Salah Bourennane

Monte-Carlo-based channel characterization for underwater optical communication systems

We consider channel characterization for underwater wireless optical communication (UWOC) systems. We focus on the channel impulse response and, in particular, quantify the channel time dispersion for different water types, link distances, and transmitter/receiver characteristics, taking into account realistic parameters. We use the Monte Carlo approach to simulate the trajectories of emitted photons propagating in water from the transmitter towards the receiver. During their propagation, photons are absorbed or scattered as a result of their interaction with different particles present in water. To model angle scattering, we use the two-term Henyey-Greenstein model in our channel simulator. We show that this model is more accurate than the commonly used Henyey-Greenstein model, especially in pure sea waters. Through the numerical results that we present, we show that, except for highly turbid waters, the channel time dispersion can be neglected when working over moderate distances. In other words, under such conditions, we do not suffer from any inter-symbol interference in the received signal. Lastly, we study the performance of a typical UWOC system in terms of bit-error-rate using the simple on-off-keying modulation. The presented results give insight into the design of UWOC systems.

Multidimensional filtering based on a tensor approach

A new multidimensional modelling of data has recently been suggested, which can be applied in a wide range of signal processing fields. Many studies have proposed new tensorial mathematical tools in order to process multidimensional data. With a view of perfecting this multidimensional model, this paper presents a new tensor approach for multidimensional data filtering. A theoretical expression of n-mode filters is established based on a specific modelling of the desired information. The optimization criterion used in this tensorial filtering is the minimization of the mean square error between the estimated signal and the desired signal. This minimization leads to some estimated n-mode filters which can be considered as an extension of the well-known Wiener filter in a particular mode. An alternating least square algorithm is proposed to determine each n-mode Wiener filter. This new multimode Wiener filtering method is tested for noise reduction in multicomponent seismic data. A comparative study with classical bidimensional filtering methods based on principal component analysis is also proposed and presents encouraging results.

Survey of hyperspectral image denoising methods based on tensor decompositions

A hyperspectral image (HSI) is always modeled as a three-dimensional tensor, with the first two dimensions indicating the spatial domain and the third dimension indicating the spectral domain. The classical matrix-based denoising methods require to rearrange the tensor into a matrix, then filter noise in the column space, and finally rebuild the tensor. To avoid the rearranging and rebuilding steps, the tensor-based denoising methods can be used to process the HSI directly by employing multilinear algebra. This paper presents a survey on three newly proposed HSI denoising methods and shows their performances in reducing noise. The first method is the Multiway Wiener Filter (MWF), which is an extension of the Wiener filter to data tensors, based on the TUCKER3 decomposition. The second one is the PARAFAC filter, which removes noise by truncating the lower rank K of the PARAFAC decomposition. And the third one is the combination of multidimensional wavelet packet transform (MWPT) and MWF (MWPT-MWF), which models each coefficient set as a tensor and then filters each tensor by applying MWF. MWPT-MWF has been proposed to preserve rare signals in the denoising process, which cannot be preserved well by using the MWF or PARAFAC filters. A real-world HYDICE HSI data is used in the experiments to assess these three tensor-based denoising methods, and the performances of each method are analyzed in two aspects: signal-to-noise ratio and improvement of subsequent target detection results.

Multiway filtering based on fourth-order cumulants

We propose a new multiway filtering based on fourth-order cumulants for the denoising of noisy data tensor with correlated Gaussian noise. The classical multiway filtering is based on the TUCKALS3 algorithm that computes a lower-rank tensor approximation. The presented method relies on the statistics of the analyzed multicomponent signal. We first recall how the well-known lower rank- tensor approximation processed by TUCKALS3 alternating least square algorithm exploits second-order statistics. Then, we propose to introduce the fourth-order statistics in the TUCKALS3-based method. Indeed, the use of fourth-order cumulants enables to remove the Gaussian components of an additive noise. In the presented method the estimation of the-mode projector on the-mode signal subspace are built from the eigenvectors associated with the largest eigenvalues of a fourth-order cumulant slice matrix instead of a covariance matrix. Each projector is applied by means of the-mode product operator on the-mode of the data tensor. The qualitative results of the improved multiway TUCKALS3-based filterings are shown for the case of noise reduction in a color image and multicomponent seismic data.

Efficient Tensor-Based 2D+3D Face Verification

We propose a novel approach for face verification by encoding 2D and 3D face images as a high order tensor. To perform tensor dimensionality reduction for both the unsupervised and supervised cases, we propose multilinear whitened principal component analysis (MWPCA) and tensor exponential discriminant analysis (TEDA), respectively. MWPCA is utilized to solve the small sample size problem in the high-dimensional space and to improve the discrimination power achieved by classical MPCA. In the supervised case, we extend multilinear discriminant analysis to TEDA in order to emphasize the discriminant data included in the null space of the within-class scatter matrix of each tensor’s mode. Additionally, TEDA enlarges the margin between samples belonging to different classes via distance diffusion mappings. Our proposed approach can be seen as a novel data fusion method based on tensor representation. Indeed, the histograms of different local descriptors extracted from both 2D and 3D face modalities are combined through different tensor modes. The extensive experimental evaluation carried out on FRGC v2.0, Bosphorus, and CASIA 2D and 3D face databases indicates that the proposed approach performs significantly better than the state-of-the-art approaches.

Investigation of solar noise impact on the performance of underwater wireless optical communication links

We investigate the effect of environmental noise, caused by solar radiations under water, on the performance of underwater wireless optical communication (UWOC) systems. Presenting an analytical and generic model for this noise, we examine its impact on the link performance in terms of the bit error rate (BER). This study is conducted for different photo-detector types in the aim of highlighting practical limitations of establishing UWOC links in the presence of subsea solar noise. We show how the solar noise can impact the performance of UWOC links for relatively low operation depths. The results we present provide valuable insight for the design of UWOC links, which are likely to be established at relatively low depths. They can be exploited not only for the purpose of practical UWOC system deployment but also for in-pool experimental set-ups, since they elucidate the effect of ambient light on the measurements.

Brain region ranking for 18FDG-PET computer-aided diagnosis of Alzheimer's disease

Abstract Positron emission tomography (PET) is a functional molecular imaging, which helps to diagnose neurodegenerative diseases, such as Alzheimers disease (AD), by evaluating cerebral metabolic rate of glucose after administration of (18)F-fluoro-deoxy-glucose ((18)FDG). A quantitative evaluation, using computer aided methods, is of importance to improve medical care. In this paper a novel ranking method of the effectiveness of brain region of interest to classify healthy and AD brain is developed. Brain images are first segmented into 116 regions according to an anatomical atlas. A spatial normalization and four gray level normalization methods are used for comparison. Each extracted region is then characterized by a feature set based on gray level histogram moments, as well as age and gender of a subject. Using a receiver operating characteristic curve for each region, it was possible to define a Separating Power Factor (SPF) to rank regions ability to separate healthy from AD brain images. Using a set of selected regions, according to their rank, and when inputting them to a support vector machine classifier, it was possible to show that classification results were similar or slightly better than those obtained when using the whole gray matter voxels of the brain or the 116 regions as input features to the classifier. Computational time was reduced compared to the other methods to which our approach was compared.

Brain region of interest selection for 18FDG positrons emission tomography computer-aided image classification

Alzheimer disease (AD) is a neurodegenerative disease which can be diagnosed using Positron Emission Tomography (PET). A quantitative evaluation of this disease, using computer aided methods, is of importance. In this paper a novel ranking method of the effectiveness of brain region of interest to classify healthy and AD brain is developed. Brain images are first segmented into 116 regions according to an anatomical atlas. A spatial normalization and four grey level normalization methods are used for comparison. Each extracted region is then characterized by a feature set based on grey level histogram moments and age and gender. Using a receiver Operating Characteristic curve for each region, it was possible to rank regions ability to separate healthy from AD brain images. Using a set of selected regions, according to their rank, and when inputting them to a Support Vector Machine, it was possible to show that classification results were similar or slightly better to those obtained when using the whole voxels or the 116 regions as input features to the classifier.

Small Target Detection Improvement in Hyperspectral Image

Target detection is an important issue in the HyperSpectral Image (HSI) processing field. However, current spectral-identification-based target detection algorithms are sensitive to the noise and most denoising algorithms cannot preserve small targets, therefore it is necessary to design a robust detection algorithm that can preserve small targets. This paper utilizes the recently proposed multidimensional wavelet packet transform with multiway Wiener filter (MWPT-MWF) to improve the target detection efficiency of HSI with small targets in the noise environment. The performances of the our method are exemplified using simulated and real-world HSI.

Spatio-temporal-based joint range and angle estimation for wideband signals

Object localization using active sensor network exploiting the scattering of the emitted waves by a transmitter has been drawing a lot of research interest in the last years. For most applications, the environment leads to the arrival of multiple signals corresponding to emitted signal, signals which are scattered by the objects, and noise. In practical systems, the signals impinging on an array are frequently correlated, and the object number rapidly exceeds the number of sensors, making unsuitable most high-resolution methods used in array processing. We propose a solution to overcome these two experimental constraints. Firstly, frequential smoothing is used to decorrelate the scattered signals, enabling the estimation of their time delays of arrival (TDOA), using subspace-based methods. Secondly, an efficient algorithm for source localization using the TDOA is proposed. The advantage of the developed method is its efficiency even if the number of sources is larger than the number of sensors, in the presence of correlated signals. The performances of the proposed method are assessed on simulated signals. The results on real-world data are also presented and analyzed.