We consider the problem of reconstructing two signals from the autocorrelation and cross-correlation measurements. This inverse problem is a fundamental one in signal processing, and arises in many applications, including phase retrieval and blind channel estimation. In a typical phase retrieval setup, only the autocorrelation measurements are obtainable. We show that, when the measurements are...

In many applications (for example if the effect of an filtering of a random process is tested) a sequence of pseudorandom numbers with a prescribed autocorrelation function must be generated. In the paper a method is discussed allowing to generate sequences of pseudorandom numbers with a autocorrelation function near the autocorrelation function of the white noise after the filtering by an filt...

In this paper, we propose an improved autocorrelation function (ACF)-based water21 marking that is robust against combined geometric and removal attacks. ACF-based watermarking is known to be effective against geometric attacks. In this watermark23 ing scheme, both the embedded watermark and autocorrelation peaks of the watermark should survive a variety of attacks. However, the autocorrelation...

For a binary pseudorandom sequence {Si} with period N , the partial period autocorrelation function AS(τ, k,D) is defined by correlating the portion of the sequence within a window of size D, and start position k, with the portion in another window of the same size but starting τ steps later in the sequence. A distribution of possible partial period autocorrelation values is obtained by allowin...

Discrete autocorrelation wavelets have recently been applied in the statistical analysis of locally stationary time series. This form of time series analysis also requires the construction of the inner product matrix of such wavelets. To date, both discrete autocorrelation wavelets and the inner product matrix have been constructed via a brute force technique which proves to be computationally ...

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when...

We investigate how model populations respond to stochastic harvesting in a stochastic environment. In particular, we show that the effects of variable harvesting on the variance in population density and yield depend critically on the autocorrelation of environmental noise and on whether the endogenous dynamics of the population display over- or undercompensation to density. These factors inter...

This paper describes an algorithm, which performs monophonic music transcription. A pitch tracker calculates the fundamental frequency of the signal from the autocorrelation function. A continuity-restoration block takes the extracted pitch and determines the score corresponding to the original performance. The signal envelope analysis completes the transcription system, calculating attack-sust...

We present an extension of sparse Canonical Correlation Analysis (CCA) designed for finding multiple-tomultiple linear correlations within a single set of variables. Unlike CCA, which finds correlations between two sets of data where the rows are matched exactly but the columns represent separate sets of variables, the method proposed here, Canonical Autocorrelation Analysis (CAA), finds multiv...

Phonoangiography, as a noninvasive quantitative analysis of arterial bruits, was conducted just prior to standard invasive radiographic angiography in 135 patients. Sound records from 162 carotid arteries were analyzed with a new processing technique employing a high speed analog acoustic analyzer, the autocorrelator. In 18 arteries with carotid stenosis, a correlation coefficient of 0.87 resul...