RESUMEN
The fast orthogonal search (FOS) algorithm has been shown to accurately model various types of time series by implicitly creating a specialized orthogonal basis set to fit the desired time series. When the data contain periodic components, FOS can find frequencies with a resolution greater than the discrete Fourier transform (DFT) algorithm. Frequencies with less than one period in the record length, called subharmonic frequencies, and frequencies between the bins of a DFT, can be resolved. This paper considers the resolution of subharmonic frequencies using the FOS algorithm. A new criterion for determining the number of non-noise terms in the model is introduced. This new criterion does not assume the first model term fitted is a dc component as did the previous stopping criterion. An iterative FOS algorithm called FOS first-term reselection (FOS-FTR), is introduced. FOS-FTR reduces the mean-square error of the sinusoidal model and selects the subharmonic frequencies more accurately than does the unmodified FOS algorithm.
Asunto(s)
Algoritmos , Modelos Biológicos , Procesamiento de Señales Asistido por Computador , Simulación por Computador , Difusión , Análisis de Fourier , Movimiento (Física) , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Análisis Espectral/métodos , Procesos EstocásticosRESUMEN
Simulated annealing (SA) is a robust, stable, but computationally costly method for solving ill-posed image-restoration problems. We describe the use of a backprojection operator that identifies those regions of an object estimate that have the greatest likelihood of being in error at each step of the SA process. This reduces computational time by concentrating the computing effort of SA on those pixels most effective in reducing the reconstruction error. The performance of an area-adaptive SA algorithm is evaluated for the restoration of images blurred by a simple pillbox space-invariant and a biconical space-variant point-spread function typical of a depth-measuring optical system.
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Accurate prediction of the short-term future behavior of atmospherically distorted wave fronts would permit the elimination of delays inherent in current adaptive-optics systems. It is shown by using astronomical image data that atmospherically induced wave-front distortions as represented by time series of wave-front tips and tilts measured in the visible and piston values measured in the infrared are predictable to a degree that would be useful in an adaptive-optics system. Adaptive linear predictors as well as predictors based on the back-propagation neural network are employed in this study.
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The observed motion of stellar-image centroids is shown to have a chaotic attractor with a correlation dimension of ~6. The existence of a chaotic attractor in star wander, or equivalently in wave-front tilts, indicates that the atmospheric processes that cause image degradation may be more accurately described as chaotic, not so random as is usually assumed. This new result has important implications for the accurate modeling of atmospheric processes, the operation of adaptive optics systems, and the processing of stellar images.
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The phase-gradient process for reconstructing stellar speckle images is implemented in a photon-address mode for application to images containing small numbers of photons. Comparisons are made with an address-mode Knox-Thompson process which show that the phase-gradient approach has computational advantages and better SNR performance. Reconstructions from simulated data and real data for the binary source Beta Delphinius are presented.
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Synchronous detection of randomly phase-modulated interferograms is examined for the cases of low and high SNR. Long-average phase estimates exhibit (time x bandwidth)(-1) dependence and give useful results with low SNR and sigma(phi) < pi. Optimum averaging times are determined for phase tracking in the case of relatively high SNR.
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The location of a downed aircraft can be estimated to within kilometers by optically processing the Dopplershifted emergency signal transmitted by the aircraft and received by a satellite. The process is described together with practical problems. A multichannel processor is proposed in which the output information is contained in the x, y, and z coordinates of the image space. An experimental processor is described.
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This paper describes an optical processor capable of forming real-time visible images of the radio sky directly from the output signals of a low-frequency T-type antenna array. Current modulator and light-valve technology could accommodate an array with 50-element crossbar and 25-element stem. Experimental confirmation of the correlator principle is described.
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A real-time optical correlator based on the principle of one-bit correlation, often used to facilitate the construction of real-time digital signal processors, is shown to have a greater immunity to mechanical noise than a comparable analog optical correlator. One-bit quantization also avoids some of the difficulties encountered with nonlinear light modulators. The one-bit optical correlator was studied by means of a computer simulation and an experimental system. Application of the technique to a multichannel correlator is discussed.
RESUMEN
This paper describes a multichannel optical correlator that uses two spatial dimensions to achieve multi-channel capability and operates in real time. Given I signals, r(i)(t), and J signals, s(j)(t), it can produce the I x J cross products, r(i)(t)s(j)(t), averaged over some interval. Multiplication is based on the fact that the square of the sum of two signals contains their cross product. Light fields modulated by the signals are added and their sum squared by measuring its intensity. An experimental correlator and its performance are described along with applications.