in the micro-scale abrasion test to test conditions and material hardness

Micro-scale abrasion (ball cratering) tests were performed with different combinations of ball and bulk specimen materials, under different test conditions, such as load and abrasive slurry concentration. Wear modes were classified into two types: with rolling particle motion and with grooving particle motion. Wear rates observed with rolling particle motion were relatively insensitive to test conditions, whereas with grooving motion they varied much more. It is suggested that rolling abrasion is therefore a more appropriate mode if reproducible test results are desired. The motion of the abrasive particles can be reliably predicted from the knowledge of hardnesses and elastic properties of the ball and the specimen, and from the normal load and the abrasive slurry concentration. General trends in wear resistance measured in the micro-scale abrasion test with rolling particle motion are similar to those reported in tests with fixed abrasives with sliding particle motion, although the variation in wear resistance with hardness is significantly smaller. © 2004 Published by Elsevier B.V.

Robust pairwise matching of interest points with complex wavelets

We present a matching framework to find robust correspondences between image features by considering the spatial information between them. To achieve this, we define spatial constraints on the relative orientation and change in scale between pairs of features. A pairwise similarity score, which measures the similarity of features based on these spatial constraints, is considered. The pairwise similarity scores for all pairs of candidate correspondences are then accumulated in a 2-D similarity space. Robust correspondences can be found by searching for clusters in the similarity space, since actual correspondences are expected to form clusters that satisfy similar spatial constraints in this space. As it is difficult to achieve reliable and consistent estimates of scale and orientation, an additional contribution is that these parameters do not need to be determined at the interest point detection stage, which differs from conventional methods. Polar matching of dual-tree complex wavelet transform features is used, since it fits naturally into the framework with the defined spatial constraints. Our tests show that the proposed framework is capable of producing robust correspondences with higher correspondence ratios and reasonable computational efficiency, compared to other well-known algorithms. © 1992-2012 IEEE.

Shape vs. volume: Invariant shape descriptors for 3D region of interest characterization in MRI

We propose a new approach for quantifying regions of interest (ROIs) in medical image data. Rotationally invariant shape descriptors (ISDs) were applied to 3D brain regions extracted from MRI scans of 5 Parkinson's patients and 10 control subjects. We concentrated on the thalamus and the caudate nucleus since prior studies have suggested they are affected in Parkinson's disease (PD). In the caudate, both the ISD and volumetric analyses found significant differences between control and PD subjects. The ISD analysis however revealed additional differences between the left and right caudate nuclei in both control and PD subjects. In the thalamus, the volumetric analysis showed significant differences between PD and control subjects, while ISD analysis found significant differences between the left and right thalami in control subjects but not in PD patients, implying disease-induced shape changes. These results suggest that employing ISDs for ROI characterization both complements and extends traditional volumetric analyses. © 2006 IEEE.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.

Convergence of the auxiliary particle implementation of the PHD filter

Optimal Bayesian multi-target filtering is in general computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency was proposed by Whiteley et. al. Numerical examples were presented for two scenarios, including a challenging nonlinear observation model, to support the claim. This paper studies the theoretical properties of this auxiliary particle implementation. $\mathbb{L}_p$ error bounds are established from which almost sure convergence follows.

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.

A performance evaluation of volumetric 3D interest point detectors

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 3D shape acquisition techniques and the diminishing returns to be had from appearance alone have seen a surge in 3D shape-based methods. In this work, we investigate the performance of several state of the art interest points detectors 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. © 2012 Springer Science+Business Media, LLC.

Application of fast oxygen sensors for investigations into air-path dynamics and EGR distribution in a diesel engine

The control of NOX emissions by exhaust gas recirculation (EGR) is of widespread application. However, despite dramatic improvements in all aspects of engine control, the subtle mixing processes that determine the cylinder-to-cylinder distribution of the recirculated gas often results in a mal-distribution that is still an issue for the engine designer and calibrator. In this paper we demonstrate the application of a relatively straightforward technique for the measurement of the absolute and relative dilution quantity in both steady state and transient operation. This was achieved by the use of oxygen sensors based on standard UEGO (universal exhaust gas oxygen) sensors but packaged so as to give good frequency response (∼ 10 ms time constant) and be completely insensitivity to the sample pressure and temperature. Measurements can be made at almost any location of interest, for example exhaust and inlet manifolds as well as EGR path(s), with virtually no flow disturbance. At the same time, the measurements yield insights into air-path dynamics. We argue that "dilution", as indicated by the deviation of the oxygen concentration from that of air, is a more appropriate parameter than EGR rate in the context of NOX control, especially for diesel engines. Experimental results are presented for the EGR distribution in a current production light duty 4-cylinder diesel engine in which significant differences were found in the proportion of the recirculated gas that reached each cylinder. Even the individual inlet runners of the cylinders exhibited very different dilution rates - differences of nearly 50% were observed at some conditions. An application of such data may be in the improvement of calibration and validation of CFD and other modelling techniques. Copyright © 2014 SAE International.