Face recognition with lattice independent component analysis and extreme learning machines

We focus on two aspects of the face recognition, feature extraction and classification. We propose a two component system, introducing Lattice Independent Component Analysis (LICA) for feature extraction and Extreme Learning Machines (ELM) for classification. In previous works we have proposed LICA for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. In the second step, the set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. The resulting abundance values are used as features for classification, specifically for face recognition. Extreme Learning Machines are accurate and fast-learning innovative classification methods based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-to-output weights. The LICA-ELM system has been tested against state-of-the-art feature extraction methods and classifiers, outperforming them when performing cross-validation on four large unbalanced face databases.     

Neural network wind retrieval from ERS-1 scatterometer data

This paper presents a neural network methodology to retrieve wind vectors from ERS1 scatterometer data. First, a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated direction are given. At least 75% of the most probable wind directions are consistent with ECMWF winds (at ±20°). Then the remaining ambiguities are solved by an adapted PRESCAT method, which uses the probabilities provided by NN-INVERSE. Several statistical tests are presented to evaluate the accuracy of the method. Its good performance is mainly due to the use of a spatial context and to the probabilistic approach for estimating the direction. Comparisons with other methods are also presented. The good performance of the neural method suggests that self-consistent wind retrieval is possible.     

Multigrid Convergence of Calculated Features in Image Analysis

This paper informs about number-theoretical and geometrical estimates of worst-case bounds for quantization errors in calculating features such as moments, moment based features, or perimeters in image analysis, and about probability-theoretical estimates of error bounds (e.g. standard deviations) for such digital approximations. New estimates (with proofs) and a review of previously known results are provided.     

Modelling and characterization of AlN-actuated microcantilevers vibrating in the first in-plane mode

The characterization of the first in-plane mode of aluminum nitride-actuated piezoelectric microcantilevers was carried out by using electrical and optical techniques. The top electrode of the cantilever was specifically designed to allow for an efficient electrical actuation of these in-plane modes. In order to confirm the in-plane nature of the modal vibration, the detection of the electrically induced movement was performed optically with the help of a stroboscopic microscope. In parallel, resonances were also measured electrically by means of an impedance analyzer. The quality factor and the resonant frequencies of the in-plane modes were estimated from the corresponding measurement data when applying both detection techniques. Our results show quality factor values as high as 3,000 for the first in-plane mode in air.     

The Kosko Subsethood Fuzzy Associative Memory (KS-FAM): Mathematical Background and Applications in Computer Vision

Many well-known fuzzy associative memory (FAM) models can be viewed as (fuzzy) morphological neural networks (MNNs) because they perform an operation of (fuzzy) mathematical morphology at every node, possibly followed by the application of an activation function. The vast majority of these FAMs represent distributive models given by single-layer matrix memories. Although the Kosko subsethood FAM (KS-FAM) can also be classified as a fuzzy morphological associative memory (FMAM), the KS-FAM constitutes a two-layer non-distributive model. In this paper, we prove several theorems concerning the conditions of perfect recall, the absolute storage capacity, and the output patterns produced by the KS-FAM. In addition, we propose a normalization strategy for the training and recall phases of the KS-FAM. We employ this strategy to compare the error correction capabilities of the KS-FAM and other fuzzy and gray-scale associative memories in terms of some experimental results concerning gray-scale image reconstruction. Finally, we apply the KS-FAM to the task of vision-based self-localization in robotics.     

Multiple Testing Correction in Medical Image Analysis

We present a novel method for correcting the significance level of hypothesis testing that requires multiple comparisons. It is based on the spectral graph theory, in which the variables are seen as the vertices of a complete undirected graph and the correlation matrix as the adjacency matrix that weights its edges. The method increases the statistical power of the analysis by refuting the assumption of independence among variables, while keeping the probability of false positives low. By computing the eigenvalues of the correlation matrix, it is possible to obtain valuable information about the dependence levels among the variables of the problem, so that the effective number of independent variables can be estimated. The method is compared to other available models and its effectiveness illustrated in case studies involving high-dimensional sets of variables.     

The effect of price promotions on consumer shopping behavior across online and offline channels: differences between frequent and non-frequent shoppers

Information Systems and e-Business Management - This study evaluates the differential effect of price promotions on brand choice across the offline and online channels of a grocery retailer. We use...     

On the sliding mode control of MIMO nonlinear systems: An input‐output approach

We address the sliding mode control design problem for output reference trajectory tracking problems in the special class of MIMO flat systems known as static feedback linearizable systems. We assume unavailable system state components but rely on available inputs and measurable flat outputs. Each controller will largely ignore state and control input couplings by adopting a standard sliding mode controller scheme derived from the SISO case and used this as decoupled input‐to‐flat‐output model. The standard controller arises from a vastly simplified pure integration, additively perturbed, system. The simplified pure integration system controlled trajectories are shown to be time‐scale homotopically equivalent to those of the nonlinear flat system. The basic sliding surface coordinate function design is approached from the perspective of structural integral reconstructors requiring only the inputs and the flat outputs of the system. Integral structural reconstructors were introduced by Fliess et al for the control of linear SISO and MIMO systems, giving rise to the generalized proportional integral control method. Simulations are presented for SISO and MIMO systems and experimental results are reported for a two‐degree‐of‐freedom fully actuated robotic manipulator.     

An invitation to Friendly Testing

1IntroductionSinceitspresentationin[1,2]TestingSemanticshasbeenwidelystudiedandusedasanaturalwaytodefineanobservationalbehaviorwithareasonablepowertodistinguishsemanticallydifferentprocesses.TestingSemanticsisdefinedbyobservingtheoperationalsemanticsofprocessesbymeansoftests.Testsarejustprocesseswhichmayexecuteanewactionwreportingsuccessofthetestapplication.Todefinetheapplicationofatesttoaprocesslweconsiderthedifferentcomputationsoftheexperimentalsystemwhichisobtainedbycomposinginparallelthe…