Signal segmentation in time-frequency plane using R&amp;#x00E9;nyi entropy - Application in seismic signal processing

Popescu, Theodor D.; Aiordăchioaie, Dorel

doi:10.1109/systol.2013.6693812

Cited by 27 publications

(13 citation statements)

References 25 publications

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“…Other popular entropy measure is proposed by Renyi (1961). Some recent applications of the Reenyi entropy can be seen in Popescu & Aiordachioaie (2013). If T has the probability density function (1) then Renyi entropy is defined by…”

Section: Entropymentioning

confidence: 99%

The generalized weighted Lindley distribution: Properties, estimation, and applications

Ramos

Louzada

2016

Cogent Mathematics

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In this paper, we proposed a new lifetime distribution namely generalized weighted Lindley (GLW) distribution. The GLW distribution is a useful generalization of the weighted Lindley distribution, which accommodates increasing, decreasing, decreasingincreasing-decreasing, bathtub, or unimodal hazard functions, making the GWL distribution a flexible model for reliability data. A significant account of mathematical properties of the new distribution are presented. Different estimation procedures are also given such as, maximum likelihood estimators, method of moments, ordinary and weighted least-squares, percentile, maximum product of spacings and minimum distance estimators. The different estimators are compared by an extensive numerical simulations. Finally, we analyze two data sets for illustrative purposes, proving that the GWL outperform several usual three parameters lifetime distributions.

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Section: Entropymentioning

confidence: 99%

The generalized weighted Lindley distribution: Properties, estimation, and applications

Ramos

Louzada

2016

Cogent Mathematics

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“…Entropy is used to measure the randomness of systems and it is widely used in areas like physics, molecular imaging of tumours, sparse kernel density estimation, high-resolution scalar quantization, estimation of the number of components of a multi-component non-stationary signal, identification of cardiac autonomic neuropathy in diabetes and signal segmentation in time-frequency plane (Popescu and Aiordachioaie [38]).…”

Section: R ́Nyi Entropy Of the Generalized Weighted Rama Distributionmentioning

confidence: 99%

Generalized Weighted Rama Distribution: Properties and Application to Model Lifetime Data

Enogwe

John

Obiora-Ilouno

et al. 2021

ARJOM

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In this paper, we propose a new lifetime distribution called the generalized weighted Rama (GWR) distribution, which extends the two-parameter Rama distribution and has the Rama distribution as a special case. The GWR distribution has the ability to model data sets that have positive skewness and upside-down bathtub shape hazard rate. Expressions for mathematical and reliability properties of the GWR distribution have been derived. Estimation of parameters was achieved using the method of maximum likelihood estimation and a simulation was performed to verify the stability of the maximum likelihood estimates of the model parameters. The asymptotic confidence intervals of the parameters of the proposed distribution are obtained. The applicability of the GWR distribution was illustrated with a real data set and the results obtained show that the GWR distribution is a better candidate for the data than the other competing distributions being investigated.

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“…There are various segmentation methods presented in the literature [5]- [7] applied in a wide range of domains, from music [8], and seismology [9], to astrology [10], and medicine [1]- [3]. Most commonly these methods utilize more or less complex conventional techniques like wavelet transformation [4], and frequency analysis [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks

Vrbančič¹,

Fister²,

Podgorelec³

2019

ElAEE

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The analysis of non-stationary signals commonly includes the signal segmentation process, dividing such signals into smaller time series, which are considered stationary and thus easier to process. Most commonly, the methods for signal segmentation utilize complex filtering, transformation and feature extraction techniques together with various kinds of classifiers, which especially in the field of biomedical signals, do not perform very well and are generally prone to poor performance when dealing with signals obtained in highly variable environments. In order to address these problems, we designed a new method for the segmentation of heart sound signals using deep convolutional neural networks, which works in a straightforward automatic manner and does not require any complex pre-processing. The proposed method was tested on a set of heartbeat sound clips, collected by non-experts with mobile devices in highly variable environments with excessive background noise. The obtained results show that the proposed method outperforms other methods, which are taking advantage of using domain knowledge for the analysis of the signals. Based on the encouraging experimental results, we believe that the proposed method can be considered as a solid basis for the further development of the automatic segmentation of highly variable signals using deep neural networks.

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Signal segmentation in time-frequency plane using Rényi entropy - Application in seismic signal processing

Cited by 27 publications

References 25 publications

The generalized weighted Lindley distribution: Properties, estimation, and applications

The generalized weighted Lindley distribution: Properties, estimation, and applications

Generalized Weighted Rama Distribution: Properties and Application to Model Lifetime Data

Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks

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