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Search Results: 1 - 10 of 224 matches for " Driss Aboutajdine "
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A Novel Energy Aware Clustering Technique for Routing in Wireless Sensor Networks  [PDF]
Ouadoudi Zytoune, Youssef Fakhri, Driss Aboutajdine
Wireless Sensor Network (WSN) , 2010, DOI: 10.4236/wsn.2010.23031
Abstract: Cluster-based architectures are one of the most practical solutions in order to cope with the requirements of large-scale wireless sensor networks (WSN). Cluster-head election problem is one of the basic QoS requirements of WSNs, yet this problem has not been sufficiently explored in the context of cluster-based sensor networks. Specifically, it is not known how to select the best candidates for the cluster head roles. In this paper, we investigate the cluster head election problem, specifically concentrating on applications where the energy of full network is the main requirement, and we propose a new approach to exploit efficiently the network energy, by reducing the energy consumed for cluster forming.
AN OPTIMIZED ENERGY AWARE CHAINING TECHNIQUE FOR DATA GATHERING IN WIRELESS SENSOR NETWORKS
Ouadoudi Zytoune,Driss Aboutajdine
International Journal of Digital Information and Wireless Communications , 2013,
Abstract: Wireless Sensor Network (WSN) is a collection of small sensor nodes with aptitude to sense, compute and transmit data that are deployed to observe a physical environment. The sensor node has limited capabilities, especially its energy reserve, its processing ability and its memory storage. Data dissemination and gathering protocols design for WSN are crucial challenges since those protocols should be easy, energy-efficient, and robust to deal with a very large number of nodes. Also, they should be self-configurable to node failures and dynamic changes of the network topology. In this paper, we present a new algorithm for gathering sensor reading based on chain forming using Ant Colony Optimization (ACO) technique. To allow network lifetime extension, the ACO provides the shortest network nodes chaining instead of starting from the furthest node and using Greedy algorithm as PEGASIS do. The leader role duration is defined for each node based on its required energy to do this role in the established chain. Which avoids fast nodea€ s energy depletion and then, the network lifetime would be extended. Through simulation, it is proved that the proposed algorithm allows network stability extension compared to the most known chaining algorithm.
Adaptive Throughput Optimization in Downlink Wireless OFDM System  [PDF]
Youssef FAKHRI, Benayad NSIRI, Driss ABOUTAJDINE, Josep VIDAL
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2008, DOI: 10.4236/ijcns.2008.11002
Abstract: This paper presents a scheduling scheme for packet transmission in OFDM wireless system with adaptive techniques.The concept of efficient transmission capacity is introduced to make scheduling decisions based on channel conditions.We present a mathematical technique for determining the optimum transmission rate, packet size, Forward Error Correction and constellation size in wireless system that have multi-carriers for OFDM modulation in downlink transmission. The throughput is defined as the number of bits per second correctly received. Trade-offs between the throughput and the operation range are observed, and equations are derived for the optimal choice of the design variables. These parameters are SNR dependent and can be adapted dynamically in response to the mobility of a wireless data terminal. We also look at the joint optimization problem involving all the design parameters together. In the low SNR region it is achieved by adapting the symbol rate so that the received SNR per symbol stays at some preferred value. Finally, we give a characterization of the optimal parameter values as functions of received SNR Simulation results are given to demonstrate efficiency of the scheme.
3D-Mesh denoising using an improved vertex based anisotropic diffusion
Mohammed EL Hassouni,Driss Aboutajdine
Computer Science , 2010,
Abstract: This paper deals with an improvement of vertex based nonlinear diffusion for mesh denoising. This method directly filters the position of the vertices using Laplace, reduced centered Gaussian and Rayleigh probability density functions as diffusivities. The use of these PDFs improves the performance of a vertex-based diffusion method which are adapted to the underlying mesh structure. We also compare the proposed method to other mesh denoising methods such as Laplacian flow, mean, median, min and the adaptive MMSE filtering. To evaluate these methods of filtering, we use two error metrics. The first is based on the vertices and the second is based on the normals. Experimental results demonstrate the effectiveness of our proposed method in comparison with the existing methods.
3D Object Recognition by Classification Using Neural Networks  [PDF]
Mostafa Elhachloufi, Ahmed El Oirrak, Aboutajdine Driss, M. Najib Kaddioui Mohamed
Journal of Software Engineering and Applications (JSEA) , 2011, DOI: 10.4236/jsea.2011.45033
Abstract: In this Paper, a classification method based on neural networks is presented for recognition of 3D objects. Indeed, the objective of this paper is to classify an object query against objects in a database, which leads to recognition of the former. 3D objects of this database are transformations of other objects by one element of the overall transformation. The set of transformations considered in this work is the general affine group.
Application of Symmetric Uncertainty and Mutual Information to Dimensionality Reduction and Classification of Hyperspectral Images
Elkebir Sarhrouni,Ahmed Hammouch,Driss Aboutajdine
International Journal of Engineering and Technology , 2012,
Abstract: Remote sensing is a technology to acquire data for disatant substances, necessary to construct a model knowledge for applications as classification . Recently Hyperspectral Images (HSI) becomes a high technical tool that the main goal is to classify the point of a region. The HIS is more than a hundred bidirectional measures, called bands (or simply images), of the same region called Ground Truth Map (GT). But some bands are not relevant because they are affected by different atmospheric effects; others contain redundant information; and high dimensionality of HSI features make the accuracy of classification lower. All these bands can be important for some applications; but for the classification a small subset of these is relevant. The problematic related to HSI is the dimensionalityreduction. Many studies use mutual information (MI) to select the relevant bands. Others studies use the MI normalized forms, like Symmetric Uncertainty, in medical imagery applications. In this paper we introduce an algorithm based also on MI to select relevant bands and it apply the Symmetric Uncertainty coefficient to control redundancy and increase the accuracy of classification. This algorithm is feature selection tool and a Filter strategy. We establish this study on HSI AVIRIS 92AV3C. This is an effectiveness, and fast scheme to control redundancy.
Corner Detection Using Mutual Information
Guelzim Ibrahim, Hammouch Ahmed, Aboutajdine Driss
International Journal of Image Processing , 2011,
Abstract: This work presents a new method of corner detection based on mutual information and invariant toimage rotation. The use of mutual information, which is a universal similarity measure, has theadvantage of avoiding the derivation which amplifies the effect of noise at high frequencies. In thecontext of our work, we use mutual information normalized by entropy. The tests are performed ongrayscale images.
Unsupervised multispectral image Classification By fuzzy hidden Markov chains model For SPOTHRV Images
Faiza DAKKA, Ahmed HAMMOUCH & Driss ABOUTAJDINE
International Journal of Image Processing , 2011,
Abstract: This paper deals with unsupervised classification of multi-spectral images, we propose to usea new vectorial fuzzy version of Hidden Markov Chains (HMC).The main characteristic of the proposed model is to allow the coexistence of crisp pixels(obtained with the uncertainty measure of the model) and fuzzy pixels (obtained with thefuzzy measure of the model) in the same image. Crisp and fuzzy multi-dimensional densitiescan then be estimated in the classification process, according to the assumption consideredto model the statistical links between the layers of the multi-band image. The efficiency of theproposed method is illustrated with a Synthetic and real SPOTHRV images in the region ofRabat.The comparisons of two methods: fuzzy HMC and HMC are also provided. The classificationresults show the interest of the fuzzy HMC method.
A Color-texture Approach Based on Mutual Information for Multispectral Image Classi cation
Hassan El Maia,Ahmed Hammouch,Driss Aboutajdine
Journal of Multimedia , 2010, DOI: 10.4304/jmm.5.5.481-487
Abstract: In this work we propose an approach to improve the results of color texture image classi cation. We construct a new space called hybrid color-texture space by selecting the most discriminating attributes for the textures. Attributes are calculating from the co-occurrence matrix. The selection is done by the algorithm MRMR based on the mutual information. The Support Vectors Machine classi er (SVM)is used. A comparison with an iterative selection is also performed. The effectiveness of the proposed approach is evaluated on the VisTex database and on a SPOT HRV (XS) image representing two forest areas in the region of Rabat.
Bridging the Semantic Gap for Texture-based Image Retrieval and Navigation
Najlae Idrissi,José Martinez,Driss Aboutajdine
Journal of Multimedia , 2009, DOI: 10.4304/jmm.4.5.277-283
Abstract: In this study, we propose a new semantic approach for interpreting textures in natural terms. In our system, the user can reach desired textures by navigating into a hierarchy of sub collections previously held (offline). The originality of the proposed approach stems from two reasons: (1)- the intrinsic properties of the texture features extracted from the co-occurrence matrices have never been used before and (2)- it provides some degree of tolerance to generate the classes semantic which is not available with the standard unsupervised clustering algorithms such as kmeans. Thus, our contibutions in this study are threefold. (1)- Our approach maps low-level visual statistical features to high-level semantic concepts; it bridges the gap between the two levels enabling to retrieve and browse image collections by their high-level semantic concepts. (2)- Our system models the human perception subjectivity with the degree of tolerance and (3)- it provides an easy interface for navigating and browsing image collections to reach target collections. A comparative study with the unsupervised clustering algorithm k-means reveals the effectiveness of the proposed approach.
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