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The time-frequency (TF) representations are used to introduce new methods for the underdetermined blind source separation (UBSS) of TF-nondisjoint nonstationary sources. The advantages of the new separation algorithms include a weaker assumption on the source 'sparseness' and an explicit treatment of the overlapping points using subspace projection, which has improved the performance.

The problem of estimating parameters of multicomponent polynomial-phase signals is addressed when impinging on a multisensor array. It is demonstrated that the use of a multisensor array allows the exploitation of spatial information and leads to the consideration of multiple filters with different observation equations.

The problem of joint direction of arrival and Doppler frequency estimation using an array in partially known additive noise is addressed. A specular reflection model with a radar cross-section fluctuating from one pulse repetition interval to another is considered.

Research was conducted to examine an efficient blind beamformer dedicated to the problem of interference mitigation in direct sequence spread spectrum (DSSS) communication systems by means of a two sensor array. The receiver is implemented in two different structures. One contains the blind beamformer followed by the spread spectrum demodulator. The other has a reversed cascading order where the spread spectrum demodulators are applied at both sensors. The performance of each structure is described based on the achieved signal-to-interference and noise ratio and the computational cost.

A new carrier tracking approach with guaranteed global convergence is presented based on the statistical independence of the real and imaginary parts of the emitted signal. Numerical simulations revealed satisfactory features and performance of the proposed approach. These characteristics allow the new technique to be applied in QAM modems for digital cable TV broadcasting requiring higher level modulation schemes.

A blind identification technique for temporally correlated sources based on a joint diagonalization of several covariance matrices is proposed. The technique capitalizes on the time coherence of the source signals. It allows the separation of Gaussian sources and is more robust due to its use of several covariances. Numerical experiments have proven the benefit of capitalizing on several covariance matrices in complex contexts.

We propose a new blind minimum mean square error (MMSE) equalization algorithm of noisy multichannel finite impulse response (FIR) systems, that relies only on second-order statistics. The proposed algorithm offers two important advantages: a low computational complexity and a relative robustness against channel order overestimation errors. Exploiting the fact that the columns of the equalizer matrix filter belong both to the signal subspace and to the kernel of truncated data covariance matrix, the proposed algorithm achieves blindly a direct estimation of the zero-delay MMSE equalizer parameters. We develop a two-step procedure to further improve the performance gain and control the equalization delay. An efficient fast adaptive implementation of our equalizer, based on the projection approximation and the shift invariance property of temporal data covariance matrix, is proposed for reducing the computational complexity from to , where is the number of emitted signals, the data vector length, and the dimension of the signal subspace. We then derive a statistical performance analysis to compare the equalization performance with that of the optimal MMSE equalizer. Finally, simulation results are provided to illustrate the effectiveness of the proposed blind equalization algorithm.

We examine the problem of blind separation of nonstationary sources in the underdetermined case, where there are more sources than sensors. Since time-frequency (TF) signal processing provides effective tools for dealing with nonstationary signals, we propose a new separation method that is based on time-frequency distributions (TFDs). The underlying assumption is that the original sources are disjoint in the time-frequency (TF) domain. The successful method recovers the sources by performing the following four main procedures. First, the spatial time-frequency distribution (STFD) matrices are computed from the observed mixtures. Next, the auto-source TF points are separated from cross-source TF points thanks to the special structure of these mixture STFD matrices. Then, the vectors that correspond to the selected auto-source points are clustered into different classes according to the spatial directions which differ among different sources; each class, now containing the auto-source points of only one source, gives an estimation of the TFD of this source. Finally, the source waveforms are recovered from their TFD estimates using TF synthesis. Simulated experiments indicate the success of the proposed algorithm in different scenarios. We also contribute with two other modified versions of the algorithm to better deal with auto-source point selection.

In this paper, a novel antenna decoupling approach is investigated. The introduced concept is based on expelling the currents flowing between the closely spaced antennas through a tapered slot etched on the array ground plane. The method is free from extra-reactive components and transmission lines. Although they are closely spaced and strongly coupled, the technique allows a high improvement in port isolation. A practical implementation of this mechanism is applied to a monopole array of two elements working at 2.45 GHz and closely packed ($0.069\lambda _{0}$). The obtained results are very attractive, and a decoupling of about 40 dB is achieved with good impedance matching. Moreover, radiation diversity is obtained with a gain of 1.9 dBi. Furthermore, the decoupled array is integrated in a real receiving system, and the proposed decoupling technique is checked through the array signal processing. Hence, the obtained results confirm the effectiveness of the proposed decoupling mechanism. In addition to its efficient port isolation, the most attractive features of the proposed decoupling technique are simplicity and ease of implementation in compact multiantenna devices. [ABSTRACT FROM AUTHOR]

Word-length allocation is the most important design phase to optimize hardware resources while guaranteeing a determined accuracy for circuits with fixed-point numbers. This paper presents an enhanced precision analysis for degree-n polynomial Horner's rule. It is based on affine arithmetic and introduces an error propagating formula for a degree-n polynomial Horner's rule. It takes into account quantization error of all the circuit's connections including the inputs. Furthermore, a tighter upper bound error is defined, exploiting the dependencies between intermediate connections. Hardware implementations show that the proposed upper bound results in an area reduction that reaches 70 percent. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm to approximate non-orthogonal joint diagonalization (NOJD) of a set of complex matrices. This algorithm is based on the Frobenius norm formulation of the joint diagonalization (JD) problem and takes advantage from combining Givens and Shear rotations to attempt the approximate JD. It represents a non-trivial generalization of the JDi (Joint Diagonalization) algorithm (Souloumiac 2009) to the complex case. The JDi is first slightly modified then generalized to the CJDi (i.e., Complex JDi) using complex to real matrix transformation. Several methods already exist in the literature. Consequently, we provide herein a brief overview of existing NOJD algorithms. Then we provide an extensive comparative study to illustrate the effectiveness and stability of the CJDi with respect to various system parameters and application contexts. [ABSTRACT FROM AUTHOR]

A method for solving blind source separation problems was developed based on a joint diagonalization of a combined set of spatial time-frequency (t-f) distribution (STFD) resulting from t-f distribution to a vector signal. The structure of the STFD resembled the data spatial correlation matrix commonly used in array signal processing. The technique permitted the separation of Gaussian sources with the same spectral forms but with different t-f localization features.

In recent work sea clutter has been modeled as the product of two components. The first one, referred to as speckle, is modeled as a stationary Gaussian process. It is characterized by a short correlation time. The second component, referred to as texture, is modeled as a stationary or cyclostationary process. It is characterized by a long correlation time. Two approaches for the estimation of the texture are introduced here. The first approach involves the correlation properties of texture and speckle. The texture spectrum is modeled as an autoregressive (AR) process, while the texture range profile within one pulse repetition interval (PRI) is identified by combining the AR estimation of the sea clutter sample auto-covariance with the empirical orthogonal functions (EOF) analysis. The second approach involves a mixed $\ell_2$-$\ell_1$ norm minimization criterion to account for the sparse harmonic structure of the texture, considered herein as a cyclostationary process within multiple coherent processing intervals (CPI). [ABSTRACT FROM AUTHOR]

This paper presents two new time-frequency distributions (TFDs) based on kernels with compact support (KCS) namely the separable (CB) (SCB) and the polynomial CB (PCB) TFDs. The implementation of this family of TFDs follows the method developed for the Cheriet–Belouchrani (CB) TFD. The mathematical properties of these three TFDs are analyzed and their performance is compared to the best classical quadratic TFDs using several tests on multicomponent signals with linear and nonlinear frequency modulation (FM) components including the noise effects. Instead of relying solely on visual inspection of the time-frequency domain plots, comparisons include the time slices' plots and the evaluation of the Boashash–Sucic's normalized instantaneous resolution performance measure that permits to provide the optimized TFD using a specific methodology. In all presented examples, the KCS-TFDs show a significant interference rejection, with the component energy concentration around their respective instantaneous frequency laws yielding high resolution measure values. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new system for ECG beat classification using support vector machines classifier with a double hinge loss. The proposed classifier rejects samples that cannot be classified with enough confidence. Specifically in medical diagnoses, the consequence of a wrong classification can be so harmful that it is convenient to reject such sample. After ECG preprocessing, feature selection and extraction, our decision rule uses dynamic reject thresholds according to the cost of rejecting or misclassifying a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrythmia database. The achieved results are represented by the error reject tradeoff. We obtained 98.2% of sensitivity with no rejection and more than 99% of sensitivity for the optimal classification cost being competitive to other published studies. [ABSTRACT FROM AUTHOR]

The Arctangent function is used in several signal processing applications and it is widely needed for hardware implementations. However, it often constitutes the performance bottleneck because of its inaccuracy and/or its poor performances. We present a generic VLSI design to respond to some applications' need of fast Arctangent functions handling high precision data. The method presented here contrasts with the usual Look Up table based solutions which allow the output customization but limit the precision of the corresponding tangent input. Detailed logical design and FPGA implementation results are provided to show our solution as the best candidate to comply with fast execution as well as high precision for both input and output data. [ABSTRACT FROM AUTHOR]

This paper deals with fault diagnosis of induction machines based on the discrete wavelet transform. By using the wavelet decomposition, the information on the health of a system can be extracted from a signal over a wide range of frequencies. This analysis is performed in both time and frequency domains. The Daubechies wavelet is selected for the analysis of the stator current. Wavelet components appear to be useful for detecting different electrical faults. In this paper, we will study the problem of broken rotor bars, end-ring segment, and loss of stator phase during operation. [ABSTRACT FROM AUTHOR]

This paper considers the blind separation of nonstationary sources in the underdetermined case, when there are more sources than sensors. A general framework for this problem is to work on sources that are sparse in some signal representation do- main. Recently, two methods have been proposed with respect to the time-frequency (TF) domain. The first uses quadratic time-frequency distributions (TFDs) and a clustering approach, and the second uses a linear TFD. Both of these methods assume that the sources are disjoint in the TF domain; i.e., there is, at most, one source present at a point in the TF domain. In this paper, we relax this assumption by allowing the sources to be TF-nondisjoint to a certain extent. In particular, the number of sources present at a point is strictly less than the number of sensors. The separation can still be achieved due to subspace projection that allows us to identify the sources present and to estimate their corresponding TFD values. In particular, we propose two subspace-based algorithms for TF-nondisjoint sources: one uses quadratic TFDs and the other a linear TFD. Another contribution of this paper is a new estimation procedure for the mixing matrix. Finally, then numerical performance of the proposed methods are provided highlighting their performance gain compared to existing ones. [ABSTRACT FROM AUTHOR]

This paper describes a method for the classification of heartbeats based on electrocardiogram QRS complex and T wave autoregressive (AR) features and interbeat distance (RR interval) features. The QRS complex (length n samples) is modelled using AR model of order P. A second AR model of order P1 is applied to the electrocardiogram segment including ST segment and T wave (ST/T segment) with a length of n1 samples. In addition, RR interval features such as the distance between one heartbeat and its preceding (pre-RR) and following (post-RR) heartbeats are extracted. The use of AR modelling is motivated by the fact that QRS complex and ST/T segment corresponding to normal beats present different patterns (in shapes and amplitudes) than those corresponding to ventricular ectopic beats. Lengths and AR model orders that minimize the number of misclassifications are determined. Obtained features are used to train and test support vector machine classifier. The proposed method is tested on MIT/BIH Arrhythmia Database and it is compared to state of the art methods. Overall classification accuracy reached 97.02%, whereas it reached 98.86% in the case of subject-specific scheme. Results are better or comparable to those obtained by state of the art methods. [ABSTRACT FROM AUTHOR]