Automated Mode Separation In Oblique Ionograms

x-mode traces is then one of deconvolution. . A convolution may be written y = Hx + e, where x is the signal to be estimated, y is the measured or convolved signal and e is white Gaussian noise. Least squares estimation of x gives x = (H H) -1 H y, but the calculation of the inverse can be numerically unstable. . The LMS algorithm [3] estimates x recursively, taking the (k+1)th estimate of the signal, x k+1 , to be x k+1 = x k + H T (y - Hx k ), where m is a parameter chosen to make the solution stable. Variations of LMS can incorporate the fact that x is positive. . The convolution kernel is not know in advance so a range of kernels are tried, and only the best result kept. . The convolution kernel may vary with group range so each row of the ionogram is deconvolved separately. 4 DECONVOLUTION: PREPROCESSING The ionogram contains noise, overlapping parts of traces and traces which are not part of the F-layer. These can be partially removed through preprocessing. 5 Th

A Combined Approach for Object Detection and Deconvolution

. The Multiscale Vision Model is a recent object detection method, based on the wavelet transform. It allows us to extract all objects contained in an image, whatever their size or their shape. From each extracted object, information concerning flux or shape can easily be determined. We show that such an approach can be combined with deconvolution, leading to the reconstruction of deconvolved objects. We discuss the advantages of this approach, such as how we can perform deconvolution with a space-variant point spread function. We present a range of examples and applications, in the framework of the ISO, XMM and other projects, to illustrate the e#ectiveness of this approach. Key words: methods: data analysis --- techniques: image processing 1. Introduction Astronomical images contain typically a large set of pointlike sources (the stars), some quasi point-like objects (faint galaxies, double stars) and some complex and diffuse structures (galaxies, nebulae, planetary stars, cluster..

Skewness Maximization for Impulsive Sources in

In blind deconvolution problems, a deconvolution filter is often determined in an iterative manner, where the filter taps are adjusted to maximize some objective function of the filter output signal. The kurtosis of the filter output is a popular choice of objective function. In this paper, we investigate some advantages of using skewness, instead of kurtosis, in situations where the source signal is impulsive, i.e. has a sparse and asymmetric distribution. The comparison is based on the error surface characteristics of skewness and kurtosis

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Impulse Response Measurements Using All-Pass Deconvolution

Introduction It is often desirable to measure the impulse response of a particular system. In many cases the best way to do this is to apply a signal to the system which is close to a delta function - a click - and to measure the resulting output. In room acoustics this is traditionally done by using a blank pistol as the sound source, and recording the impulse response on a tape recorder. In seismology an explosive charge is often used for the same result. A problem with explosives is that they are not very repeatable, and the spectrum of the impulse that they produce is frequently complicated. In addition the high peak power they produce can present problems for the system under test, creating nonlinearities which can change the result of the test. For example, in a hall with an electronic reverberation system the high peak pressure of the pistol will probably overload the electronics, and the response of the electronics will be much lower than if music were used as an excitation. I