Research Article  Open Access
Julio Ramirez Pacheco, Deni Torres Román, Homero Toral Cruz, "Distinguishing Stationary/Nonstationary Scaling Processes Using Wavelet Tsallis Entropies", Mathematical Problems in Engineering, vol. 2012, Article ID 867042, 18 pages, 2012. https://doi.org/10.1155/2012/867042
Distinguishing Stationary/Nonstationary Scaling Processes Using Wavelet Tsallis Entropies
Abstract
Classification of processes as stationary or nonstationary has been recognized as an important and unresolved problem in the analysis of scaling signals. Stationarity or nonstationarity determines not only the form of autocorrelations and moments but also the selection of estimators. In this paper, a methodology for classifying scaling processes as stationary or nonstationary is proposed. The method is based on wavelet Tsallis entropies and particularly on the behaviour of these entropies for scaling signals. It is demonstrated that the observed wavelet Tsallis entropies of signals can be modeled by sumcosh apodizing functions which allocates constant entropies to a set of scaling signals and varying entropies to the rest and that this allocation is controlled by . The proposed methodology, therefore, differentiates stationary signals from nonstationary ones based on the observed wavelet Tsallis entropies for signals. Experimental studies using synthesized signals confirm that the proposed method not only achieves satisfactorily classifications but also outperforms current methods proposed in the literature.
1. Introduction
The theory of scaling processes has shown to be meaningful in several fields of applied science [1]. Some aspects of scaling behaviour have been reported in finance [2, 3], in the analysis of heart rate variability and EEGs in physiology [4, 5], in the characterization of mood and other behavioural variables in psychology [6], in the modelling of computer network traffic and delays in LANs and WANs [7, 8], and in the study of the velocity field of turbulent flows in turbulence [9–12] among others. The scaling signals studied in these fields can be modelled by a wide variety of stochastic processes, the majority of which are characterized by the single scaling index or the associated Hurst index . Theoretically, the scaling index determines not only the nature of the signal in terms of smooth, stationarity, nonstationarity, and correlations but also the selection of the methodologies employed to estimate . The boundary is of special importance since processes for which are categorized as stationary in the sense that their statistical properties are invariant to translations and processes with are nonstationary. The stationary/nonstationary condition is fundamental for analysis and estimation purposes as many estimators have been devised for stationary signals while others have been formulated for nonstationary ones. The application of a nonstationary signal to an analysis/estimation technique designed to work in stationary conditions will result in an ambiguous analysis/estimate. For a review on the methodologies used to estimate and the range of the scaling index over which they are applicable please refer to the work of Serinaldi [13], Malamud and Turcotte [14], and Gallant et al. [15]. In practice, a scaling process analyst does not known apriori the nature of the signal, and usually the estimation of is performed somewhat arbitrarily without a stage of signal classification. Many articles in the literature, claiming that a given phenomena can be modelled by scaling signals, have performed the estimation of without a phase of signal classification, and therefore their results remain questionable. Moreover, the scaling property of signals, in particular the longmemory characteristic, can also be caused by structural breaks in the mean, a common nonstationarity embedded in the signal [16, 17]. Because of this, the phase of signal classification is not only important but also necessary. The phase of signal classification was first recognized as important by Eke and coworkers in physiological signal analysis [4–6]. In their work, Eke et al. emphasized the importance of signal classification, the implications of omitting this phase, and the necessity of including this phase as a first step for enhancing the estimation and analysis of scaling signals. They claimed that by integrating this step in the traditional estimation and analysis methodologies, significant improvements can be achieved, and the possibility of misinterpreting the phenomena is decreased. Traditionally, signal classification has been performed by methods based on PSD [5, 6] on fractional Brownian motions, fBms, and fractional Gaussian noises, fGns. The characteristics of fBms and fGns, however, are visually different, and the signal classification process can even be performed by eye. Motivated by this, the present paper not only extends the results presented in [5] for the case of PPL signals but also proposes a methodology based on wavelet Tsallis entropies to differentiate scaling processes as stationary or nonstationary. The method is based on the observed sumcosh window behaviour of these entropies which allocates constant entropies to stationary signals and varying entropies to nonstationary ones reducing the classification process to the constant/nonconstant character of the observed estimated entropies of the signals under study. Experimental and comparison studies not only confirm the capabilities of the method but also their advantages over standard methodologies based on PSD. The remainder of the paper is structured as follows. Section 2 provides a brief review of scaling processes, their definitions, and some standard results concerning its wavelet analysis. Section 3 describes wavelet entropies and its applications and derives the sumcosh window behaviour observed in wavelet Tsallis entropies for signals with behaviour. The techniques employed for classifying scaling signals as stationary or nonstationary as well as their advantages and disadvantages are briefly reviewed in Section 4 along with a description of the proposed methodology based on wavelet Tsallis entropies to discriminate signals. Section 5 describes the methodology used in the paper for testing the accuracy and robustness of the proposed technique and also details the comparative study to be performed among the techniques used for scaling signal classification. Section 6 presents the experimental results, and finally the conclusions of the paper are drawn in Section 7.
2. Wavelet Analysis of Scaling Processes
2.1. Scaling Processes
Scaling processes of parameter , also called or powerlaw processes, have been extensively applied and studied in the scientific literature since they model diverse phenomena [2, 3] within these fields. These processes are sufficiently characterized by the parameter , called the scalingindex, which determines many of their properties. Various definitions have been proposed in the scientific literature, some based on their characteristics such as selfsimilarity or long memory, others based on the behaviour of their PSD. In this paper, a scaling process is a random process for which the associated PSD behaves as a power law in a range of frequencies [8, 9], that is, where is a constant, the scaling index, and , represent the lower and upper bound frequencies upon which the powerlaw scaling holds. Depending upon , , and , several particular scaling processes and behaviours can be identified. Independently of , local regularity and bandpass powerlaw behaviour are observed whenever and , respectively. When the scalingindex is taken into consideration, longmemory behaviour is observed when both and . Selfsimilar features (in terms of distributional invariance under dilations) are observed in all the scalingindex range for all . Scalingindex determines not only the stationary and nonstationary condition of the scaling process but also the smoothness of their sample path realizations. The greater the scaling index , the smoother their sample paths. As a matter of fact, as long as , the scaling process is stationary (or stationary with long memory for small and ) and nonstationary when . Some transformations can make a stationary process appear nonstationary and vice versa. Outside the range , several other processes can be identified, for example, the socalled extended fBms and fGns defined in the work of Serinaldi [13]. The persistence of scaling processes can also be quantified by the index , and within this framework, scaling processes possess negative persistence as long as , positive weak long persistence when , and positive strong long persistence whenever . Scaling signals encompasses a large family of wellknown random signals, for example, fBms, fGns [18], pure powerlaw processes [9], multifractal processes [8], and so forth. FBm, , comprises a family of Gaussian, selfsimilar processes with stationary increments, and because of the Gaussianity, it is completely characterized by its autocovariance sequence ACVS, which is given by where is the Hurstindex. FBm is nonstationary, and as such no spectrum can be defined on it; however, fBm possesses an average spectrum of the form as which implies that [19]. FBm has been applied very often in the literature; however it is its related process, fGn, which has gained widespread prominence because of the stationarity of its realizations.
FGn, , obtained by sampling a fBm process and computing increments of the form , (i.e., by differentiating fBm), is a wellknown Gaussian process. The ACVS of this process is given by where is the Hurstindex. The associated PSD of fGn is given by [9] where is the process’ variance and is a constant. FGn is stationary and for large enough and under the restriction of possesses longmemory or longrange dependence (LRD). The scaling index associated to fGn signals is given by as its PSD, given by (2.4), behaves asymptotically as for . Another scaling process of interest is the family of discrete pure powerlaw processes (dPPL) which are defined as processes for which their PSD behaves as for , where and represent a constant. PPL signals are stationary when the powerlaw parameter and nonstationary whenever . As stated in the work of Percival [9], the characteristics of these processes and those of fBms/fGns are similar; however, the differences between fBms and PPLs with are more evident. As a matter of fact, differentiation of stationarity/nonstationarity is far more difficult for PPL than for fBms/fGns. Figure 1 displays some realizations of fGn, fBm, and PPL processes. The scalingindex of the PPL signals is identical to the scalingindex of the associated fGn and fBm. Note that the characteristics of the sample paths of fGn are fairly different from those of fBm. In the case of PPL processes, this differentiation is not so evident, and as a matter of fact when the scaling indexes approach the boundary , classification becomes complex. For further information on the properties, estimators, and analysis techniques of scaling processes please refer to [2, 3, 8–10, 13, 14].
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2.2. Wavelet Analysis of Scaling Signals
Wavelets and wavelet transforms have been applied for the analysis of deterministic and random signals in almost every field of science [20–22]. The advantages of wavelet analysis over standard techniques of signal analysis have been widely reported and its potential for nonstationary signal analysis proven. Wavelet analysis represents a signal in timescale domain by the use of an analyzing or mother wavelet, [23]. For the purposes of the paper, and the family of shifted and dilated form an orthonormal basis of . In addition, the finiteness of the mean average energy () on the scaling process allows to represent it as a linear combination of the form: where is the DWT of and is a family of dilated (of order ) and shifted (of order ) versions of . The coefficients in (2.5), obtained by DWT, represent a random process for every , a random variable for fixed and , and as such many statistical analyses can be performed on them. Equation (2.5) represents signal as a linear combination of detail signals, obtained by means of the DWT. DWT is related to the theory of multiresolution signal representation (MRA), in which signals (or processes) can be represented at different resolutions based on the number of detail signals added to the lowfrequency approximation signal. Detail random signals () are obtained by projections of signal into wavelet spaces , and approximation coefficients () are obtained by projections of into related approximation spaces . In the study of scaling processes, wavelet analysis has been primarily applied in the estimation of the wavelet variance [20, 24]. Wavelet variance or spectrum of a random processes accounts for computing variances of wavelet coefficients at each scale. Wavelet variance not only has permitted to propose estimation procedures for the scalingindex but also to compute entropies associated with the scaling signals. Wavelet spectrum has also been used for detecting nonstationarities embedded in Internet traffic [20]. For stationary zeromean processes, wavelet spectrum is given by where is the Fourier integral of and represents the PSD of . Table 1 summarizes the wavelet spectrum for some standard scaling processes. For further details on the analysis, estimation, and synthesis of scaling processes please refer to the works of Abry and Veitch [23] and Bardet [25] and references therein.

3. WaveletBased Entropies
The concept of entropy has traditionally been employed to measure the information content of random signals and systems [26, 27]. Recently, entropic functionals, such as Shannon, Rényi, and Tsallis, have been extensively applied to quantify the complexities associated with random and nonlinear phenomena [28]. Information planes, which consist of the product of positive measures of entropic functional and the Fisher information (and also of entropy/disequilibrium product), are now being applied in numerous systems (e.g., atomic, molecular, geophysical, etc.). Entropic quantities involve the calculation of functionals on probability densities or probability mass functions (pmf). Depending upon the domain in which the pmfs are obtained, entropies usually inherit their name. Entropies are called spectral entropies when entropic functionals are applied to pmfs derived from the Fourier spectrum representation of the process. When the densities are determined in the timescale domain by discrete wavelet transformations, the associated entropy functionals are called wavelet entropies [29, 30]. If the pmf is obtained via the continuous wavelet transform, CWT, the entropy is called continuous multiresolution entropy (CMqE) [31]. Wavelet Shannon entropy, tantamount to computing a Shannon entropy functional on a pmf derived from the wavelet variance, has found applications in eventrelated potentials in neuroelectrical signals [32, 33], structural damage identification [34], segmentation of EEG signals [35], characterization of complexity in random signals [36–38] among others. Entropic measures of order (hereafter entropies) generalize Shannon entropy and provide the flexibility of fine tuning to a desired behaviour with the value of . The pmf in timescale domain for which all entropies in this paper are computed is obtained by where represents the variance or secondorder moment of the , (resp., ) stands for the total number of wavelet coefficients at scale (resp., ), and is the length of the process. For signals with PSD, the socalled wavelet spectrumbased pmf is determined by direct substitution of the wavelet spectrum of the process under study (see Table 1) into (3.1), which results in where . The density given in (3.2) represents the probability that the energy of the scaling signal is located at scale . The pmf of (3.2) can be used to compute numerous information theoretic functionals such as entropies, Fisher information, and information planes. Zunino and coworkers computed Shannon entropy functional on (3.2) and called it wavelet entropy. Later explicit formulas for wavelet Rényi and Tsallis entropies were derived and some applications suggested. Normalized Shannon entropy functional of scaling signals is given by where . Wavelet Rényi entropies, as in the case of Shannon entropies, are extensive entropies in the sense that for any two independent random variables and , the joint entropy . For scaling signals, Rényi entropy functional, , results in where denotes the extensivity parameter. Tsallis entropies are nonextensive entropies in the sense that the extensivity property no longer holds. For a pmf it is defined as where is the logarithm function and the nonextensivity parameter. Tsallis entropies provide a valuable and interesting tool for the analysis of systems with longrange interactions, long memories, and so forth. The application of Tsallis entropies is vast, from the characterization of complexities in EEG signals [28] to the study of nonlinear systems [31]. Normalized Tsallis functional applied to (3.2) results in wavelet Tsallis entropies which is given by where is a normalizing factor and is a polynomial of order , that is,
Figure 2 displays the wavelet Rényi entropies of scaling processes. Note that independently of signal length, wavelet Rényi entropies display a bellshaped form for these processes. Parameter stretches the bellshaped form as is varied. Parameter has, in view of these entropy planes, no effect on the form of the observed entropies as the bellshaped form is maintained. The maximum entropy is achieved when the scaling process is a pure white noise (), and as the process becomes nonstationary their entropies decrease. The form and behaviour of these entropies are similar as those observed in the literature [32] and reflect the extensivity character of the entropy functionals. Note that both Shannon and Rényi entropies describe appropriately the complexities associated to processes: maximum for highly disordered systems and minimum for smooth signals. For further information on wavelet entropy please refer to [30, 32].
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Figure 3 illustrates the wavelet Tsallis entropies for scaling processes of parameter . Note that as long as , wavelet Tsallis entropies are identical as those observed in wavelet Rényi entropies (i.e., they have the same bellshaped form). As long as , the behaviour of wavelet Tsallis entropies changes and differs from that of Shannon and Rényi. Wavelet Tsallis entropies, therefore, comprise the behaviour of wavelet Shannon and Rényi and provide greater flexibility in describing the process. Observe that, unlike Rényi entropies, Tsallis entropies allocate maximum (and constant) entropies to a set of scaling processes. In addition, the set of scaling signals for which this constant behaviour is observed is controlled by the nonextensivity parameter of Tsallis entropies. The constant behaviour observed means that wavelet Tsallis entropies regard some set of scaling processes as totally random or disordered and, in some sense, randomizes the scaling signal under study. This particular behaviour of wavelet Tsallis entropies is further explored in next section, and a model for this is derived.
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3.1. SumCosh Window Behaviour of Wavelet Tsallis Entropies
Wavelet Tsallis entropies allocates constant entropies to a set of scaling processes and varying entropies to the rest. This particular behaviour can be modelled by the theory of windowing or apodizing functions. This theory has been important for the design of digital filters; however, in this paper it is used to model the observed wavelet Tsallis entropies of signals. As a matter of fact, (3.8) resembles in some sense the cosh window observed in the work of Avci and Nacaroglu [39] and can be regarded as a sumcosh window provided , , and conditions are satisfied. The symmetry condition () is easily verified, and the is computed based on the observation that which results in . A similar reasoning for the expression to the right of in (3.6) results in Derivation of the second limit is performed by means of the asymptotic relation for large , consequently as . The above demonstrates that wavelet Tsallis entropies can be modelled by sumcosh windowing functions which in turn implies that for particular , rectangularlike behaviour can be observed. The quasirectangular behaviour implies that constant regions of entropies are observed for a range of scaling processes and varying for the rest. The set of scaling processes for which constant wavelet Tsallis entropies are observed is controlled by the nonextensivity parameter of Tsallis entropies. Figure 4 displays the shape of the wavelet Tsallis entropies for fixed length and different values of the nonextensivity parameter. For the cases and , a bellshaped form is observed which in some sense is identical to the ones observed for wavelet Shannon and Rényi entropies. Note from the figure that as , constant entropies are assigned to scaling processes in a symmetric range of . This quasirectangular form can be set up to allocate constant entropies to stationary scaling processes and varying entropies to nonstationary ones. As a matter of fact, constant wavelet Tsallis entropies can be obtained for stationary signals and varying entropies to nonstationary ones as long as . This behaviour is important since a potential application of this feature is on the classification of scaling processes as stationary or nonstationary.
4. Classification of Scaling Signals
The classification of scaling signals as stationary or nonstationary has already been recognized as an important and unresolved problem in many areas of signal analysis [5, 12, 40–42]. Signal classification not only enhances the estimation process (i.e., estimation of the scaling index ) but also provides a correct interpretation of the phenomena, which in turn eases the application of a given technique in the process under study. Much of the literature on selfsimilar, longmemory, and fractal processes lack a step of signal classification, the parameters were estimated under the assumption of stationary, and therefore their results remain questionable. The process of signal classification becomes harder as we approach the boundary of stationarity and nonstationarity, that is, when . The reason for this is that as , stationary signals incorporate some features of nonstationarity and viceversa. Signal classification techniques often fail to distinguish fractal noises from motions within this boundary. The signal classification phase is sometimes more straightforward in some families of scaling signals than in others. For example, fBms and fGns are visually different, and the classification is simpler than that for the case of PPLs which are more difficult to classify. In this respect, any signal classification procedure must differentiate scaling signals independently of signal family and also provide meaningful classifications in the boundary . Classification of scaling signals has traditionally been accomplished by using standard methodologies based on the PSD. PSD and PSDbased signal summation conversion (SSC) were recently proposed as methodologies for distinguishing fractal noises and motions in [5] by using synthesized fBms and fGns. In that work, fGns and fBms were generated in the interval and with sufficiently large lengths. The present paper proposes a methodology based on wavelet Tsallis entropies and its sumcosh window behaviour. In the following, we briefly review current techniques employed to perform the signal classification phase and describe the proposed methodology based on wavelet Tsallis entropies.
4.1. Power Spectral Density
Spectral density function (SDF) characterizes stationary random signals in frequency domain. According to the work of Eke and coworkers [5, 41], SDF can be used to classify signals based on the fact that the observed PSD of processes follows a powerlaw dependence (). When the estimated parameter of powerlaw dependence () is less than 1 (), the process is stationary; on the other hand if , the process is nonstationary. Signal classification in the SDF framework is therefore accomplished by first estimating the SDF of the scaling process under study using some standard methodology (e.g., Periodogram), plot versus , fit a line, compute the slope which corresponds to the estimated and finally based on the observed slope determine the nature of the process. Authors in [5] reported on the classification properties of the SDF method using synthesized signals of the fGn/fBm type. Eke and coauthors [5, 41] claimed that PSD performs satisfactorily when the process’ scaling parameter lies in the intervals and but misclassifies signals in the range . Because of this, they proposed a methodology specially designed to enhance the classification of signals in the interval . Figure 5 displays the algorithm based on PSD to classify scaling signals.
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4.2. Signal Summation Conversion
As stated in the previous section, PSD offers limited classification when the scaling process studied has a scaling index lying in the interval . The work of Eke et al. [5] not only identified this limitation but also proposed a solution based on the cumulative sum operation. The technique, called signal summation conversion, is only necessary whenever the estimated scaling index of the process lies in . The solution posed by Eke was to classify the process in the nonstationarity domain by the use of some standard nonstationarity technique. The use of the cumulative sum technique allowed the conversion of a stationary process into a nonstationary one and also maintaining the nonstationarity condition in a nonstationary process. Once the process to be classified is transformed to exhibit nonstationarity features, the following step is to estimate the Hurst index of this process by using some standard technique, for example, bridgedetrended scaledwindowed variance (bdSWV). Depending upon the estimated Hurst index obtained by bdSWV (), a process is classified as stationary whenever and nonstationary when . If the estimated lies outside this interval, the scaling process is regarded as unclassifiable. Eke and coworkers showed that the SSC enhances the classification observed in PSD at the expense of higher computational time. Even though SSC enhanced the classification of processes, many disadvantages can be identified in this technique. First, extended fGn cannot be classified as stationary within this framework as its cumulative sum is still stationary; secondly, SSC is based on PSD, a technique which has traditionally been attached to stationary signals. In addition, SSC has not been tested on signals displaying more complex behaviour such as PPLs, and the signals used to perform the classification are long. Figure 5 displays the algorithms for performing scaling signal classification in the PSD and SSC framework.
4.3. Wavelet Tsallis Entropies
Section 3 demonstrated that wavelet Tsallis entropies can be modelled by sumcosh apodizing functions which among other properties display constant regions of entropies and regions of decreasing entropies (i.e., quasirectangular behaviour). The length of the constant region, which usually lies in a symmetric range of the scaling index , can be controlled by the nonextensivity parameter of Tsallis entropies. If the constant region of entropies lies in the interval , then, every stationary scaling process will present maximum wavelet Tsallis entropy (). On the other hand if the process has a scaling index outside this range it will present fluctuations of entropy. The above suggest that wavelet Tsallis entropies can be used to differentiate scaling signals as stationary or nonstationary based on the observed entropies. If the estimated entropies are constant, then the scaling process is stationary, otherwise it is nonstationary.
Figure 6 captures the rationale behind the signal classification procedure based on wavelet Tsallis entropies. As long as , constant regions of entropies are observed for scaling signals with and varying for . If , then classification of fractional noises and motions can be accomplished, and when , classification of fractional motions from extended fractional motions is accomplished. Therefore, wavelet Tsallis entropies not only allows distinguishing stationary from nonstationary but also nonstationary from nonstationary.
5. Methodology
In [5], a comparison of PSD and SSC was performed by using synthesized signals of length . SSC was reported to present better classifications of fBms (as true fBms) and fGns (as true fGns). The present paper extends the results reported in [5] to PPL signals, which are known to present more complex behaviour than fBms and fGns, and proposes a novel methodology for scaling signal classification based on wavelet Tsallis entropies. The paper uses PPL signals with length , which are more realistic in the sense that many studies, reported in the literature with measured data, claimed that the nature of the phenomena does not permit to obtain higher signal lengths [6]. Also, estimation techniques often increase their MSE for shortlength signals. Therefore, the present study not only proposes a methodology for classifying scaling processes as stationary or nonstationary but also compares the techniques for signal classification in nonstandard conditions (i.e., by using complex signals with short lengths). PPL signals were synthesized by using the package fractal, which simulates signals using the circular embedding algorithm of Davies and Harte [43]. To test the performance of each technique, PPL signals were generated in the range in steps of . For each (in the range ), 100 traces were simulated; therefore, a total of 19900 traces were studied. The selection of the range: . is because of the fact that techniques of signal classification often fail in the limit and perform better outside this range. SSC often considers a signal as unclassifiable; however, for the purposes of comparison, an unclassifiable signal is regarded as misclassified in this paper. SSC was implemented in using the PSD algorithm of the fractal package. Wavelet entropy was implemented in (and also in MATLAB), and the classification of signals was based on fluctuations of entropy (by computing wavelet entropy in sliding windows). To study the fluctuations, subsets of the original scaling signal, , were taken in sliding windows of the form: where , is the sliding factor, and is the standard rectangular function. Once the signals were classified, their results were summarized by plotting where stand for the number of signals classified correctly for the technique for signals with scaling index .
6. Experimental Results
Figure 7 displays the results of the experimental study detailed (methodology) detailed in previous section. Note that for PSD, stationary PPL signals are classified correctly (i.e., classified as stationary). This was expected, since as previously stated, PSD was primarily designed to work for timeinvariant (stationary) random signals. For nonstationary signals, PSD classifies nonstationary signals as stationary, misclassifying every nonstationary PPL process. PSD, therefore, do not provide reliable classifications of PPL signals, and it is not recommended for use in a signal classification scheme. In [5], SSC was shown to enhance the classification of scaling signals for the range . Note, however, that SSC enhances the classification mostly for stationary signals. Moreover, SSC is only applicable as the estimated scaling index lies in , otherwise only the PSD is applied. Based on this, it is expected to have a similar behaviour of classifications as the PSD for the SSC technique. Middle plot of Figure 7 displays the classifications of the SSC technique for PPL signals of length . As expected, SSC presents identical behaviour as that of PSD and, as the case of PSD, is not recommended as a signal classification tool for signals with PPL behaviour. The results of PSD and those of SSC differ from those presented in [5]. Note, however, that the signals studied in this paper are of different nature than those studied in [5]. First, the signals studied in the work of Eke et al. [5] are fBms and fGns, which in some sense are more easily classifiable since their smoothness properties are visually different. Finally, the length of the signals studied in the work of Eke are longer than the length of the signals considered in this paper. It is well known that as the length of the studied signals increases, the MSE of the estimated decreases. Thus, the synthesized signals studied in this paper possess not only higher complexities but also shorter length. Rightmost plot of Figure 7 presents the classifications of PPL signals using the methodology proposed in this paper based on wavelet Tsallis entropies. Note that the classifications of the proposed technique are better than those observed in PSD and SSC. The proposed technique based on wavelet Tsallis entropies classifies correctly stationary as well as nonstationary PPL signals and that this classification is somewhat unacceptable in the limit of . The technique based on wavelet Tsallis entropies is fast enough and can also classify extended fGns from fGns and fBms from extended fBms. The classifications of signals with these characteristics are not supported by either the PSD and SSC techniques. In performing the classifications with the technique based on wavelet Tsallis entropies, the entropies were computed in sliding windows, and the boundary of fluctuations was taken as . The nonextensivity parameter was set to but similar results are observed for .
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7. Conclusions
This paper presented a novel methodology for classifying scaling signals as stationary or nonstationary based on wavelet Tsallis entropies. It was shown that the sumcosh window behaviour of wavelet Tsallis entropies allocated constant entropies to a set of scaling signals and varying to the rest and that the length of the constant region is controlled by , the nonextensivity parameter of Tsallis entropies. It was also shown that by setting the constant regions to the range of stationary scaling signals, the problem of signal classification can be reduced to the observation of constant/nonconstant entropies. The classification properties of the PSD and SSC were extended to signals with pure powerlaw behaviour with length , and a comparison procedure was performed among PSD, SSC, and the technique based on wavelet Tsallis entropy. The results not only confirm that the technique based on wavelet Tsallis entropies provides meaningful classification but also outperforms PSD and SSC techniques. The results presented in this paper are meaningful in many areas of scaling signal analysis since many estimation/analysis results presented in the literature have been performed without a phase of signal classification.
Acknowledgments
The present paper was jointly funded by the National Council of Science and Technology (CONACYT) under Grant 47609, FOMIXCOQCYT Grant no. 126031, and University of Caribe internal funds.
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Copyright © 2012 Julio Ramirez Pacheco et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.