Ouail Ouchetto

Homogenization of 3-D Periodic Bianisotropic Metamaterials

A novel homogenization technique, combining an asymptotic multiscale method with wave-field conception, is proposed for computing the quasi-static effective parameters of three-dimensional lattices of general bianisotropic composite materials. This technique is based on the decomposition of the fields into an averaged nonoscillating part and a corrected term with microoscillation. This paper provides an original and accurate way to model the electromagnetic fields in fine microstructures of bianisotropic particles with complex inclusion shapes when the wavelength is larger than the periodicity of the microstructure. The effects of the interaction between edges and corners of adjacent inclusions on the macroscopic effective parameters have been studied, and numerical results and verifications have been presented

Homogenization of Maxwell's Equations in Lossy Biperiodic Metamaterials

A novel homogenization technique is proposed for computing the quasi-static effective parameters of the lossy bi-periodic artificial structure materials. This technique is based on the Floquets Theorem which allows reducing the studied domain to the elementary cell with pseudo-periodic conditions on the lateral sides. The studied domain is extended by adding a vacuum layer in order to impose correctly the Silver-Müler absorbing boundary condition. This homogenization technique is a numerical method using the Finite Element Method and based on the evaluation of the macroscopic fields by averaging the local fields on the elementary cell. The effective constitutive parameters are obtained from the macroscopic fields and inductions. The numerical validation of this approach is presented in 2D and 3D by computing the effective conductivities for square cylinders and cubes suspended in a host isotropic medium. The obtained results are compared to our previous approach based on Unfolding Method and Finite Element Method (UFEM). On the one hand this technique can be applied to homogenize any bi-periodic metamaterial with elementary cells having inclusions of arbitrary geometry and on the other hand it takes into account the effect of the inclusions shape on the effective parameters.

Homogenization of Periodic Structured Materials With Chiral Properties

This paper presents a new and efficient method to compute the quasi-static homogenized constitutive parameters of biperiodic chiral artificial material. In this method, the studied domain is reduced to an elementary cell with pseudo-periodic conditions on the lateral sides and the local electromagnetic properties are computed by using the finite-element method. This chiral media is decomposed into two equivalent isotropic media and each one is treated separately. Indeed, the homogenized constitutive parameters of each isotropic media are expressed as a function of the macroscopic electromagnetic properties which are obtained by averaging the local electromagnetic properties. The homogenized constitutive parameters of the initial chiral material are expressed using the two equivalent isotropic materials. The validation of the numerical results is presented in the case of the lattices with 3-D inclusions. In addition, the obtained results are compared to existing results in literature. Finally, the incidence wave angle impact on the homogenized parameters is studied.

Effective Electromagnetic Properties of Structured Chiral Metamaterials

A novel methodology to evaluate the effective parameters of a three-dimensional lattice of chiral inclusions is presented. The homogenization is based upon mathematical arguments. The finite element technique is used to compute the constitutive parameters.

Homogenization of periodic bi-isotropic composite materials

Abstract In this paper, we present a new method for homogenizing the bi-periodic materials with bi-isotropic components phases. The presented method is a numerical method based on the finite element method to compute the local electromagnetic properties. The homogenized constitutive parameters are expressed as a function of the macroscopic electromagnetic properties which are obtained from the local properties. The obtained results are compared to Unfolding Finite Element Method and Maxwell–Garnett formulas.

Content based video retrieval based on bounded coordinate of motion histogram

In this paper, the authors present a novel Content-Based Video Retrieval (CBVR) system based on Bounded Coordinate of Motion Histogram (BCMH). The authors propose to characterize videos by using spatio-temporal features (e.g. motion direction, intensity and the residual information features). Bounded Coordinate of Motion Histogram is introduced to characterize and compare video subsequences in a reduced computation time. Moreover, the proposed approach is adaptive: a training procedure is presented. For instance, the cost — expressed in similarity measurement — has its precision increased by more than twice 38%) in comparison with extended fast dynamic time warping approach on a dataset of 1707 movie clips.

Similarity Performance of Keyframes Extraction on Bounded Content of Motion Histogram

The paper studies the influence on the similarity by extracting and using m from n frames on videos, the purpose is to evaluate the amount of the proportion similarity between them, and propose a new Content-Based Video Retrieval (CBVR) system. The proposed system uses a Bounded Coordinate of Motion Histogram (BCMH) [1] to characterize videos which are represented by spatio-temporal features (eg. motion vectors) and the Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD). However, a global representation of a video is compared pairwise with all those of the videos in the Hollywood2 dataset using the k-nearest neighbors (KNN). Moreover, this approach is adaptive: a training procedure is presented, and an accuracy of 58.1% is accomplished in comparison with the state-of-the-art approaches on the dataset of 1707 movie clips.

Fast-Tracking Application for Traffic Signs Recognition

Traffic sign recognition is among the major tasks on driver assistance system. The convolutional neural networks (CNN) play an important role to find a good accuracy of traffic sign recognition in order to limit the dangerous acts of the driver and to respect the road laws. The accuracy of the Detection and Classification determines how powerful of the technique used is. Whereas SSD Multibox (Single Shot MultiBox Detector) is an approach based on convolutional neural networks paradigm, it is adopted in this paper, firstly because we can rely on it for the real-time applications, this approach runs on 59 FPS (frame per second). Secondly, in order to optimize difficulties in multiple layers of DeeperCNN to provide a finer accuracy. Moreover, our experiment on German traffic sign recognition benchmark (GTSRB) demonstrated that the proposed approach could achieve competitive results (83.2% in 140.000 learning steps) using GPU parallel system and Tensorflow.