Flow Intensity Parameter in Pier Scour Experiments

Technical note discusses a detailed approach to dimensional analysis for the bridge pier scour phenomenon and the introduction of flow intensity. It demonstrates the dependence of critical upstream velocity on the rest of the parameters describing the process and its implications on dimensional analysis. Assuming that the viscous effects are negligible in the local scour phenomenon, it is concluded that the flow intensity of the approaching undisturbed flow is not an adequate parameter to describe the process in usual laboratory conditions. A new proposal is established.     

Nonhydrolytic sol-gel synthesis and characterization of YAG

Yttrium–aluminum oxides are interesting compounds, which are widely used as hosts for lasers and phosphors due to their stable physical and chemical properties. The manufacture of YAG has been investigated thoroughly. YAG powders are traditionally synthesized through the reaction of aluminum and yttrium powders at high temperatures. The work reported here involved an investigation into the preparation of YAG by a nonhydrolytic sol-gel route and the influence of heating time at low temperatures to obtain YAG from inorganic precursors (yttrium and aluminum chloride). AlCl₃, YCl₃ and ethanol were reflux reacted under an argon atmosphere. Europium III chloride was added as a structural probe. The powder was treated at 800 °C for 1, 2, 4, 8 and 16 h. The YAG structure was analyzed by X-ray diffraction (XRD), nuclear magnetic resonance (NMR), thermal analysis (TA) and photoluminescence (PL). The XRD revealed only peaks corresponding to the YAG phase. PL data showed that the YAG phase was formed in 2 min with samples pretreated at 50 °C. For the samples pretreated at 800 °C, the YAG phase appeared in 30 s. The excitation spectra presented a maximum of 394 nm corresponding to the ⁵L₆ level, while the emission spectra of Eu III ions showed characteristic transition bands arising from the ⁵D₀ → ⁷F _J (J = 1, 2, 3, 4) manifolds at maximum excitation. The magnetic dipole ⁵D₀ → ⁷F₁ transition exhibited greater intensity than the electric dipole ⁵D₀ → ⁷F₂ transition. This methodology proved efficient for obtaining YAG phase.     

Modeling and Analysis of Composite Antenna Superstrates Consisting on Grids of Loaded Wires

We study the characteristics and radiation mechanism of antenna superstrates based on closely located periodical grids of loaded wires. An explicit analytical method based on the local field approach is used to study the reflection and transmission properties of such superstrates. It is shown that as a result of proper impedance loading there exists a rather wide frequency band over which currents induced to the grids cancel each other, leading to a wide transmission maximum. In this regime radiation is produced by the magnetic dipole moments created by circulating out-of-phase currents flowing in the grids. An impedance matrix representation is derived for the superstrates, and the analytical results are validated using full-wave simulations. As a practical application example we study numerically the radiation characteristics of dipole antennas illuminating finite-size superstrates.     

Ultrasound image segmentation with shape priors: application to automatic cattle rib-eye area estimation.

Automatic ultrasound (US) image segmentation is a difficult task due to the quantity of noise present in the images and the lack of information in several zones produced by the acquisition conditions. In this paper, we propose a method that combines shape priors and image information to achieve this task. In particular, we introduce knowledge about the rib-eye shape using a set of images manually segmented by experts. A method is proposed for the automatic segmentation of new samples in which a closed curve is fitted taking into account both the US image information and the geodesic distance between the evolving curve and the estimated mean rib-eye shape in a shape space. This method can be used to solve similar problems that arise when dealing with US images in other fields. The method was successfully tested over a database composed of 610 US images, for which we have the manual segmentations of two experts.     

A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.     

Early diagnosis of Alzheimer’s disease based on Partial Least Squares and Support Vector Machine

An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance for the patient medical treatment. It has been shown that pathological manifestations of AD may be detected thought functional images even before that the patients becomes symptomatic. This fact has led researchers to propose new ways for analyzing functional data in order to get more accurate Computer-Aided Diagnosis (CAD) systems for this disorder. In this paper we show an effective approach for Single Photon Emission Computed Tomography feature extraction that improves the accuracy of CAD systems for AD. The proposed methodology uses a Partial Least Squares algorithm for extracting score vectors and the Out-Of-Bag error for selecting a number of scores that are used as features. Subsequently, a Support Vector Machine (SVM) based classifier determines the underlying class of the images, thus performing diagnostics. In order to test this approach we have used an image database for AD with 97 SPECT images from controls and AD patients. The images were visually labeled by experienced clinicians after the properly normalization. Several experiments have been developed to compare the proposed methodology and previous approaches. The results show that our method yields accuracy rates over 90%, outperforming several recently reported CAD systems for AD diagnosis.     

Fault tolerant embedded systems design by multi-objective optimization

The design of fault tolerant systems is gaining importance in large domains of embedded applications where design constrains are as important as reliability. New software techniques, based on selective application of redundancy, have shown remarkable fault coverage with reduced costs and overheads. However, the large number of different solutions provided by these techniques, and the costly process to assess their reliability, make the design space exploration a very difficult and time-consuming task. This paper proposes the integration of a multi-objective optimization tool with a software hardening environment to perform an automatic design space exploration in the search for the best trade-offs between reliability, cost, and performance. The first tool is commanded by a genetic algorithm which can simultaneously fulfill many design goals thanks to the use of the NSGA-II multi-objective algorithm. The second is a compiler-based infrastructure that automatically produces selective protected (hardened) versions of the software and generates accurate overhead reports and fault coverage estimations. The advantages of our proposal are illustrated by means of a complex and detailed case study involving a typical embedded application, the AES (Advanced Encryption Standard).     

Perspectives on control-relevant identification through the use of interactive tools

System identification is an essential part of control design. Control engineers must devote substantial effort to identification issues in order to obtain suitable models for closed-loop control. Control-relevant identification seeks to both simplify the modelling task and improve the usefulness of the model by taking into account controller requirements during system identification. The advantages of this methodology can be better understood and appreciated through the interactive software tool described in this paper. The Interactive Tool for Control Relevant Identification (ITCRI) comprehensively captures the control-relevant identification process for the monovariable problem, from input design to closed-loop control, depicting these stages simultaneously and interactively in one screen. By simultaneously displaying both open- and closed-loop responses of the estimated models, ITCRI enables the user to readily assess how design variable choices during identification and control performance requirements impact model error and ultimately, closed-loop performance. Moreover, the work presents several examples which the aim to illustrate the tool and the considerations that arise when control requirements are taken into account during the identification stage.