Convergence of long-memory discrete kth order Volterra processes

Low-Order Volterra Long-Term Predictors

Models based on linear prediction have been used for several decades in different areas of speech signal processing. While the linear approach has led to great advances in the last 40 years, it neglects nonlinearities present in the speech production mechanism. This paper compares the results of long-term nonlinear prediction based on second-order and third-order Volterra filters. Additional im...

The KTH Visit in Semi-Markov Processes

Strong order of convergence of a fully discrete approximation of a linear stochastic Volterra type evolution equation

In this paper we investigate a discrete approximation in time and in space of a Hilbert space valued stochastic process {u(t)}t∈[0,T ] satisfying a stochastic linear evolution equation with a positive-type memory term driven by an additive Gaussian noise. The equation can be written in an abstract form as du+ (∫ t 0 b(t− s)Au(s) ds ) dt = dW , t ∈ (0, T ]; u(0) = u0 ∈ H, where W is a Q-Wiener p...

The kth-Order Nonhomomorphicity of S-Boxes

Nonhomomorphicity is a new nonlinearity characterization of a mapping or S box An important advantage of nonhomomorphicity over other criteria is that it is easy to estimate by a fast statistical method with a high reliability due to the Law of Large Numbers In this paper we explicitly express the kth order nonhomomorphicity of S boxes by other criteria identify the tight upper and lower bounds...

Generation of kth-order random toposequences

The model presented in this paper derives toposequences from a digital elevation model (DEM). It is written in ArcInfo Macro Language (AML). The toposequences are called kth-order random toposequences, because they take a random path uphill to the top of a hill and downhill to a stream or valley bottom from a randomly selected seed point, and they are located in a streamshed of order k accordin...