The Rician inverse Gaussian distribution: a new model for non-Rayleigh signal amplitude statistics.

In this paper, we introduce a new statistical distribution for modeling non-Rayleigh amplitude statistics, which we have called the Rician inverse Gaussian (RiIG) distribution. It is a mixture of the Rice distribution and the inverse Gaussian distribution. The probability density function (pdf) is given in closed form as a function of three parameters. This makes the pdf very flexible in the sense that it may be fitted to a variety of shapes, ranging from the Rayleigh-shaped pdf to a noncentral chi2-shaped pdf. The theoretical basis of the new model is quite thoroughly discussed, and we also give two iterative algorithms for estimating its parameters from data. Finally, we include some modeling examples, where we have tested the ability of the distribution to represent locale amplitude histograms of linear medical ultrasound data and single-look synthetic aperture radar data. We compare the goodness of fit of the RiIG model with that of the K model, and, in most cases, the new model turns out as a better statistical model for the data. We also include a series of log-likelihood tests to evaluate the predictive performance of the proposed model.     

Synthesis and characterization of dehydroabietic acid derivatives suitable for surfactant synthesis

The syntheses of derivatives of dehydroabietic acid suitable for further manipulation into surfactants are presented. The compounds are produced at very high yields and purity. They contain several different functional groups, e.g., carboxylic acid, amine, ester, alcohol, and an aromatic group. The functional groups are useful as the linking unit to the hydrophilic part in synthesizing surfactants.     

Surface properties of surfactants derived from natural products. Part 1: Syntheses and structure/property relationships—Solubility and emulsification

Several novel and some previously known, mostly sugar-based, surfactants have been synthesized and some of their surface properties have been characterized and compared with those of commercial nonylphenol ethoxylates. The surfactant solubility in water, ethanol, and dodecane was studied. The properties of these compounds as emulsification agents in systems composed of the surfactant with water/isopropyl myristate, water/rapeseed oil, and water/dodecane are presented. The aqueous solubility of the surfactants follows the general trend expected from their hydrophilic-lipophilic balance according to Griffin (HLBG), but it is also clear that the nature of the headgroup and the structure of the nonpolar part affect the solubility in a manner not aptured in the standard HLBG concept. An ester or amine group as the connecting unit between the hydrophile and the hydrophobe produces a more water-soluble surfactant than the corresponding amide derivative. Some effective emulsifiers were found. For instance, the surfactants with a dehydroabietic nonpolar group appear to be promosing emulsifiers. Most sugar-based surfactants were able to form macroemulsions of up to around 2 wt/vol% of oil. The stability of many of these amulsions was very high, extending for months.     

Enantiomeric Composition of Monoterpene Hydrocarbons in Some Conifers and Receptor Neuron Discrimination of α-Pinene and Limonene Enantiomers in the Pine Weevil, Hylobius abietis

The enantiomeric composition of seven monoterpene hydrocarbons in headspace volatiles of spruce sawdust and seedlings (Picea abies), pine seedlings (Pinus sylvestris), and branches of juniper (Juniperus communis) was determined by gas chromatographic separation on a -Cyclodextrin column. For the six monoterpenes, -pinene, camphene, -pinene, sabinene, limonene, and -phellandrene, both enantiomers were present, whereas for 3-carene only the (+)-configuration was found. The amount of each enantiomer varied considerably both in relation to total amount of all of them, and for the six pairs also in relation to the opposite enantiomer. One olfactory receptor neuron in the pine weevil (Hylobius abietis) showed a strong response to -pinene when stimulated with all four headspace materials via a GC equipped with a DB-WAX column. The same neuron was subsequently tested with repeated stimulations via the GC effluent containing the (+)- or (–)-enantiomer. A marked better response to (+)- than to (–)--pinene was elicited. Another olfactory receptor neuron that responded strongly to limonene when stimulated with the spruce volatiles was tested for enantiomers of limonene. This neuron responded more strongly to (–)- than to (+)-limonene, when stimulated alternately with each of the limonene enantiomers. Discrimination between enantiomers by plant olfactory receptor neurons suggests that the enantiomeric ratios of volatile compounds may be important in host location by the pine weevil.     

Implementation of a Modelica compiler using JastAdd attribute grammars

We have implemented a compiler for key parts of Modelica, an object-oriented language supporting equation-based modeling and simulation of complex physical systems. The compiler is extensible, to support experiments with emerging tools for physical models. To achieve extensibility, the implementation is done declaratively in JastAdd, a metacompilation system supporting modern attribute grammar mechanisms such as reference attributes and nonterminal attributes.This paper reports on experiences from this implementation. For name and type analyses, we illustrate how declarative design strategies, originally developed for a Java compiler, could be reused to support Modelica’s advanced features of multiple inheritance and structural subtyping. Furthermore, we present new general design strategies for declarative generation of target ASTs from source ASTs. We illustrate how these strategies are used to resolve a generics-like feature of Modelica called modifications, and to support flattening, a fundamental part of Modelica compilation. To validate that the approach is practical, we have compared the execution speed of our compiler to two existing Modelica compilers.     

Robust pose estimation and recognition using non-gaussian modeling of appearance subspaces

We present an original appearance model that generalizes the usual Gaussian visual subspace model to non-Gaussian and nonparametric distributions. It can be useful for the modeling and recognition of images under difficult conditions such as large occlusions and cluttered backgrounds. Inference under the model is efficiently solved using the mean shift algorithm     

Critical success factors for implementing lean production: the effect of contingencies

To facilitate the implementation of lean production, practitioners and researchers have suggested an array of critical success factors (CSFs). However, despite a broad consensus about what needs to be done, companies still struggle to implement lean. Contingency theory posits the explanation that the common advice is not universal but is instead contingent on the situation. This paper investigates how contingency variables influence what practitioners see as success factors for implementing lean. A survey asked 432 practitioners from 83 factories belonging to two multinational companies for their opinions about what managers should do to ensure the success of the factory-level implementation of lean production. The survey responses were grouped into general success factors, which were then tested for differences across four contingency variables: corporation, factory size, stage of lean implementation and national culture. In general, the analysis supports a generic list of CSFs, but with some minor exceptions. For example, the stage of lean implementation in a factory influences to a slight extent which factors are perceived as more effectual than others. The paper contributes to the literature on lean production and offers several recommendations for managers striving to implement lean in their firms.     

Phase-based multidimensional volume registration

We present a method for accurate image registration and motion compensation in multidimensional signals, such as two-dimensional (2-D) X-ray images and three-dimensional (3-D) computed tomography/magnetic resonance imaging volumes. The method is based on phase from quadrature filters, which makes it robust to noise and temporal intensity variations. The method is equally applicable to signals of two, three or higher number of dimensions. We use parametric models, e.g., affine models, finite elements or local affine models with global regularization. Experimental results show high accuracy for 2-D and 3-D motion compensation.