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Now showing items 1 - 16 of 40

  • Low Cost Sparse Subspace Tracking Algorithms

    Lassami, Nacerredine   Aïssa-El-Bey, Abdeldjalil   Abed-Meraim, Karim  

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  • A new detection method for EMG activity monitoring

    Bengacemi, Hichem   Abed-Meraim, Karim   Buttelli, Olivier   Ouldali, Abdelaziz   Mesloub, Ammar  

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  • Generalized Minimum Noise Subspace For Array Processing

    Viet-Dung Nguyen   Abed-Meraim, Karim   Nguyen Linh-Trung   Weber, Rodolphe  

    Based on theminimum noise subspace (MNS) method previously introduced in the context of blind channel identification, generalized minimum noise subspace (GMNS) is proposed in this paper for array processing that generalizes MNS with respect to the availability of only a fixed number of parallel computing units. Different batch and adaptive algorithms are then introduced for fast and parallel computation of signal (principal) and noise (minor) subspaces. The computational complexity of GMNS and its related estimation accuracy are investigated by simulated experiments and a real-life experiment in radio astronomy. It is shown that GMNS represents an excellent tradeoff between the computational gain and the subspace estimation accuracy, as compared to several standard subspace methods.
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  • A method for the automated detection of solar radio bursts in dynamic spectra

    Salmane, Houssam   Weber, Rodolphe   Abed-Meraim, Karim   Klein, Karl-Ludwig   Bonnin, Xavier  

    The variability of the solar corona, including flares and coronal mass ejections, affects the space environment of the Earth (heating and ionization of the atmosphere, magnetic field disturbances, and bombardment by high-energy particles). Electromagnetic emissions are the first signatures of a solar eruptive event which by modifying the electron density in the ionosphere may affect airborne technology and radio communications systems. In this paper, we present a new method to detect automatically radio bursts using data from the Nancay Decametre Array (NDA) in the band 10 MHz-80 MHz. This method starts with eliminating unwanted signals (Radio-Frequency Interference, RFI and Calibration signals) by analyzing the dynamic spectrum of the signal recorded in time. Then, a gradient median filter is applied to smooth and to reduce the variability of the signal. After denoising the signal, an automated solar radio burst detection system is applied. This system is based on a sequential procedure with adaptive constant-false-alarm rate (CFAR like detector) aimed to extract the spectra of major solar bursts. To this end, a semi-automatic software package is also developed to create a data base of all possible events (type II, III, IV or other) that could be detected and used for our performance assessment.
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  • Robust, Blind Multichannel Image Identification And Restoration Using Stack Decoder

    Boudjenouia, Fouad   Jennane, Rachid   Chetouani, Aladine   Abed-meraim, Karim  

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  • Multi-channel EEG epileptic spike detection by a new method of tensor decomposition

    Thanh, Le Trung   Dao, Nguyen Thi Anh   Dung, Nguyen Viet   Trung, Nguyen Linh   Abed-Meraim, Karim  

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  • Reduction of Buffering Requirements: Another Advantage of Cooperative Transmission

    Bader, Ahmed   Abed-Meraim, Karim   Alouini, Mohamed-Slim  

    Yet another advent of cooperative transmission is exposed in this letter. It is shown that cooperation lends itself to the reduction of buffer sizes of wireless sensor nodes. It is less likely to find the channel busy when cooperative transmission is employed in the network. Otherwise, in the lack of cooperation, the probability of build up of packet queues in transmission buffers increases.
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  • Time frequency and array processing of non-stationary signals

    Belouchrani, Adel   Abed-Meraim, Karim   Boashash, Boualem  

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  • Blind source separation for robot audition using fixed HRTF beamforming

    Maazaoui, Mounira   Abed-Meraim, Karim   Grenier, Yves  

    In this article, we present a two-stage blind source separation (BSS) algorithm for robot audition. The first stage consists in a fixed beamforming preprocessing to reduce the reverberation and the environmental noise. Since we are in a robot audition context, the manifold of the sensor array in this case is hard to model due to the presence of the head of the robot, so we use pre-measured head related transfer functions (HRTFs) to estimate the beamforming filters. The use of the HRTF to estimate the beamformers allows to capture the effect of the head on the manifold of the microphone array. The second stage is a BSS algorithm based on a sparsity criterion which is the minimization of the l (1) norm of the sources. We present different configuration of our algorithm and we show that it has promising results and that the fixed beamforming preprocessing improves the separation results.
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  • Quasi-Convexity of the Asymptotic Channel MSE in Regularized Semi Blind Estimation

    Kammoun, Abla   Abed-Meraim, Karim   Affes, Sofiene  

    In this paper, the quasi-convexity of a sum of quadratic fractions in the form Sigma(n)(i=1) 1+c(i)x(2)/(1+d(i)x)(2) is demonstrated where c(i) and d(i) are strictly positive scalars, when defined on the positive real axis R(+). It will be shown that this quasi-convexity guarantees it has a unique local (and hence global) minimum. Indeed, this problem arises when considering the optimization of the weighting coefficient in regularized semi-blind channel identification problem, and more generally, is of interest in other contexts where we combine two different estimation criteria. Note that V. Buchoux et al. have noticed by simulations that the considered function has no local minima except its unique global minimum but this is the first time this result, as well as the quasi-convexity of the function is proved theoretically.
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    Gazzah, Houcem   Abed-Meraim, Karim  

    Based on the CRB of the 2D-DOA estimation problem, we prove a condition on the sensor coordinates of a planar array to be ambiguity-free and isotropic. A systematic search of such antenna arrays is conducted leading to the identification of all possible ambiguity-free isotropic arrays. In particular, we select the arrays that outperform the popular Uniform Circular Array (UCA). It is shown that these arrays allow to enhance the DOA estimation by as much as 25%, in comparison with UCA. As the number of sensors increases, the best isotropic array tends towards the non-intuitive V-shape.
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    Kammoun, Abla   Abed-Meraim, Karim   Affes, Sofiene  

    In semi-blind channel estimation techniques, the choice of the regularizing parameter that weights the blind criterion when linearly combined to the training-based least square criterion has a great impact on channel estimation performance. If a scalar regularization is considered, it has been noted that the optimal value of the regularizing factor has no closed-form expression. In a recent work, we proved that by using a regularization matrix instead, we not only enhance the performance but also can determine a closed-form expression for the optimal regularizing matrix that minimizes the asymptotic mean-square-error of the channel estimate. In this paper, we generalize our work to the context of Multiple-Input-Multiple-Output-Orthogonal-Frequency-Division-Multiplexin g (MIMO-OFDM). As an application, we propose to make a performance comparison between linear prediction and subspace semi-blind estimators. In particular, we assess by simulations the accuracy of the derived results and investigate the Bit Error Rate performance as well as the impact of channel overmodeling.
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  • A New Look to Multichannel Blind Image Deconvolution

    Souidene, Wided   Abed-Meraim, Karim   Beghdadi, Azeddine  

    The aim of this paper is to propose a new look to MBID, examine some known approaches, and provide a new MC method for restoring blurred and noisy images. First, the direct image restoration problem is briefly revisited. Then a new method based on inverse filtering for perfect image restoration in the noiseless case is proposed. The noisy case is addressed by introducing a regularization term into the objective function in order to avoid noise amplification. Second, the filter identification problem is considered in the MC context. A new robust solution to estimate the degradation matrix filter is then derived and used in conjunction with a total variation approach to restore the original image. Simulation results and performance evaluations using recent image quality metrics are provided to assess the effectiveness of the proposed methods.
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  • Cross Psi(B)-energy operator-based signal detection RID F-4185-2010

    Boudraa, Abdel-Ouahab   Cexus, Jean-Christophe   Abed-Meraim, Karim  

    In this paper, two methods for signal detection and time-delay estimation based on the cross Psi(B)-energy operator are proposed. These methods are well suited for mono-component AM-FM signals. The Psi(B) energy operator measures how much one signal is present in another one. The peak of the Psi(B) operator corresponds to the maximum of interaction between the two signals. Compared to the cross-correlation function, the Psi(B) operator includes temporal information and relative changes of the signal which are reflected in its first and second derivatives. The discrete version of the continuous-time form of the Psi(B) operator, which is used in its implementation, is presented. The methods are illustrated on synthetic and real signals and the results compared to those of the matched filter and the cross correlation. The real signals correspond to impulse responses of buried objects obtained by active sonar in iso-speed single path environments. (C) 2008 Acoustical Society of America.
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  • Performance Bounds Analysis for Semi-Blind Channel Estimation in MIMO-OFDM Communications Systems

    Ladaycia, Abdelhamid   Mokraoui, Anissa   Abed-Meraim, Karim   Belouchrani, Adel  

    Most communications systems require channel estimation for equalization and symbol detection. Currently, this is achieved by using dedicated pilot symbols, which consume a non-negligible part of the throughput and power resources, especially for large dimensional systems. The main objective of this paper is to quantify the rate of reduction of this overhead due to the use of a semi-blind channel estimation. Different data models and different pilot design schemes have been considered in this paper. By using the Cramer Rao Bound (CRB) tool, the estimation error variance bounds of the pilot-based and semi-blind based channel estimators for a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system are compared. In particular, for large MIMO-OFDM systems, a direct computation of the CRB is prohibitive, and hence, a dedicated numerical technique for its fast computation has been developed. Many key observations have been made from this comparative study. The most important one is that, thanks to the semi-blind approach, one can skip about 95% of the pilot samples without affecting the channel estimation quality.
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  • Higher order tensor-based method for delayed exponential fitting RID F-4185-2010

    Boyer, Remy   De Lathatrwer, Lieven   Abed-Meraim, Karim  

    We present subspace-based schemes for the estimation of the poles (angular frequencies and damping factors) of a sum of damped and delayed sinusoids. In our model, each component is supported over a different time frame, depending on the delay. parameter. Classical subspace-based methods are not suite to handle signals with varying time supports. In this contribution, we propose solutions based on the approximation of a partially structured Hankel-type tensor on which the data are mapped. We show, by means of several examples, that the approach based on the best rank-(R-1, R-2, R-3) approximation of the data tensor out-' performs the current tensor and matrix-based techniques in terms of the accuracy of the angular frequency and damping factor parameter estimates, especially in the context of difficult scenarios as in the low signal-to-noise ratio regime and for closely spaced sinusoids.
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