Multiresolution Wavelet Decomposition Research Articles

A hybrid two-dimensional orthogonal wavelet multiresolution and proper orthogonal decomposition technique for the analysis of turbulent wake flow

A hybrid two-dimensional orthogonal wavelet multiresolution and proper orthogonal decomposition technique is developed to analyze the wake flow behind a semi cylinder. The two-dimensional orthogonal wavelet analysis is first applied to decompose the measured velocity fields into four wavelet components based on their spatial scales. Then the extracted multi-scale structures are further investigated based on proper orthogonal decomposition. The modal energy distributions of multi-scale flow structures suggest that the dominance of first two POD modes is reduced and the relative energy becomes more distributed as the flow structure changes from large-scale to small-scale. The first two POD modes of intermediate-scale structure also appear in a mode pair and they are related to secondary resonance accompanied with large-scale flow oscillation. The POD mode 3 of intermediate-scale structure suggest the existence of symmetric vortex shedding pattern. The first two POD modes of small-scale structure also display a symmetrically distributed vortex pair and the distance between adjacent vortices is about half of the case of mode 3 of intermediate-scale structure. The PSD distributions of POD modes of multi-scale structures suggest that the bandwidth of PSD becomes less concentrated as the flow structure varies from large-scale to small-scale. Distinctive flow patterns associated with first and second order harmonics of fundamental flow oscillation can be successfully extracted from the dominating POD modes of intermediate-scale and small-scale structures. The proposed hybrid technique provides a tool for relate the most energetic flow events over a period of time with multi-scale structures cascaded in the turbulence flow.

An objective fabric pilling evaluation based on wavelet transform and Gabor filter

A new objective fabric pilling detection method is proposed for monochrome fabric without pattern. Firstly, through wavelet multi-resolution decomposition and reconstruction, remove the high-frequency noise such as fluff which is significantly different from the size of pills and low-frequency noise such as uneven illumination. After wavelet multi-resolution decomposition of fabric image, through analyzing the energy change law between each wavelet decomposition layer, and then the fabric is judged whether it is rough or fine texture. Then, according to the different color characteristics of fine texture and rough texture fabrics, the even symmetry two-dimensional Gabor filter with smoothing effect and the odd symmetry two-dimensional Gabor filter with edge extraction ability in different directions are selected for texture removal. Finally, fabric pilling parameters are extracted to realize fabric pilling grade evaluation. The main contribution of this method is: on the premise of removing the noise such as uneven illumination and fluff, on the basis of considering the different color change laws of fine texture and rough texture, using the directionality of fabric texture and the randomness of pills direction to effectively remove the texture noise, so as to improve the accuracy of fabric pilling grade evaluation.

Fault Protection in Microgrid Using Wavelet Multiresolution Analysis and Data Mining

The protection problems in microgrid effect the reliability of the power system caused due to high distributed generator penetrations. Therefore, fault protection in microgrid is extremely important and needs to be resolved to enhance the robustness of the power system. This manuscript proposes a combined signal processing and data mining-based approach for microgrid fault protection. In this study, first the multiresolution decomposition of wavelet transform is employed to preprocess the voltage and current signals to compute the total harmonic distortion of the voltage and current. Then, the statistical indices of standard deviation, mean, and median of the total harmonic distortion and the negative sequence components of active and reactive power are used to collect the input data. After that, all the available data is provided to the random forest-based classifier to evaluate the efficiency of the proposed scheme in terms of the detection, identification, and classification of faults. This study used different aspects for data collection by simulating various fault and no-fault cases for both looped and radial configurations under grid-connected and islanded modes of operation. The simulations were performed on a standard medium voltage microgrid using MATLAB/SIMULINK, whereas the analysis for testing and training of the random forest were conducted in Python. It is recognized that the proposed method performed better than support vector machines and decision tree that are reported in the literature. The results further demonstrate that the proposed method can also detect simultaneous faults, and it is also effective against measurement noise.

Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction

Turbomachinery often suffers various defects such as impeller cracking, resulting in forced outage, increased maintenance costs, and reduced productivity. Condition monitoring and damage prognostics has been widely used as an increasingly important and powerful tool to improve the system availability, reliability, performance, and maintainability, but still very challenging due to multiple sources of data uncertainties and the complexity of analytics modeling. This paper presents an intelligent probabilistic methodology for anomaly prediction of high-fidelity turbomachine, considering multiple data imperfections and multivariate correlation. The proposed method adeptly integrates several advanced state-of-the-art signal processing and artificial intelligence techniques: wavelet multi-resolution decomposition, Bayesian hypothesis testing, probabilistic principal component analysis, and fuzzy stochastic neural network modeling. The advanced signal processing is employed to reduce dimensionality and to address multivariate correlation and data uncertainty for damage prediction. Instead of conventionally using raw time series data, principal components are utilized in the establishment of stochastic neural network model and anomaly prediction. Bayesian interval hypothesis testing metric is then presented to quantitatively compare the predicted and measured data for model validation and anomaly evaluation, thus providing a confidence indicator to judge the model quality and evaluate the equipment status. Bayesian method is developed in this study for denoising the raw data with multiresolution wavelet decomposition, quantifying the model accuracy, and assessing the equipment status. The dynamic stochastic neural network model is established via the nonlinear autoregressive moving average with exogenous inputs approach. It seamlessly integrates the fuzzy clustering and independent Bernoulli random function into radial basis function neural network. A natural gradient method based on Kullback-Leibler distance criterion is employed to maximize the log-likelihood loss function. The effectiveness of the proposed methodology and procedure is demonstrated with the 11-variable time series data and the forced outage event of a real-world centrifugal compressor.

Multiresolution decomposition and wavelet analysis of urban aerosol fluxes in Italy and Austria

Observations of turbulent aerosol fluxes are fundamental to understand basic transport processes that govern changes of particle concentrations in the atmospheric boundary layer. The turbulent surface-atmosphere exchange of atmospheric particles can be quantified using several methods, including the eddy-covariance (EC) method and spectral flux estimation methods such as wavelet analysis and multiresolution decomposition. In this work, turbulent time series obtained by EC measurements in two different cities, Lecce (Italy) and Innsbruck (Austria), are spectrally analysed applying wavelet analysis and multiresolution decomposition, and the respective turbulent spectra are compared in these two European cities to quantify the contributions to turbulent fluxes in both the time and the frequency domains. As expected, particle emission is dominant in both cities following a similar diurnal cycle. Multiresolution decomposition reveals a similar cospectral peak of particle fluxes in both cities, with a median normalized frequency of n = 0.087 in Lecce and n = 0.086 in Innsbruck. Wavelet analysis shows that the 2–20 s time scales contribute very strongly to the particle flux in Lecce, while in Innsbruck the 20–200 s time scales are clearly dominant. In both cities, larger-sized eddies contribute only sporadically to turbulent aerosol fluxes. These results suggest that spectral similarity of urban particle number fluxes holds, to a large extent, even when comparing two very different urban environments and different meteorological conditions.

A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra.

Adulterated sesame oil seriously damages the interests of consumers and the health of market. In this paper, a simple, fast and real-time model for identifying adulterated sesame oil (ASO) was proposed by combining 3D fluorescence spectra with wavelet moments (WMs). First, noise and data volume of the experimental data were reduced by wavelet multiresolution decomposition (WMRSD), which improved the stability and real-time of the model. Next, WMs were used to extract the features of the 3D fluorescence spectra and proved to be effective by hierarchical clustering results. Then, the qualitative quality of WMs of the same orders, different orders and the combinations were evaluated by Dunn's validity index (DVI), and the rules were given, respectively. Finally, the target WMs for identifying ASO were determined. This model is simple and fast, and expandable to online measurement, providing a reference for identification and adulteration of vegetable oils.

Machine learning and multiresolution wavelet decomposition to improve automated cardiac arrhythmia detection and classification

Machine learning and multiresolution wavelet decomposition to improve automated cardiac arrhythmia detection and classification

Identification of time-varying systems with partial acceleration measurements by synthesis of wavelet decomposition and Kalman filter

Structural systems often exhibit time-varying dynamic characteristics during their service life due to serve hazards and environmental erosion, so the identification of time-varying structural systems is an important research topic. Among the previous methodologies, wavelet multiresolution analysis for time-varying structural systems has gained increasing attention in the past decades. However, most of the existing wavelet-based identification approaches request the full measurements of structural responses including acceleration, velocity, and displacement responses at all dynamic degrees of freedom. In this article, an improved algorithm is proposed for the identification of time-varying structural parameters using only partial measurements of structural acceleration responses. The proposed algorithm is based on the synthesis of wavelet multiresolution decomposition and the Kalman filter approach. The time-varying structural stiffness and damping parameters are expanded at multi-scale profile by wavelet multiresolution decomposition, so the time-varying parametric identification problem is converted into a time-invariant one. Structural full responses are estimated by Kalman filter using partial observations of structural acceleration responses. The scale coefficients by the wavelet expansion are estimated via the solution of a nonlinear optimization problem of minimizing the errors between estimated and observed accelerations. Finally, the original time-varying parameters can be reconstructed. To demonstrate the efficiency of the proposed algorithm, the identification of several numerical examples with various time-varying scenarios is studied.

Spatial scales and locality of magnetic helicity

Context. Magnetic helicity is approximately conserved in resistive magnetohydrodynamic models. It quantifies the entanglement of the magnetic field within the plasma. The transport and removal of helicity is crucial in both dynamo development in the solar interior and active region evolution in the solar corona. This transport typically leads to highly inhomogeneous distributions of entanglement. Aims. There exists no consistent systematic means of decomposing helicity over varying spatial scales and in localised regions. Spectral helicity decompositions can be used in periodic domains and is fruitful for the analysis of homogeneous phenomena. This paper aims to develop methods for analysing the evolution of magnetic field topology in non-homogeneous systems. Methods. The method of multi-resolution wavelet decomposition is applied to the magnetic field. It is demonstrated how this decomposition can further be applied to various quantities associated with magnetic helicity, including the field line helicity. We use a geometrical definition of helicity, which allows these quantities to be calculated for fields with arbitrary boundary conditions. Results. It is shown that the multi-resolution decomposition of helicity has the crucial property of local additivity. We demonstrate a general linear energy-topology conservation law, which significantly generalises the two-point correlation decomposition used in the analysis of homogeneous turbulence and periodic fields. The localisation property of the wavelet representation is shown to characterise inhomogeneous distributions, which a Fourier representation cannot. Using an analytic representation of a resistive braided field relaxation, we demonstrate a clear correlation between the variations in energy at various length scales and the variations in helicity at the same spatial scales. Its application to helicity flows in a surface flux transport model show how various contributions to the global helicity input from active region field evolution and polar field development are naturally separated by this representation. Conclusions. The multi-resolution wavelet decomposition can be used to analyse the evolution of helicity in magnetic fields in a manner which is consistently additive. This method has the advantage over more established spectral methods in that it clearly characterises the inhomogeneous nature of helicity flows where spectral methods cannot. Further, its applicability in aperiodic models significantly increases the range of potential applications.

Effects of Global Climate Oscillations on Intermonthly to Interannual Variability of Sea levels along the English Channel Coasts (NW France)

Summary This work examines the multiscale variability in sea level along the English Channel coasts (NW France) using a wavelet multiresolution decomposition of water level values and climate oscillations in order to gain insights in the connection between the global atmospheric circulation and the local-scale variability of the monthly extreme surges. Changes in surges have exhibited different oscillatory components from the intermonthly (∼3–6-months) to the interannual scales (∼1.5-years, ∼2–4-years, ∼5–8-years) with mean explained variances of ∼40% and ∼25% of the total variability respectively. The correlation between the multiresolution components of surges and 28 exceptional stormy events with different intensities has revealed that energetic events are manifested at all timescales while moderate events are limited to short scales. By considering the two hypotheses of (1) the physical mechanisms of the atmospheric circulation change according to the timescales and (2) their connection with the local variability improves the prediction of the extremes, the multiscale components of the monthly extreme surges have been investigated using four different climate oscillations (Sea Surface Temperature (SST), Sea-Level Pressure (SLP), Zonal Wind (ZW), and North Atlantic Oscillation (NAO)); results show statistically significant correlations with ∼3–6-months, ∼1.5-years, ∼2–4-years, and ∼5–8-years, respectively. Such physical links, from global to local scales, have been considered to model the multiscale monthly extreme surges using a time-dependent Generalized Extreme Value (GEV) distribution. The incorporation of the climate information in the GEV parameters has considerably improved the fitting of the different timescales of surges with an explained variance higher than 30%. This improvement exhibits their nonlinear relationship with the large-scale atmospheric circulation.

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors.

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.

A Temporal Neural Trace of Wavelet Coefficients in Human Object Vision: An MEG Study.

Wavelet transform has been widely used in image and signal processing applications such as denoising and compression. In this study, we explore the relation of the wavelet representation of stimuli with MEG signals acquired from a human object recognition experiment. To investigate the signature of wavelet descriptors in the visual system, we apply five levels of multi-resolution wavelet decomposition to the stimuli presented to participants during MEG recording and extract the approximation and detail sub-bands (horizontal, vertical, diagonal) coefficients in each level of decomposition. Apart from, employing multivariate pattern analysis (MVPA), a linear support vector classifier (SVM) is trained and tested over the time on MEG pattern vectors to decode neural information. Then, we calculate the representational dissimilarity matrix (RDM) on each time point of the MEG data and also on wavelet descriptors using classifier accuracy and one minus Pearson correlation coefficient, respectively. Given the time-courses calculated from performing the Pearson correlation between the wavelet descriptors RDMs and MEG decoding accuracy in each time point, our result shows that the peak latency of the wavelet approximation time courses occurs later for higher level coefficients. Furthermore, studying the neural trace of detail sub-bands indicates that the overall number of statistically significant time points for the horizontal and vertical detail coefficients is noticeably higher than diagonal detail coefficients, confirming the evidence of the oblique effect that the horizontal and vertical lines are more decodable in the human brain.

Linking sea level dynamic and exceptional events to large-scale atmospheric circulation variability: A case of the Seine Bay, France

Summary In this study, the multi-time-scale variability of the South English Channel (case of the Seine Bay, North France) sea level and its exceptional events have been investigated in relation with the global climate patterns by the use of wavelet multi-resolution decomposition techniques. The analysis has been focused on surges demodulating by an envelope approach. The low-frequency components of the interannual (2.1-yr, 4-yr, 7.8-yr) and the interdecadal (15.6-yr and 21.2-yr) time-scales, extracted from 46-years demodulated surges, have been correlated to 36 exceptional stormy events according to their intensity. Results have revealed five categories of storms function on their correlation with the interannual and the interdecadal demodulated surges: events with high energy are manifested at the full scales while moderate events are only observed at the interannual scales. The succession of storms is mainly carried by the last positive oscillations of the interannual and the interdecadal scales. A statistical downscaling approach integrating the discrete wavelet multi-resolution analysis for each time-scale has been used to investigate the connection between the local dynamic of surges and the global atmospheric circulation from SLP composites. This relation illustrates dipolar patterns of high-low pressures suggesting positive anomalies at the interdecadal scales of 15.6-yr and 21.3-yr and the interannual scales of 4-yr while negative anomalies at 7.8-yr should be related to a series of physical mechanisms linked to the North-Atlantic and ocean/atmospheric circulation oscillating at the same time-scales. The increasing storm frequency is probably related to the Gulf Stream variation and its weakening trend in the last years.

Watermelon ripeness detection by wavelet multiresolution decomposition of acoustic impulse response signals

Ripeness is an important index for evaluating the quality of watermelons. This paper presents a simple, effective, and inexpensive approach to detecting watermelon ripeness based on wavelet multi-resolution decomposition (WMRD) and a statistical hypothesis test. The key idea in the proposed method is to model each WMRD coefficient as a random variable. For a specific coefficient, two samples are obtained by the multi-scale decomposition of watermelon acoustic signals, where one is for ripe watermelons and the other is for unripe ones. It is found that that many coefficients can be regarded as normal random variables, both for ripe and unripe watermelons. A discrimination index is proposed to estimate the separation degree of two samples, in order to determine the best WMRD coefficient. The probability density functions for ripe and unripe watermelons are obtained using the samples with the best coefficient. As a result, an inequality based on the hypothesis test at a 5% significant level is established for detecting watermelon ripeness. Using the proposed method, the training and test accuracies can reach 91.67% and 91.76%, respectively.

Classification of User Trajectories in LTE HetNets Using Unsupervised Shapelets and Multiresolution Wavelet Decomposition

The classification of user trajectories in Long-Term Evolution (LTE) heterogeneous networks (HetNets) is investigated in this paper. We propose a methodology to classify user trajectories based on the measurement reports submitted to the serving base station as part of the handover process; we propose to consider each measurement report as a time series. This methodology allows base stations to automatically and autonomously discover the radio-frequency (RF) conditions of their cell edge (e.g., signal strength degradation and interference levels). We propose the application of machine learning and data mining techniques to identify patterns in the reference signal received power measurement reports submitted by users as they approach the edge of the service area. Our time-series clustering algorithm based on unsupervised shapelets and multiresolution wavelet decomposition provided superior performance compared to a discrete Fourier transform (DFT)-based clustering algorithm. Our algorithm was able to provide clustering results with an average accuracy of 95%. Furthermore, the quality measure of the resulting clusters was up to 75% better, compared to the clustering results provided by the DFT-based algorithm. We also proposed a novel methodology to calculate a suitable number of clusters with no prior knowledge regarding the data; an average accuracy close to 90% was achieved.

Wavelet Multiresolution Complex Network for Analyzing Multivariate Nonlinear Time Series

Characterizing complicated behavior from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. We in this paper propose a novel wavelet multiresolution complex network (WMCN) for analyzing multivariate nonlinear time series. In particular, we first employ wavelet multiresolution decomposition to obtain the wavelet coefficients series at different resolutions for each time series. We then infer the complex network by regarding each time series as a node and determining the connections in terms of the distance among the feature vectors extracted from wavelet coefficients series. We apply our method to analyze the multivariate nonlinear time series from our oil–water two-phase flow experiment. We construct various wavelet multiresolution complex networks and use the weighted average clustering coefficient and the weighted average shortest path length to characterize the nonlinear dynamical behavior underlying the derived networks. In addition, we calculate the permutation entropy to support the findings from our network analysis. Our results suggest that our method allows characterizing the nonlinear flow behavior underlying the transitions of oil–water flows.

Day-ahead Forecasting of Non-stationary Electric Power Demand in Commercial Buildings: Hybrid Support Vector Regression Based

Abstract Accurate and highly-generalized forecasting models of hourly electric power demand are in urgent need for buildings, as to be the basis of operation management and bottom-up regional energy forecasting. Combined with multi-resolution wavelet decomposition (MWD), a hybrid support vector regression model was applied in a non-stationary operated hotel to predict the hourly electric power. With 15-dimensional parameters of 29 clustered days as the training sample, a nonlinear SVR model was carried out. Relative errors (RE) with and without MWD were compared at different ɛ-non-insensitive values. Results show that the MWD processing can reduce the deviations slightly only when ɛ is higher than 0.1, and the optimal daily mean RE of a typical day is around 5.6%. This paper aims to offer engineers and planners a feasible method for energy prediction based on the historical meter readings.

Short-term prediction of electric demand in building sector via hybrid support vector regression

Reliable and highly-generalized prediction models of short-term electric demand are urgently needed for the building sector, as the crucial basis of sophisticated building energy management. Advances in metering technologies and machine learning methods provide both opportunities and challenges for modified approaches. With multi-resolution wavelet decomposition (MWD) as a preprocessing from the point of view of signal analysis, the hybrid support vector regression (SVR) model was applied in two case study buildings to predict the hourly electric demand intensity. Taking ten-dimensional parameters of 29 workdays as the training sample, this model was carried out in a mall and a hotel, the consumed electric demand sequences of which represented the stationary and non-stationary series respectively.By comparisons between the hybrid SVR and the pure SVR, results indicated that the introduction of MWD can always improve the predicting accuracy for the hotel, while it is not necessary for the mall. Specifically, the similar steady level around 0.65W/m2 of absolute error was obtained for the mall and the hotel buildings, when ε was lower than 0.1. At the same time, the steady quantitative values of relative errors tended to be around 4% and 6% respectively for the hotel and the mall. Based on the limited historical readings, this paper offers an on-line prediction method of short-term electric demand, which is applicable for the further smart energy management.

Multi-time-scale hydroclimate dynamics of a regional watershed and links to large-scale atmospheric circulation: Application to the Seine river catchment, France

In the present context of global changes, considerable efforts have been deployed by the hydrological scientific community to improve our understanding of the impacts of climate fluctuations on water resources. Both observational and modeling studies have been extensively employed to characterize hydrological changes and trends, assess the impact of climate variability or provide future scenarios of water resources. In the aim of a better understanding of hydrological changes, it is of crucial importance to determine how and to what extent trends and long-term oscillations detectable in hydrological variables are linked to global climate oscillations.In this work, we develop an approach associating correlation between large and local scales, empirical statistical downscaling and wavelet multiresolution decomposition of monthly precipitation and streamflow over the Seine river watershed, and the North Atlantic sea level pressure (SLP) in order to gain additional insights on the atmospheric patterns associated with the regional hydrology. We hypothesized that: (i) atmospheric patterns may change according to the different temporal wavelengths defining the variability of the signals; and (ii) definition of those hydrological/circulation relationships for each temporal wavelength may improve the determination of large-scale predictors of local variations.The results showed that the links between large and local scales were not necessarily constant according to time-scale (i.e. for the different frequencies characterizing the signals), resulting in changing spatial patterns across scales. This was then taken into account by developing an empirical statistical downscaling (ESD) modeling approach, which integrated discrete wavelet multiresolution analysis for reconstructing monthly regional hydrometeorological processes (predictand: precipitation and streamflow on the Seine river catchment) based on a large-scale predictor (SLP over the Euro-Atlantic sector). This approach basically consisted in three steps: 1 – decomposing large-scale climate and hydrological signals (SLP field, precipitation or streamflow) using discrete wavelet multiresolution analysis, 2 – generating a statistical downscaling model per time-scale, 3 – summing up all scale-dependent models in order to obtain a final reconstruction of the predictand. The results obtained revealed a significant improvement of the reconstructions for both precipitation and streamflow when using the multiresolution ESD model instead of basic ESD. In particular, the multiresolution ESD model handled very well the significant changes in variance through time observed in either precipitation or streamflow. For instance, the post-1980 period, which had been characterized by particularly high amplitudes in interannual-to-interdecadal variability associated with alternating flood and extremely low-flow/drought periods (e.g., winter/spring 2001, summer 2003), could not be reconstructed without integrating wavelet multiresolution analysis into the model. In accordance with previous studies, the wavelet components detected in SLP, precipitation and streamflow on interannual to interdecadal time-scales could be interpreted in terms of influence of the Gulf-Stream oceanic front on atmospheric circulation.

OP.1C.02] SPECTRAL ESTIMATION OF PETERSON'S MODULUS VARIABILITY FROM ARTERIAL BLOOD PRESSURE AND WAVELET ANALYSIS

Objective: Arterial stiffness is mainly characterized by its Peterson's Modulus (EP). Increased values of EP are associated with a reduced heart function and an increased arterial blood pressure. Assessment of EP requires simultaneous measurements of arterial diameter and pressure signals. However, instantaneous determination of arterial diameter signals implies optical and ultrasonic techniques and neither of them are portables. Aims: To evaluate a novel method for estimating the Ep spectral variability of arterial stiffness, without arterial diameter signals. Design and method: Arterial pressure (Konigsberg micro-transducer) was measured in 10 sheeps (25–35 kg) during 24 hours. Instantaneous cardiac cycle of pressure signals were determined using five–level multi-resolution wavelet analysis and decomposition. A state machine processed and analysed different linear combinations of details d3, d4 and d5 of mother wavelet Symlet11. EP spectral estimation assessment involved the study of various temporal locations of the fiducial elasticity starting point within each beat. Moreover, discard criteria were added from the heart rate variability studio and coherence index between systolic pressure and beat to beat intervals around 0.1 Hz. Agreement between the actual direct measurement of EP and the proposed method of spectral stiffness estimation was analyzed according to the Bland–Altman approach. Results: Results showed a high coherence (more than 70%) between the actual and proposed method for a wide frequency range (0 to 0.5 Hz). Bland & Altman test showed a standard deviation of the mean less of 10% between the proposed methodology and the direct assessment of EP. Conclusions: The methodology presented in this work evidenced high levels of spectral coherence in EP variability studios and no significantly difference between the actual and the proposed estimation method. So, this novel method is able to estimate the Peterson's modulus spectral variability by solely using the arterial blood pressure signal.