Speckle is being used as a characterization tool for the analysis of the dynamics of slow-varying phenomena occurring in biological and industrial samples at the surface or near-surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about the inner dynamics of the biological or physical process taking place in the sample. Principal component analysis (PCA) is able to split the original data set into a collection of classes. These classes are related to processes showing different dynamics. In addition, statistical descriptors of speckle images are used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, PCA requires a longer computation time, but the results contain more information related to spatial–temporal patterns associated to the process under analysis. This contribution merges both descriptions and uses PCA as a preprocessing tool to obtain a collection of filtered images, where statistical descriptors are evaluated on each of them. The method applies to slow-varying biological and industrial processes.
Speckle is being used as a characterization tool for the analysis of the dynamic of slow varying phenomena occurring in biological and industrial samples. The retrieved data takes the form of a sequence of speckle images. The analysis of these images should reveal the inner dynamic of the biological or physical process taking place in the sample. Very recently, it has been shown that principal component analysis is able to split the original data set in a collection of classes. These classes can be related with the dynamic of the observed phenomena. At the same time, statistical descriptors of biospeckle images have been used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, principal component analysis requires longer computation time but the results contain more information related with spatial-temporal pattern that can be identified with physical process. This contribution merges both descriptions and uses principal component analysis as a pre-processing tool to obtain a collection of filtered images where a simpler statistical descriptor can be calculated. The method has been applied to slow-varying biological and industrial processes
One of the main technical difficulties in the fabrication of optical antennas working as light detectors is the proper
design and manufacture of auxiliary elements as load lines and signal extraction structures. These elements need
to be quite small to reach the location of the antennas and should have a minimal effect on the response of
the device. Unfortunately this is not an easy task and signal extraction lines resonate along with the antenna
producing a complex signal that usually masks the one given by the antenna. In order to decouple the resonance
from the transduction we present in this contribution a parametric analysis of the response of a bolometric stripe
that is surrounded by resonant dipoles with different geometries and orientations. We have checked that these
elements should provide a signal proportional to the polarization state of the incoming light.
Fractal antennas have been proposed to improve the bandwidth of resonant structures and optical antennas. Their multiband characteristics are of interest in radiofrequency and microwave technologies. In this contribution we link the geometry of the current paths built-in the fractal antenna with the spectral response. We have seen that the actual currents owing through the structure are not limited to the portion of the fractal that should be geometrically linked with the signal. This fact strongly depends on the design of the fractal and how the different scales are arranged within the antenna. Some ideas involving materials that could actively respond to the incoming radiation could be of help to spectrally select the response of the multiband design.
The practical application of optical antennas in detection devices strongly depends on its ability to produce
an acceptable signal-to-noise ratio for the given task. It is known that, due to the intrinsic problems arising
from its sub-wavelength dimensions, optical antennas produce very small signals. The quality of these signals
depends on the involved transduction mechanism. The contribution of different types of noise should be adapted
to the transducer and to the signal extraction regime. Once noise is evaluated and measured, the specific
detectivity, D*, becomes the parameter of interest when comparing the performance of antenna coupled devices
with other detectors. However, this parameter involves some magnitudes that can be defined in several ways
for optical antennas. In this contribution we are interested in the evaluation and comparison of D_ values for
several bolometric optical antennas working in the infrared and involving two materials. At the same time,
some material and geometrical parameters involved in the definition of noise and detectivity will be discussed to
analyze the suitability of D_ to properly account for the performance of optical antennas.
We demonstrate the first long wave infrared (LWIR) transmission line design and characterization. Two of the widely used transmission-lines: coplanar striplines (CPS) and microstrip (MS) lines are characterized at IR frequency (28.3THz), in terms of transmission line parameters: characteristic impedance (Zo), attenuation constant (α) and effective index of refraction (neff), through modeling, fabrication and measurement. These transmission-line parameters cannot be directly measured, what can be measured is the antenna response. So we compute, measure and compare the response of the dipole antenna connected to these transmission lines as a function of transmission-line length. The response depends on the transformation of antenna impedance along the transmission-line length according to the transmission-line parameters (Zo, α and neff ) of the line. Comparison of measured and computed response validates extracted transmission-line parameters. This paper demonstrates excellent agreement between measured and computed response for both types of transmission-lines under study.
KEYWORDS: Land mines, Imaging systems, Signal to noise ratio, Extremely high frequency, Principal component analysis, Metals, Millimeter wave imaging, Aluminum, Reflection, Mining
With over 110 million landmines buried throughout the world, the ability to detect and identify objects beneath the soil is crucial. The increased use of plastic landmines requires the detection technology to be able to locate both metallic and non-metallic targets. 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 90-140 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.
Infrared antennas are a novel type of detectors that couples electromagnetic radiation into metallic structures and feed it to a rectifying element. As their radio and millimeter counterparts, they can be characterized by parameters explaining their response in a variety of situations. The size of infrared antennas scales with the detected wavelength. Then, specifically designed experimental set-ups
need to be prepared for their characterization. The measurement of the spatial responsivity map of infrared antennas is one of the parameters of interest, but other parameters can be defined to
describe, for example, their directional response, or polarization response. One of the inputs to measure the spatial responsivity map is the spatial distribution of the beam irradiance illuminating
the antenna-coupled detector. The measured quantity is actually a map of the response of the detector when it moves under the beam illumination. This measurement is given as the convolution of the actual responsivity map and the beam irradiance distrbution. The uncertainties, errors, and artifacts incorporated along the measurement procedure are analyzed by using the Principal Component Analysis (PCA). By means of this method is possible to classify different sources of uncertainty. PCA is applied as a metrology tool to characterize the accuracy and repeatability of the experimental set-up. Various examples are given to describe the application of the PCA to the characterization of the deconvolution procedure, and to define the responsivity and the signal-to-noise ratio of the measured results.
The use of lasers as probe sources is very extended in micro and nano technologies. Therefore, the characterization of the beam is critical for the utter development of the measurement. Typically, the beam is projected on the detectors using optical elements and lenses. The alignment procedure is not always very good, and the difficulties increases when infrared radiation is involved. Even with very accurate positioning elements some misalignments are produced. The misalignment is most responsible for the appearance of coma aberration. In the case of a pure Gaussian beam shape they are going to produce a slightly comatic aberrated beam. In this paper we propose a method to characterize the direction and amplitude of this comatic aberration. The method is sensible enough to characterize slightly aberrated beams normally used to deconvolve detector's spatial response. It is based on a statistical analysis of the beam shape in different directions respect to its center. Simulations including the effect of noise are presented too and some applications to micro and nano metrology are exposed.
A test procedure is developed for an infrared laser scene projector, and applied to a projection system that we develop based on digital micromirror technology. The intended use will be for simulation and target training. Resolution and noise are significant parameters for target perception models of infrared imaging systems. System resolution is normally measured as the modulation transfer function (MTF), and its noise modeled through an appropriate signal standard deviation metric. We compare MTF measurements for both mid-wave (MWIR) and long-wave IR (LWIR) bands for an infrared laser scene projector based on the digital micromirror device (DMD). Moreover, we use two complimentary models to characterize imaging camera noise. This provides a quantitative image-quality criterion of system performance.
KEYWORDS: Light emitting diodes, LED displays, Manufacturing, Eye, Visualization, Process control, Modulation transfer functions, Monte Carlo methods, Control systems, Calibration
The use of Light Emitting Diodes (LED) offers a multitude of advantages over conventional illuminating displays in various fields (traffic, publicity, screens for public events and so on). Each pixel of these displays is normally formed by a collection of different color LED's. These "clusters" of LED are used to correctly reproduce colors. In this paper we developed a color performance characterization method. The input is the light emitting spectrums of LED's and their uncertainties derived through the control of manufacturing processes.
Photonic crystal microcavities are defined by the spatial arrangement of materials. In the analysis of their spatial-temporal mode distributions Finite-Difference Time-Domain (FDTD) methods have proved its validity. The output of the FDTD can be seen as the realizations of a multidimensional statistic variable. At the same time, fabrication tolerances induce an added and unavoidable variability in the performance of the microcavity. In this contribution we have analyzed the modes of a defective photonic crystal microcavity. The location, size, and shape of the cylinders configuring the microcavity are modelled as having a normal distribution of their parametric descriptors. A principal component analysis is applied to the output of the FDTD for a population of defective microcavities. The relative importance of the defects is evaluated, along with the changes induced in the spatial temporal distribution of electromagnetic field obtained from the calculation.
The Finite-Difference Time Domain method has encountered several difficulties when analyzing dispersive materials. This is the case of the metal structures that configure an optical antenna. These devices couple the electromagnetic radiation to conform currents that are rectified by another physical element attached to the antenna. Both elements: antenna and rectifier configures an optical detector with sub-wavelength dimensions. In this contribution we analyze the effect on the currents induced by the incident electromagnetic field using FDTD and taking into account the dispersive character of metal at optical frequencies. The analysis is done in a 2 dimensional framework and it serves as an analytical tool for the election of material and structures in the fabrication of optical antennas.
IR lasers are widely used in electro-optical applications, especially in detector characterization systems. These lasers can be extremely sensitive to fluctuations in the operational temperature of their cavity and other environmental factors. Due to these influences, the laser output signal normally fluctuates randomly. These variations make it difficult to characterize the laser waist position and exact focus, which in turn causes difficulty with detector measurement. We apply a multivariate statistical approach to characterize and filter these variations and to calculate the "best focus" of a carbon dioxide laser operating at 10.6 µm. Using this method, the "best focus" can be calculated with great accuracy and can be easily implemented during postsignal processing. Also, this technique can potentially be applied to other situations in which laser signal instability is significant.
Fresnel Zone Plate Lenses (FZPLs) have been successfully coupled to infrared (IR) antennas producing a responsivity enhancement of about two orders of magnitude. However, their lateral extension may compromise their applicability in focal-plane-arrays (FPA) IR imagers, where the dimensions of the pixel are constrained by the FPA spacing. When designing optimum-gain FZPLs for FPAs, we are lead to the requirement of FZPLs operating at very low F/#s (marginal rays propagating at a large angle in image space). In this case, Finite-Difference Time-Domain techniques (FDTD) are used to refine the physical-optics modelling results, producing a result closer to the actual case encountered in a high-fill-factor FPA. In this contribution, we analyze the FZPL designs by using FDTD techniques. The main result of the FDTD computation is the gain factor defined as the ratio of the response of the IR antennas coupled with the FZPL, compared to the same antennas without the FZPL.
Multiband and hyperspectral imagers are widespread nowadays. Different approaches and technologies are used with this purpose. One of the main problems is to deal with the huge amount of information involved in hyperspectral images. Moreover, in the majority of the cases, the kind of technology used introduces some artifacts into the images. It is necessary to take them into account depending on types of application. In this paper we have applied a multivariate statistical technique known as principal components to characterize different artifacts introduced in hyperspectral imagers. The technique permits to obtain a set of "relevant filters" that could substitute the original system under special conditions.
FDTD algorithms are being used as a numeric tool for the analysis of photonic crystals. The definition of the modes associated with them is of interest for the study of the capabilities of photonic crystal devices. The Principal Component Analysis (PCA) has been applied here to a sequence of images corresponding to the electromagnetic fields obtained from the FDTD simulations. PCA has revealed and quantified the importance of the modes appearing in the photonic crystals. The capability of PCA to produce spatial structures, or maps, associated with temporal evolutions has made possible the calculation of the modulus and phase of the modes existing in the photonic crystal. Some other modes, contributing with an almost negligible amount to the total variance of the original data, are also revealed by the method. Besides, PCA has been used to quantify the contribution of the numerical noise of the algorithm and to identify the effect of artifacts related with the matching of the computational grid and the inner geometry of the photonic crystal.
The miniaturization of light detectors in the visible and infrared has produced devices with micrometric and sub-micrometric spatial features. Some of these spatial features are closely linked with the physical mechanism of detection. An example of these devices is an optical antennas. To spatially characterize optical antennas it is necessary to scan a probe beam on the plane of the optical antenna. The mapping of this response is then treated and analyzed. When the response of the antenna is monitorized at visible or near-infrared frequencies, a sub-micron scanning step is necessary. In this paper we show the experimental set-up of a measurement station having a spatial resolution of 50 nanometers. This station is devoted to spatially characterize micrometric detectors, and specially optical antennas. The origin of the uncertainties of the measurement protocol is shown and practically analyzed. This station is also applied for characterizing the temporal, spectral, and polarization sensitivity specifications of light detectors with the previously mentioned resolution.
The spatial resolution of Focal Plane Arrays (FPA) is affected by sampling. The artifacts introduced by the sampling procedure are usually referred as "aliasing". Phase artifacts are introduced by the non-isoplanatic nature of the sampling mechanism in FPA. In this paper we introduce a stochastic description of these artifacts. This approach allows us to elucidate similarities and differences between "aliasing" and "phase effects". Figures of merit are introduced in order to characterize regions of isoplanatism in the Fourier Space. The relation between these figures of merit and target perception models is explored in order to clarify further research.
Noise processes are often modelled as stochastic processes. We have used a multivariate method based on the application of Principal Component Analysis (PCA) in order to classify different spatial-temporal structures taken as noise. When the structures have a correlation in time, a parameter distinguishing between fast and slow dynamics appears naturally. We have found this parameter in previous contributions with a different meaning depending on the context. Especially interesting is the application to the characterization of 1/f noise. In this paper we have extended the method in order to apply it to different kind of systems exhibiting, for example, self-organizing properties or brownian motion. One goal is trying to define a criterion to distinguish between fast and slow dynamics parameters. Finally, a statistical analysis is made in order to find the conditions for the application of the method to a wide range of different systems.
The behaviour of stock markets has been modelled actively during recent years. In some cases the market is modelled as a whole through the time series analysis of some indexes. But the market is made of companies whose time series can be studied independently. In this paper we have paid attention to the characterization of correlations and covariance among different companies in order to extract information about the market. We have used a statistical technique based on the analysis of the covariance matrix between the indexes of companies. When taking into account the sampling uncertainties and high order cumulants of index probability distribution, it is possible to classify automatically trends or clusters of companies in order to identify some independent “submarkets.” The method is applied to some finance data sets coming from the Spanish financial market IBEX35.
Imaging digital systems are widely used nowadays. CCD and CMOS sensors are embedded in a lot of metrologic devices for metrology in a lot of devices. One of these applications is the characterization of laser beams. For these kinds of applications, it is necessary to use cameras with high dynamic range. Some algorithms have been proposed in the past for this purpose. But, normally they enhance not only the dynamic range but the noise sensor too. In this paper we have applied an automatic algorithm to classify the different noise processes appearing in a CCD matrix. The method is based on a principal component expansion of the covariance matrix of some frames taken with the camera. It is possible to classify not only non-uniformity noise of the detector matrix, but also those contributions due to electronics and electronic interference and vibrations. Some of these noise processes represent only a very low amount of the total noise. A method to filter these noises is also presented.
The number of imager devices using multiband or hyperspectral scenes has increased in recent years. For surveillance, or even remote sensing applications, it is necessary to reduce the amount of collected information in order to be useful for automatic or human classification tasks, with affordable performance. In this sense it is very important to filter out only redundant information still preserving the relevant information. In this paper we present an approach in order to compact this information based on a multivariate statistical analysis of spectrums that uses an automatized principal component analysis. Possible applications and use for imagers using color outputs are also given.
The Principal Component Analysis (PCA) has been successfully applied to the characterization of noise in a sequence of frames and to the identification of bad pixels in an imaging array. Remote sensing scenery includes visualization through atmospheric turbulence and sea surfaces. These conditions produce spatial-temporal patterns that can be properly treated with the PCA method. A faint or weak source may be masked by the spatial features of the scene, or even by a fluctuating structure embedded on it. The PCA method is able to filter out these contributions related with global correlations of the set of data. In this sense the identification of the sources with the PCA is not based on the values of the Signal-to-Noise Ratio (SNR). It pays more attention to the spatio-temporal structure of the signals. Therefore, it is possible to identify sources below the classical SNR threshold. Another advantage is that the method corresponds with linear transformations, therefore it is easily implemented requiring a low computational effort. The approach used in this contribution is based on the same reasoning applied to the identification and classification of bad pixels. When the source is a point source, its image will fall on a small cluster of pixels (in the limit it will be only one pixel). This cluster is identified because the spatial-temporal evolution is different from the rest of the array. The method is applied to simulated sceneries as those found in images through atmospheric turbulence and detection of targets in sea images.
Web cameras are widely used as an image acquisition systems that are cheap, available and easily configured. Their settings are implemented electronically and the software drivers have a limited access to the internal characteristic of the cameras. These settings are prepared to cover the most common of the illumination conditions and scenes. The images provided by these cameras not only have to do with the physical characteristics of the detector array, but also with the compression algorithm used to present the images onto a display. In this contribution we apply the principal component analysis to a set of frames obtained by web cameras. This method allows to extract different spatial-temporal patterns of the noise of the cameras.
The noise characterization of a set of frames can be treated by means of the principal component analysis. The main advantage of this method is that it provides a set of eigenimages that can be grouped into processes. These processes may be identified with actual sources of noise. In this scheme, bad pixels are extracted as those pixels showing an anomalous behaviour. The principal component analysis also allows to extract information about the character of the temporal evolution of the signal of the pixels. The bad pixels are identified by evaluating their place in the distribution of signal of the whole data set.
Noise characterization and classification is an important task to evaluate the performance of an infrared imaging system. The focal plane array infrared cameras present several types of noises: fixed pattern noise, 1/f noise, pure temporal noise, etc. The existence of bad pixels showing a singular behavior must be included in the noise description. In this paper we show how the principal component analysis is able to classify the noise of a set of frames into different subsets. The classification method is integrated into a software package that performs the classification of the obtained eigenimages into processes. This method is specially adapted to the analysis of noise in a set of frames because it produces a corresponding set of images characterizing the noise. A result of the analysis provided with this method is the extraction of the fixed pattern noise, the bad pixel identification, the 1/f nosie components and analysis, the pure temporal noise, and some other processes having intermediate time scales.
Aliasing characterization is a critical problem in the description of pixelated image acquisition system such as focal plane array cameras for infrared image-forming systems. The aliased spatial frequency spectrum contains only those frequencies below the Nyquist limit. The higher- than- Nyquist frequency components are aliased onto lower- than-Nyquist frequencies. This effect can be described by means of a matricial transformation that is a folded version of the non-aliased transfer function of the system. This matricial analysis helps to understand the effect of the sampling. The transformation can be related with an aliased Modulation Transfer Function (MTF). Several examples of the application of the method are presented along with the description of the matricial formalism.
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