Asymptotic Performance of Weak Signal Detectors in Multiplicative and First-Order Markov Additive Noise Environment

2006 ◽  
Author(s):  
Jumi Lee ◽  
Hyun Kang ◽  
Jongho Oh ◽  
Taehoon Ahn ◽  
Iickho Song
Keyword(s):  
Additive Noise ◽  
Weak Signal ◽  
Asymptotic Performance ◽  
Noise Environment ◽  
First Order

Generic top-down discrimination for sorting and partitioning in linear time

2012 ◽  
Vol 22 (3) ◽  
pp. 300-374 ◽  
Author(s):  
FRITZ HENGLEIN
Keyword(s):  
Linear Time ◽  
Source Code ◽  
Equivalence Relations ◽  
Asymptotic Performance ◽  
Data Types ◽  
Worst Case ◽  
First Order ◽  
Structural Recursion ◽  
Algebraic Data Types ◽  
Abstract Types

AbstractWe introduce the notion ofdiscriminationas a generalization of both sorting and partitioning, and show thatdiscriminators(discrimination functions) can be definedgenerically, by structural recursion on representations oforderingandequivalence relations. Discriminators improve the asymptotic performance of generic comparison-based sorting and partitioning, and can be implemented not to expose more information than the underlying ordering, respectively equivalence relation. For a large class of order and equivalence representations, including all standard orders for regular recursive first-order types, the discriminators execute in the worst-case linear time. The generic discriminators can be coded compactly using list comprehensions, with order and equivalence representations specified using Generalized Algebraic Data Types. We give some examples of the uses of discriminators, including the most-significant digit lexicographic sorting, type isomorphism with an associative-commutative operator, and database joins. Source code of discriminators and their applications in Haskell is included. We argue that built-in primitive types, notably pointers (references), should come with efficient discriminators, not just equality tests, since they facilitate the construction of discriminators for abstract types that are both highly efficient and representation-independent.

Nonequilibrium first-order phase transition induced by additive noise

1999 ◽  
Vol 60 (6) ◽  
pp. R6275-R6278 ◽  
Author(s):  
A. A. Zaikin ◽  
J. García-Ojalvo ◽  
L. Schimansky-Geier
Keyword(s):  
Phase Transition ◽  
Order Phase Transition ◽  
Additive Noise ◽  
First Order ◽  
First Order Phase Transition ◽  
First Order Phase

scholarly journals Numerical discretization of stochastic oscillators with generalized numerical integrators

2021 ◽  
Vol 25 (Spec. issue 1) ◽  
pp. 65-75
Author(s):  
Ali Sirma ◽  
Resat Kosker ◽  
Muzaffer Akat
Keyword(s):  
Numerical Experiments ◽  
Numerical Scheme ◽  
Additive Noise ◽  
First Order ◽  
Variation Of Constants Formula ◽  
Variation Of Constants ◽  
Numerical Integrators ◽  
One Step ◽  
Numerical Discretization ◽  
Stochastic Oscillators

In this study, we propose a numerical scheme for stochastic oscillators with additive noise obtained by the method of variation of constants formula using generalized numerical integrators. For both of the displacement and the velocity components, we show that the scheme has an order of 3/2 in one step convergence and a first order in overall convergence. Theoretical statements are supported by numerical experiments.

Comparative Research on Stochastic Resonance of Piecewise-Linear Model and Bi-Stable Model

2010 ◽  
Vol 44-47 ◽  
pp. 2701-2706 ◽  
Author(s):  
Lin Ze Wang ◽  
Wen Li Zhao ◽  
Bei Bei Xia
Keyword(s):  
Stochastic Resonance ◽  
Input Signal ◽  
Comparative Research ◽  
Additive Noise ◽  
Piecewise Linear ◽  
Stable Model ◽  
Weak Signal ◽  
Resonance Model ◽  
Saturation Phenomenon ◽  
One Dimensional

A new piecewise-linear stochastic resonance model was proposed in this paper. Using numerical simulation, the model was compared with nonlinear one-dimensional continuous bi-stable system on response to period signals under both noisy and non-noisy circumstances. Comparing with contrasting model, there is no output response saturation phenomenon, and the output will increase greatly with the increasing of input signal. To input signal with noise, there is larger applicability to noise intense, especially to weak signal with large additive noise, still there is stochastic resonance. It is superior to contrasting model.

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