On body-fitted co-ordinates for separated laminar flows past wall-mounted obstacles

A body-fitted curvilinear co-ordinate system is used to solve the equations of two-dimensional incompressible laminar flow over bluff obstructions by finite differences. Arbitrary conditions at the corner are removed by this method. Results for a backward-facing step are in reasonable agreement with those obtained with conventional mesh systems, and the differences are explained. A treatment of a channel expansion, in comparison with empirical data, is also included. The capability of the present method to handle arbitrary two-dimensional geometries is stressed and demonstrated, using a triangle and a semi-circle as examples.     

Computation of 2D discrete Hartley transform

Based on a decimation-in-time decomposition, a fast split-radix algorithm for the 2D discrete Hartley transform is presented. Compared to other reported algorithms, the proposed algorithm achieves substantial savings on the number of operations and provides a wider choice of transform sizes     

Eigenfeature regularization and extraction in face recognition

This work proposes a subspace approach that regularizes and extracts eigenfeatures from the face image. Eigenspace of the within-class scatter matrix is decomposed into three subspaces: a reliable subspace spanned mainly by the facial variation, an unstable subspace due to noise and finite number of training samples and a null subspace. Eigenfeatures are regularized differently in these three subspaces based on an eigenspectrum model to alleviate problems of instability, over-fitting or poor generalization. This also enables the discriminant evaluation performed in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR and GT databases show that our method consistently outperforms others.     

Complete discriminant evaluation and feature extraction in kernel space for face recognition

This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show that our approach consistently outperforms other kernel based face recognition methods.

Isoprostanes-biomarkers of lipid peroxidation: their utility in evaluating oxidative stress and analysis

Isoprostanes (IsoPs) are key biomarkers for investigating the role of free radical generation in the pathogenesis of human disorders. To solve IsoPs-related problems with regard to isoprostanes, analytical tools are required. This paper reviews the problems and trends in this field focusing on the methodology for assaying biomarkers in exhaled breath condensate (EBC) samples. A large amount of work has been done in the qualitative and quantitative analysis of IsoPs, but a standardized method has yet to emerge. The methodologies described differ, either in the sample preparation steps or in the detection techniques, or both. Requiring a number of chromatographic steps, the relevant extraction and purification procedures are often critical and time-consuming, and they lead to a substantial loss of target compounds. Recent data show that EBC is a promising non-invasive tool for the evaluation of different diseases. Two main analytical approaches have been adopted for IsoPs measurement: immunological methods and mass spectrometry. The methodologies for the extraction, purification and analysis of IsoPs in EBC samples are presented.     

Effect of Surface Roughness and Structure Features on Tribological Properties of Electrodeposited Nanocrystalline Ni and Ni/Al<Subscript>2</Subscript>O<Subscript>3</Subscript> Coatings

Metal matrix composite coatings obtained by electrodeposition are one of the ways of improving the surfaces of materials to enhance their durability and properties required in different applications. This paper presents an analysis of the surface topography, microstructure and properties (residual stresses, microhardness, wear resistance) of Ni/Al₂O₃ nanocomposite coatings electrodeposited on steel substrates from modified Watt’s-type baths containing various concentrations of Al₂O₃ nanoparticles and a saccharin additive. The residual stresses measured in the Ni/Al₂O₃ coatings decreased with an increasing amount of the co-deposited ceramics. It was established that the addition of Al₂O₃ powder significantly improved the coatings’ microhardness. The wear mechanism changed from adhesive-abrasive to abrasive with a rising amount of Al₂O₃ particles and coating microhardness. Nanocomposite coatings also exhibited a lower coefficient of friction than that of a pure Ni-electrodeposited coating. The friction was found to depend on the surface roughness, and the smoother surfaces gave lower friction coefficients.     

Kleene Algebra and Bytecode Verification

Most standard approaches to the static analysis of programs, such as the popular worklist method, are first-order methods that inductively annotate program points with abstract values. In [6] we introduced a second-order approach based on Kleene algebra. In this approach, the primary objects of interest are not the abstract data values, but the transfer functions that manipulate them. These elements form a left-handed Kleene algebra. The dataflow labeling is not achieved by inductively labeling the program with abstract values, but rather by computing the star (Kleene closure) of a matrix of transfer functions. In this paper we show how this general framework applies to the problem of Java bytecode verification. We show how to specify transfer functions arising in Java bytecode verification in such a way that the Kleene algebra operations (join, composition, star) can be computed efficiently. We also give a hybrid dataflow analysis algorithm that computes the closure of a matrix on a cutset of the control flow graph, thereby avoiding the recalculation of dataflow information when there are cycles in the graph. This method could potentially improve the performance over the standard worklist algorithm when a small cutset can be found.