Search results for: Shahram Hosseini

Items from 1 to 20 out of 45 results

article

A new modified Bessel‐type radial basis function for meshless methods: Utilized in the analysis of free vibration in 2D functionally graded Euler–Bernoulli beams

Shahram Hosseini, Fatemeh Abbaspour, Romina Nazari

International Journal of Numerical Modelling: Electronic Networks, Devices... > 37 > 5 > n/a - n/a

Radial basis functions (RBFs) are extensively employed in mesh‐free methods owing to their distinct properties. This study presents a novel RBF formulation based on a modified first‐kind Bessel function, introduced for the first time. The efficacy and precision of the proposed function are assessed through an examination of the free vibrations of Euler–Bernoulli beams composed of two‐directional functionally...

article

A meshless collocation method on nonlinear analysis of functionally graded hyperelastic plates using radial basis function

Shahram Hosseini, Gholamhossein Rahimi, Davoud Shahgholian‐Ghahfarokhi

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für... > 102 > 2 > n/a - n/a

In the present work, the nonlinear static analysis of functionally graded hyperelastic thin plate is investigated. The neo‐Hookean strain energy function and right Cauchy‐Green tensor are utilized to formulate the potential energy function. Also, the first‐order shear deformation theory of the plate is used to form the strain‐displacement relations. The strong form of the governing equations is derived...

chapter

A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

Yasmine Kheira Benkouider, Moussa Sofiane Karoui, Yannick De ville, Shahram Hosseini

2017 25th European Signal Processing Conference (EUSIPCO) > 483 - 487

2017 25th European Signal Processing Conference (EUSIPCO)

In these investigations, a novel algorithm is proposed for linearly unmixing hyperspectral images combined with multispectral data. This algorithm, which is used to unmix the considered hyperspectral image, is founded on nonnegative matrix factorization. It minimizes, with new multiplicative update rules, a novel cost function, which includes multispectral data and a spectral degradation model between...

article

A second-order blind source separation method for bilinear mixtures

Lina Jarboui, Yannick Deville, Shahram Hosseini, Rima Guidara, more

Multidimensional Systems and Signal Processing > 2018 > 29 > 3 > 1153-1172

In this paper, we are interested in the problem of Blind Source Separation using a Second-order Statistics (SOS) method in order to separate autocorrelated and mutually independent sources mixed according to a bilinear (BL) model. In this context, we propose a new approach called Bilinear Second-order Blind Source Separation, which is an extension of linear SOS methods, devoted to separate sources...

chapter

Modified nonnegative matrix factorization for endmember spectra extraction from highly mixed hyperspectral images combined with multispectral data

Moussa Sofiane Karoui, Shahram Hosseini, Yannick Deville, Abdelaziz Ouamri, more

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) > 1572 - 1576

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

In this paper, a new approach is proposed for linear endmember spectra extraction from a highly mixed hyperspectral image combined with high spatial resolution multispectral data containing pure pixels. This new approach, which is applied to unmix the considered hyperspectral image, is based on a modified version of nonnegative matrix factorization (NMF) coupled with nonnegative least squares (NLS)...

chapter

Blind Separation of Nonstationary Sources by Spectral Decorrelation

Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Independent Component Analysis and Blind Signal Separation > Linear Mixture Models > 279-286

This paper demonstrates and exploits some interesting frequency-domain properties of nonstationary signals. Considering these properties, two new methods for blind separation of linear instantaneous mixtures of mutually uncorrelated, nonstationary sources are proposed. These methods are based on spectral decorrelation of the sources. The second method is particularly important because it allows the...

chapter

Blind Separation of Non-stationary Images Using Markov Models

Rima Guidara, Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Independent Component Analysis and Signal Separation > Miscellaneous > 722-729

In recent works, we presented a blind image separation method based on a maximum likelihood approach, where we supposed the sources to be stationary, spatially autocorrelated and following Markov models. To make this method more adapted to real-world images, we here propose to extend it to non-stationary image separation. Two approaches, respectively based on blocking and kernel smoothing, are then...

chapter

Mixing model in slitless spectroscopy and resulting blind methods for separating galaxy spectra

Ahmed Selloum, Shahram Hosseini, Yannick Deville, Thierry Contini

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) > 1 - 6

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

We first derive a mathematical model describing data generated by the spectrograph of the future Euclid space mission. Our model takes into account the special mixing effect resulting from slitless spectroscopy. We then propose two new blind source separation methods, exploiting the non-negativity of data involved in these mixtures and adapted to this special model, to separate the spectra from the...

chapter

Extending NMF to blindly separate linear-quadratic mixtures of uncorrelated sources

Shahram Hosseini, Yannick Deville, Leonardo T. Duarte, Ahmed Selloum

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) > 1 - 6

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

This paper proposes a new constrained method, based on nonnegative matrix factorization, for blindly separating linear-quadratic (LQ) mixtures of mutually uncorrelated source signals when the sources and mixing parameters are all nonnegative. The uncorrelatedness of the sources is used as a regularization term in the cost function. The main advantage of exploiting uncorrelatedness in this manner is...

chapter

Blind separation of linear-quadratic mixtures of real sources using a recurrent structure

Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Artificial Neural Nets Problem Solving Methods > Sources Separation > 241-248

In this paper, we propose an approach for separating linearquadratic mixtures of independent real sources. The method is based on parametric identification of a recurrent separating structure by means of an adaptive algorithm which uses the higher-order statistics of the outputs of this structure. We study the local stability of the recurrent structure and show experimentally that when it is stable...

chapter

Blind Operation of a Recurrent Neural Network for Linear-Quadratic Source Separation: Fixed Points, Stabilization and Adaptation Scheme

Yannick Deville, Shahram Hosseini

Lecture Notes in Computer Science > Latent Variable Analysis and Signal Separation > Theory > 237-244

Retrieving unknown sources from nonlinear mixtures of them requires one to define a separating structure, before proceeding to methods for estimating mixing or separating parameters in blind configurations. Recurrent neural networks are attractive separating structures for a wide range of nonlinear mixing models. In a previous paper, we proposed such a network for the non-blind version of linear-quadratic...

chapter

Blind Separation of Convolutive Mixtures of Non-stationary Sources Using Joint Block Diagonalization in the Frequency Domain

Hicham Saylani, Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Latent Variable Analysis and Signal Separation > Convolutive Signal Separation > 97-105

We recently proposed a new method based on spectral decorrelation for blindly separating linear instantaneous mixtures of non-stationary sources. In this paper, we propose a generalization of this method to FIR convolutive mixtures using a second-order approach based on block-diagonalization of covariance matrices in the frequency domain. Contrary to similar time or time-frequency domain methods,...

chapter

Markovian Source Separation in Post-nonlinear Mixtures

Anthony Larue, Christian Jutten, Shahram Hosseini

Lecture Notes in Computer Science > Independent Component Analysis and Blind Signal Separation > Nonlinear ICA and BSS > 702-709

In linear mixtures, priors, like temporal coloration of the sources, can be used for designing simpler and better algorithms. Especially, modeling sources by Markov models is very efficient, and Markov source separation can be achieved by minimizing the conditional mutual information [1,2]. This model allows to separate temporally colored Gaussian sources. In this paper, we extend this result for...

chapter

Stable Higher-Order Recurrent Neural Network Structures for Nonlinear Blind Source Separation

Yannick Deville, Shahram Hosseini

Lecture Notes in Computer Science > Independent Component Analysis and Signal Separation > Algorithms > 161-168

This paper concerns our general recurrent neural network structures for nonlinear blind source separation, especially suited to polynomial mixtures. We here focus on linear-quadratic mixtures. We introduce an extended structure, with additional free parameters as compared to the structure that we previously proposed. We derive the equilibrium points of our new structure, thus showing that it has no...

chapter

Blind Maximum Likelihood Separation of a Linear-Quadratic Mixture

Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Independent Component Analysis and Blind Signal Separation > Nonlinear ICA and BSS > 694-701

We proposed recently a new method for separating linear-quadratic mixtures of independent real sources, based on parametric identification of a recurrent separating structure using an ad hoc algorithm. In this paper, we develop a maximum likelihood approach providing an asymptotically efficient estimation of the model parameters. A major advantage of this method is that the explicit form of the inverse...

chapter

Neural network modeling of ambulatory systolic blood pressure for hypertension diagnosis

Shahram Hosseini, Christian Jutten, Sylvie Charbonnier

Lecture Notes in Computer Science > Artificial Neural Nets Problem Solving Methods > Medical Applications > 599-606

A neural network model is used for estimating Ambulatory Systolic Blood Pressure (ASBP) variations from corporal acceleration and heart rate measurements. The temporal correlation of the estimation residual, modeled by a first order autoregressive (AR) process, is used for training the neural network in a maximum likelihood framework, which yields a better estimation performance. As data are collected...

chapter

Markovian Blind Image Separation

Shahram Hosseini, Rima Guidara, Yannick Deville, Christian Jutten

Lecture Notes in Computer Science > Independent Component Analysis and Blind Signal Separation > Algorithms and Architectures > 106-114

We recently proposed a markovian image separation method. The proposed algorithm is however very time consuming so that it cannot be applied to large-size real-world images. In this paper, we propose two major modifications i.e. utilization of a low-cost parametric score function estimator and derivation of a modified equivariant version of Newton-Raphson algorithm for solving the estimating equations...

chapter

Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation

Hicham Saylani, Shahram Hosseini, Yannick Deville

Lecture Notes in Computer Science > Independent Component Analysis and Signal Separation > Algorithms and Architectures > 322-329

In this paper, we propose a new approach for blind separation of noisy, over-determined, linear instantaneous mixtures of non-stationary sources. This approach is an extension of a new method based on spectral decorrelation that we have recently proposed. Contrary to classical second-order blind source separation (BSS) algorithms, our proposed approach only requires the non-stationary sources and...

chapter

Weight freezing in constructive neural networks: A novel approach

Shahram Hosseini, Christian Jutten

Lecture Notes in Computer Science > Engineering Applications of Bio-Inspired Artificial Neural Networks > 11-20

Constructive algorithms can be classified in two main groups: freezing and non-freezing, each one having its own advantages and inconveniences. In large scale problems, freezing algorithms are more suitable thanks to their speed. The main problem of these algorithms, however, comes from the fixed-size nature of the new units that they use. In this paper, we present a new freezing algorithm which constructs...

chapter

Differential Fast Fixed-Point BSS for Underdetermined Linear Instantaneous Mixtures

Yannick Deville, Johanna Chappuis, Shahram Hosseini, Johan Thomas

Lecture Notes in Computer Science > Independent Component Analysis and Blind Signal Separation > Algorithms and Architectures > 48-56

This paper concerns underdetermined linear instantaneous blind source separation (BSS), i.e. the case when the number P of observed mixed signals is lower than the number N of sources. We propose a partial BSS method, which separates P supposedly non-stationary sources of interest one from the others (while keeping residual components for the other N – P, supposedly stationary, “noise” sources). This...

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BLIND SOURCE SEPARATION (14)
HYPERSPECTRAL IMAGING (9)
MATHEMATICAL MODEL (7)
SOURCE SEPARATION (7)
INDEPENDENT COMPONENT ANALYSIS (6)
CORRELATION (5)
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ADAPTATION MODELS (4)
NMF (4)
SPARSE COMPONENT ANALYSIS (4)
SPECTRAL UNMIXING (4)
HYPERSPECTRAL IMAGES (3)
INDEXES (3)
MAXIMUM LIKELIHOOD (3)
NON-NEGATIVE MATRIX FACTORIZATION (NMF) (3)
NONLINEAR MIXTURE (3)
VECTORS (3)
CLUSTERING (2)
CONVERGENCE (2)
COST FUNCTION (2)
DATA MODELS (2)
ENDMEMBER SPECTRA EXTRACTION (2)
EQUATIONS (2)
ESTIMATION (2)
ESTIMATION ERROR (2)
EUCLID MISSION (2)
HYPERSPECTRAL UNMIXING (2)
ICA (2)
INSTRUMENTS (2)
LINEAR-QUADRATIC MIXING MODEL (2)
LINEAR-QUADRATIC MIXTURE (2)
MATRIX DECOMPOSITION (2)
MULTISPECTRAL SPATIAL UNMIXING (2)
MUTUAL INFORMATION (2)
NOISE MEASUREMENT (2)
NON-NEGATIVE MATRIX FACTORIZATION (2)
NONNEGATIVE MATRIX FACTORIZATION (2)
OPTIMIZATION (2)
SEMI-BLIND SOURCE SEPARATION (2)
SHAPE (2)
SIGNAL PROCESSING ALGORITHMS (2)
SLITLESS SPECTROSCOPY (2)
SPATIAL RESOLUTION (2)
SPECTRUM DECONTAMINATION (2)
URBAN AREAS (2)
URBAN IMAGES (2)
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ANALYTICAL MODELS (1)
ASTRONOMY (1)
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BLIND MIXTURE IDENTIFICATION (1)
BLIND SOURCE SEPARATION (BSS) (1)
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Search results for: Shahram Hosseini

A new modified Bessel‐type radial basis function for meshless methods: Utilized in the analysis of free vibration in 2D functionally graded Euler–Bernoulli beams

A meshless collocation method on nonlinear analysis of functionally graded hyperelastic plates using radial basis function

A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

A second-order blind source separation method for bilinear mixtures

Modified nonnegative matrix factorization for endmember spectra extraction from highly mixed hyperspectral images combined with multispectral data

Blind Separation of Nonstationary Sources by Spectral Decorrelation

Blind Separation of Non-stationary Images Using Markov Models

Mixing model in slitless spectroscopy and resulting blind methods for separating galaxy spectra

Extending NMF to blindly separate linear-quadratic mixtures of uncorrelated sources

Blind separation of linear-quadratic mixtures of real sources using a recurrent structure

Blind Operation of a Recurrent Neural Network for Linear-Quadratic Source Separation: Fixed Points, Stabilization and Adaptation Scheme

Blind Separation of Convolutive Mixtures of Non-stationary Sources Using Joint Block Diagonalization in the Frequency Domain

Markovian Source Separation in Post-nonlinear Mixtures

Stable Higher-Order Recurrent Neural Network Structures for Nonlinear Blind Source Separation

Blind Maximum Likelihood Separation of a Linear-Quadratic Mixture

Neural network modeling of ambulatory systolic blood pressure for hypertension diagnosis

Markovian Blind Image Separation

Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation

Weight freezing in constructive neural networks: A novel approach

Differential Fast Fixed-Point BSS for Underdetermined Linear Instantaneous Mixtures

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