Traveling-wave dual-gate MESFET: A multimode S-matrix derivation based on the quasistatic approach

A theoretical model of the dual-gate MESFET including electromagnetic propagation effects is presented. The quasistatic approach is induced by the presence of a constant volumic charge density in the depletion layer when the device operates under the slow-wave Schottky mode. Device features are analyzed, through its S-parameter variation vs. electrode length, frequency, loading impedances, and bias voltages. We found that the different propagating mode attenuation factors are determined principally by the conductor losses. © 1998 John Wiley & Sons, Inc. Int J RF and Microwave CAE 8: 11–19, 1998.     

Modeling of surface plasmon resonance nanobiosensor with the transverse resonance method

This article describes a new approach for the detection and identification of a molecular interaction in real‐time and non‐label with surface plasmon resonance (SPR). This approach is based on the transverse resonance method, modeling the nanobiosensor by an equivalent circuit that allows studying the dispersion characteristics of surface plasmon and the reflectivity. The results obtained from these two studies show that the dispersive study is very precise than the reflectivity one to determine the presence and nature of molecular interactions.     

A new multi-scale framework for convolutive blind source separation

This paper presents a new multi-scale decomposition algorithm which enables the blind separation of convolutely mixed images. The proposed algorithm uses a wavelet-based transform, called Adaptive Quincunx Lifting Scheme (AQLS), coupled with a geometric demixing algorithm called Deds. The resulting deconvolution process is made up of three steps. In the first step, the convolutely mixed images are decomposed by AQLS. Then, Deds is applied to the more relevant component to unmix the transformed images. The unmixed images are, thereafter, reconstructed using the inverse of the AQLS transform. Experiments carried out on images from various origins show the superiority of the proposed method over many widely used blind deconvolution algorithms.     

The Characters of a Sylow p-Subgroup of the Group G2(pn), p≠2, 3

 We compute the irreducible complex characters of a Sylow p-subgroup of the Chevalley group G ₂(q), q=p ⁿ , p≠2, 3 and show that the non linear irreducible characters are induced from linear characters of subgroups of order q ⁵ and q ⁴. Received April 6, 1995     

Bayesian learning of inverted Dirichlet mixtures for SVM kernels generation

We describe approaches for positive data modeling and classification using both finite inverted Dirichlet mixture models and support vector machines (SVMs). Inverted Dirichlet mixture models are used to tackle an outstanding challenge in SVMs namely the generation of accurate kernels. The kernels generation approaches, grounded on ideas from information theory that we consider, allow the incorporation of data structure and its structural constraints. Inverted Dirichlet mixture models are learned within a principled Bayesian framework using both Gibbs sampler and Metropolis-Hastings for parameter estimation and Bayes factor for model selection (i.e., determining the number of mixture’s components). Our Bayesian learning approach uses priors, which we derive by showing that the inverted Dirichlet distribution belongs to the family of exponential distributions, over the model parameters, and then combines these priors with information from the data to build posterior distributions. We illustrate the merits and the effectiveness of the proposed method with two real-world challenging applications namely object detection and visual scenes analysis and classification.     

An efficient algorithm for electromagnetic scattering by a set of perfect conducting cylindrical objects using the artificial neural network

In this article an efficient algorithm based on the wave concept iterative process is formulated and applied in order to investigate the electromagnetic scattering by a set of conducting arbitrarily shaped objects placed in the free space. In this approach, the cylindrical coordinates system is used as a modal base in order to develop the modal coefficients of the diffraction operator. This operator models the reaction of the environment expressing the electromagnetic coupling between each two pixels of the discretized surface. A study of electromagnetic coupling between two pixels positioned and oriented somehow in the free space is highlighted so as to determine the coupling operator for different positions and orientations of the emitted and received waves. Twelve coupling operators are developed. For a complex geometric shape structure, the determination of these characteristics involved the determination of interactions between each pixel and the others that constitute the discretized surface. This interaction involves the wave formulation already highlighted for each couple of pixels which implies a higher calculation time. In order to optimize the calculation time, the artificial neural networks are adopted with the feed‐forward architecture. The supervised learning has been chosen with the use of the resilient backpropagation algorithm.