Linear combinations of iterates of Bernstein polynomials exponentially converging to the Lagrange interpolating polynomial are given. The results are applied in CAGD to get an exponentially fast weighted progressive iterative approximation technique to fit data with finer and finer precision.

The SAP4PRISMA project research activities aimed at supporting the Italian hyperspectral PRISMA mission by developing preliminary processing chains suitable for PRISMA to obtain high level hyperspectral data products for agriculture, land degradation, natural and human hazards.

Additive partial linear models with nonparametric additive components of heterogeneous smoothness are studied. To achieve optimal rates in large sample situations we use block wavelet penalisation techniques combined with adaptive (group) LASSO procedures for selecting the variables in the linear part and the the additive components in the nonparametric part of the models. Numerical implementations of our procedures for proximal like algorithms are discussed. Large sample properties of the estimates and of the model selection are presented and the results are illustrated with simulated examples and a real data analysis.

Methods coming from statistics and pattern recognition to estimate the cloud mask from radiance measured by visible and infrared sensors on board satellites are gaining greater consideration for their ability to properly exploit the increasing number of channels available with current and next-generation sensors. Endowed with physical arguments, they give rise to robust methods for accurately estimating the cloud mask. Application of such classification methods to Moderate Resolution Imaging Spectroradiometer (MODIS) data is discussed in this paper. Three different types of MODIS datasets are considered: synthetic (radiance is simulated by proper radiative transfer models); annotated (real MODIS data labeled by a meteorologist as clear or cloudy); and real MODIS data, whose truth is obtained from the official MODIS cloud mask product. A full assessment of the MODIS spectral bands is performed, aimed at understanding the role of the spectral bands in detecting clouds and at achieving top performance with very few properly chosen spectral channels. Local methods that use spatial correlation of images to improve classification, reducing the pseudonuisance of nonlocal methods, have also been tested on real data.

We introduce a classification method (cumulative discriminant analysis) of the discriminant analysis type to discriminate between cloudy and clear-sky satellite observations in the thermal infrared. The tool is intended for the high-spectral-resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The cumulative discriminant analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A principal component analysis prior step is also introduced, which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) imagers. The agreement with these independent cloud masks is generally well above 80 %, except at high latitudes in the winter seasons.

This paper presents a new hybrid approach integrating the cross-entropy (CE) algorithm and the sequential quadratic programming (SQP) technique to solve the economic load dispatch (ELD) problem related to electrical power generating units. Due to the introduction of the valve-point effect in the ELD objective function, the optimization task requires tools appropriate for a nonconvex optimization landscape. In this respect, we employ the CE approach, which constructs a random sequence of solutions probabilistically converging to a near-optimal solution and, thus, facilitating the exploration capability. Additionally, to fine-tune the solution in promising basins of attraction, the SQP algorithm is invoked, which performs a local search. Despite its reliance on a global heuristic scheme, CE-SQP is vested with fast convergence capability, which may entail its use for online power dispatch. The effectiveness and the robustness of the proposed method in comparison with several state-of-the-art approaches have been demonstrated with four standard test systems that are widely reported in the ELD literature.

This paper aims at understanding the role of clients in the innovation of Creativity- Intensive Business Services (CIBS), namely advertising services, to provide useful managerial indications for the proactive management of the collaboration with clients for the purpose of innovation. We adopt a multi-stage approach. Firstly, we propose a conceptual multidimensional framework by combining three streams of research: a) research on customer involvement in New Service Development; b) service innovation studies with specific regard to CIBS; c) literature on business relationships marketing. Secondly, we refine such framework through an exploratory qualitative analysis based on a case study of a successful advertising agency. Thirdly, we undertake a large-scale survey of European advertising agencies. Our preliminary findings provide empirical evidence to the hidden and multidimensional nature of CIBS' innovation, and contribute to advance the understanding of the concept of "soft innovation" in creative services. Results also suggest that efforts to improve the interaction and collaborative work practices with clients are conducive to changes in other technological as well as non-technological dimensions. Moreover, the empirical analysis provides indications on additional innovation enabling characteristics of clients and on their potential roles as catalyst and co-developer of innovation.

This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighbouring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images.

The upcoming launch of the next generation of hyperspectral satellites (PRISMA, EnMap, HyspIRI, etc.) will meet the increasing demand for the availability/accessibility of hyperspectral information on agricultural land use from the agriculture community. To this purpose, algorithms for the classification of remotely sensed images are here considered for agricultural monitoring of cultivated area, exploiting remotely sensed high spectral resolution images. Classification is accomplished by procedures based on discriminant analysis tools that well suit hyperspectrality, circumventing what in statistics is called "the curse of dimensionality". As a byproduct of classification, a full assessment of the spectral bands of the sensor is obtained, ranking them with the purpose of understanding their role in segmentation and classification. The methodology has been validated on two independent image datasets gathered by the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor for which ground validations were available. A comparison with the popular multiclass SVM (Support Vector Machines) classifier is also presented. Results show that a good classification (minimum global success rate 95% through all experiments) is achieved by using the 10 spectral bands selected as the most discriminant by the proposed procedure; moreover, it also appears that nonparametric techniques generally outperform parametric ones. The present study confirms that the new generation of hyperspectral satellite data like PRISMA can ripen an end-user application for agricultural land-use of cultivated area.

The convergence quality of the cross-entropy (CE) optimizer relies critically on the mechanism meant for randomly generating data samples, in agreement with the inference drawn in the earlier works--the fast simulated annealing (FSA) and fast evolutionary programming (FEP). Since tracing a near-global-optimum embedded on a nonconvex search space can be viewed as a rare event problem, a CE algorithm constructed using a longtailed distribution is intuitively attractive for effectively exploring the optimization landscape. Based on this supposition, a set of CE algorithms employing the Cauchy, logistic and Laplace distributions are experimentally validated in a wide range of optimization functions, which are shifted, rotated, expanded and/or composed, characterized by convex, unimodal, discontinuous, noisy and multimodal fitness landscapes. The Laplace distribution has been demonstrated to be more suitable for the CE optimization, since the samples drawn have jump-lengths long enough to elude local optima and short enough to preserve sufficient candidates in the global optimum neighborhood. Besides, a theoretical analysis has been carried out to understand the following: (i) benefits offered by the long-tailed distributions towards evasion of local optima; (ii) link between the variation in scale parameter estimate and the probability of producing candidate solutions arbitrarily close to the global optimum.

A Riemannian manifold optimization strategy is proposed to facilitate the relaxation of the orthonormality constraint in a more natural way in the course of performing independent component analysis (ICA) that employs a mutual information-based source-adaptive contrast function. Despite the extensive development of manifold techniques catering to the orthonormality constraint, only a limited number of works have been dedicated to oblique manifold (OB) algorithms to intrinsically handle the normality constraint, which has been empirically shown to be superior to other Riemannian and Euclidean approaches. Imposing the normality constraint implicitly, in line with the ICA definition, essentially guarantees a substantial improvement in the solution accuracy, by way of increased degrees of freedom while searching for an optimal unmixing ICA matrix, in contrast with the orthonormality constraint. Designs of the steepest descent, conjugate gradient with Hager-Zhang or a hybrid update parameter, quasi-Newton, and cost-effective quasi-Newton methods intended for OB are presented in this paper. Their performance is validated using natural images and systematically compared with the popular state-of-the-art approaches in order to assess the performance effects of the choice of algorithm and the use of a Riemannian rather than Euclidean framework. We surmount the computational challenge associated with the direct estimation of the source densities using the improved fast Gauss transform in the evaluation of the contrast function and its gradient. The proposed OB schemes may find applications in the offline image/signal analysis, wherein, on one hand, the computational overhead can be tolerated, and, on the other, the solution quality holds paramount interest.

Knowledge of the exact spatial distribution of brain tissues in images acquired by magnetic resonance imaging (MRI) is necessary to measure and compare the performance of segmentation algorithms. Currently available physical phantoms do not satisfy this requirement. State-of-the-art digital brain phantoms also fall short because they do not handle separately anatomical structures (e.g. basal ganglia) and provide relatively rough simulations of tissue fine structure and inhomogeneity. We present a software procedure for the construction of a realistic MRI digital brain phantom. The phantom consists of hydrogen nuclear magnetic resonance spin-lattice relaxation rate (R1), spin-spin relaxation rate (R2), and proton density (PD) values for a 24 x 19 x 15.5 cm volume of a ''normal'' head. The phantom includes 17 normal tissues, each characterized by both mean value and variations in R1, R2, and PD. In addition, an optional tissue class for multiple sclerosis (MS) lesions is simulated. The phantom was used to create realistic magnetic resonance (MR) images of the brain using simulated conventional spin-echo (CSE) and fast field-echo (FFE) sequences. Results of mono-parametric segmentation of simulations of sequences with different noise and slice thickness are presented as an example of possible applications of the phantom. The phantom data and simulated images are available online at http://lab.ibb.cnr.it/.

Soil N2O emissions were monitored throughout a 3-year crop rotation including maize, fennel and a ryegrass-clover. sward, at Borgo Cioffi NitroEurope site. N2O emission rates were highly variable in time and space and controlled by soil nitrogen and soil water content. The N2O effluxes were low for most of the monitored period. The highest N2O emissions were recorded throughout the 2007 maize cropping season, ranged from 15.2 to 196.2 mug m-2 h-1 whereas the lowest ones ranged from -5 to 10 mug m-2 h-1 during the 2007 2008 ryegrass-clover winter crop. For the maize crops, N2O peaks were detected after fertilization but with a delay of some weeks from applications, probably due to the presence of DMPP nitrification inhibitor in the applied fertilizer. A properly designed ANOVA model was developed to explain the influence of the main chemical-physical factors. This model also allowed the quantification of the delay time in peak emissions following fertilization, which resulted variable over the years and ranged between 2 and 21 days. A dependence of emissions from soil temperature and moisture was found, with significant interactions in some instances. Calculated Emission Factors (maize 2007: 0.48%; ryegrass-clover sward 2007 2008: 0.05%; maize 2008: 0.14%; fennel: 0.28% 2008 2009; maize 2009: .015%) resulted well below the values reported in the literature and the 1% reference value indicated by IPCC, probably due to a suboptimal water regime inducing low Water Filled Pore Space (WFPS) values.

Quantifying trace gas emissions and the influence of surface exchange processes on the atmosphere is a necessary step towards the control of global greenhouse gas emissions and reliability of air quality models. This paper proposes a procedure based on the mass balance method and implemented on highly resolved aircraft data. It allows one to estimate surface exchanges on areas of several km2 and heterogeneous features exploiting the characteristics of convective boundary layer during steady state conditions that permit the estimation of emission/absorption terms as functions of advective fluxes only. A nonparametric approach is adopted and the fluxes on the surface of a virtual box surrounding the area of interest are reconstructed on the basis of scalar densities and wind vectors using Shepard functions. Two different techniques are also proposed to face lack of data on the top surface of the box. The method has been applied to experimental data coming from measurement campaigns on two different sites. It provides realistic estimates of the CO2 emission/absorption in the considered areas that are in good agreement with CO2 fluxes evaluated by Airborne Eddy Covariance and confirm the suitability of the proposed approach for the assessment of turbulent exchange of trace gases by composite landscapes. Uncertainties on the estimated emissions due to both propagation of the experimental error and interpolation have been quantified by bootstrap analysis as 6%.