Segmentation of non-stationary signals with applications

Lovell, Brian C.; Boashash, Boualem

doi:10.1109/icassp.1988.197203

Cited by 5 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following applications of the WVD have been reported and can be consulted: Oceanography: [19], [30], [44]; Underwater Acoustics: [33], [48], [52]. Seismic Processing: [12], [31], [53], [54], [55].…”

Section: Applicationsmentioning

confidence: 99%

Section: Estimation Procedurementioning

confidence: 99%

“…The choice of optimal windows to use for the analysis was given in [30] and further discussed in [19]. A segmentation procedure for estimating this ergodicity width is presented in [44], and its influence on the accuracy of the estimate of the evolutive spec trum, S°(t,f), and the instantaneous frequency, fA (t) of the signal z(t), is discussed in [45]. This methodology for obtaining a smoothed version of the EV of a signal provides the analyst with an estimate of the power spectrum of the signal as a function of time, and specifies the best averaging to use for a particular application.…”

Section: Pattern Recognitionmentioning

confidence: 99%

“…(44) where A^ is the Airy function defined as follows [38]: A_^(x) = 1 e x du. (45) J-oo Let us consider the signal with the following parameters: a(t) = rip(t -T/2) and T ~ 1ms; B -90 kHz; f 0 , =100kHz; BT = 90.…”

Section: 3mentioning

confidence: 99%

See 3 more Smart Citations

Time-Frequency Signal Analysis And Synthesis The Choice Of A Method And Its Application

Boashash

1988

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

Section: Applicationsmentioning

confidence: 99%

Section: Estimation Procedurementioning

confidence: 99%

Section: Pattern Recognitionmentioning

confidence: 99%

Section: 3mentioning

confidence: 99%

See 2 more Smart Citations

Time-Frequency Signal Analysis And Synthesis The Choice Of A Method And Its Application

Boashash

1988

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…The segmentation problem can be found in several applications. We only mention here a few of them like condition monitoring [9,14], machine performance analysis [15], experimental physics [1,16,17], biomedical signals (like ECG signals) [7,[18][19][20][21][22][23], speech [24][25][26], and econometrics [27,28].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modelling as a Tool for Seismic Signals Segmentation

Kucharczyk

Wyłomańska

Obuchowski

et al. 2016

Shock and Vibration

View full text Add to dashboard Cite

In order to model nonstationary real-world processes one can find appropriate theoretical model with properties following the analyzed data. However in this case many trajectories of the analyzed process are required. Alternatively, one can extract parts of the signal that have homogenous structure via segmentation. The proper segmentation can lead to extraction of important features of analyzed phenomena that cannot be described without the segmentation. There is no one universal method that can be applied for all of the phenomena; thus novel methods should be invented for specific cases. They might address specific character of the signal in different domains (time, frequency, time-frequency, etc.). In this paper we propose two novel segmentation methods that take under consideration the stochastic properties of the analyzed signals in time domain. Our research is motivated by the analysis of vibration signals acquired in an underground mine. In such signals we observe seismic events which appear after the mining activity, like blasting, provoked relaxation of rock, and some unexpected events, like natural rock burst. The proposed segmentation procedures allow for extraction of such parts of the analyzed signals which are related to mentioned events.

show abstract

Structural break detection method based on the Adaptive Regression Splines technique

Kucharczyk

Wyłomańska

Zimroz

2017

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

For many real data, long term observation consists of different processes that coexist or occur one after the other. Those processes very often exhibit different statistical properties and thus before the further analysis the observed data should be segmented. This problem one can find in different applications and therefore new segmentation techniques have been appeared in the literature during last years. In this paper we propose a new method of time series segmentation, i.e. extraction from the analysed vector of observations homogeneous parts with similar behaviour. This method is based on the absolute deviation about the median of the signal and is an extension of the previously proposed techniques also based on the simple statistics. In this paper we introduce the method of structural break point detection which is based on the Adaptive Regression Splines technique, one of the form of regression analysis. Moreover we propose also the statistical test which allows testing hypothesis of behaviour related to different regimes. First, the methodology we apply to the simulated signals with different distributions in order to show the effectiveness of the new technique. Next, in the application part we analyse the real data set that represents the vibration signal from a heavy duty crusher used in a mineral processing plant.

show abstract

Segmentation of non-stationary signals with applications

Cited by 5 publications

References 6 publications

Time-Frequency Signal Analysis And Synthesis The Choice Of A Method And Its Application

Time-Frequency Signal Analysis And Synthesis The Choice Of A Method And Its Application

Stochastic Modelling as a Tool for Seismic Signals Segmentation

Structural break detection method based on the Adaptive Regression Splines technique

Contact Info

Product

Resources

About