An adaptive filtering approach to spectral estimation and SARimaging

We present an adaptive FIR filtering approach, which is referred to as the amplitude and phase estimation of a sinusoid (APES), for complex spectral estimation. We compare the APES algorithm with other FIR filtering approaches including the Welch (1967) and Capon (1969) methods. We also describe how to apply the FIR filtering approaches to target range signature estimation and synthetic aperture radar (SAR) imaging. We show via both numerical and experimental examples that the adaptive FIR filtering approaches such as Capon and APES can yield more accurate spectral estimates with much lower sidelobes and narrower spectral peaks than the FFT method, which is also a special case of the FIR filtering approach. We show that although the APES algorithm yields somewhat wider spectral peaks than the Capon method, the former gives more accurate overall spectral estimates and SAR images than the latter and the FFT method     

Array processing in correlated noise fields based on instrumentalvariables and subspace fitting

Accurate signal parameter estimation from sensor array data is a problem which has received much attention in the last decade. A number of parametric estimation techniques have been proposed in the literature. In general, these methods require knowledge of the sensor-to-sensor correlation of the noise, which constitutes a significant drawback. This difficulty can be overcome only by introducing alternative assumptions that enable separating the signals from the noise. In some applications, the raw sensor outputs can be preprocessed so that the emitter signals are temporally correlated with correlation length longer than that of the noise. An instrumental variable (IV) approach can then be used for estimating the signal parameters without knowledge of the spatial color of the noise. A computationally simple IV approach has recently been proposed by the authors. Herein, a refined technique that can give significantly better performance is derived. A statistical analysis of the parameter estimates is performed, enabling optimal selection of certain user-specified quantities. A lower bound on the attainable error variance is also presented. The proposed optimal IV method is shown to attain the bound if the signals have a quasideterministic character     

Uniqueness of prediction error estimates of multivariable moving average models

In prediction error (PE) identification of the parameter estimates is given by the global minimum of a scalar-valued function of the innovation sample covariance matrix. It may happen that the loss function has multiple local minimum points so that a numerical search routine can fail to find the global minimum. Such a situation, usually referred to as lack of uniqueness of the estimates, was experienced in practice and also theoretically examined for various model structures. A unique minimum of the criterion is also crucial for convergence of recursive PE algorithms. In this paper multivariable moving average (MA) models are considered. It is proved that for such models any reasonable PE criterion has asymptotically a unique stationary point. Furthermore it is shown that this stationary point is a (global) minimum which corresponds to the true parameter vector. This extends the result known for univariate MA models to the multivariate case.     

Materials Based on Quaternized Polysulfones with Potential Applications in Biomedical Field: Structure–Properties Relationship

Starting from the bactericidal properties of functionalized polysulfone (PSFQ) and due to its excellent biocompatibility, biodegradability, and performance in various field, cellulose acetate phthalate (CAP) and polyvinyl alcohol (PVA), as well as their blends (PSFQ/CAP and PSFQ/PVA), have been tested to evaluate their applicative potential in the biomedical field. In this context, because the polymer processing starts from the solution phase, in the first step, the rheological properties were followed in order to assess and control the structural parameters. The surface chemistry analysis, surface properties, and antimicrobial activity of the obtained materials were investigated in order to understand the relationship between the polymers’ structure–surface properties and organization form of materials (fibers and/or films), as important indicators for their future applications. Using the appropriate organization form of the polymers, the surface morphology and performance, including wettability and water permeation, were improved and controlled—these being the desired and needed properties for applications in the biomedical field. Additionally, after antimicrobial activity testing against different bacteria strains, the control of the inhibition mechanism for the analyzed microorganisms was highlighted, making it possible to choose the most efficient polymers/blends and, consequently, the efficiency as biomaterials in targeted applications.     

Zero-Energy-Buildings in Different Climates： Design Strategies,Simulation and Prognosis METHOD for Energy Demand

The European Directive 2010/31 claims that by 2020 only （nearly-） ZEB （zero-energy-buildings） may be built. To reach this goal, it is pertinent for buildings to be energetically optimized first. The remaining energy demand must then be covered by on-site renewable energies （PV, geothermal, etc.）. With the area of use （energy demand） and the size of the building envelope/estate （renewable energy supply） in competition with each other, the maximum number of building stories will be most likely limited. For 15 different climatic locations worldwide, the energy demand of optimised office rooms has been simulated and compared with the possible renewable energy production on site. For every location, a good correlation has been found between the simulated energy demand and data like heating and cooling degree hours. Correspondent linear equations are given here. As another result, the maximum numbers of possible stories for ZEBS have been derived, being between 3 and 10 depending on the location.