Extraction d'information, amélioration du SNR et compression des données dans les images SAR multifréquences multipolarisées

Une méthode statistique permettant d'obtenir une nouvelle représentation optimale des images SAR multifréquences multipolarisées est proposée. La méthode est inspirée de l'analyse en composantes principales à bruit additif ajusté (NAPC) et de la théorie de diagonalisation simultanée de deux matrices symétriques par une seule matrice. Un nombre restreint des nouvelles images transformées décrivant fidèlement les données image SAR originales avec un rapport signal sur bruit amélioré sont alors retenues. Le développement théorique ainsi que la mise en oeuvre de la méthode en question, avec les résultats obtenus sur des images SAR réelles des capteurs radar du système SIR-C, font l'objet de ce présent article

Fusion de classifieurs en utilisant la théorie de l'évidence pour l'amélioration de la classification d'image

Le problème traité dans cet article concerne l'amélioration de la classification d'image dans les conditions d'insuffisance d'informations a priori déterministes et fiables sur l'état et la nature de la formation de l'image à l'instant de la prise de vue. Une méthode de fusion des classifieurs d'imagerie pour une meilleure classification des scènes imagées est proposée. La méthode est basée sur la théorie de l'évidence. Elle est générale et applicable à tout type de classifieur. On utilise le taux de fiabilité de la classification comme critère d'évaluation, les résultats obtenus, en utilisant des images simulées et réelles de télédétection, montrent que la méthode proposée donne de meilleurs résultats en comparaison avec les résultats des classifieurs considérés séparément

Approches neuronales pour l'extraction des composantes principales d'images multispectrales de télédétection

Le problème traité dans le présent article consiste en l'extraction des composantes principales les plus significatives d'images multispectrales de télédétection sans avoir à calculer la matrice de covariance des images spectrales. L'originalité du travail réside dans l'élaboration des algorithmes d'apprentissage spécifiques pour deux approches neuronales d'Analyse en Composantes Principales (ACP). Les deux approches possèdent des convergences rapides. L'application sur une image multispectrale réelle a montré leur efficacité dans l'extraction des composantes principales les plus significatives. The problem addressed in the présent paper is the most significant principal components extraction of remotely sensed multispectral images without having to calculate the covariance matrix of spectral images. The originality of the work resides in the elaboration of specific training algorithms for two neural network-based approaches of Principal Component Analysis (PCA). The convergence of a proposed approaches are rapid. The application on a real multispectral image has shown their efficiency in the extraction of the most significant principal components

Digital Implementation of an Improved LTE Stream Cipher Snow-3G Based on Hyperchaotic PRNG

SNOW-3G is a stream cipher used by the 3GPP standards as the core part of the confidentiality and integrity algorithms for UMTS and LTE networks. This paper proposes an enhancement of the regular SNOW-3G ciphering algorithm based on HC-PRNG. The proposed cipher scheme is based on hyperchaotic generator which is used as an additional layer to the SNOW-3G architecture to improve the randomness of its output keystream. The objective of this work is to achieve a high security strength of the regular SNOW-3G algorithm while maintaining its standardized properties. The originality of this new scheme is that it provides a good trade-off between good randomness properties, performance, and hardware resources. Numerical simulations, hardware digital implementation, and experimental results using Xilinx FPGA Virtex technology have demonstrated the feasibility and the efficiency of our secure solution while promising technique can be applied to secure the new generation mobile standards. Thorough analysis of statistical randomness is carried out demonstrating the improved statistical randomness properties of the new scheme compared to the standard SNOW-3G, while preserving its resistance against cryptanalytic attacks

Electrodeposition of Ni and Te-doped Cobalt Triantimonide in Citrate Solutions

Skutterudite compounds form a new class of potential candidates for thermoelectric applications. Cobalt triantimonide (CoSb3) shows good thermoelectric properties at medium and high temperatures. Doping this system with substitution elements, for either Co or Sb or both, may result in an increase of the thermoelectric figure of merit (ZT). This work focused on the electrochemical doping and characterization of films and nanowires of Co-Sb system in citrate solutions using gold-coated PCTE templates. The electrodeposition was performed on gold surface that was pre-treated electrochemically to ensure reproducible results. The electrochemical treatment acted as an annealing process for the surface, which resulted in an increase in Au(111) as demonstrated by XRD. Detailed electrochemical studies including deposition-stripping experiments was performed in order to develop a better understanding of the co-deposition kinetics and a better control over the composition of doped Co-Sb system. Scanning electron microscopy (SEM/EDS) helped study the morphology and the composition of the doped and undoped Co-Sb system. Co-deposition of Co-Sb showed that the amount of Co is higher in nanowires than in film or mushroom caps due to the slow Sb deposition rate dictated by slow Sb(III) complex diffusion. Doped nanowires have been also obtained. Both Ni and Te electrochemical doping of the Co-Sb system affected the composition of the deposit but there was no effect on nanowire morphology

An efficient palmprint identification system using multispectral and hyperspectral imaging

Ensuring the security of individuals is becoming an increasingly important problem in a variety of applications. Biometrics technology that relies on the physical and/or behavior human characteristics is capable of providing the necessary security over the standard forms of identification. Palmprint recognition is a relatively new one. Almost all the current palmprint- recognition systems are mainly based on image captured under visible light. However, multispectral and hyperspectral imaging have been recently used to improve the performance of palmprint identification. In this paper, the MultiSpectral Palmprint (MSP) and HyperSpectral Palmprint (HSP) are integrated in order to construct an efficient multimodal biometric system. The observation vector is based on Principal Components Analysis (PCA). Subsequently, HiddenMarkov Model (HMM) is used for modeling this vector. The proposed scheme is tested and evaluated using 350 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate

An efficient multi-spectral palmprint identification using contourlet decomposition and Hidden Markov Model

Automatic personal identification is playing an important role in security systems. Biometrics technologies has been emerging as a new and effective methods to achieve accurate and reliable identification results. A number of biometric traits exist and are in use in various applications. Palmprint is one of the relatively new biometrics due to its stable and unique characteristics. In this paper, multi-spectral information for the unique palmprint are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. For that, the palm lines are characterized by the contourlet coefficients sub-bands and compressed using the Principal Components Analysis (PCA). Subsequently, we use the Hidden Markov Model (HMM) for modeling. Finally, log-likelihood scores are used for palmprint matching. Experimental results show that our proposed scheme yields the best performance for identifying palmprints and it is able to provide an excellent identification rate and provide more security

Efficient person identification by fusion of multiple palmprint representations

The automatic person identification is a significant component in any security biometric system because of the challenges and the significant number of the applications that require a high safety. A biometric system based solely on one template (representation) is often not able to meet such desired performance requirements. Identification based on multiple representations represents a promising tendency. In this context, we propose here a multi-representation biometric system for person recognition using palm images and by integrating two different representations of the palmprint. Two ensembles of matchers that use two different feature representation schemes of the images are considered. The two different feature extraction methods are the block based 2D Discrete Cosine Transform (2D-DCT) and the phase information in 2D Discrete Fourier Transform (2D-DFT) that are complementing each other in terms of identification accuracy. Finally the two ensembles are combined and the fusion is applied at the matching-score level. Using the PolyU palmprint database, The results showed the effectiveness of the proposed multi-representation biometric system in terms of the recognition rate