Thomas Grosges

A new image encryption scheme based on a chaotic function

In recent years, several methods of secure image encryption were studied and developed through chaotic processes or functions. In this paper, a new image encryption scheme based on a coupling of chaotic function and xor operator is presented. The main advantages of such a method are the abilities to produce a large key space to resist brute-force attacks, and to encrypt securely images with any entropy structure assuring indistinguishability, confusion and diffusion properties in the corresponding cipher-images. The results of several statistical analysis about randomness, sensitivity and correlation of the cipher-images show that the proposed cryptosystem is efficient and secure enough to be used for the image encryption and transmission. Moreover, the implementation of the corresponding algorithm is easy and only integers are used.

Fitting the optical constants of gold, silver, chromium, titanium, and aluminum in the visible bandwidth

Abstract. The fitting of metal optical properties is a topic that has applications in advanced simulations of spectroscopy, plasmonics, and optical engineering. In particular, the finite difference time domain method (FDTD) requires an analytical model of dispersion that verifies specific conditions to produce a full spectrum in a single run. Combination of Drude and Lorentz models, and Drude and critical points models, are known to be efficient, but the number of parameters to be adjusted for fitting data can prevent accurate results from simulated annealing or Nelder-Mead. The complex number relative permittivities of Au, Ag, Al, Cr, and Ti from either Palik or Johnson and Christy experimental data in the visible domain of wavelengths are successfully fitted by using the result of the particle swarm optimization method with FDTD constraint, as a starting point for the Nelder-Mead method. The results are well positioned compared to those that can be found in the literature. The results can be used directly for numerical simulations in the visible domain. The method can be applied to other materials, such as dielectrics, and to other domain of wavelengths.

Nanoshells for photothermal therapy: a Monte-Carlo based numerical study of their design tolerance.

The optimization of the coated metallic nanoparticles and nanoshells is a current challenge for biological applications, especially for cancer photothermal therapy, considering both the continuous improvement of their fabrication and the increasing requirement of efficiency. The efficiency of the coupling between illumination with such nanostructures for burning purposes depends unevenly on their geometrical parameters (radius, thickness of the shell) and material parameters (permittivities which depend on the illumination wavelength). Through a Monte-Carlo method, we propose a numerical study of such nanodevice, to evaluate tolerances (or uncertainty) on these parameters, given a threshold of efficiency, to facilitate the design of nanoparticles. The results could help to focus on the relevant parameters of the engineering process for which the absorbed energy is the most dependant. The Monte-Carlo method confirms that the best burning efficiency are obtained for hollow nanospheres and exhibit the sensitivity of the absorbed electromagnetic energy as a function of each parameter. The proposed method is general and could be applied in design and development of new embedded coated nanomaterials used in biomedicine applications.

Design of nanostructures for imaging and biomedical applications by plasmonic optimization

An adapted method of optimization of coated metallic nanoparticles is introduced to perform the optimal choice of material and sizes for better scattering or absorption efficiency. This design of nanoshells, involving plasmon resonance, is achieved to maximize the efficiency factors. The presented method is turned to tune the efficiency of nanoshells for biomedical applications and an increasing of the efficiency factors by 1 or 2 orders of magnitude is predicted with realistic materials.

A Poincaré's approach for plasmonics: the plasmon localization

A Poincarés approach is employed to characterize the excitation of a plasmon, which in essence corresponds to a zero of a complex S‐matrix. Throughout this work we study the plasmonic behaviour of gold, as this metal not only is frequently used in experimental arrays, but also requires an accurate dispersion model to properly excite plasmons. We investigate the plasmonic behaviour of gold nanogratings by means of Borns approximation and the Finite‐Elements Method. Also, a method based on the Poincarés approach is proposed to optimize this kind of structures.

Localized surface plasmon resonance in arrays of nano-gold cylinders: inverse problem and propagation of uncertainties

The plasmonic nanostructures are widely used to design sensors with improved capabilities. The position of the localized surface plasmon resonance (LSPR) is part of their characteristics and deserves to be specifically studied, according to its importance in sensor tuning, especially for spectroscopic applications. In the visible and near infra-red domain, the LSPR of an array of nano-gold-cylinders is considered as a function of the diameter, height of cylinders and the thickness of chromium adhesion layer and roughness. A numerical experience plan is used to calculate heuristic laws governing the inverse problem and the propagation of uncertainties. Simple linear formulae are deduced from fitting of discrete dipole approximation (DDA) calculations of spectra and a good agreement with various experimental results is found. The size of cylinders can be deduced from a target position of the LSPR and conversely, the approximate position of the LSPR can be simply deduced from the height and diameter of cylinders. The sensitivity coefficients and the propagation of uncertainties on these parameters are evaluated from the fitting of 15500 computations of the DDA model. The case of a grating of nanodisks and of homothetic cylinders is presented and expected trends in the improvement of the fabrication process are proposed.

Improved scheme for accurate computation of high electric near-field gradients.

We present an improved adaptive mesh process that allows the accurate control of the numerical solution of interest derived from the solution of the partial differential equation. In the cases of two-dimensional studies, such an adaptive meshing is applied to compute phenomenon involving high field gradients in near-field (electric intensity, Poyntings vector, optical forces,...). We show, that this improved scheme permits to decrease drastically the computational time and the memory requirements.

Adaptive Non-Uniform Particle Swarm Application to Plasmonic Design

The metaheuristic approach has become an important tool for the optimization of design in engineering. In that way, its application to the development of the plasmonic based biosensor is apparent. Plasmonics represents a rapidly expanding interdisciplinary field with numerous transducers for physical, biological and medicine applications. Specific problems are related to this domain. The plasmonic structures design depends on a large number of parameters. Second, the way of their fabrication is complex and industrial aspects are in their infancy. In this study, the authors propose a non-uniform adapted Particle Swarm Optimization (PSO) for rapid resolution of plasmonic problem. The method is tested and compared to the standard PSO, the meta-PSO (Veenhuis, 2006) and the ANUHEM (Barchiesi, 2009).These approaches are applied to the specific problem of the optimization of Surface Plasmon Resonance (SPR) Biosensors design. Results show great efficiency of the introduced method.

New adaptive mesh development for accurate near-field enhancement computation

An accurate computation of the near‐field enhancement is a key factor for the optimization of nanostructures in plasmonics. This problem has been addressed for Greens dyadic method but remains open for finite element method (FEM) where the use of non‐Cartesian meshes is known to be the most efficient. We present a new adaptive mesh process based on the a posteriori error indicator estimation on the physical solution. This new procedure accelerates drastically the convergence of the solution and minimizes both the memory requirement and the computational time.

Particle swarm optimization and evolutionary methods for plasmonic biomedical applications

In this paper the Evolutionary Method (EM) and the Particle Swarm Optimization (PSO), which are based on competitiveness and collaborative algorithms respectively, are investigated for plasmonic design. Actually, plasmonics represents a rapidly expanding interdisciplinary field with numerous devices for physical, biological and medicine applications. In this study, four EM and PSO algorithms are tested in two different plasmonic applications: design of surface plasmon resonance (SPR) based biosensors and optimization of hollow nanospheres used in curative purposes (cancer photothermal therapy). Specific problems-in addition of being multimodal and having different topologies — are related to plasmonic design; therefore the most efficient optimization method should be determined through a comparative study. Results of simulations enable also to characterize the optimization methods and depict in which case they are more efficient.