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In this paper, we propose a low-complexity decision-directed channel-estimation scheme, which offers improved performance for space-frequency-block-coded orthogonal frequency-division multiplexing (SFBC-OFDM) systems under doubly selective (i.e., frequency selective and fast fading) channels. First, we propose a square-root-free inverse-QR-decomposition-based groupwise recursive (SIQR-GR) channel-estimation algorithm employing sequences of scaled Givens rotations. The proposed SIQR-GR scheme recursively processes data decisions corresponding to the SFBC groups within each OFDM block to improve system performance under doubly selective channels. Furthermore, the avoidance of square roots and the utilization of scaled Givens rotations ensure that the proposed SIQR-GR scheme is computationally efficient. Second, we derive semi-analytical expressions for the normalized mean square error (NMSE) performance of SIQR-GR and verify the accuracy of the NMSE analysis via numerical simulations. Finally, we provide performance and complexity comparisons between SIQR-GR and previously proposed schemes. These comparisons show an excellent performance-complexity tradeoff achieved by SIQR-GR over the previous solutions under various channel conditions. [ABSTRACT FROM AUTHOR]

Abstract-Subspace-based estimation of multiple real-valued sine wave frequencies is considered in this paper. A novel data covariance model is proposed. In the proposed model, the dimension of the signal subspace equals the number of frequencies present in the data, which is half of the signal. subspace dimension for the conventional model. Consequently, an ESPRIT-like algorithm using the proposed data model is presented. The proposed algorithm is then extended for the case of complex-valued sine waves. Performance analysis of the proposed algorithms are also carried out. The algorithms are tested in numerical simulations. When compared with ESPRIT, the newly proposed algorithm results in a significant reduction in computational burden without any compromise in the accuracy. [ABSTRACT FROM AUTHOR]

Abstract: In the framework of dependent risks it is a crucial task for risk management purposes to quantify the probability that the aggregated risk exceeds some large value . Motivated by Asmussen et al. (2011) [1] in this paper we introduce a modified Asmussen–Kroese estimator for simulation of the rare event that the aggregated risk exceeds . We show that in the framework of log-Gaussian risks our novel estimator has the best possible performance i.e., it has asymptotically vanishing relative error. For the more general class of log-elliptical risks with marginal distributions in the Gumbel max-domain of attraction we propose a modified Rojas-Nandayapa estimator of the rare events of interest, which for specific importance sampling densities has a good logarithmic performance. Our numerical results presented in this paper demonstrate the excellent performance of our novel Asmussen–Kroese algorithm. [Copyright &y& Elsevier]

Abstract: This paper describes a numerical procedure for the blank shape design of thin metallic parts obtained by stamping. The objective is to determine the initial blank shape knowing the geometry of the desired 3D CAD part. The numerical procedure consists of two stages: At first, an estimation of the initial blank shape is given using the one step inverse approach (IA). Then, update of the blank shape is continued by iterations combining optimization algorithms and finite element analysis (FEA). The numerical procedure for the blank shape design is tested in the case of an industrial stamping process where the part is formed using a manual press without blank-holder. The proposed numerical procedure can provide very quickly the optimal blank shape in a few iterations. [Copyright &y& Elsevier]

Abstract: We introduce new criteria for model discrimination and use these and existing criteria to evaluate standard orthogonal designs. We show that the capability of orthogonal designs for model discrimination is surprisingly varied. In fact, for specified sample sizes, number of factors, and model spaces, many orthogonal designs are not model discriminating by the definition given in this paper, while others in the same class of orthogonal designs are. We also use these criteria to construct optimal two-level model-discriminating designs for screening experiments. The efficacy of these designs is studied, both in terms of estimation efficiency and discrimination success. Simulation studies indicate that the constructed designs result in substantively higher likelihoods of identifying the correct model. [Copyright &y& Elsevier]

CG, SYMMLQ, and MINRES are Krylov subspace methods for solving symmetric systems of linear equations. When these methods are applied to an incompatible system (that is, a singular symmetric least-squares problem), CG could break down and SYMMLQ's solution could explode, while MINRES would give a learnt-squares solution but not necessarily the minimum-length (pseudoinverse) solution. This understanding motivates us to design a MINRES-like algorithm to compute minimum-length solutions to singular symmetric systems. MINRES uses QR factors of the tridiagonal matrix from the Lanczos process (where R is upper-tridiagonal). MINRES-QLP uses a QLP decomposition (where rotations on the right reduce R to lower-tridiagonal form). On ill-conditioned systems (singular or not), MINRES-QLP can give more accurate solutions than MINRES. We derive preconditioned MINRES-QLP, new stopping rules, and better estimates of the solution and residual norms, the matrix norm, and the condition number. [ABSTRACT FROM AUTHOR]

This study deals with the estimation of amplitude and phase of an analogue multi-harmonic band-limited signal from irregularly spaced sampling values. To this end, assuming the signal fundamental frequency is known in advance (i.e. estimated at an independent stage), a complexity-reduced algorithm for signal reconstruction in time domain is proposed. The reduction in complexity is achieved owing to completely new analytical and summarised expressions that enable a quick estimation at a low numerical error. The proposed algorithm can be applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The proposed method of processing can be used for precise root mean square measurements (for power and energy) of a periodic signal based on the presented signal reconstruction. The authors investigate the errors related to the signal parameter estimation, and there is a computer simulation that demonstrates the accuracy of these algorithms. [ABSTRACT FROM AUTHOR]

The problem of estimating the parameters of a noisy autoregressive (AR) signal is considered. We propose a new least-squares (LS) method for estimating AR parameters that uses both low-order and high-order Yule-Walker equations in a new way. This estimate is biased. We derive a new method for noise variance estimation to yield unbiased LS estimate of the AR parameters. To evaluate the performance of the proposed method, computer simulations are performed. Simulation results illustrate that the performance of the proposed method is much better than the other estimation methods. [ABSTRACT FROM AUTHOR]

We consider the uplink channel estimation of a multipath wireless channel used for orthogonal frequency division multiple access (OFDMA) transmission, where the uplink uses a pseudo-random "tile" allocation pattern. A tile is made of small number of physically adjacent data subcarriers along with a few embedded pilot subcarriers and an uplink sub-channel allocated to an user in OFDMA systems such as IEEE 802.16d/e wireless MAN consists of several such pseudo-randomly chosen tiles. While the embedded pilots enable intra-tile channel interpolation, such an estimation will have an error floor which degrades performance substantially for highly frequency selective channels. We propose a parametric channel estimation method applicable to such irregular and sparsely spaced pilots, that does not exhibit an error-floor over the nominal operating range of signal to noise ratios, even for highly selective channels. The proposed algorithm exploits the pilot structure in each tile in estimating the delay subspace corresponding to the parametric channel description. Although this algorithm is more computationally complex when compared to the intra-tile linear interpolator, it offers a greatly enhanced bit-error probability (BEP) performance with a significantly lower pilot overhead. The uncoded BEP expression for the proposed estimator are analytically derived. Simulation results provided compares the mean squared error performance of this parametric channel estimator with the Cramer-Rao bound and also illustrates the significantly improved BEP performance over the existing methods. [ABSTRACT FROM AUTHOR]