Bootstrap-based bandwidth choice for log-periodogram regression

Abstract.  The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data-driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean-squared error (MSE). Its behaviour is compared with other existing techniques for optimal bandwidth selection in a MSE sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analysed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins.     

CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems

This paper presents a new evolutionary cooperative–competitive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, CO²RBFN, promotes a cooperative–competitive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. The proposal considers, in order to measure the credit assignment of an individual, three factors: contribution to the output of the complete RBFN, local error and overlapping. In addition, to decide the operators’ application probability over an RBF, the algorithm uses a Fuzzy Rule Based System. It must be highlighted that the evolutionary algorithm considers a distance measure which deals, without loss of information, with differences between nominal features which are very usual in classification problems. The precision and complexity of the network obtained by the algorithm are compared with those obtained by different soft computing methods through statistical tests. This study shows that CO²RBFN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other methods considered.     

Methods for automatic and assisted image annotation

Personal memories composed of digital pictures are very popular at the moment. To retrieve these media items annotation is required. During the last years, several approaches have been proposed in order to overcome the image annotation problem. This paper presents our proposals to address this problem. Automatic and semi-automatic learning methods for semantic concepts are presented. The automatic method is based on semantic concepts estimated using visual content, context metadata and audio information. The semi-automatic method is based on results provided by a computer game. The paper describes our proposals and presents their evaluations.     

Study of strengthening solutions for glued-laminated wood beams of maritime pine wood

The application of the concept of glued-laminated wood (glulam) to improve the mechanical properties of the solid wood of the maritime pine (Pinus pinaster) is the subject of this work. Two reinforced glued-laminated wood beams are proposed and assessed. The first is based on a concept of laminated wood composite with fiber glass; the second is based on the application of pultruded lamellas glued to the most stressed tensile region of the glued-laminated beams. In order to demonstrate the potential of the proposed strengthening solutions, a comparison between their mechanical behaviors and the mechanical behaviors of the conventional glued-laminated and solid beams are performed. Static load–deflection curves, an equivalent Young’s modulus, the ultimate strain and modulus of rupture are determined using 3-point quasi-static bending tests. In general, it is demonstrated the beneficial effect of the proposed strengthening solutions both in terms of resistance and ductility.     

Coupled model for the nonlinear analysis of sections made of anisotropic materials, subjected to general 3D loading. Part 2: Implementation and validation

A numerical model for the coupled analysis of cross-sections made of anisotropic materials under general combined loading was formulated in an accompanying paper (1). In this paper, additional aspects concerning its implementation and the scheme for nonlinear analysis are discussed. The model is validated by analyzing several isotropic and anisotropic elastic problems; excellent accuracy was obtained compared to closed-form solutions. Further, the case of a RC section presenting crack-induced anisotropy is investigated. The capability of the model to capture interactions between tangent and normal forces is proved. The conclusion drawn is that the developed model is a suitable sectional constitutive equation for 3D beam elements for realistic structural analysis.     

Towards embedded model predictive control for System-on-a-Chip applications

We propose a framework for embedding model predictive control for Systems-on-a-Chip applications. In order to allow the implementation of such a computationally expensive controller on chip, we propose reducing the precision of the microprocessor to the minimum while maintaining near optimal control performance. Taking advantage of the low precision, a logarithmic number system based microprocessor architecture is used, that allows the design of a reduced size processor, providing further energy and computational cost savings. The design parameters for this high-performance embedded controller are chosen using a combination of finite element method simulations and bit-accurate hardware emulations in a number of parametric tests. We provide the methodology for choosing the design parameters for two particular control problems; the temperature regulation in a wafer cross-section geometry, and the control of temperature in a non-isothermal fluid flow problem in a microdevice. Finally, we provide the microprocessor architecture details and estimates for the performance of the resulting embedded model predictive controller.     

Volatile compounds on the surface and within Iberian dry-cured loin

The profile of volatile compounds from external and internal areas of Iberian dry-cured loin was studied by headspace solid-phase microextraction. Higher levels of 16 volatile compounds (hexane, decane, 3-methylbutanal, several sulphur compounds and some aromatic hydrocarbons) were detected in the outer part of the loins. These differences could be owing to several factors, such as greater exposure to oxygen and dehydration conditions in the surface of the product, which favour oxidation reactions and Strecker degradation of amino acids. Moreover, the addition of spices on the surface and the proximity of a mould layer growing on the surface of the product could also contribute to the higher levels of these compounds on the external layer. However, other sulphur compounds coming from spices showed similar levels on the surface and within the loins. This different behaviour could be a consequence of different diffusion rates depending on the features of the compound and the matrix.     

Tight closed-form approximation for the ergodic capacity of orthogonal STBC

In this letter we derive a simple and tight closed-form approximation for the ergodic capacity of orthogonal space-time block coding in arbitrary fading channels. The expression is an analytical function of the power covariance matrix of the channel. In the case of uncorrelated channels the expression only depends on the variances of the channel power gains. These channel statistics can be easily obtained from both analytical and physical fading channel models. Simulations results show the accuracy of the proposed expression     

Aging fingerprint characterization of beer using electronic nose

In this work, attempts were made in order to characterize the change of aroma of alcoholic and non alcoholic beers during the aging process by use of a metal oxide semiconductor based electronic nose. The aged beer samples were statistically characterized in several classes. Linear techniques as principal component analysis (PCA) and Linear Discriminant Analaysis (LDA) were performed over the data that revealed non alcoholic beer classes are separated except a partial overlapping between zones corresponding to two specified classes of the aged beers. A clear discrimination was not found among the alcoholic beer classes showing the more stability of such type of beer compared with non alcoholic beer. In this research, to classify the classes, two types of artificial neural networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Backpropagation (BP) learning method. The classification success was found to be 90% and 100% for alcoholic and non alcoholic beers, respectively. Application of PNN showed the classification accuracy of 83% and 100%, respectively for the aged alcoholic and non alcoholic beer classes as well. Finally, this study showed the capability of the electronic nose system for the evaluation of the aroma fingerprint changes in beer during the aging process.