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...

We present an improved Markov chain Monte Carlo (MCMC) algorithm for posterior computation in chaotic dynamical systems. Recent Bayesian approaches to estimate the parameters of chaotic maps have used the Gibbs sampler which exhibits slow convergence due to high posterior correlations. Using the extended Kalman filter to compute the likelihood function by integrating out all unknown system stat...

1 Techniques to recreate sounds from perceptual displays known as cochleagrams and correlograms are developed using a convex projection framework. Prior work on cochlear-model inversion is extended to account for rectiÞcation and gain adaptation. A prior technique for phase recovery in spectrogram inversion is combined with the synchronized overlap-and-add technique of speech rate modiÞcation, ...

This paper introduces a relational function approximation technique based on McCallum’s UTree algorithm (McCallum, 1995). We have extended the original approach to handle relational observations using an attribute graph of observable objects and relationships (McGovern et al., 2003). Furthermore, we address the inherent challenges that arise with a relational representation. We use stochastic s...

Dealing with several structures of different complexities, we adopt various strategies to extract information. The general idea is to find appropriate tools to analyze the variation of the corresponding autocorrelation functions. First, for homogeneous media under different conditions, we recover, in a statistical way, a relationship between porosity and the autocorrelation function. Then, for ...

Spatial autocorrelation in species abundances indicates a lack of independence between sample locations and causes problems in distribution modelling. Knowing the cause of such spatial autocorrelation is vital for selecting the best suited modelling methods. Most autocorrelation in distributions is caused by autocorrelation in the underlying environmental conditions. The aim of the study was to...

BACKGROUND AND AIMS During the development of an even-aged plant population, the spatial distribution of individuals often changes from a clumped pattern to a random or regular one. The development of local size hierarchies in an Abies forest was analysed for a period of 47 years following a large disturbance in 1959. METHODS In 1980 all trees in an 8 x 8 m plot were mapped and their height g...

This paper proposes a new musical instrument estimation of polyphony using autocorrelation functions. We notice that each musical instrument has each autocorrelation function. Polyphony can be separated into each monophony using comb filters ( ). We can obtain the autocorrelation functions for the outputs of comb filters from the autocorrelation functions of the monophony. By the pattern patchi...

The autocorrelation function of the all-pole filter given by the conventional linear prediction (LP) matches exactly the autocorrelation function of the input signal between indices 0 and m, when the prediction order equals m. This study describes a recently developed technique, Weighted-Sum Line Spectrum Pair (WLSP), where an all-pole filter is defined by using a sum of weighted LSP (Line Spec...