The fractional Fourier transform and quadratic field magnetic resonance imaging

The fractional Fourier transform (FrFT) is revisited in the framework of strongly continuous periodic semigroups to restate known results and to explore new properties of the FrFT. We then show how the FrFT can be used to reconstruct Magnetic Resonance (MR) images acquired under the presence of quadratic field inhomogeneity. Particularly, we prove that the order of the FrFT is a measure of the distortion in the reconstructed signal. Moreover, we give a dynamic interpretation to the order as time evolution of a function. We also introduce the notion of ρ-α space as an extension of the Fourier or k-space in MR, and we use it to study the distortions introduced in two common MR acquisition strategies. We formulate the reconstruction problem in the context of the FrFT and show how the semigroup theory allows us to find new reconstruction formulas for discrete sampled signals. Finally, the results are supplemented with numerical examples that show how it performs in a standard 1D MR signal reconstruction.     

A decision support system for the diagnosis of melanoma: A comparative approach

Melanoma is the most deathful of all skin cancers and the number of cases grows every year. The extirpation in early phases implies a high degree of survival so it is fundamental to diagnose it as soon as possible. In this paper we present a clinical decision support system for melanoma diagnosis using as input an image set of the skin lesion to be diagnosed. The system analyses the image sequence to extract the affected area, determinates the characteristics which indicate the degree of damage and, according to them, it makes a decision. Several methods of classification are proposed: a multilayered perceptron, a Bayesian classifier and the algorithm of the K nearest neighbours. These methods work independently and also in combination making a collaborative decision support system. The classification rates obtained are around 87%.     

Introducing requirements traceability support in model-driven development of web applications

In this work, we present an approach that introduces requirements traceability capabilities in the context of model-driven development of Web applications. This aspect allows us to define model-to-model transformations that not only provide a software artifact of lower abstraction (as model-to-model transformations usually do) but also to provide feedback about how they are applied. This feedback helps us to validate whether transformations are correctly applied. In particular, we present a model-to-model transformation that allows us to obtain navigational models of the Web engineering method OOWS from a requirements model. This transformation is defined as a set of mappings between these two models that have been implemented by means of graph transformations. The use of graph transformations allows us to develop a tool-supported strategy for applying mappings automatically. In addition, mechanisms for tracing requirements are also included in the definition of graph transformations. These mechanisms allow us to link each conceptual element to the requirements from which it is derived. In particular, we focus on tracing requirements throughout the navigational model, which describe the navigational structure of a Web application. To take advantage of these traceability mechanisms, we have developed a tool that obtains traceability reports after applying transformations. These reports help us to study aspects such as whether requirements are all supported, the impact of changing a requirement, or how requirements are modelled.     

Solving differential matrix Riccati equations by a piecewise-linearized method based on diagonal Padé approximants

Differential Matrix Riccati Equations (DMREs) appear in several branches of science such as applied physics and engineering. For example, these equations play a fundamental role in control theory, optimal control, filtering and estimation, decoupling and order reduction, etc. In this paper a new method based on a theorem proved in this paper is described for solving DMREs by a piecewise-linearized approach. This method is applied for developing two block-oriented algorithms based on diagonal Padé approximants. MATLAB versions of the above algorithms are developed, comparing, under equal conditions, accuracy and computational costs with other piecewise-linearized algorithms implemented by the authors. Experimental results show the advantages of solving stiff or non-stiff DMREs by the implemented algorithms.     

Stability analysis in spanwise-periodic double-sided lid-driven cavity flows with complex cross-sectional profiles

Three-dimensional linear instability analyses are presented of steady two-dimensional laminar flows in the lid-driven cavity defined by [15] and further analyzed in the present volume [1], as well as in a derivative of the same geometry. It is shown that in both of the geometries considered three-dimensional BiGlobal instability leads to deviation of the flow from the two-dimensional solution; the analysis results are used to define low- and high-Reynolds number solutions by reference to the flow physics. Critical conditions for linear global instability and neutral loops are presented in both geometries.     

The Poisson-exponential lifetime distribution

In this paper we proposed a new two-parameters lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk problem base. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulae for its reliability and failure rate functions, quantiles and moments, including the mean and variance. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived analytically in order to obtaining the asymptotic covariance matrix. The methodology is illustrated on a real data set.     

Equivalence of some common linear feature extraction techniques for appearance-based object recognition tasks

Recently, a number of empirical studies have compared the performance of PCA and ICA as feature extraction methods in appearance-based object recognition systems, with mixed and seemingly contradictory results. In this paper, we briefly describe the connection between the two methods and argue that whitened PCA may yield identical results to ICA in some cases. Furthermore, we describe the specific situations in which ICA might significantly improve on PCA     

GPGPU implementation of growing neural gas: Application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6×$6\times$ compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.     

Impact of cycling temperatures on Fusarium verticillioides and Fusarium graminearum growth and mycotoxins production in soybean

BACKGROUND: Fusarium graminearum and F. verticillioides are two very important mycotoxigenic species as they cause diverse diseases in crops. The effects of constant and cycling temperatures on growth and mycotoxin production of these species were studied on soybean based medium and on irradiated soya beans. RESULTS: F. graminearum grew better when was incubated at 15, 20 and 15–20 °C (isothermal or cycling temperature) during 21 days of incubation. Maximum levels of zearalenone and deoxynivalenol (39.25 and 1040.4 µg g^?1, respectively) were detected on soya beans after 15 days of incubation and the optimal temperature for mycotoxin production was 15 °C for zearalenone and 20 °C for deoxynivalenol. F. verticillioides grew better at 25 °C in culture medium and at 15/20 °C and 15/25 °C on soybean seeds. Fumonisin B₁ was produced only in culture medium, and the maximum level (7.38 µg g^?1) was found at 15 °C after 7 days of incubation. CONCLUSION: When growth and mycotoxin production under cycling temperatures were predicted from the results under constant conditions, observed values were different from calculated for both species and substrate medium. Therefore, care should be taken if data at constant temperature conditions are to be extrapolated to real field conditions. Copyright © 2012 Society of Chemical Industry