The bootstrap and its application in signal processing

The bootstrap is an attractive tool for assessing the accuracy of estimators and testing hypothesis for parameters where conventional techniques are not valid, such as in small data-sample situations. We highlight the motivations for using the bootstrap in typical signal processing applications and give several practical examples. Bootstrap methods for testing statistical hypotheses are described and we provide an analysis of the accuracy of bootstrap tests. We also discuss how the bootstrap can be used to estimate a variance-stabilizing transformation to define a pivotal statistic, and we demonstrate the use of the bootstrap for constructing confidence intervals for flight parameters in a passive acoustic emission problem     

On tasks synchronization with the MMS protocol

The MMS protocol is a powerful tool for communication as well as for synchronization between manufacturing equipment such as robots, programmable logic controllers, etc. This paper shows how the MMS services can be used to handle tasks synchronization in a distributed environment. The MMS weaknesses regarding temporal aspects and possible directions to alleviate these weaknesses are also presented.     

Clinical and Molecular Characterization of Patients with Mucopolysaccharidosis Type I in an Algerian Series

Mucopolysaccharidoses (MPS’s) represent a subgroup of lysosomal storage diseases related to a deficiency of enzymes that catalyze glycosaminoglycans degradation. Mucopolysaccharidosis type I (MPS I) is a rare autosomal recessive disorder caused by a deficiency of α-l-iduronidase encoded by the IDUA gene. Partially degraded heparan sulfate and dermatan sulfate accumulate progressively and lead to multiorgan dysfunction and damage. The aim of this study is to describe the clinical, biochemical, and molecular characteristics of 13 Algerian patients from 11 distinct families. MPS I diagnosis was confirmed by molecular study of the patients’ IDUA gene. Clinical features at the diagnosis and during the follow-up are reported. Eighty-four percent of the studied patients presented with a mild clinical phenotype. Molecular study of the IDUA gene allowed the characterization of four pathological variations at the homozygous or compound heterozygote status: IDUA NM_00203.4:c.1598C>G-p.(Pro533Arg) in 21/26 alleles, IDUA NM_00203.4:c.532G>A-p.(Glu178Lys) in 2/26 alleles, IDUA NM_00203.4:c.501C>G-p.(Tyr167*) in 2/26 alleles, and IDUA NM_00203. 4: c.1743C>G-p.(Tyr581*) in 1/26 alleles. This molecular study unveils the predominance of p.(Pro533Arg) variation in our MPS I patients. In this series, the occurrence of some clinical features linked to the Scheie syndrome is consistent with the literature, such as systematic valvulopathies, corneal opacity, and umbilical hernia; however, storage signs, facial dysmorphic features, and hepatomegaly were more frequent in our series. Screening measures for these debilitating diseases in highly consanguineous at-risk populations must be considered a priority health problem.     

On the performance of cyclic moments-based parameter estimators ofamplitude modulated polynomial phase signals

Parameter estimation of amplitude-modulated polynomial phase signals embedded in additive white Gaussian noise is considered. The amplitude modulation is modeled as the sum of a real-valued deterministic function and a zero mean correlated stationary random process. It is shown that cyclic moments-based estimators, previously proposed for parameter estimation of polynomial phase signals modulated by stationary random processes, can be adapted to the more general signal model considered here. The covariance matrices of the cyclic moments-based amplitude and phase parameter estimators are derived for large sample lengths. Using this result, it is shown how the lags can be chosen to minimize the large-sample variances of the cyclic moments-based phase parameter estimators. Comparisons with the Cramer-Rao bounds are performed under the assumption of a Gaussian modulating process. The theoretical derivations are confirmed by simulation results     

Target Detection in Single- and Multiple-View Through-the-Wall Radar Imaging

A detector of targets behind walls and in enclosed structures is presented. The detector is applied to through-the-wall radar images obtained by wideband delay and sum beamforming. We consider the detection problem using single- and multiple-view imaging. The statistics of noise, clutter, and target images are examined and formulated using sample scenes. The effects of wall parameter errors on the image statistics are shown. An iterative detection scheme, which adapts itself to the image statistics, is presented. The proposed detection schemes are evaluated using real data.     

Estimation of SiC JFET temperature during short-circuit operations

This paper presents results showing the robustness of different SiC JFET transistors from SiCED in current limitation regime or short-circuit operation. Crystal temperature during failure was estimated after different electrical characterizations and using appropriate models of saturation current which is used as a thermal indicator. This work shows the exceptional robustness of SiC JFET transistors in current limitation mode compared to Si devices (MOSFETS and IGBTs).     

Efficient Subspace-Based Estimator for Localization of Multiple Incoherently Distributed Sources

In this paper, a new subspace-based algorithm for parametric estimation of angular parameters of multiple incoherently distributed sources is proposed. This approach consists of using the subspace principle without any eigendecomposition of the covariance matrix, so that it does not require the knowledge of the effective dimension of the pseudosignal subspace, and therefore the main difficulty of the existing subspace estimators can be avoided. The proposed idea relies on the use of the property of the inverse of the covariance matrix to exploit approximately the orthogonality property between column vectors of the noise-free covariance matrix and the sample pseudonoise subspace. The resulting estimator can be considered as a generalization of the Pisarenko's extended version of Capon's estimator from the case of point sources to the case of incoherently distributed sources. Theoretical expressions are derived for the variance and the bias of the proposed estimator due to finite sample effect. Compared with other known methods with comparable complexity, the proposed algorithm exhibits a better estimation performance, especially for close source separation, for large angular spread and for low signal-to-noise ratio.