Blind Unitary Prewhitening with a Real-Valued Eigendecomposition

Prewhitening is a standard step for the processing of noisy signals. Typically, eigenvalue decomposition (EVD) of the sample data covariance matrix is used to calculate the whitening matrix. From a computational point of view, an important problem here is to reduce the complexity of the EVD of the complex-valued sample data covariance matrix. In this paper, we show that the computational complexity of the prewhitening step for complex-valued signals can be reduced approximately by a factor of four when the real-valued EVD is used instead of the complex-valued one. Such complexity reduction can be achieved for any axis-symmetric array. The performance of the proposed procedure is studied in application to a blind source separation (BSS) problem. For this application, the performance of the proposed prewhitening scheme is illustrated by means of simulations, and compared with the conventional prewhitening scheme. Among a number of BSS methods which use prewhitening, the second-order blind identification procedure has been adopted in this paper.     

Effect of Heat Treatment on the Mechanical Properties of Aluminum Alloy 2219 and its Welded Joints at Cryogenic and Elevated Temperatures

Strength of Materials - The effect of heat treatment on mechanical properties of welded joints of aluminum alloy 2219 for different types of heat treatment (T62, T81, and T851), types of rolled...     

External Quantum Efficiency of Bifacial HIT Solar Cells

Semiconductors - It is shown that the use of both sides of solar cells created with the heterojunction technology makes possible an increase in the solar-cell efficiency. The difference in...     

Maximum likelihood direction-of-arrival estimation in unknown noise fields using sparse sensor arrays

We address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields using sparse sensor arrays composed of multiple widely separated subarrays. In such arrays, intersubarray spacings are substantially larger than the signal wavelength, and therefore, sensor noises can be assumed to be uncorrelated between different subarrays. This leads to a block-diagonal structure of the noise covariance matrix which enables a substantial reduction of the number of nuisance noise parameters and ensures the identifiability of the underlying DOA estimation problem. A new deterministic ML DOA estimator is derived for this class of sparse sensor arrays. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters. In contrast to the analytic concentration used in conventional ML techniques, the implementation of the proposed estimator is based on an iterative procedure, which includes a stepwise concentration of the log-likelihood (LL) function. The proposed algorithm is shown to have a straightforward extension to the case of uncalibrated arrays with unknown sensor gains and phases. It is free of any further structural constraints or parametric model restrictions that are usually imposed on the noise covariance matrix and received signals in most existing ML-based approaches to DOA estimation in spatially correlated noise.     

Blind spatial signature estimation via time-varying user power loading and parallel factor analysis

In this paper, the problem of blind spatial signature estimation using the parallel factor (PARAFAC) analysis model is addressed in application to wireless communications. A time-varying user power loading in the uplink mode is proposed to make the model identifiable and to enable application of PARAFAC analysis. Then, identifiability issues are studied in detail and closed-form expressions for the corresponding modified Crame/spl acute/r-Rao bound (CRB) are obtained. Furthermore, two blind spatial signature estimation algorithms are developed. The first technique is based on the PARAFAC fitting trilinear alternating least squares (TALS) regression procedure, whereas the second one makes use of the joint approximate diagonalization algorithm. These techniques do not require any knowledge of the propagation channel and/or sensor array manifold and are applicable to a more general class of scenarios than earlier approaches to blind spatial signature estimation.     

Computer analysis of the configuration of the magnetic fields of surface discontinuity flaws of finite dimensions in a ferromagnetic plate of finite dimensions by the spatial integral equation method

The spatial integral equation method is used to perform a numerical analysis of the magnetic-field configuration for ferromagnetic objects of finite dimensions having surface discontinuity flaws of arbitrary shape and finite dimensions. The range of application of the software developed for the design of technical devices for inspecting objects of irregular geometry was determined.     

Mathematical simulation of magnetization processes of arbitrarily shaped ferromagnetic test objects in fields of given spatial configurations

On the basis of the method of spatial integral equations and developed software, a technique for simulating magnetization processes of ferromagnetic objects of complex geometric shapes, which are monitored by a magnetic method, has been developed. This procedure takes into account the spatial character of the informative magnetic field and nonlinear magnetic properties of the object material. The probable simulation-error level is estimated.     

Computer analysis of the configurations of magnetic fields of subsurface discontinuities of finite dimensions and arbitrary shapes in tested objects of finite length by the method of spatial integral equations

Using software developed by the authors and based on the method of spatial integral equations, the spatial configurations of the magnetic fields of tested objects of finite length that contain subsurface discontinuity flaws of finite dimensions and arbitrary shapes are studied. It is possible to take into account the influence of both uniform and nonuniform magnetizing fields, including real sources; an arbitrary geometry of tested objects; and a nonlinear character of the magnetic properties of materials. It is shown that software can be used in studies of the influence of the geometrical parameters of a defect on the topography of the informative magnetic field in the testing zone.     

The Undecidability of the First-Order Theories of One Step Rewriting in Linear Canonical Systems

By reduction from the halting problem for Minsky's two-register machines we prove that there is no algorithm capable of deciding the -theory of one step rewriting of an arbitrary finite linear confluent finitely terminating term rewriting system (weak undecidability). We also present a fixed such system with undecidable *-theory of one step rewriting (strong undecidability). This improves over all previously known results of the same kind.