José Manuel López-Alonso

Optical antennas for nano-photonic applications

Antenna-coupled optical detectors, also named optical antennas, are being developed and proposed as alternative detection devices for the millimetre, infrared, and visible spectra. Optical and infrared antennas represent a class of optical components that couple electromagnetic radiation in the visible and infrared wavelengths in the same way as radioelectric antennas do at the corresponding wavelengths. The size of optical antennas is in the range of the detected wavelength and they involve fabrication techniques with nanoscale spatial resolution. Optical antennas have already proved and potential advantages in the detection of light showing polarization dependence, tuneability, and rapid time response. They also can be considered as point detectors and directionally sensitive elements. So far, these detectors have been thoroughly tested in the mid-infrared with some positive results in the visible. The measurement and characterization of optical antennas requires the use of an experimental set-up with nanometric resolution. On the other hand, a computation simulation of the interaction between the material structures and the incoming electromagnetic radiation is needed to explore alternative designs of practical devices.

Principal-component characterization of noise for infrared images

Principal-component decomposition is applied to the analysis of noise for infrared images. It provides a set of eigenimages, the principal components, that represents spatial patterns associated with different types of noise. We provide a method to classify the principal components into processes that explain a given amount of the variance of the images under analysis. Each process can reconstruct the set of data, thus allowing a calculation of the weight of the given process in the total noise. The method is successfully applied to an actual set of infrared images. The extension of the method to images in the visible spectrum is possible and would provide similar results.

Bad pixel identification by means of principal components analysis

Jose ´Manuel Lopez-AlonsoJavier AldaUniversity Complutense of MadridSchool of OpticsDepartment of OpticsAv. Arcos de Jalo´n s/n. 28037 MadridSpainE-mail: jmlopez@opt.ucm.esAbstract. Bad pixels are defined as those pixels showing a temporalevolution of the signal different from the rest of the pixels of a givenarray. Principal component analysis helps us to understand the definitionof a statistical distance associated with each pixels, and using this dis-tance it is possible to identify those pixels labeled as bad pixels. Thespatiality of a pixel is also calculated. An assumption about the normalityof the distribution of the distances of the pixels is revised. Although theinfluence on the robustness of the identification algorithm is negligible,the definition of a parameter related with this nonnormality helps to iden-tify those principal components and eigenimages responsible for the de-parture from a multinormal distribution. The method for identifying thebad pixels is successfully applied to a set of frames obtained from a CCDvisible and a focal plane array (FPA) IR camera.

Millimeter wave imaging system for land mine detection

Applications using millimeter wave (mmW) and THz radiation have increased during the past few years. One of the principal applications of these technologies is the detection and identification of objects buried beneath the soil, in particular land mines and unexploded ordnances. A novel active mmW scanning imaging system was developed for this purpose. It is a hyperspectral system that collects images at different mmW frequencies from 90 to 140 GHz using a vector network analyzer collecting backscattering mmW radiation from the buried sample. A multivariate statistical method, principal components analysis, is applied to extract useful information from these images. This method is applied to images of different objects and experimental conditions.

Demonstration of a single-layer meanderline phase retarder at infrared

Meanderline wave plates are in common use at radio frequencies as polarization retarders. We present initial results of a gold meanderline structure on a silicon substrate that functions at a wavelength of 10.6 microm in the IR. The measured results show a distinct change in the polarization state of the incident beam after passing through the device, inducing a 74 degrees phase retardance between horizontal and vertical components. A high degree of polarization (88%) is maintained in the transmitted beam with an overall power transmittance of 38% and a beam profile that remains essentially unchanged.

Photonic crystal characterization by FDTD and principal component analysis

We demonstrate the capabilities of principal component analysis (PCA) for studying the results of finite-difference time-domain (FDTD) algorithms in simulating photonic crystal microcavities. The spatial-temporal structures provided by PCA are related to the actual electric field vibrating inside the photonic microcavity. A detailed analysis of the results has made it possible to compute the phase maps for each mode of the arrangement at their respective resonant frequencies. The existence of standing wave behavior is revealed by this analysis. In spite of this, some numerical artifacts induced by FDTD algorithms have been clearly detailed through PCA analysis. The data we have analyzed are a given set of maps of the electric field recorded during the simulation.

Operational parametrization of the 1/f noise of a sequence of frames by means of the principal component analysis in focal plane arrays

Principal component analysis is applied to the calculation of the power α of the 1/ f α dependence of the power spectrum density of a sequence of frames affected by a 1/ f noise and obtained from a focal plane array. This method provides an operational alternative of characterization of this type of noise that has been successfully applied to simulated and experimental data. The α parameter obtained by this method corresponds with a global characterization of the whole array. Besides, a calibration lifetime is calculated. This temporal parameter indicates when the 1/ f noise begins to be more noticeable than the temporal noise in the images of the array.

Characterization of artifacts in fully digital image-acquisition systems: Application to web cameras

We apply principal component analysis (PCA) to the characterization of artifacts in a digital image-acquisition system containing image-compression algorithms. The method is successfully applied to web cameras. The classification done with the PCA method produces three processes. The pure spatial process retrieves the luminance distribution of a static object. The pure temporal process is directly related with the temporal noise of the system. An intermediate spatial-temporal process reveals the interaction between the compression algorithms and the spatial-frequency contents of the object. Without prior information, the PCA method is able to distinguish this interaction from the classical temporal noise. The analysis of the anomalous pixels also reveals the location in the scene where the compression algorithms work harder. An extension of this analysis identifies the origin of the anomalous behavior in terms of its spatial or temporal character.

Uncertainty analysis in the measurement of the spatial responsivity of infrared antennas

The measurement of a two-dimensional spatial responsivity map of infrared antennas can be accomplished by use of an iterative deconvolution algorithm. The inputs of this algorithm are the spatial distribution of the laser beam irradiance illuminating the antenna-coupled detector and a map of the measured detector response as it moves through the illuminating beam. The beam irradiance distribution is obtained from knife-edge measurements of the beam waist region; this data set is fitted to a model of the beam. The uncertainties, errors, and artifacts of the measurement procedure are analyzed by principal-component analysis. This study has made it possible to refine the measurement protocol and to identify, classify, and filter undesirable sources of noise. The iterative deconvolution algorithm stops when a well-defined threshold is reached. Spatial maps of mean values and uncertainties have been obtained for the beam irradiance distribution, the scanned spatial response data, and the resultant spatial responsivity of the infrared antenna. Signal-to-noise ratios have been defined and compared, and the beam irradiance distribution characterization has been identified as the statistically weakest part of the measurement procedure.

Toy model to describe the effect of positional blocklike disorder in metamaterials composites

We study theoretically the effect of a new type of blocklike positional disorder on the effective electromagnetic properties of one-dimensional chains of resonant, high-permittivity dielectric particles, where particles are arranged into perfectly well-ordered blocks whose relative position is a random variable. This creates a finite order correlation length that mimics the situation encountered in metamaterials fabricated through self-assembled techniques, whose structures often display short-range order between near neighbors but long-range disorder, due to stacking defects. Using a spectral theory approach combined with a principal component statistical analysis, we study, in the long-wavelength regime, the evolution of the electromagnetic response when the composite filling fraction and the block size are changed. Modifications in key features of the resonant response (amplitude, width, etc.) are investigated, showing a regime transition for a filling fraction around 50%.