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     

Supercomputing and grid computing on the verification of covering arrays

The Covering Arrays (CAs) are mathematical objects with minimal coverage and maximum cardinality that are a good tool for the design of experiments. A?covering array is an N×k matrix over an alphabet v s.t. each N×k subset contains at least one time each combination from {0,1,??,v?1} ^t , given a positive integer value?t. The process of ensuring that a CA contains each of the v ^t combinations is called verification of CA. In this paper, we present an algorithm for CA verification and its implementation details in three different computation paradigms: (a)?sequential approach (SA); (b)?parallel approach (PA); and (c)?Grid approach (GA). Four different PAs were compared in their performance of verifying a matrix as a CA; the PA with the best performance was included in a different experimentation where the three paradigms, SA, PA, and GA were compared in a benchmark composed by 45 possible CA instances. The results showed the limitations of the different paradigms when solving the verification of CA problem, and points out the necessity of a Grid approach to solve the problem when the size of a CA grows.     

Detection of Channel Variations to Improve Channel Estimation Methods

In current digital communication systems, channel information is typically acquired by supervised approaches that use pilot symbols included in the transmit frames. Given that pilot symbols do not convey user data, they penalize throughput spectral efficiency, and transmit energy consumption of the system. Unsupervised channel estimation algorithms could be used to mitigate the aforementioned drawbacks although they present higher computational complexity than that offered by supervised ones. This paper proposes a simple decision method suitable for slowly varying channels to determine whether the channel has suffered a significant variation, which requires to estimate the matrix of the recently changed channel. Otherwise, a previous estimate is used to recover the transmitted symbols. The main advantage of this method is that the decision criterion is only based on information acquired during the time frame synchronization, which is carried out at the receiver. We show that the proposed criterion provides a considerable improvement of computational complexity for both supervised and unsupervised methods, without incurring in a penalization in terms of symbol error ratio. Specifically, we consider systems that make use of the popular Alamouti code. Performance evaluation is accomplished by means of simulated channels as well as making use of indoor wireless channels measured using a testbed.     

Adaptive input shaping for single-link flexible manipulators using an algebraic identification

This work proposes an adaptive control scheme applied to single link-flexible manipulators, which combines a feedback controller of the joint angle with an adaptive input shaper updated by an algebraic non-asymptotic identification. The feedback controller is designed to guarantee trajectory tracking of the joint angle, simplifying thus the input shaper, which can be designed for the arm dynamics only. The input shaper is updated by an algebraic identification of the natural frequency corresponding to the first vibration mode of the arm. In addition, the influence of the assumptions adopted to derive the algebraic identification on the performance of the estimation is studied. Finally, the proposed adaptive control strategy is implemented in practice.