Near-Field Plasmonics of an Individual Dielectric Nanoparticle above a Metallic Substrate

We simulate and discuss the local electric-field enhancement in a system of a dielectric nanoparticle placed very near to a metallic substrate. We use finite-element numerical simulations in order to understand the field-enhancement mechanism in this dielectric NP-on-mirror system. Under appropriate excitation conditions, the gap between the particle and the substrate becomes a "hot spot", i.e., a region of intense electromagnetic field. We also show how the optical properties of the dielectric NP placed on a metallic substrate affect the plasmonic field enhancement in the nanogap and characterize the confinement in the gap. Our study helps to understand and design systems with dielectric NPs on metallic substrates which can be equally as effective for SERS, fluorescence, and nonlinear phenomena as conventional all plasmonic structures.

A Single-Frame Super-Resolution Innovative Approach

Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is given as an input. The proposed method is based on statistical learning for inferring the high frequencies regions which helps to distinguish a high resolution image from a low resolution one. These inferences are obtained from the correlation between regions of low and high resolution that come exclusively from the image to be super-resolved, in term of small neighborhoods. The Markov random fields are used as a model to capture the local statistics of high and low resolution data when they are analyzed at different scales and resolutions. Experimental results show the viability of the method.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Buried-object detection using free-space time-domain near-field measurements

The detection of buried objects using time-domain freespace measurements was carried out in the near field. The location of a hidden object was determined from an analysis of the reflected signal. This method can be extended to detect any number of objects. Measurements were carried out in the X- and Ku-bands using ordinary rectangular pyramidal horn antennas of gain 15 dB. The same antenna was used as the transmitter and recei er. The experimental results were compared with simulated results by applying the two-dimensional finite-difference time-domain(FDTD)method, and agree well with each other. The dispersi e nature of the dielectric medium was considered for the simulation.

Heat transfer between nanoparticles: Thermal conductance for near-field interactions

We analyze the heat transfer between two nanoparticles separated by a distance lying in the near-field domain in which energy interchange is due to the Coulomb interactions. The thermal conductance is computed by assuming that the particles have charge distributions characterized by fluctuating multipole moments in equilibrium with heat baths at two different temperatures. This quantity follows from the fluctuation-dissipation theorem for the fluctuations of the multipolar moments. We compare the behavior of the conductance as a function of the distance between the particles with the result obtained by means of molecular dynamics simulations. The formalism proposed enables us to provide a comprehensive explanation of the marked growth of the conductance when decreasing the distance between the nanoparticles.

An Improved Color Video Super-Resolution Using Kernel Regression and Fuzzy Enhancement

An improved color video super-resolution technique using kernel regression and fuzzy enhancement is presented in this paper. A high resolution frame is computed from a set of low resolution video frames by kernel regression using an adaptive Gaussian kernel. A fuzzy smoothing filter is proposed to enhance the regression output. The proposed technique is a low cost software solution to resolution enhancement of color video in multimedia applications. The performance of the proposed technique is evaluated using several color videos and it is found to be better than other techniques in producing high quality high resolution color videos

Single Frame Image Super Resolution Using Learned Directionlets

In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions of a set of available high resolution images .This information is used as the training set for super resolving a low resolution input image and the Directionlet coefficients at finer scales of its high-resolution image are learned locally from this training set and the inverse Directionlet transform recovers the super-resolved high resolution image. The simulation results showed that the proposed approach outperforms standard interpolation techniques like Cubic spline interpolation as well as standard Wavelet-based learning, both visually and in terms of the mean squared error (mse) values. This method gives good result with aliased images also.

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images

Particle acceleration and magnetic field structure in PKS 2155-304: optical polarimetric observations

In this paper, we present multiband optical polarimetric observations of the very-high energy blazar PKS 2155-304 made simultaneously with a HESS/Fermi high-energy campaign in 2008, when the source was found to be in a low state. The intense daily coverage of the data set allowed us to study in detail the temporal evolution of the emission, and we found that the particle acceleration time-scales are decoupled from the changes in the polarimetric properties of the source. We present a model in which the optical polarimetric emission originates at the polarized mm-wave core and propose an explanation for the lack of correlation between the photometric and polarimetric fluxes. The optical emission is consistent with an inhomogeneous synchrotron source in which the large-scale field is locally organized by a shock in which particle acceleration takes place. Finally, we use these optical polarimetric observations of PKS 2155-304 at a low state to propose an origin for the quiescent gamma-ray flux of the object, in an attempt to provide clues for the source of its recently established persistent TeV emission.

Small-angle X-ray scattering and X-ray absorption near-edge structure study of iron-doped siloxane-polyoxyethylene nanocomposites

The local and medium-range structures of siloxane-POE hybrids doped with Fe(III) ions and prepared by the sol-gel process were investigated by X-ray absorption near-edge structure (XANES)/extended X-ray absorption fine structure (EXAFS) and small-angle X-ray scattering (SAXS), respectively. The experimental results show that the structure of these composites depends on the doping level. EXAFS data reveal that, for low doping levels ([O]/[Fe] > 40, oxygens being of the ether-type of the POE chains), Fe(III) ions are surrounded essentially by a shell of chlorine atoms, suggesting the formation of FeCl4- anions. At high doping levels ([O]/[Fe] < 20), Fe(III) ions interacts mainly with oxygen atoms and form FeOx species. The relative proportion of FeOx species increases with iron concentration, this result being consistent with the results of SAXS measurements showing that increasing iron doping induces the formation of iron-rich nanodomains embedded in the polymer matrix.

Comparative study on the far-field spectra and near-field amplitudes for silver and gold nanocubes irradiated at 514, 633 and 785 nm as a function of the edge length

We describe a systematic investigation by the discrete dipole approximation on the optical properties of silver (Ag) and gold (Au) nanocubes as a function of the edge length in the 20-100 nm range. Our results showed that, as the nanocube size increased, the plasmon resonance modes shifted to higher wavelengths, the contribution from scattering to the extinction increased, and the quadrupole modes became more intense in the spectra. The electric field amplitudes at the surface of the nanocubes were calculated considering 514, 633 and 785 nm as the excitation wavelengths. While Ag nanocubes displayed the highest electric field amplitudes (vertical bar E vertical bar(max)) when excited at 514 nm, the Au nanocubes displayed higher vertical bar E vertical bar(max) values than Ag, for all sizes investigated, when the excitation wavelength was either 633 or 785 nm. The variations in vertical bar E vertical bar(max) as a function of size for both Ag and Au nanocubes could be explained based on the relative position of the surface plasmon resonance peak relative to the wavelength of the incoming electromagnetic wave. Our results show that not only size and composition, but also the excitation wavelength, can play an important role over the maximum near-field amplitudes values generated at the surface of the nanocubes.