4,275 research outputs found
Possibility between earthquake and explosion seismogram differentiation by discrete stochastic non-Markov processes and local Hurst exponent analysis
The basic purpose of the paper is to draw the attention of researchers to new
possibilities of differentiation of similar signals having different nature.
One of examples of such kind of signals is presented by seismograms containing
recordings of earthquakes (EQ's) and technogenic explosions (TE's). We propose
here a discrete stochastic model for possible solution of a problem of strong
EQ's forecasting and differentiation of TE's from the weak EQ's. Theoretical
analysis is performed by two independent methods: with the use of statistical
theory of discrete non-Markov stochastic processes (Phys. Rev. E62,6178 (2000))
and the local Hurst exponent. Time recordings of seismic signals of the first
four dynamic orthogonal collective variables, six various plane of phase
portrait of four dimensional phase space of orthogonal variables and the local
Hurst exponent have been calculated for the dynamic analysis of the earth
states. The approaches, permitting to obtain an algorithm of strong EQ's
forecasting and to differentiate TE's from weak EQ's, have been developed.Comment: REVTEX +12 ps and jpg figures. Accepted for publication in Phys. Rev.
E, December 200
Estimation, Analysis and Smoothing of Self-Similar Network Induced Delays in Feedback Control of Nuclear Reactors
This paper analyzes a nuclear reactor power signal that suffers from network
induced random delays in the shared data network while being fed-back to the
Reactor Regulating System (RRS). A detailed study is carried out to investigate
the self similarity of random delay dynamics due to the network traffic in
shared medium. The fractionality or selfsimilarity in the network induced delay
that corrupts the measured power signal coming from Self Powered Neutron
Detectors (SPND) is estimated and analyzed. As any fractional order randomness
is intrinsically different from conventional Gaussian kind of randomness, these
delay dynamics need to be handled efficiently, before reaching the controller
within the RRS. An attempt has been made to minimize the effect of the
randomness in the reactor power transient data with few classes of smoothing
filters. The performance measure of the smoothers with fractional order noise
consideration is also investigated into.Comment: 6 pages, 6 figure
Tsallis non-extensive statistics, intermittent turbulence, SOC and chaos in the solar plasma. Part one: Sunspot dynamics
In this study, the nonlinear analysis of the sunspot index is embedded in the
non-extensive statistical theory of Tsallis. The triplet of Tsallis, as well as
the correlation dimension and the Lyapunov exponent spectrum were estimated for
the SVD components of the sunspot index timeseries. Also the multifractal
scaling exponent spectrum, the generalized Renyi dimension spectrum and the
spectrum of the structure function exponents were estimated experimentally and
theoretically by using the entropy principle included in Tsallis non extensive
statistical theory, following Arimitsu and Arimitsu. Our analysis showed
clearly the following: a) a phase transition process in the solar dynamics from
high dimensional non Gaussian SOC state to a low dimensional non Gaussian
chaotic state, b) strong intermittent solar turbulence and anomalous
(multifractal) diffusion solar process, which is strengthened as the solar
dynamics makes phase transition to low dimensional chaos in accordance to
Ruzmaikin, Zeleny and Milovanov studies c) faithful agreement of Tsallis non
equilibrium statistical theory with the experimental estimations of i)
non-Gaussian probability distribution function, ii) multifractal scaling
exponent spectrum and generalized Renyi dimension spectrum, iii) exponent
spectrum of the structure functions estimated for the sunspot index and its
underlying non equilibrium solar dynamics.Comment: 40 pages, 11 figure
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Holder exponents of irregular signals and local fractional derivatives
It has been recognized recently that fractional calculus is useful for
handling scaling structures and processes. We begin this survey by pointing out
the relevance of the subject to physical situations. Then the essential
definitions and formulae from fractional calculus are summarized and their
immediate use in the study of scaling in physical systems is given. This is
followed by a brief summary of classical results. The main theme of the review
rests on the notion of local fractional derivatives. There is a direct
connection between local fractional differentiability properties and the
dimensions/ local Holder exponents of nowhere differentiable functions. It is
argued that local fractional derivatives provide a powerful tool to analyse the
pointwise behaviour of irregular signals and functions.Comment: 20 pages, Late
Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms.
In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose KayÂżs method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.Postprint (author's final draft
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