REFLECTIONS OF ARABIC LITERATURE ON ARCHITECTURAL REPRESENTATION

Literature and architecture have long been connected where both are forms of art; literature is a verbal form of art whereas architecture takes a social form. Both domains play a major role in heritage inheritance and representation and are immensely linked to each other; each is capable of representing and embodying the other. Gloomily, in light of digital technology, Information Age, and onslaught of globalization, the reflections of the practices of Arabic literature on architectural representations, after being practiced together since ancient periods, have retracted in recent times thus architecture in the Arab cities has started to veer far away from reflecting the Arab heritage. Accordingly, the purpose of this paper is primarily to propose a number of approaches that enhance the amalgamation of both domains in order to nourish the cultural heritage through such notions and to introduce the Arabic culture to different types of users directly and indirectly. To achieve this aim, the research follows a scientific methodology that relies on deskwork, literature review, and observations making it a qualitative type of work. The research highlights previous readings that analyse the relationship between Arabic literature and architecture and then tackles the methods of reflections of literature in architectural representations. After that, recently executed case studies will be investigated in the paper which are Quranic Park by OBE Architects in Dubai, UAE and Qatar Faculty of Islamic Studies by Mangera Yvars Architects in Doha, Qatar. Finally, a field survey is carried out for the purpose of gathering further information. In conclusion, several design approaches were conducted to employ the reflections of Arabic literature in architectural representations in order to achieve the previously mentioned aim

UNDERDETERMINED BLIND SEPARATION OF AUDIO SOURCES IN TIME-FREQUENCY DOMAIN

International audienceThis paper considers the blind separation of audio sources in the underdetermined case, where we have more sources than sensors. A recent algorithm applies time-frequency distributions (TFDs) to this problem and gives good separation performance in the case where sources are disjoint in the time-frequency (TF) plane. However, in the non-disjoint case, the reconstruction of the signals requires some interpolation at the intersection points in the TF plane. In this paper, we propose a new algorithm that combines the abovementioned method with subspace projection in order to explicitly treat non-disjoint sources. Another contribution of this paper is the estimation of the mixing matrix in the underdetermined case

Underdetermined blind source separation of audio sources in time-frequency domain

International audienceThis paper considers the blind separation of audio sources in the underdetermined case, where we have more sources than sensors. A recent algorithm applies time-frequency distributions (TFDs) to this problem and gives good separation performance in the case where sources are disjoint in the time-frequency (TF) plane. However, in the non-disjoint case, the reconstruction of the signals requires some interpolation at the intersection points in the TF plane. In this paper, we propose a new algorithm that combines the abovementioned method with subspace projection in order to explicitly treat non-disjoint sources. Another contribution of this paper is the estimation of the mixing matrix in the underdetermined case

Adaptive Blind Identification of Sparse SIMO Channels using Maximum a Posteriori Approach

International audienceIn this paper, we are interested in adaptive blind channel identification of sparse single input multiple output (SIMO) systems. A generalized Laplacian distribution is considered to enhance the sparsity of the channel coefficients with a maximum a posteriori (MAP) approach. The resulting cost function is composed of the classical deterministic maximum likelihood (ML) term and an additive $\ell_p$ norm of the channel coefficient vector which represents the sparsity penalization. The proposed adaptive optimization algorithm is based on a simple gradient step. Simulations show that our method outperforms the existing adaptive versions of cross-relation (CR) method

Blind system identification using cross-relation methods : further results and developments

International audienceWe consider the problem of blind identification of FIR systems using the cross-relations (CR) method first introduced in [1]. Our contribution in this paper are as follows: (i) We introduce an extended formulation of the CR identification criterion which generalizes the standard CR criterion used in [2]. It can be shown that many existing multichannel blind identification methods belong to the class of generalized CR methods. (ii) We introduce a new identification method referred to as Minimum Cross-Relations (MCR) method which exploits with minimum redundancy the spatial diversity among the channel outputs. Simulation-based performance analysis of the MCR method and comparisons with CR method are also presented. (iii) Then, we present a modified version of the MCR referred to as the "unbiased MCR" (UMCR) method that leads to unbiased estimation of the channel parameters and better estimation performances without need of noise whitening as in the MCR. (iv) Finally, we discuss the multi-input case and show how additional difficulties arise due to the non-linear parameterization of the noise vectors in terms of the channel parameters

Sparsity-Based Algorithms for Blind Separation of Convolutive Mixtures with Application to EMG Signals

International audienceIn this paper we propose two iterative algorithms for the blind separation of convolutive mixtures of sparse signals. The first one, called Iterative Sparse Blind Separation (ISBS), minimizes a sparsity cost function using an approximate Newton technique. The second algorithm, referred to as Givens-based Sparse Blind Separation (GSBS) computes the separation matrix as a product of a whitening matrix and a unitary matrix estimated, via a Jacobi-like process, as the product of Givens rotations which minimize the sparsity cost function. The two sparsity based algorithms show significantly improved performance with respect to the time coherence based SOBI algorithm as illustrated by the simulation results and comparative study provided at the end of the paper

Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain

International audienceThis paper considers the blind separation of non-stationary 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 domain. 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 thanks 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

Séparation aveugle sous-déterminée de sources audio par la méthode EMD (Empirical Mode Decomposition)

- Dans le cadre de la séparation aveugle de sources, nous montrons dans cet article comment effectuer la séparation de mélanges instantanés de sources audio en utilisant une méthode basée sur l'algorithme de Décomposition Modale Empirique (ou EMD, pour Empirical Mode Décomposition). Cette approche nous permet, en particulier de traiter le cas sous déterminé (c'est à dire le cas où l'on a moins de capteurs que de sources). L'approche EMD se base sur le fait que les signaux audio (et particulièrement les signaux musicaux) peuvent être bien modélisés localement par une somme de signaux périodiques. Ces signaux seront donc décomposés en utilisant l'algorithme EMD et recombinés par classification suivant leurs directions spatiales regroupant ainsi les composantes de chacune des sources. Nous présenterons quelques résultats de simulation qui permettent d'évaluer les performances de cette nouvelle méthode