Search

From the beginning of COVID-19 pandemic, numerous mutants of SARS-CoV-2 have since been evolved owing to high transmissibility and virulence. Due to the limited effectiveness of previously imposed vaccines and preventive therapies, these strains are still causing concern. This paper proposes comparative evaluation of three novel genomic signal processing-based methods employing discrete wavelet decomposition with lifting (DWT), discrete Fourier transform (DFT), and singular value decomposition (SVD) for the classification of emerging SARS-CoV-2 variants utilizing feature extraction from collected SARS-CoV-2 variants acquired from the NCBI virus database. The efficiency and accuracy of the proposed alignment-free algorithms have been tested using three Coronavirus datasets including human Coronavirus (HCoV), SARS-CoV-2 variants (CoV-Variants and Omicron). The viral nucleotide sequences which are converted into numerical representation leveraging purine-pyrimidine mapping, DNA walk & Z-curve are fed into DWT, SVD, & DFT processors, respectively. In the approach with DWT, the second-generation wavelet transform employs two best wavelet bases Daubechies (Db) and Biorthogonal (Bior) based on the validation of the HCoV dataset for the feature extraction of the CoV-Variants dataset. Various machine learning algorithms, such as Support Vector Machine, K-nearest neighbors, and ensemble, are used to classify the virus strains and evaluate the efficacy of the algorithm. Finally, hyper-parametric tuning is done utilizing the Bayesian optimization technique to select the best fit model for KNN and SVM. The proposed algorithm has successfully classified the CoV-Variants dataset with an average accuracy of 98.76% utilizing the DWT, DFT, and SVD, while the best-achieved accuracy for this dataset is 98.9% using the DWT technique employing purine–pyrimidine mapping. The best-achieved accuracy rate for predicting Omicron is 99.8% using SVD-based technique. The best-obtained accuracy for HCoV dataset is 100% resulted in all three methods. [ABSTRACT FROM AUTHOR]

In response to the problem that seismic wave signals are usually non-stationary, traditional Fourier transform cannot analyze non-stationary signals and cannot reflect the local time-frequency distribution relationship. The advantages and disadvantages of short-term Fourier transform, continuous wavelet transform, and Chirplet transform in analyzing non-stationary signals at single component, second single component, and multi-component instantaneous frequencies were introduced in sequence. The modified kernel function was used to improve the Chirplet transform, effectively improving the time-frequency resolution of the signal to be analyzed. With the increasing difficulty of oil exploration and development, the prediction of thin interbedded reservoirs has gradually become a research focus. Cyclic thin interbedded reservoirs are one of the typical models and also a sedimentary combination of sequence stratigraphy. Compared with the original Chirplet transform and traditional wavelet transform, the improved Chirplet transform can more clearly depict the trend of stratigraphic cycle changes in practical applications, and has important research significance for solving the prediction problem of general thin interbedded sedimentary cycles in the future. [ABSTRACT FROM AUTHOR]

Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that a decision is made through several independent information channels with the subsequent aggregation of these decisions. The method of forming descriptors for classifiers of the functional state of the respiratory system includes the study of the spectral range of the respiratory rhythm and the construction of the wavelet plane of the monitoring electrocardiosignal overlapping this range. Then, variations in the breathing rhythm are determined along the corresponding lines of the wavelet plane. Its analysis makes it possible to select slow waves corresponding to the breathing rhythm and systemic waves of the second order. Analysis of the spectral characteristics of these waves makes it possible to form a space of informative features for classifiers of the functional state of the respiratory system. To construct classifiers of the functional state of the respiratory system, hierarchical classifiers were used. As an example, we took a group of patients with pneumonia with a well-defined diagnosis (radiography, X-ray tomography, laboratory data) and a group of volunteers without pulmonary pathology. The diagnostic sensitivity of the obtained classifier was 76% specificity with a diagnostic specificity of 82%, which is comparable to the results of X-ray studies. It is shown that the corresponding lines of the wavelet planes are correlated with the respiratory system and, using their Fourier analysis, descriptors can be obtained for training neural network classifiers of the functional state of the respiratory system. [ABSTRACT FROM AUTHOR]

Nowadays expectations against modern communication services involve not just Quality of Service (QoS) enhancement for real-time applications but also increased transmission rate between the storing and processing of Big Data nodes. Transmission Control Protocol (TCP) has strict flow control of the data stream providing automatic adaptation to the path load of the process-to-process communication. User Datagram Protocol (UDP) based solutions are proposed to settle the communication efficiency. In this paper, we analyse the effect of three independent communication parameters on the efficiency of looped UDP communication: the size of the Maximum Transfer Unit (MTU), the bandwidth of the end-to-end session, and the segment size of the UDP protocol data unit. The usage of nonstationary multi-resolution methods helps to identify three characteristic patterns offering identification of the objective qualitative features of the looped datagram communication services. [ABSTRACT FROM AUTHOR]

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time–frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications. [ABSTRACT FROM AUTHOR]

This article presents selected physical diagnostic methods used in otorhinolaryngology and results of their application. In addition to the applications of methods using the capabilities of selective sensors, selected methods of hybrid diagnostics were also presented - for assessment of parameters of respiratory processes, with polysomnography as an example of using both typical diagnostic methods dedicated to otolaryngology, as well as standard EEG and ECG methods. It has been shown that in some special cases of respiratory disorders, measurements of the air flow in the respiratory tract can be supplemented with pressure measurements in selected positions within the airways. The presented optical methods and diagnostic systems are very often used in the diagnosis of diseases not specific for otolaryngology occurring in the area of the head and neck. The presented material is the second part of the study discussing both standard and widely used diagnostic methods. All presented methods are dedicated to otolaryngology. This text is a continuation of the material published in No 4 of 2021 [1]. [ABSTRACT FROM AUTHOR]

This study compares two methods to quantify the amplitude and frequency of head movements in patients with head tremor: one based on video-based motion analysis, and the other using a miniature wireless inertial magnetic motion unit (IMMU). Concomitant with the clinical assessment of head tremor severity, head linear displacements in the frontal plane and head angular displacements in three dimensions were obtained simultaneously in forty-nine patients using one video camera and an IMMU in three experimental conditions while sitting (at rest, counting backward, and with arms extended). Head tremor amplitude was quantified along/around each axis, and head tremor frequency was analyzed in the frequency and time-frequency domains. Correlation analysis investigated the association between the clinical severity of head tremor and head linear and angular displacements. Our results showed better sensitivity of the IMMU compared to a 2D video camera to detect changes of tremor amplitude according to examination conditions, and better agreement with clinical measures. The frequency of head tremor calculated from video data in the frequency domain was higher than that obtained using time-frequency analysis and those calculated from the IMMU data. This study provides strong experimental evidence in favor of using an IMMU to quantify the amplitude and time-frequency oscillatory features of head tremor, especially in medical conditions. [ABSTRACT FROM AUTHOR]

In this paper, we study the Mexican hat wavelet formulated from the Gaussian function. The Mexican hat wavelet transform (MHWT) is defined using this basic wavelet. A standard method is introduced to obtain the gap formula for the MHWT. Further, an example for the gap formula is also presented. [ABSTRACT FROM AUTHOR]

Issues are brought to light in the optimization of combustion processes of fuel, depending on the spraying capability of rocket and aircraft engine nozzles. Using the example of the flame of a fuel-injection nozzle, the problem of determining the size of droplets of spray of an optically transparent fluid by the IPI method, based on the analysis of interferometric images of the particles, was studied. A method is proposed for analysis of the IPI image of a drop using a continuous wavelet transform. The spatial distribution of wavelet coefficients was used for the introduction of an integrated measurement, analogous to the spectral density of a signal. The reliability of the estimated sizes of droplets was verified from the results of processing glistening images that are obtained by the direct optical GPT method on focused images of droplets. The focused images and interferometric images of droplets are taken simultaneously. The sizes of droplets obtained by the IPI method and processed with the aid of wavelet and Fourier transforms were compared with the data of the GPT method. It was shown that the wavelet transform application substantially reduces the frequency of occurrence of systematic error of the determination of size, which is most characteristic for large droplets. It was established that the false maxima causing systematic error in the Fourier spectrum were caused by the occurrence of distortions on the boundary of the interferometric image of a drop. It was shown that it is possible to remove these distortions from the basic maximum in the neighborhood of the center of a drop by means of wavelet transform. [ABSTRACT FROM AUTHOR]

High Impedance Faults (HIFs) occur by the contact between an energized conductor and a high impedance surface. Due to the low fault current level, HIFs cannot be detected by conventional protection and there is no fully efficient solution to this problem. HIF detection methods often extract metrics using signal processing techniques, such as Fourier Transform, Wavelet Transform, Stockwell Transform, and Mathematical Morphology. However, these techniques are applied under specific conditions, which hinders comparative and critical analyses among them. Therefore, this paper presents a critical review of HIF detection methods based on the aforementioned techniques, and also shows a detailed investigation of the performance of the metrics commonly used with them. To do this efficiently, the authors proposed a set of assessment indices based on the ratio between the metrics’ characteristics and another one based on the repeating of the metrics features. The proposed indices revealed that some of these metrics fail to distinguish HIF from other typical occurrences in power distribution systems, and their performances are negatively affected by the fault location and by the existence of noise in the measurements. Additionally, the results showed a need to specify system conditions in which any HIF detection technique is valid. [ABSTRACT FROM AUTHOR]

The rapidly developing measurement techniques and emerging new physical methods are frequently used in otolaryngological diagnostics. A wide range of applied diagnostic methods constituted the basis for the review study aimed at presenting selected modern diagnostic methods and achieved diagnostic results to a wider group of users. In this part, the methods based on measuring the respiratory parameters of patients were analysed. Respiration is the most important and necessary action to support life and its effective duration. It is an actual gas exchange in the respiratory system consisting of removing CO2 and supplying O2. Gas exchange occurs in the alveoli, and an efficient respiratory tract allows for effective ventilation. The disruption in the work of the respiratory system leads to measurable disturbances in blood saturation and, consequently, hypoxia. Frequent, even short-term, recurrent hypoxia in any part of the body leads to multiple complications. This process is largely related to its duration and the processes that accompany it. The causes of hypoxia resulting from impaired patency of the respiratory tract and/or the absence of neuronal respiratory drive can be divided into the following groups depending on the cause: peripheral, central and/or of mixed origin. Causes of the peripheral form of these disorders are largely due to the impaired patency of the upper and/or lower respiratory tract. Therefore, early diagnosis and location of these disorders can be considered reversible and not a cause of complications. Slow, gradually increasing obstruction of the upper respiratory tract (URT) is not noticeable and becomes a slow killer. Hypoxic individuals in a large percentage of cases have a shorter life expectancy and, above all, deal with the consequences of hypoxia much sooner. [ABSTRACT FROM AUTHOR]

In this paper, we study and discuss the wavelet transform of periodic Boehmians by using wavelet coefficients in terms of Fourier coefficients. A uniqueness theorem is defined for the wavelet transform for the Boehmians of analytic functions. Furthermore, we investigate the wavelet transform of Boehrnians of analytic type. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the advantages and drawbacks of four signal processing methods for spectrophotometric quantitation of mixtures of dapagliflozin and saxagliptin. Methods: The methods studied were numerical differentiation (ND), Savitzky-Golay filter (SG), discrete Fourier transform (DFT) and continuous wavelet transform (CWT). The resolution powers of the methods were compared via quantitative determination of dapagliflozin (DAP) and saxagliptin (SAX) in laboratory prepared mixtures. Furthermore, a new approach for validating robustness in spectrophotometric methods was developed, and the methods were compared using their robustness. Results: Continuous wavelet transform (CWT) produced the best results regarding the analysis of the two drugs in different ratios. It also showed a lower limit of quantification (LOQ), when compared to each of the other methods. When the four methods were used for quantitation of pharmaceutical drug formulations, and subjected to validation in line with ICH regulations, they were found to be satisfactorily specific, accurate, precise and robust. Conclusion: These results show that CWT technique is superior to the other three methods for analysis of drug mixtures with regard to sensitivity and resolving power. Thus, CWT can be used in the routine spectrophotometric analysis of pharmaceuticals in quality control laboratories. [ABSTRACT FROM AUTHOR]

Ageing of civil engineering infrastructures such as dams, bridges, tunnels and buildings causes many problems with great consequences, both from practical and economical points of view. The main aim of this paper, together with 11], is to describe the state of the art of known methods and how to tackle this important problem. In particular we make a brief description and comparison between the methods. The major challenge for damage detection is the large amount of noise, and presence of harmonic components that leads to erroneous modal identification. New developments in signal processing techniques along with the use of artificial intelligence can play a crucial role in finding solutions for such problems. In this article we also include a new application of OMA, on a high-rise building in Luled, Sweden, where some of the most popular OMA techniques have been applied. [ABSTRACT FROM AUTHOR]

The use of renewable power generation brings new challenges related to power quality issues. Furthermore, with the changing power system nature due to the presence of new components such as power electronics in large numbers and distributed generation systems, the tools used for more than a century to analyze signals in this type of systems are no longer providing accurate information with a good resolution in time and frequency domain. To contribute with a new view of the problem, this paper presents a hybrid technique for the analysis of oscillations in low voltage distribution systems considering photovoltaic generation. The aim is to characterize the behavior of the system in a time–frequency domain and get the different instantaneous frequencies that appear. The results obtained with this technique are compared with three well-known methods of analysis. The validation of the methodology is carried out in a real-time digital simulator of a distributed system with photovoltaic generation. [ABSTRACT FROM AUTHOR]

For an efficient and directional representation of signals in higher dimensions, we propose the non-isotropic angular Stockwell transform in the context of time-frequency analysis. The proposed transform is aimed at rectifying the conventional Stockwell transform by employing an angular and scalable localized window which offers directional flexibility and thereby results in the multi-scale and directional analysis of signals in higher dimensions. The basic properties of the proposed transform such as orthogonality relation, reconstruction formula, derivation of the admissibility condition and characterization of the range are discussed using the machinery of operator theory and Fourier transforms. In addition, we introduce the discrete version of the non-isotropic angular Stockwell transform and establish a sufficient condition for the corresponding discrete family to be a frame in L 2 (R 2). Furthermore, some generalizations of the well-known Heisenberg-type inequalities are derived for the non-isotropic angular Stockwell transform in the Fourier domain. [ABSTRACT FROM AUTHOR]

This study focuses on demonstrating the multiannual patterns of cyclical fluctuations of monthly and annual precipitation in the Polish Carpathian Mountains and their foreland. The study is based on secular series of precipitation totals from 18 meteorological stations for the 138-year period from 1881 to 2018. Use is made of Fourier analysis (FFT) and wavelet transform nonlinear estimation. The study has found a 35-year Brückner cycle in annual precipitation across the area. The number of days during the year with the types of cyclonic circulation that lead to extremely high precipitation, i.e. with the northerly and north-easterly (Nc + NEc) types and a cyclonic trough (Bc), is not clearly correlated with the 35-year harmonic variability derived from annual precipitation totals. However, the Fourier transform demonstrates a local maximum at T = 35 years. A harmonic 6-month cycle has been discovered as the only harmonic cycle in monthly precipitation. Its stability over the 138-year timespan is investigated. [ABSTRACT FROM AUTHOR]

• Fault type identification can be achieved using data with a length of one cycle. • The combination of THD and normalized wavelet energy coefficients are used as characteristic quantities to reflect the fault characteristics from multiple perspectives. • In comparison with other methods, this method has the advantages of speed and accuracy. Different types of faults pose different degrees of threat to the distribution network. Accurate identification of fault types is essential for distribution network maintenance and hazard prevention. The simulation of a typical single-phase arc grounding fault in a distribution network is carried out based on a 10 kV test platform, and the zero-sequence current of cable fault, tree touch, line break, and insulator flashover is obtained. The BP neural network is established and trained to recognize the feature data, which is extracted by Fourier transform and wavelet transform. The identification results prove the effectiveness of the proposed method for single-phase arc grounding fault type identification in distribution networks. [ABSTRACT FROM AUTHOR]

In the present article, the authors study the heat equation in the form of the fractional wavelet transform with certain initial conditions, by exploiting the technique of fractional Fourier transform. The properties of the fundamental solution of heat equation are investigated and the relation between the fractional wavelet transform and fractional integrals obtained. The authors also study the computational aspect of the solution and heat flux of the heat equation as well as the Schrödinger equation. The graphical representations of both solutions and the heat flux for are shown and their values are compared with the classical solutions and found to have some better properties. [ABSTRACT FROM AUTHOR]

Data on frequency and time is not simultaneously available in a Fourier transform. Problems with the Fourier transform have led to the emergence of short-window Fourier analysis which itself has several limitations. The main problem with short-time Fourier transform is related to Heisenberg's uncertainty principle. In the wavelet transform, the signal is analyzed on a set of wavelet functions (coherent states). In quantum mechanics, the coherent state is a type of quantum state which has the minimum value of uncertainty. In the present study, the concepts of quantum mechanics such as the uncertainty principle, Dirac's notation and quantum coherent states are used to present a method for obtaining wavelet functions. [ABSTRACT FROM AUTHOR]

Several studies focus on improving forecasting techniques to capture multiple patterns in time series. The evolution of computing hardware has made possible to solve complex equations with large amount of data, such as the one used in neural networks. On the other hand, time series methods such as ARIMA (Autoregressive Integrated Moving Average) could also have a good approximation with low computational resources. Nonetheless, to improve the ARIMA approximations, it could be combined with other techniques such as Wavelet Transform or Fourier Transform. Therefore, this work evaluates the appropriate utilization to make predictions for different time horizons (2, 5 and 10 years) and different time frequencies (days, months, and years) using artificial neural network, ARIMA combined with Wavelet Transform, or Fourier Transform. The results show that Artificial Neural Networks provides a better approach for short-term horizons considering either time frequency, ARIMA with Fourier Transform has the best approximation for the monthly time series and either time horizons and ARIMA with Wavelet Transform has the best approximation for medium-term and long-term periods with either time frequency. • Comparison of time series analysis methods and machine learning for forecasting. • Time series analysis for long-term predictions with seasonality index. • Fourier combined with ARIMA has high performance for monthly data predictions. • Wavelet combined with ARIMA, has high performance for large amounts of data. • Machine learning is a powerful tool for predictions with large amount of data. [ABSTRACT FROM AUTHOR]

In this paper, wavelet analysis of the cavitating flow over a sphere is reported. Unsteady and dynamic behaviors of cavitation were captured using large eddy simulation (LES) turbulence approach and Sauer mass transfer models. Numerical simulation is implemented under the framework of OpenFOAM within the interPhaseChangeFoam solver. The simulation is conducted over a wide range of cavitation numbers. Two more essential variations, pressures and kinetic energies, were considered at specific points in front and behind of the sphere's body for sufficient simulation period. The oscillations global frequency modes and spectral content of the cavity cloud are computed and analyzed using Fourier and continuous wavelet transformations. The computed results show that the flow fluctuations enhance by increasing the cavitation number. The low-frequency fluctuations play a pivotal role in the cavitating flow and possess almost the same magnitude in all investigated cavitation numbers. The frequencies enhance as the simulation time increases in all cases. One of the primary frequencies that happened in all cavitation numbers in the cavity cloud separation is due to a Strouhal number within the range of 0.046 and 0.05. Therefore, this Strouhal number can be used for the purpose of cloud cavitation detection. • LES simulation of cavitating flow around sphere in OpenFOAM. • Providing a thorough understanding of the wavelet analysis around a sphere for the first time. • Reporting the oscillations global frequency modes and spectral content of the sphere cavity cloud. [ABSTRACT FROM AUTHOR]

In the present paper, we discuss about extension of the wavelet transform on distribution space of compact support and develop the Paley–Wiener–Schwartz type theorem for the wavelet transform on the same. Furthermore, Paley–Wiener–Schwartz type theorem for the wavelet transform is also established using the relation between the wavelet transform and double Fourier transform. [ABSTRACT FROM AUTHOR]

In the present paper, a fractional wavelet transform of real order α is introduced, and various useful properties and results are derived for it. These include (for example) Perseval's formula and inversion formula for the fractional wavelet transform. Multiresolution analysis and orthonormal fractional wavelets associated with the fractional wavelet transform are studied systematically. Fractional Fourier transforms of the Mexican hat wavelet for different values of the order α are compared with the classical Fourier transform graphically, and various remarkable observations are presented. A comparative study of the various results, which we have presented in this paper, is also represented graphically. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Effect of electrostatic charge on fluidization hydrodynamics was investigated. • Macro-structure had the highest contribution in the bed of uncharged particle. • Effect of initial charge of particles on the fluidization structures was studied. • Transition velocity decreases by increasing the initial charge of particles. • Turbulent transition velocity of uncharged experiments was higher than other cases. Abstract The aim of this work was to investigate effect of electrostatic charge of particles on the fluidization hydrodynamics. Behavior of bubbles in beds of polyethylene particles was studied through analysis of pressure fluctuations in the frequency domain. Fluidized beds of uncharged, pre-charged and bed-charged particles were used in the experiments. Results revealed that in the bed of pre-charged particles, compared to uncharged experiments, particle-particle repulsive force increases the bed voidage and reduces equilibrium bubble size while the transition velocity to turbulent fluidization is decreased. In the case of bed-charged particles, at low gas velocities bubble fraction is greater compare to the other cases due to faster bubble coalescence in the presence of particle-wall attractive electrostatic force. Electrostatic charge of bulk increases by increasing the gas velocity. At high gas velocities, the repulsion force between highly charged particles overcomes the particle-wall effect on bubble formation and reduces the bubble size to less than in uncharged experiments. Accumulation of particles near the wall in the bed od bed-charged particles affects the hydrodynamics in two ways: first it accelerates bubble growth via bubble coalescence at low gas velocities, second it limits the bubble growth and reduces the transition velocity to turbulent regime to a value less than for pre-charged particles. [ABSTRACT FROM AUTHOR]

We define wavelets and wavelet transforms associated with spherical mean operator. We establish a Plancherel theorem, orthogonality property and inversion formula for the wavelet transform. Next, we define the Toeplitz operators Tφ,ψ(σ) associated with two wavelets φ,ψ and with symbol σ. We establish the boundedness and compactness of these operators. Last, we define the Schatten-von Neumann class Sp;p∈[1,+∞], and we show that the Toeplitz operators belong to the class Sp and we prove a formula of trace. [ABSTRACT FROM AUTHOR]

Long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data have inherent noise due to clouds and poor atmospheric conditions that limit its applicability for environmental applications. This study was carried out with an objective of noise removal and reconstruction of time series MODIS EVI data (16 day) for the period 2010-2014 using de-noising algorithms. Relative evaluation of de-noising algorithms for smoothing temporal data with ideal noise free data is not possible in actual scenario. Hence, synthetic signals were generated and introduced Gaussian noise at different variance levels for evaluation purpose. Spatial analysis was carried out by introducing noise at different variance levels into the noise free EVI images from the raw EVI stacked image. Spatio-temporal analyses of noise signals in the reconstructed EVI images were evaluated in terms of performance indicators, namely Peak Signal-to-Noise Ratio and Mean Square Error. [ABSTRACT FROM AUTHOR]

Corrosion detection and monitoring is a relevant task in many different fields including automotive, naval, civil etc. In the case of artifacts, corrosion is a relevant topic because this process can introduce huge damages on really ancient objects so that preventive procedures and long term monitoring are required. In this specific field the monitoring task presents, in addition, some extra requirements. In fact, monitoring the surface condition without contact, without sampling the object and, possibly, without diminishing the fruition of the handwork is preferable. Optical techniques, in general, are successfully used in many fields where non-contact, high accuracy measurements are required. In particular, an approach based on fringe projection will be explored in this paper. This method mainly consists in projecting a given intensity pattern on the object and in observing the fringe modulation introduced by the object itself; modulation contains information about the contour of the object itself. In this study corrosion tests on bronze alloy were performed both under salt spray and acid rain condition. A sinusoidal fringe pattern was projected on the analyzed samples before starting the measurement and at each advancement step of the corrosion test. Fringe patterns were analyzed using two approaches. Fast Fourier Transform of the recorded patterns was studied to detect if formation of corrosion patina can be related to some variation in the frequency spectrum. Moreover, continuous wavelet analysis was performed to detect where corrosive patina forms. [ABSTRACT FROM AUTHOR]

SUMMARY: Online partial discharge (PD) measurement is very important for insulation monitoring of high‐voltage equipment. One of the biggest challenges performing online monitoring is discrimination between PD detect signals and external noises. Experimental results using high‐frequency current transformer (CT) sensor show that the current waveforms associated with various PD signals are all damped cosine waveforms, and have unique frequency and decay times of damped oscillation. Therefore, we focused attention on the damped oscillation waveform of the PD current, especially damped cosine waveform having a different number of oscillations. In addition to this, the noise discrimination method using wavelet transform or short‐time Fourier transform is effective. Those noise discriminating processes are applicable to online PD current measurement system. [ABSTRACT FROM AUTHOR]

The aim of this study is providing a comprehensive background information related to the roots of both Fourier Transform (FT) and Wavelet Transform (WT) along with an experiment related to applications of WT techniques. The paper describes several applications of WT and provides background information on FT. Fourier Transform (FT) is a concept that has a long history yet several issues related to resolution and uncertainty of time-frequency. Even though there are several adapted forms of FT such as Short Time Fourier Transform (STFT), which intend to solve the problems, certain limitations remain. Wavelet Transform (WT) is an alternative transformation technique emerged in order to fully tackle these diverse and complicated issues. In this paper, the background information related to the roots of FT and WT are given. Some of the problems that WT addresses are examined. WT is a tool that has many advantages among them is noise reduction and compression. We reviewed several studies that use the noise reduction capability of WT alone or combined with other signal processing tools. Discrete Wavelet Transform (DWT) based algorithm is also examined as a noise reduction technique and carried out in MATLAB setting. Analysis on a speech signal which contaminated with keyboard sound also a number spelling female voice containing unknown noise are performed. Different types of thresholding and mother wavelets were in consideration and it was revealed that Daubechies family along with the soft thresholding technique suited our application the most. [ABSTRACT FROM AUTHOR]

Background and purpose: Retinitis Pigmentosa (RP) is one of the retinal degeneration diseases affecting the eye signals. Electroretinogram (ERG) is a signal that plays an important role in diagnosis and treatment of RP. This signal includes useful information that cannot be revealed just in time domain. We aimed to investigate the effect of RP on time, frequency, and time-frequency parameters of ERG using the Fourier and wavelet transform processing methods. Materials and methods: In this experimental study Flash Xenon ERG was recorded from 18 eyes of RP patients and 20 eyes of healthy individuals. After extracting the latency and amplitude of the ERG signals, the Fourier and wavelet transforms were performed on the signals by MATLAB software. Then, the frequency mode and main frequencies along with the occurring time constituting the ERG signals were extracted. Finally, the differences between the means of all parameters were analyzed. Results: Findings indicated an increase in the latency and a decrease in amplitude of the ERG. But, no significant difference was found between the mean frequency mode of the RP patients and healthy individuals. A significant decrease was observed in the main constituent frequencies of the ERG signals and their occurrence time. In addition, with further development of the disease in the RP patients, one or two main frequencies were omitted. Conclusion: RP disease can cause variations in time and time-frequency components of the ERG signals. By applying the wavelet transform on the ERG signals of the RP patients to convert their indices into frequency domain, the position of the retina affected mainly by this disease can be found. [ABSTRACT FROM AUTHOR]

Accurate and real-time measurement of the power system signals’ phasor is an open challenge in future smart grids. This article analyzes the performance of three recently proposed phasor estimators and compares them with the proposed estimator algorithm. The article proposes an improved recursive wavelet transform for real-time estimation of phasor and frequency in smart power systems. The proposed algorithm performance is compared with the commonly used full-cycle discrete Fourier transform (DFT), the improved discrete Fourier transform, and the wavelet transform based methods. It is shown that improved recursive wavelet transform possesses an improvement over a wide range of decaying DC components, harmonic distortions, frequency deviations, and sampling frequency. These characteristics of the improved recursive wavelet transform make it a good candidate for real-time applications in any smart power system. [ABSTRACT FROM AUTHOR]

Microscopy is going through a digital renaissance and new schemes are developed where computer and algorithms constitute an integral part of the imaging process itself. Computational microscopy increases performance by offering better resolution, larger field of view, quantitative contrast and also reduced size, weight and economic cost. Fourier Ptychographic microscopy utilizes multiple images of a sample taken at lower resolution, each illuminated with a unique incidence angle coherent source, and synthesizes one high resolution complex valued image by iterative phase retrieval algorithms. The recorded images are often corrupted with background noise and pre-processing is needed to improve the quality of the FP recovered image. The pre-processing involves data denoising, thresholding and intensity balancing. We have investigated different wavelet families to test their performance in terms of having compact support and giving the desired level of decomposition for optimal intensity thresholding and denoising in Fourier Ptychography (FP). The wavelet families Daubechies, Biorthogonal, Reverse Biorthogonal, Coiflet, Fejer-Korovkin, Discrete Meyer and Symlet with different compact support have been studied. The obtained threshold was used with noisy synthetic and experimental images for a variety of objects to evaluate the performance of the described framework. In particular, Reverse Biorthogonal wavelets were found to preserve useful signal in corrupted images to a great extent (RMS error 0.39) with low computational cost. Consequently, quantitatively more correct amplitude and phase images with uniform and homogeneous background could be recovered. • In FPM, Experimental images contain background noise and pre-processing is always needed. • Pre-processing involves data denoising, thresholding and intensity balancing. • Different wavelet families have been investigated to evaluate performance for denoising in FPM • Daubechies, Biorthogonal, Reverse Biorthogonal, Coiflet, FejerKorovkin, Discrete Meyer and Symlet wavelets have been studied. • Reverse Biorthogonal wavelets were found to preserve useful signal in corrupted images to a great extent. [ABSTRACT FROM AUTHOR]

The pipe roof system is widely used in the New Austrian Tunneling Method (NATM) as the main support system. Thus, the integrity of the pipe roof system influences the tunnel stability. The purpose of this study is to evaluate the grouted ratio of a pipe roof system using a non-destructive method in the laboratory and in the field. In the laboratory tests, four specimens embedded in soils and five non-embedded specimens are prepared with different grouted ratios of 0%, 25%, 50%, 75%, and 100%. The steel pipes are 6 m in length, 60.5 mm in external diameter, and 3.8 mm in thickness. Field tests are conducted with two fully grouted pipes with dimensions of 12 m in length, 60.5 mm in external diameter, and 3.8 mm in thickness. The reflection method of guided waves, which are generated by a hammer impact and are measured using an acoustic emission sensor, is used for the non-destructive testing. Experimental studies demonstrate that the group velocities and the main frequencies of the guided waves decrease as the grouted ratio increases for embedded and non-embedded specimen in soils. The variation of the main frequency, however, is more significant than the variation of the group velocity. In addition, the group velocities and main frequencies of the field specimens are lower than those of the embedded specimens. This study demonstrates that the variations of the group velocity and main frequency may be used effectively to estimate the grout ratio of a pipe roof system in tunneling. [ABSTRACT FROM AUTHOR]

From a very modest presentation as an introductory composition of wavelets by Chui in 1992 to a very specialist and advanced monographs by Meyer in 1990, and by Daubechies in 1992, one will certainly experience the beauty of this subject, which in the recent time has attracted both the pure and applied mathematicians. Wavelet transform, more correctly called the integral wavelet transform, is one of the two entities of the wavelet analysis. Possibly the window Fourier trans- form, also called the Gabor transform (first introduced by Gabor in 1946), is the initiation for wavelet transform. In this brief note we attempt to discuss some of its aspects. [ABSTRACT FROM AUTHOR]

The identification of formation interfaces is of prime importance from well log data. The interfaces are not clearly discernible due to the presence of high and low frequency noise in the log response. Accurate bed boundary information is very crucial in hydrocarbon exploration and the problem has received considerable attention and many techniques have been proposed. Frequency spectrum based filtering techniques aids us in interpretation, but usually leads to inaccurate amplification of unwanted components of the log response. Wavelet transform is very effective in denoising the log response and can be carried out to filter low and high frequency components of signal. The use of Fourier and Wavelet transform in denoising the log data for obtaining formation interfaces is demonstrated in this work. The feasibility of the proposed technique is tested so that it can be used in the industry to decipher formation interfaces. The work flow is demonstrated by testing on wells belonging to the Upper Assam Basin, which are self-potential, gamma ray, and resistivity log responses. [ABSTRACT FROM AUTHOR]

When 3D sensors such as Light Detection and Ranging (LIDAR) are employed in targeting and recognition of human actions from both ground and aerial platforms, the corresponding point clouds of body shape often comprise low-resolution, disjoint, and irregular patches of points resulted from self-occlusions and viewing angle variations. Many existing 3D shape descriptors designed for shape query and retrieval cannot work effectively with these degenerated point clouds because of their dependency on dense and smooth full-body scans. In this paper, a new degeneracy-tolerable, multi-scale 3D shape descriptor based on the discrete orthogonal Tchebichef moment is proposed as an alternative for single-view partial point cloud representation and characterization. To evaluate the effectiveness of our descriptor, named Tchebichef moment shape descriptor (TMSD), in human shape retrieval, we built a multi-subject pose shape baseline to produce simulated LIDAR captures at different viewing angles and conducted experiments of nearest neighbor search and point cloud reconstruction. The query results show that TMSD performs significantly better than the Fourier descriptor and is slightly better than the wavelet descriptor but more flexible to construct. In addition, we proposed a voxelization scheme that can achieve translation, scale, and resolution invariance, which may be less of a concern in the traditional full-body shape analysis but are crucial requirements for meaningful partial point cloud retrievals. [ABSTRACT FROM AUTHOR]

A new method based on the shearlet transform is presented for phase extraction in fringe projection profilometry (FPP) from a single fringe pattern. We apply the advanced shearlet transform to the fringe pattern to obtain the transform coefficients, and extract the fundamental frequency component of the fringe pattern in the 2-D frequency domain. We test the introduced method on a simulated pattern and two actual objects with edges or abrupt changes in height. The results show that the proposed method is more effective and accurate than the Fourier transform method and wavelet transform method. [ABSTRACT FROM AUTHOR]

We discuss the possible advantages of using the wavelet transform over the Fourier transform for the early detection of rogue waves. We show that the triangular wavelet spectra of the rogue waves can be detected at early stages of the development of rogue waves in a chaotic wave field. Compared to the Fourier spectra, the wavelet spectra are capable of detecting not only the emergence of a rogue wave but also its possible spatial (or temporal) location. Due to this fact, wavelet transform is also capable of predicting the characteristic distances between successive rogue waves. Therefore multiple simultaneous breaking of the successive rogue waves on ships or on the offshore structures can be predicted and avoided by smart designs and operations. [ABSTRACT FROM AUTHOR]

In this paper, Cauchy wavelet transform of ultra-distributions in tube domains is defined and its various properties are studied using the theory of Cauchy integrals and Poisson integrals of ultra-distributions. [ABSTRACT FROM PUBLISHER]

Ship wake transformation in the coastal zone is analysed based on field measurements of wave conditions at two measurement sites located about 20 m and 100 m from the shore. Analysis of single wake events recorded at both sites is carried out by transforming the time series of the wave amplitude into the time-frequency domain, using both a short-time Fourier transform and a wavelet transform. Analysis reveals that signature features of individual wake components can be tracked as the wake approaches the shore, but the wave amplitude and associated wave energy is transformed differently for different wake components. The wake energy is reduced as the waves propagate through the surf zone, which can be attributed mainly to wave breaking of the leading wave system and a significant reduction of the divergent wave system. However, the energy of transverse waves is stable or increasing, indicating that these waves undergo a non-breaking shoaling process. [ABSTRACT FROM AUTHOR]

High penetration of distributed generation resources (DGR) in distribution network provides many benefits in terms of high power quality, efficiency, and low carbon emissions in power system. However, efficient islanding detection and immediate disconnection of DGR is critical in order to avoid equipment damage, grid protection interference, and personnel safety hazards. Islanding detection techniques are mainly classified into remote, passive, active, and hybrid techniques. From these, passive techniques are more advantageous due to lower power quality degradation, lower cost, and widespread usage by power utilities. However, the main limitations of these techniques are that they possess a large non detection zones and require threshold setting. Various signal processing techniques and intelligent classifiers have been used to overcome the limitations of passive islanding. Signal processing techniques, in particular, are adopted due to their versatility, stability, cost effectiveness, and ease of modification. This paper presents a comprehensive overview of signal processing techniques used to improve common passive islanding detection techniques. A performance comparison between the signal processing based islanding detection techniques with existing techniques are also provided. Finally, this paper outlines the relative advantages and limitations of the signal processing techniques in order to provide basic guidelines for researchers and field engineers in determining the best method for their system. [ABSTRACT FROM AUTHOR]

Five signal processing techniques were applied to ratio spectra for quantitative determination of bisoprolol (BIS) and hydrochlorothiazide (HCT) in their binary mixture. The proposed techniques are Numerical Differentiation of Ratio Spectra (ND-RS), Savitsky–Golay of Ratio Spectra (SG-RS), Continuous Wavelet Transform of Ratio Spectra (CWT-RS), Mean Centering of Ratio Spectra (MC-RS) and Discrete Fourier Transform of Ratio Spectra (DFT-RS). The linearity of the proposed methods was investigated in the range of 2–40 and 1–22 μg/mL for BIS and HCT, respectively. The proposed methods were applied successfully for the determination of the drugs in laboratory prepared mixtures and in commercial pharmaceutical preparations and standard deviation was less than 1.5. The five signal processing techniques were compared to each other and validated according to the ICH guidelines and accuracy, precision, repeatability and robustness were found to be within the acceptable limit. [ABSTRACT FROM AUTHOR]

The comparative ability of the Fourier transform (FFT) and discrete wavelet transform (DWT) algorithms in assessing muscle fatigue during sub-maximal repetitive dynamic exertion was investigated in this study. Surface electromyography data recorded from the upper trapezius muscle during forty minutes of repetitive upper extremity exertion performed by 10 male participants were used in the analysis. Multi-model regression analysis was performed to study the trend in the power values of the different frequency bands estimated using the FFT and DWT algorithms. Less variability and higher statistical significance was observed for the power value trend computed using the DWT algorithm compared to the FFT algorithm. The regression models provided a better fit for the power values estimated under more fatigued condition compared to the less fatigued condition. The lower frequency bands of 23–46 Hz and 46–93 Hz exhibited the expected and consistent power trend independent of the algorithm (DWT or FFT) used. For the exertions tested in this study, a cubic or curvilinear model explained the fatigue development process with a higher precision than the linear models. [ABSTRACT FROM AUTHOR]

We introduce a natural convolution of two suitable quaternion valued functions on R and list down its properties. Using this convolution, first we get the convolution theorem for Fourier transform on quaternion valued functions. Next, we modify the existing definition of wavelet transform on square integrable quaternion valued functions in a natural manner so that Parseval's identity is obtained without any additional conditions. Applying the Parseval's identity, we derive the inversion formula for the wavelet transform and we also prove the other properties like linearity, continuity and injectivity. Finally, we construct two Boehmian space of quaternion valued functions and extend the wavelet transform as a continuous linear injection from one Boehmian space into the other space. [ABSTRACT FROM AUTHOR]