Wavelet Transform-based Methods Research Articles

Wavelet transform-based mode decomposition for EEG signals under general anesthesia.

Mode decomposition methods are used to extract the characteristic intrinsic mode function (IMF) from various multidimensional time series signals. We analyzed an electroencephalogram (EEG) dataset for sevoflurane anesthesia using two wavelet transform-based mode decomposition methods, comprising the empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) methods, and compared the results with those from the previously reported variational mode decomposition (VMD) method. To acquire the EEG data, we used the software application EEG Analyzer, which enabled the recording of raw EEG signals via the serial interface of a bispectral index (BIS) monitor. We also created EEG mode decomposition software to perform empirical mode decomposition (EMD), VMD, EWT, and WMD operations. When decomposed into six IMFs, the EWT enables narrow band separation of the low-frequency bands IMF-1 to IMF-3, in which all central frequencies are less than 10 Hz. However, in the upper IMF of the high-frequency band, which has a center frequency of ≥ 10 Hz, the dispersion within the frequency band covered was widespread among the individual patients. In WMD, a narrow band of clinical interest is specified using a bandpass filter in a Meyer wavelet filter bank within a specific mode-decomposition discipline. When compared with the VMD and EWT methods, the IMF that was decomposed via WMD was accommodated in a narrow band with only a small variance for each patient. Multiple linear regression analyses demonstrated that the frequency characteristics of the IMFs obtained from WMD best tracked the changes in the BIS upon emergence from general anesthesia. The WMD can be used to extract subtle frequency characteristics of EEGs that have been affected by general anesthesia, thus potentially providing better parameters for use in assessing the depth of general anesthesia.

Wavelet Transform-based Methods for Forensic Analysis of Digital Images

In recent years, Generative Adversarial Networks (GANs) have been utilized in many applications of our daily lives to create digital media that was previously impossible. This paper utilizes GANs in multimedia forensics, where the precision and accuracy of image classification are crucial. The objective of the study is to detect sophisticated manipulated images generated by advanced deep learning methods using classical transformation methods. The method combines the classical wavelet transform for feature extraction with a classifier model to distinguish between real and fake images. The proposed method extracts unique features that differentiate between fake and real images, which are then fed to several gradient-boosting-based classifiers. The proposed method was tested using the FaceForensics++ dataset, which contains low-resolution video sequences of faces. The proposed model achieves an accuracy of 95% in detecting manipulated videos compared with 87% accuracy of other classical techniques.

Enhanced detonators detection in X-ray baggage inspection by image manipulation and deep convolutional neural networks

Detecting detonators is a challenging task because they can be easily mis-classified as being a harmless organic mass, especially in high baggage throughput scenarios. Of particular interest is the focus on automated security X-ray analysis for detonators detection. The complex security scenarios require increasingly advanced combinations of computer-assisted vision. We propose an extensive set of experiments to evaluate the ability of Convolutional Neural Network (CNN) models to detect detonators, when the quality of the input images has been altered through manipulation. We leverage recent advances in the field of wavelet transforms and established CNN architectures—as both of these can be used for object detection. Various methods of image manipulation are used and further, the performance of detection is evaluated. Both raw X-ray images and manipulated images with the Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform-based methods and the mixed CLAHE RGB-wavelet method were analyzed. The results showed that a significant number of operations, such as: edges enhancements, altered color information or different frequency components provided by wavelet transforms, can be used to differentiate between almost similar features. It was found that the wavelet-based CNN achieved the higher detection performance. Overall, this performance illustrates the potential for a combined use of the manipulation methods and deep CNNs for airport security applications.

Do oil and natural gas prices affect carbon efficiency? Daily evidence from China by wavelet transform-based approaches

This study analyzes the impacts of oil and natural gas prices on China's carbon efficiency using wavelet transform-based time series methods. To this end, the study applies the continuous wavelet transform (CWT), wavelet transform coherence (WTC), and a time-varying wavelet causality test (TVWCT) to daily data during the period from October 31, 2012 to November 3, 2022. The CWT results show that there are volatilities in oil prices due to the coronavirus 2019 (COVID-19) pandemic, in natural gas prices because of the Russia-Ukraine war, and in carbon efficiency resulting from the Paris Climate Agreement. The WTC results demonstrate that increasing oil prices promote carbon efficiency in the 2014–2016 and 2018–2020 periods, while natural gas prices have a weak impact on carbon efficiency in 2022. Moreover, the TVWCT results prove that there is a strong causality leading from oil and natural gas prices to carbon efficiency in the long run. These results highlight that China can use volatilities in oil and natural gas prices as a policy tool to enhance carbon efficiency.

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra.

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.

Music Note Feature Recognition Method based on Hilbert Space Method Fused with Partial Differential Equations

Hilbert space method is an old mathematical theoretical model developed based on linear algebra and has a high theoretical value and practical application. The basic idea of the Hilbert space method is to use the existence of some stable relationship between variables and to use the dynamic dependence between variables to construct the solution of differential equations, thus transforming mathematical problems into algebraic problems. This paper firstly studies the denoising model in the process of music note feature recognition based on partial differential equations, then analyzes the denoising method based on partial differential equations and gives an algorithm for fused music note feature recognition in Hilbert space; secondly, this paper studies the commonly used music note feature recognition methods, including linear predictive cepstral coefficients, Mel frequency cepstral coefficients, wavelet transform-based feature extraction methods and Hilbert space-based feature extraction methods. Their corresponding feature extraction processes are given.

Modal identification of high-rise buildings by combined scheme of improved empirical wavelet transform and Hilbert transform techniques

Recently, the empirical wavelet transform technique has attracted much attention due to its advantage of dealing with non-stationary and nonlinear signals. For empirical wavelet transform-based methods, their performance is heavily dependent on the accuracy of Fourier spectral segmentation. Structural response signals of high-rise buildings under ambient excitations often include high-level noises, which may lead to inaccurate spectral estimation, thereby introducing errors in modal decomposition. To this end, an improved empirical wavelet transform method is presented in this paper to analyze the structural response signals with high-level noises, which employs the projection information of Fourier spectra to improve spectral representation. Then, the improved empirical wavelet transform method is combined with the natural excitation technique and Hilbert transform for identifying modal properties of super-tall buildings under ambient excitations. Through a numerical simulation study, the validity and accuracy of the combined scheme are verified even when structural response signals were embedded with high levels of noise. Furthermore, the proposed method is adopted for modal identification of a 600 m supertall building for evaluating its applicability in field measurements. It is demonstrated that the combined scheme can effectively and accurately evaluate the modal properties of the skyscraper under ambient vibrations with low amplitudes.

Study of Wavelet Transform-based Image Fusion Methods

The main objective of this paper is to get some basic knowledge about image fusion, the methods used for fusion, and the problems that still exist in image fusion technology, and to draw the attention of the research field to image fusion technology through a combination of theory and practice.

Multiscale fusion of multimodal medical images using lifting scheme based biorthogonal wavelet transform

Medical image fusion has been used to improve useful and relevant information (e.g., precise localization of abnormalities) of multimodal medical images, such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) images, and the information obtained from the fused images can then be utilized as an assistive tool for better diagnosis and treatment. In this work, we propose an algorithm for fusing multimodal medical images using lifting scheme-based biorthogonal wavelet transform. The multiscale fusion scheme is performed for the multimodal medical images in the wavelet domain at multiple scales by adopting either average or absolute maximum fusion rules. To verify the effectiveness of the proposed method, a series of visual and quantitative performance evaluations of the proposed method are compared with those of five representative wavelet-based fusion methods including contourlet transform (CLT), nonsubsampled CLT (NSCLT), lifting wavelet transform (LWT), multiwavelet transform (MWT), and stationary wavelet transform (SWT). For the quantitative performance evaluations, we adopted five metrics: fusion factor, fusion symmetry, entropy, standard deviation and edge strength. The experimental results demonstrated that the proposed method could yield better results than other wavelet transform-based fusion methods. Furthermore, from the additional comparison study of fusing noise-contaminated images, we could conclude that the proposed method is noise resilient in fusing images corrupted by Gaussian and speckle noise with varying variances.

Iterative adaptive Despeckling SAR image using anisotropic diffusion filter and Bayesian estimation denoising in wavelet domain

In this paper, a new iterative algorithm has been presented by aggregating Stationary Wavelet Transform (SWT), Bilateral filtering, Bayesian estimation, and Anisotropic Diffusion (AD) filtering to reduce the speckle noise in SAR images. For this purpose, speckle images were first decomposed using two-dimensional stationary wavelet transform and then a suitable filtering method was used to filter respective coefficients of each sub-band of the speckled images. Generally, in wavelet transform-based noise reduction methods, filtering and thresholding techniques are usually applied to the coefficients of the detail sub-bands and the residual speckle noise is ignored in the approximate sub-band. In this paper, bilateral filtering has been applied to reduce the speckle noise in the approximate sub-band. We used Bayesian estimator to calculate the noise-free signal in the horizontal and vertical sub-bands with respect to that some parts of signal coefficients are eliminated in the traditional thresholding techniques. Moreover, we applied anisotropic diffusion filtering method to preserve the edges and structure of image along the diagonal subband which has more details (the entropy is maximum) than other directions in radar and optic images. Finally, both the proposed algorithm and other speckle noise reduction methods were applied on two synthetic speckled images and an actual SAR image in San Francisco. Their efficiencies were compared according to the Structural SIMilarity(SSIM), Peak Signal to Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Speckle Suppression Index (SSI) and Speckle Suppression and Mean Preservation Index (SMPI). The experimental results indicate that the proposed algorithm efficiently reduces the speckle noise and preserves the edges and structure of image.

Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors

In this paper, a new sensing device that can simultaneously monitor traffic congestion and urban flash floods is presented. This sensing device is based on the combination of passive infrared sensors (PIRs) and ultrasonic rangefinder, and is used for real-time vehicle detection, classification, and speed estimation in the context of wireless sensor networks. This framework relies on dynamic Bayesian Networks to fuse heterogeneous data both spatially and temporally for vehicle detection. To estimate the speed of the incoming vehicles, we first use cross correlation and wavelet transform-based methods to estimate the time delay between the signals of different sensors. We then propose a calibration and self-correction model based on Bayesian Networks to make a joint inference by all sensors about the speed and the length of the detected vehicle. Furthermore, we use the measurements of the ultrasonic and the PIR sensors to perform vehicle classification. Validation data (using an experimental dual infrared and ultrasonic traffic sensor) show a 99% accuracy in vehicle detection, a mean error of 5 kph in vehicle speed estimation, a mean error of 0.7m in vehicle length estimation, and a high accuracy in vehicle classification. Finally, we discuss the computational performance of the algorithm, and show that this framework can be implemented on low-power computational devices within a wireless sensor network setting. Such decentralized processing greatly improves the energy consumption of the system and minimizes bandwidth usage.

Multidimensional Compression of ITS Data Using Wavelet-Based Compression Techniques

This paper explores the use of wavelet transform-based methods for ITS data compression. A methodology for structuring data and applying wavelet transform-based algorithms is proposed. The methodology provides the option of controlling the compression ratio at the cost of an acceptable distortion, visualizing data at different detail levels. With proper database management, this methodology will also allow faster data access without fully decompressing them. Given a high correlation of traffic data and knowing that the image data are compressed very well due to the inherent correlation of image pixels, the idea here is to restructure the traffic data, such that efficient image compression methods underlying modern image compression standards can be used. Three data structures are discussed: 1-D, 2-D, and 3-D. For a 1-D arrangement, different wavelets and decomposition levels were tested and analyzed for distortion levels in the data after decompression. The 2-D and 3-D data arrangements were compressed using embedded zero-tree wavelet and set partitioning in hierarchical trees algorithms, which are well-proved algorithms for compressing image data. A case study was performed using the traffic flow data from freeways in Las Vegas, Nevada. As could be expected, the compression ratio under the 3-D scheme has shown the best results. The 2-D and 3-D approaches yielded a 91% and 95.2% reduction ratios, respectively.

Comparison of hemispheric asymmetry measurements for emotional recordings from controls

Four asymmetry measurements (conventional coherence function (CCF), cross wavelet correlation (CWC), phase lag index (PLI), and mean phase coherence (MPC)) have been compared to each other for the first time in order to recognize emotional states (pleasant (P), neutral (N), unpleasant (UP)) from controls in EEG sub-bands (delta (0–4 Hz), theta (4–8 Hz), alpha (8–16 Hz), beta (16–32 Hz), gamma (32–64 Hz)) mediated by affective pictures from the International Affective Picture Archiving System (IAPS). Eight emotional features, computed as hemispheric asymmetry between eight electrode pairs (Fp1 − Fp2, F7 − F8, F3 − F4, C3 − C4, T7 − T8, P7 − P8, P3 − P4, and O1 − O2), have been classified by using data mining methods. Results show that inter-hemispheric emotional functions are mostly mediated by gamma. The best classification is provided by a neural network classifier, while the best features are provided by CWC in time-scale domain due to non-stationary nature of electroencephalographic (EEG) series. The highest asymmetry levels are provided by pleasant pictures at mostly anterio-frontal (F3 − F4) and central (C3 − C4) electrode pairs in gamma. Inter-hemispheric asymmetry levels are changed by each emotional state at all lobes. In conclusion, we can state the followings: (1) Nonlinear and wavelet transform-based methods are more suitable for characterization of EEG; (2) The highest difference in hemispheric asymmetry was observed among emotional states in gamma; (3) Cortical emotional functions are not region-specific, since all lobes are effected by emotional stimuli at different levels; and (4) Pleasant stimuli can strongly mediate the brain in comparison to unpleasant and neutral stimuli.

Sparse representation-based ECG signal enhancement and QRS detection

Electrocardiogram (ECG) signal enhancement and QRS complex detection is a critical preprocessing step for further heart disease analysis and diagnosis. In this paper, we propose a sparse representation-based ECG signal enhancement and QRS complex detection algorithm. Unlike traditional Fourier or wavelet transform-based methods, which use fixed bases, the proposed algorithm models the ECG signal as the superposition of a few inner structures plus additive random noise, where these structures (referred to here as atoms) can be learned from the input signal or a training set. Using these atoms and their properties, we can accurately approximate the original ECG signal and remove the noise and other artifacts such as baseline wandering. Additionally, some of the atoms with larger kurtosis values can be modified and used as an indication function to detect and locate the QRS complexes in the enhanced ECG signals. To demonstrate the robustness and efficacy of the proposed algorithm, we compare it with several state-of-the-art ECG enhancement and QRS detection algorithms using both simulated and real-life ECG recordings.

Quantitative finance for agricultural commodities: discussion and extension

Purpose– The purpose of this paper is to review three papers in this issue and contribute new results on commodity futures prices and volume using wavelet analysis.Design/methodology/approach– The paper uses time series econometrics including variance ratio tests, fractional integration estimators, and wavelet transforms.Findings– The role of time horizon is emphasized in the discussion of the three papers, and wavelet methods are shown to be a useful tool to better understand time horizon-specific risk. Moreover, changes in the time horizon of futures trading are documented and discussed.Originality/value– In addition to discussing three papers on quantitative finance for agricultural commodities, this paper also looks at how the analysis and management of short-term and long-term risk may differ. To this end, wavelet transform-based time series methods are reviewed and applied.

Inter-annual to inter-decadal streamflow variability in Quebec and Ontario in relation to dominant large-scale climate indices

Summary The impacts of large-scale climate oscillations on hydrological systems and their variability have been documented in different parts of the world. Since hydroclimatic data are known to exhibit non-stationary characteristics, spectral analyses such as wavelet transforms are very useful in extracting time–frequency information from such data. As Canadian studies, particularly those of regions east of the Prairies, using wavelet transform-based methods to draw links between relevant climate indices [e.g., the El Nino Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO)] and streamflow variability are not common, this study aims to analyze such relationships for the southern regions of Quebec and Ontario. Monthly and annual streamflow data with a record length of 55 years were used to capture streamflow variability at intra-annual, inter-annual and inter-decadal scales. The continuous wavelet transform spectra of monthly streamflow data revealed consistent significant 6- and 12-month periodicities, which are likely associated with strong seasonality factors. Its annual counterparts showed four different significant periodicities: up to 4 years, 4–6 years, 6–8 years, and greater than 8 years – all of which occurred after the late 1960s/early 1970s. Wavelet coherence analyses show that the influence of ENSO and NAO at the inter-annual scale occurs at 2–6 year periodicities, and the influence of PDO occur at periodicities up to 8 years and exceeding 16 years. Correlations between these climate indices and streamflow were computed to determine the time delay of streamflow response to the influence of ENSO, NAO, and PDO. The lag times ranged from 6–48 months (for monthly data) and 1–4 years (for annual data). This research contributes to our understanding of streamflow variability over the southern parts of Quebec and Ontario, and the role of ENSO, NAO, and PDO phenomena on this variability. These relationships can be also used to improve hydrological forecasting and water resources management in Ontario and Quebec.

High-order balanced M-band multiwavelet packet transform-based remote sensing image denoising

This article proposes high-order balanced multi-band multiwavelet packet transforms for denoising remote sensing images. First, properties of several wavelet transforms and their relationships are analyzed. The article then presents theoretical principles and a fast algorithm for constructing high-order balanced multi-band multiwavelet packet transforms. The remote sensing image denoising method based on this transform scheme is then described, and its utility is demonstrated by illustrative results of its application to denoise remote sensing images. The method provides clear improvements in denoising quality, due to the balanced order or band number, consistently outperforming traditional wavelet transform-based methods in terms of both visual quality and evaluation indicators. The method also incurs reasonable computational costs compared with the traditional methods.

Wavelet-based study of valence-arousal model of emotions on EEG signals with LabVIEW.

This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence–arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using “db5” wavelet function. Relative features were calculated to obtain further information. Impact of the emotions according to valence value was observed to be optimal on power spectral density of gamma band. The main objective of this work is not only to investigate the influence of the emotions on different frequency bands but also to overcome the difficulties in the text-based program. This work offers an alternative approach for emotion evaluation through EEG processing. There are a number of methods for emotion recognition such as wavelet transform-based, Fourier transform-based, and Hilbert–Huang transform-based methods. However, the majority of these methods have been applied with the text-based programming languages. In this study, we proposed and implemented an experimental feature extraction with graphics-based language, which provides great convenience in bioelectrical signal processing.

Bilateral Filtering-Based Enhanced Pansharpening of Multispectral Satellite Images

An efficient pansharpening method should inject the missing geometric information to the multispectral (MS) image while preserving its radiometric information. Widely used additive wavelet transform-based pansharpening methods extract the missing high-frequency information by decomposing the panchromatic (PAN) image and adding the detail layers to the low-resolution MS (LRM) image. However, this approach causes a redundant detail injection, leading to artifacts in the fusion result. In this letter, we propose to decompose the high-resolution-PAN image using an edge-preserving decomposition which will decrease the amount of redundant high-frequency injection. The missing high-frequency information of the LRM image is obtained by the decomposition of the PAN image using a multiscale bilateral filter. The spatial and range parameters of the bilateral filter are optimized so as to enhance spatial and spectral metrics. The fusion results are compared with the widely used additive wavelet luminance proportional (AWLP) and recently proposed improved AWLP fusion methods. The resulting images as well as evaluation metrics demonstrate that the proposed injection approach has better performance.

Time–time‐transform application to fault diagnosis of power transformers

Application of time–time (TT)-transform for differential protection of power transformers has been suggested. At first, external and internal disturbances are discriminated. If the disturbance is external, relay scheme restrains more analysis and tripping. Otherwise, the differential current signal is analysed by TT-transform and TT-matrix is computed. Next, a suggested index is computed, accordingly. Discrimination between inrush current and internal fault is performed by the proposed index. To investigate the effectiveness of the method, a typical power system has been modelled in EMTP software. Also, the relay scheme has been developed in MATLAB environment. Then, differential currents extracted from the system modelled in EMTP have been fed to MATLAB for analysis. Also, the performance of the method in noisy environments has been assessed. To make an analogy between the TT-transform-based method and wavelet transform-based methods, a wavelet transform-based scheme has been developed in MATLAB environment and results have been compared to the results of the TT-transform-based method. In addition, an S-transform-based method has been implemented in MATLAB and has been compared with the suggested method. The results show that the method is superior to both wavelet transform and S-transform-based methods.