Wavelet Analysis

Non-invasive fetal electrocardiography (NI-ECG) is based on the acquisition of signals from electrodes on the mother's abdominal surface. This abdominal ECG (aECG) signal consists of the maternal ECG (mECG) along with the fetal ECG (fECG) and other noises and artifacts. These records allow the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper proposes a procedure based on principal component analysis (PCA) to obtain a single-channel master abdominal ECG record that can be used as input to fetal heart rate extraction techniques. The new procedure requires three main processing stages: PCA-based analysis for fECG-component extraction, polarity test, and curve fitting. To show the advantages of the proposal, this PCA-based method has been used as the feeding stage to a previously developed clustering-based method for single-channel aECG fetal heart rate monitoring. The results obtained for a set of real abdominal ECG recordings from annotated public aECG databases, the Abdominal and Direct Fetal ECG Database and the Challenge 2013 Training Set A, show improved efficiency in fetal heart rate extraction and illustrate the benefits derived from the use of such a master abdominal ECG channel. This allows us to achieve proper fetal heart rate monitoring without the need for manual inspection and selection of channels to be processed, while also allowing us to analyze records that would have been discarded otherwise.

Wavelets and wavelet frames are important and useful mathematical tools in numerous applications, such as signal and image processing, and numerical analysis. Recently, the theory of wavelet frames plays an essential role in signal processing, image processing, sampling theory, harmonic analysis. However, multiwavelets and multiple frames are more flexible and have more freedom in their construction which can provide more desired properties than the scalar case, such as short compact support, orthogonality, high approximation order, and symmetry. These properties are useful in several applications, such as curve and surface noise-removing as studied in this dissertation. Thus, the study of multiwavelets and multiple frames construction has more advantages for many applications.

This study investigates the nexus between industrial production (IPI) and sovereign CDS spreads in Türkiye (2009-2025) using a time-frequency framework. By integrating Fourier-based unit root tests, Johansen cointegration, and cross-spectral analysis, the results reveal a long-run equilibrium where real activity significantly leads financial stress in medium-and long-term cycles. The findings identify industrial resilience as a structural anchor for sovereign creditworthiness, suggesting that long-term stability is fundamentally rooted in productive capacity rather than transitory financial flows.

Let $ N \in \mathbb{N} $, $ N \geq 2 $, be given. Motivated by wavelet analysis, we consider a class of normal representations of the $ C^* $-algebra $ \mathfrak{A}_{N} $ on two unitary generators $ U $, $ V $ subject to the relation \[ UVU^{-1}=V^{N}. \] The representations are in one-to-one correspondence with solutions $ h \in L^{1}(\mathbb{T}) $, $ h \geq 0 $, to $ R(h)=h $ where $ R $ is a certain transfer operator (positivity-preserving) which was studied previously by D. Ruelle. The representations of $ \mathfrak{A}_{N} $ may also be viewed as representations of a certain (discrete) $ N $-adic $ ax+b $ group which was considered recently by J.-B. Bost and A. Connes.

This paper suggests a novel method for robust estimation in linear regression models by fusing the Andrews weighted function with Symlet wavelet analysis. The result is a new weighted function that includes the Symlet Andrews Weighted Function. These functions try to reduce the effect of outliers in regression analysis by giving data points with high residuals lower weights. The effectiveness of these new weighted functions was evaluated using simulated experiments, using the root mean square error (RMSE) as the standard. The results show that, in terms of RMSE values, the recommended weighted function consistently performs better than the traditional weighted function. This improvement suggests that because the new function can handle outlier data, they are more reliable for linear regression analysis, including problematic data.

This article treats data noise and outliers in Bayesian ARIMA models through wavelet analysis. Apply the discrete wavelet transformation using Daubechies and Symlets wavelets for orders 10 and 15 to decompose the data of Bayesian ARIMA models into their frequency components. Threshold the wavelet coefficients using a method like soft thresholding, with the threshold selected via Stein's unbiased risk estimate and soft rule. Simulation experiments were used with real data representing the monthly inflation in the Kurdistan Region of Iraq (2009-2024) with a forecast for the next ten months. The proposed waveletbased Bayesian ARIMA method provides a robust framework for handling noisy time series data and offers significant improvements over classical methods, making it an appealing choice for practical applications in time series forecasting, particularly when dealing with outliers and noise.

In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods.

In this paper, we discuss the origin of superswell volcanism on the basis of representation and analysis of recent gravity and magnetic satellite data with wavelets in spherical geometry. We computed a refined gravity field in the south central Pacific based on the GRACE satellite GGM02S global gravity field and the KMS02 altimetric grid, and a magnetic anomaly field based on CHAMP data. The magnetic anomalies are marked by the magnetic lineation of the seafloor spreading and by a strong anomaly in the Tuamotu region, which we interpret as evidence for crustal thickening. We interpret our gravity field through a continuous wavelet analysis that allows to get a first idea of the internal density distribution. We also compute the continuous wavelet analysis of the bathymetric contribution to discriminate between deep and superficial sources. According to the gravity signature of the different chains as revealed by our analysis, various processes are at the origin of the volcanism in French Polynesia. As evidence, we show a large-scale anomaly over the Society Islands that we interpret as the gravity signature of a deeply anchored mantle plume. The gravity signature of the Cook-Austral chain indicates a complex origin which may involve deep processes. Finally, we discuss the particular location of the Marquesas chain as suggesting that the origin of the volcanism may interfere with secondary convection rolls or may be controlled by lithospheric weakness due to the regional stress field, or else related to the presence of the nearby Tuamotu plateau.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Extreme hydrological events are often triggered by exceptional co-variations of the relevant hydrometeorological processes and in particular by exceptional co-oscillations at various temporal scales. Wavelet and cross wavelet spectral analysis offers promising timescale resolved analysis methods to detect and analyze such exceptional co-oscillations. This paper presents the state-of-the-art methods of wavelet spectral analysis, discusses related subtleties, potential pitfalls and recently developed solutions to overcome them and shows how wavelet spectral analysis, if combined to a rigorous significance test, can lead to reliable new insights into hydrometeorological processes for real-world applications. The presented methods are applied to detect potentially flood triggering situations in a high Alpine catchment for which a recent re-estimation of design floods encountered significant problems simulating the observed high flows. For this case study, wavelet spectral analysis of precipitation, temperature and discharge offers a powerful tool to help detecting potentially flood producing meteorological situations and to distinguish between different types of floods with respect to the prevailing critical hydrometeorological conditions. This opens very new perspectives for the analysis of model performances focusing on the occurrence and non-occurrence of different types of high flow events. Based on the obtained results, the paper summarizes important recommendations for future applications of wavelet spectral analysis in hydrology.

With several large aperture optical and IR telescopes just coming on-line, or scheduled for the near future, solar physics is on the verge of a quantum leap in observational capabilities. An efficient use of such facilities will require new and innovative approaches to both observatory operations and data handling.This two-days long Special Session discussed the science expected with large solar telescopes, and started addressing the strategies necessary to optimize their scientific return. Cutting edge solar science as derived from state-of-the-art observations and numerical simulations and modeling was presented, and discussions were held on the role of large facilities in satisfying the demanding requirements of spatial and temporal resolution, stray-light correction, and spectro-polarimetric accuracy. Building on the experience of recently commissioned telescopes, critical issues for the development of future facilities were discussed. These included operational issues peculiar ...

We present observational evidence for the existence of a current sheet in the chromosphere above a sunspot umbra based on high angular resolution two-dimensional spectroscopic observations in the Ca ii 854.21 nm line. In the core of this line we observe a very stable bright ribbon-like structure separating magnetic field configurations that connect to different parts of the active region. We make plausible that the structure is a string of sheets carrying vertical currents that result from dissipation when the different parts of the active region are moved around in the photosphere. To our knowledge this is the first direct observation of the heating caused by the dissipation in such a current sheet in the chromosphere.

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215

We examine the decomposition of forced Taylor-Green and Arn'old-Beltrami-Childress (ABC) flows into coherent and incoherent components using an orthonormal wavelet decomposition. We ask whether wavelet coefficient thresholding based on the Donoho-Johnstone criterion can extract a coherent vortex signal while leaving behind Gaussian random noise. We find that no threshold yields a strictly Gaussian incoherent component, and that the most Gaussian incoherent flow is found for data compression lower than that achieved with the fully iterated Donoho-Johnstone threshold. Moreover, even at such low compression, the incoherent component shows clear signs of large-scale spatial correlations that are signatures of the forcings used to drive the flows.

Let N ∈ N, N ≥ 2, be given. Motivated by wavelet analysis, we consider a class of normal representations of the C * -algebra A N on two unitary generators U , V subject to the relation The representations are in one-to-one correspondence with solutions h ∈ L 1 (T), h ≥ 0, to R (h) = h where R is a certain transfer operator (positivitypreserving) which was studied previously by D. Ruelle. The representations of A N may also be viewed as representations of a certain (discrete) N -adic ax + b group which was considered recently by J.-B. Bost and A. Connes.

Abstract

Hydrological calibration in data-scarce catchments is challenged by non-stationary regimes, fragmented data, and systematic measurement errors. Conventional calibration approaches often assume continuous records and rely on standard performance metrics, which can bias calibration toward high flows and exacerbate parameter equifinality—ultimately reducing robustness under data limitations. This study provides a systematic comparison of three calibration strategies—Kling–Gupta Efficiency (KGE), a non-parametric variant (RNP), and Flow Duration Curve (FDC)-based calibration—together with their time-consistent counterparts (SKGE, SRNP, and SRMSE). All schemes are implemented for the lumped HBV-type TUW model across nine catchments in southern Italy and evaluated using independent metrics targeting overall hydrograph agreement, high-flow behavior, and FDC quantile matching (Q5–Q95). The results reveal that the time-consistent KGE-based strategy excels during in calibration (NSE = 0.56, RMSE = 4.65 m3/s) but shows notable declines in validation (NSE = 0.40, RMSE = 3.91 m3/s), indicating sensitivity to non-stationarity. The RNP-based approach demonstrates enhanced validation robustness (NSE = 0.51, RMSE = 3.60 m3/s) and low-flow accuracy, with NSElnQ = 0.30 and low-flow accuracy, leveraging its non-parametric structure. The SRNP variant further enhances performance in validation (NSE = 0.52, RMSE = 3.42 m3/s), along with superior low-flow performance (NSElnQ = 0.48). The FDC-based strategy effectively reproduces flow distributions during calibration (NSE = 0.41, minimal PBIAS = −0.03%) but exhibits limited temporal transferability (validation NSE = 0.25, RMSE = 4.50 m3/s). Time-consistent variants reduce parameter dispersion by approximately 2–8% (relative to full-period calibration) and improve validation metrics by 5–15% across all catchments. Overall, time-consistent calibration provides a practical pathway to increase robustness under non-stationary, data-scarce Mediterranean conditions, highlighting a systematic trade-off between calibration accuracy and validation reliability.

It is essential to predict what the exchange rate will be in the future. There are several factors affecting value of national currency of an economy. One of them is risk perception and after the end of "Quantitative Easing" program by Federal Reserve, risk perception for emerging market economies has changed. In this study, we aim to analyze interaction between credit default swap premium as a risk indicator and exchange rate in the Turkish economy after the global finance crisis. Results imply that risk perception has essential effects on the value of Turkish lira against U.S. dollar and to reduce volatility in the value of Turkish lira, risk perception has to be decreased.

Disclaimer This document does not constitute an operational early-warning system for earthquakes and is not intended for direct public notification of seismic hazards. The protocol describes a method for diagnosing phase regimes of seismic activity and assessing structural risk within the framework of nonlinear dynamics and complex systems theory. All decisions related to civil protection, operation of engineering structures, and official seismic risk assessments must be made by authorised organisations based on current regulatory documents and comprehensive analysis of available information. Results obtained using this methodology should be considered as auxiliary scientific and applied information and an invitation for independent verification and development within institutional research teams.

This second part of the report examines the evolution of the Phenomenological Reconstruction of Complex Systems (PRCS) method from binary anomaly detection toward expert-level diagnostic capability. Building upon multiscale analysis of structural invariants in reconstructed phase space, a meta-feature space is constructed wherein streaming clustering and dynamic pattern tracking are performed. The emergence of mechanical defects appears to be associated not merely with an increase in a scalar risk index, but with the emergence of new, quasi-stable dynamical regimesa process we term patternogenesis. By associating identied clusters with computed bearing defect frequencies and analyzing state transition dynamics, physical localization and typication of damage modes become feasible. Results are validated using the publicly available CWRU Bearing Data Center dataset.

The Vietnamese Mekong Delta is the country's agricultural powerhouse and a strategic locus for the clean-energy transition. Yet rapid salinity intrusion, subsidence, climate variability, and grid constraints threaten both energy security and livelihoods. This paper synthesizes the state of renewable energy in the VMD, identifies policy-technical gaps, and proposes an integrated portfolio of solutions-agrivoltaics, floating solar coupled with hydropower (hybrid hydrosolar), distributed biomass and biogas, and battery energy storage systems aligned with emergent national standards. We combine desk research on national power planning (PDP8 and its 2025 revision), peer-reviewed literature on salinity and land-use change, and sectoral reports to evaluate technical feasibility, system value, and socio-ecological co-benefits. Results indicate VMD-appropriate options that: (i) shift PV to water and saline-affected lands; (ii) valorize rice husk/bagasse for firm, dispatchable biomass; (iii) deploy modular BESS to reduce curtailment and support local reliability; and (iv) mainstream climate adaptation through energy-water-food nexus pilots (e.g., solar-powered desalination). We conclude with a staged roadmap for project preparation, standards adoption, and investment de-risking.

pur, for his invaluable supervision, encouragement, and guidance throughout the course of my doctoral studies. His patience, motivation, and profound knowledge have been a constant source of inspiration. I truly feel fortunate to have had him as my mentor during my Ph.D. journey. I also extend my heartfelt gratitude to Prof. (Dr.

Electroencephalogram (EEG) coherence is a mixture of phase locking interrupted by phase shifts in the spontaneous EEG. Average reference, Laplacian transforms, and independent component (ICA) reconstruction of time series can distort physiologically generated phase differences and invalidate the computation of coherence and phase differences as well as in the computation of directed coherence and phase reset. Time domain measures of phase shift and phase lock are less prone to artifact and are independent of volume conduction. Cross-frequency synchrony in the surface EEG and in Low Resolution Electromagnetic Tomography (LORETA) provides insights into dynamic functions of the brain. Coherence is a measure of the variability of time differences between two time series in a specific frequency band. The Fourier transform provides a direct relationship between the time and frequency domains and represents time difference as a phase difference or phase angle. If the phase angle is stable and constant over time i.e., phase locked then coherence = 1.0 and if time differences between two time series varies from moment-to-moment then coherence = 0. Electroencephalogram (EEG) coherence is often interpreted as a measure of "coupling" and as a measure of the functional association between two brain regions (

Research on human sensorimotor functions has hugely increased after electromyogram (EMG) analysis was replaced by functional magnetic resonance imaging (fMRI), that allows to obtain a direct visualization of the brain areas involved in motor control. Very meaningful results could be obtained if the two analysis could be correlated. Our goal is to acquire the EMG data during an fMRI task. The main problems in doing this are related to the electromagnetic compatibility between the resonance coils (very high magnetic fields) and the EMG electrodes. In this study we developed a system that can characterize the entire EMG signal corrupted by the magnetic fields generated by the magnetic resonance gradients. The entire system consists in a hardware equipment (shielded cables and wires) and a software analysis (effective mean analysis and wavelet analysis). The results show that a motor task was correctly delivered by our post processing analysis of the signal.

Most clinical measurements rely on bioelectromagnetic phenomena. These events allow us to record electric or magnetic signals during the activity of living tissues. In this paper, we put our attention on the bioelectric fields that occur in the muscle activity. In fact during the body movements, the muscle contractions produce a bioelectric potential distribution that can be measured by putting the electrodes on the skin. In clinical applications, the monitoring of muscle activity can be performed in a noninvasive way, by placing a fixed number of electrodes on the skin surface; this technique is called surface electromyography (sEMG), and it is able to reveal the electric field generated by each muscle activity. Unfortunately, the sEMG suffers from the external electromagnetic fields. In recent years, many authors investigated the correlation between muscle and brain activity by performing the sEMG during the functional magnetic resonance imaging (fMRI). The fMRI is the most reliable technique to evaluate the brain activity because it allows us to obtain some images of the human body with very high resolutions. Unfortunately, joint measurement is a very difficult task because of the high electromagnetic interference between the resonance coils (very high magnetic fields) and the sEMG electrodes. In this paper, we present a method based on wavelet analysis to reveal sEMG in voluntary contractions when the measurement is made in a fMRI environment.

In gravel bed rivers, the microtopography of the bed exerts a significant effect on the generation of turbulent flow structures. Although field and laboratory measurements have indicated that flows over gravel beds contain coherent macroturbulent flow structures, the origin of these phenomena, and their relationship to the ensemble of individual roughness elements forming the bed, is not quantitatively well understood. Here we report upon a flume experiment in which flow over a gravel surface is quantified through the application of digital particle imaging velocimetry, which allows study of the downstream and vertical components of velocity over the entire flow field. The results indicate that as the Reynolds number increases (1) the visual distinctiveness of the coherent flow structures becomes more defined, (2) the upstream slope of the structures increases, and (3) the turbulence intensity of the structures increases. Analysis of the mean velocity components, the turbulence intensity, and the flow structure using quadrant analysis demonstrates that these large-scale turbulent structures originate from flow interactions with the bed topography. Detection of the dominant temporal length scales through wavelet analysis enables calculation of mean separation zone lengths associated with the gravel roughness through standard scaling laws. The calculated separation zone lengths demonstrate that wake flapping is a dominant mechanism in the production of large-scale coherent flow structures in gravel bed rivers. Thus, we show that coherent flow structures over gravels owe their origin to bed-generated turbulence and that large-scale outer layer structures are the result of flow-topography interactions in the near-bed region associated with wake flapping.

The success or failure of an epilepsy surgery depends greatly on the localization of epileptic focus (origin of a seizure). We address the problem of identification of a seizure origin through an analysis of ictal electroencephalogram (EEG), which is proven to be an effective standard in epileptic focus localization. Summary: With a goal of developing an automated and robust way of visual analysis of large amounts of EEG data, we propose a novel approach based on multiway models to study epilepsy seizure structure. Our contributions are 3-fold. First, we construct an Epilepsy Tensor with three modes, i.e. time samples, scales and electrodes, through wavelet analysis of multi-channel ictal EEG. Second, we demonstrate that multiway analysis techniques, in particular parallel factor analysis (PARAFAC), provide promising results in modeling the complex structure of an epilepsy seizure, localizing a seizure origin and extracting artifacts. Third, we introduce an approach for removing artifacts using multilinear subspace analysis and discuss its merits and drawbacks. Results: Ictal EEG analysis of 10 seizures from 7 patients are included in this study. Our results for 8 seizures match with clinical observations in terms of seizure origin and extracted artifacts. On the other hand, for 2 of the seizures, seizure localization is not achieved using an initial trial of PARAFAC modeling. In these cases, first, we apply an artifact removal method and subsequently apply the PARAFAC model on the epilepsy tensor from which potential artifacts have been removed. This method successfully identifies the seizure origin in both cases.

Nous présentons dans ce papier deux nouveaux modèles de texture orientée, basés sur une nouvelle classe de champs gaussiens, appelés champs browniens fractionnaires localement anisotropes, à orientation locale prescrite en chaque point. Ces champs aléatoires sont une version locale d'une classe spécifique de champs anisotropes autosimilaires à incréments stationnaires. La simulation de telles textures s'appuie sur un nouvel algorithme couplant l'utilisation de la notion de champs tangents à la méthode de Cholesky ou plus récemment la méthode des bandes tournantes, cette dernière ayant prouvé son efficacité à générer des textures stationnaires anisotropes. Des applications numériques illustrent la faculté de la méthode à synthétiser des textures à orientation locale prescrite.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

This article addresses parameter identification problem in vibrating Euler-Bernoulli beams with an open crack or elastic intermediate supports. The crack is simulated by a line spring model. The boundary value problem is solved by the aid of Haar wavelets. The characteristic parameters are predicted using the artificial neural networks. Two different approaches based on natural frequencies and Haar wavelets are compared. The significance of the second approach lies in its ability to make fast and trustworthy predictions on the selected parameters.

The 10-year government bond yield is a key benchmark for sovereign financing costs and a critical reference rate for asset pricing in emerging markets. Amid increased global financial integration, understanding its drivers is therefore crucial for policymakers and market participants. This study examines the impact of domestic monetary conditions and global investor behavior on Indonesia's 10-year government bond yield, utilizing monthly data from January 2014 to December 2024. The model includes the central bank policy rate, the stock market index, inflation, the rupiah-US dollar exchange rate, and the share of foreign ownership in government bonds. Given the mixed order of integration among variables, the analysis employs the AutoRegressive Distributed Lag (ARDL) approach to estimate both short-run dynamics and long-run relationships. The results show that exchange rate movements significantly affect the 10-year yield in both the short run and the long run, underscoring the role of currency risk and external shocks in sovereign yield formation. These findings imply that strengthening exchange rate stability and managing external vulnerability can help contain government borrowing costs and support bond market resilience during shifts in global risk appetite.

The daily rainfall data of fifty eight years (1961-2018) for Shirala tahsil has been analysed to determined the trend in rainfall and rainy days on annual, seasonal and monthly basis using mann kendall test, its statistical significance along with magnitude of sen's slope methods. The trend was calculated individually for each season and each month (May to November) for 58 years. The mann kendall test showed that annual rainfall and rainy days of Shirala tahsil didn't exhibit any statistical significant trend and sen's slope method showed decreasing trend of annual rainfall. The rainfall and rainy days trend were significant decreasing, for summer and north east monsoon season. The rainfall trend was significant decreasing, for the month of May and November, the rainy days trend was significant increasing for June and decreasing for May and November.

This study describes winter climate during the last 500 yr for the greater Baltic Sea region through an examination of well-documented time series of ice cover, sea level pressure, and winter surface air temperatures. These time series have been the focus of previous studies, but here their covariation over different time scales is analyzed based on two modern descriptive statistical techniques, matching pursuit and wavelet analysis. Independently, 15 time periods were found during the last 500 yr with different climatic signatures with respect to winter severity, circulation patterns, and interannual variability. The onsets of these periods are presumably caused largely by perturbations within the system, although correspondences with solar and volcanic activity can be identified for certain of the periods. The Baltic region climate has changes on both centennial and decadal time scales, often with rapid transitions. Major warmer periods were the first half of the eighteenth centur...

— ECG signals are used to detect the heart rate and heart abnormalities. For extraction of ECG features and detection of QRS complexes it is required to remove baseline wander and minimize the noise interference. In this paper we proposed a technique to remove baseline wander using Kaiser Windowing filter and wavelet transform, among which wavelet is most powerful and effective tool for analyzing transient signal. The algorithm is developed in matlab with standard CSEECG database.

In this paper, we propose a new methodology for the automatic picking of P- and S- wave arrivals. The measurement of elastic wave velocities is crucial for characterisation of the elastic properties of rocks, as well as their physico-mechanical properties and durability. P-waves are easy to generate and acquire, whereas the acquisition and subsequent analysis of the S-waves present major difficulties in most investigations of rocks. In our proposed method the recorded signal is first pre-processed in the wavelet domain, removing low-frequency disturbances and filtering noise. The signal is then analysed in the time-domain to detect and characterise the first pulse and, subsequently, estimate the corresponding onset time. The automatic approach analyses all pulses detected in the output signal and selects the first pulse of the P or S wave relative to symmetry, amplitude and duration criteria. This triple check provides greater confidence in the obtained results. We record P- and S- ...

A beam is a common structural element designed to resist loading. Applications of beams can be found in industrial, mechanical, aerospace and other engineering disciplines. Some examples are a doorway beam, a diving board, a snowboard, a rail and an aeroplane wing.
Underestimated loads during the design stage, looseness during the manufac- turing stage, mechanical vibrations, corrosive environment, collisions may intro- duce some damage to beams. If no action is taken, the damage can turn into a fault or a breakdown of the whole system. Hereof, the entirety of beams is a crucial issue. Cracks, delaminations or changes of elastic parameters can be localised and assessed at a relatively early stage using non-destructive testing methods, such as the X-ray, the lamb waves, the acoustic emission. Inherently, these methods are expensive; they require arduous work to examine the entire structure and can- not always be applied to temperature-sensitive materials. Recently, modal-based methods have gained attention of many researchers. These methods are based on the idea that any damage alters physical properties of the structure (mass, stiffness) which in turn change the modal properties (natural frequencies, mode shapes, modal damping). Although several research papers have indicated that changes in the natural frequencies can follow damage severity, they are unable to provide spatial information. To overcome this limitation, advanced modern type signal processing techniques such as wavelets can be used.
This dissertation concerns non-inverse and inverse problems of beams with different boundary conditions. It is proposed to solve the forward problem of the vibration analysis using the Haar wavelets, their integration and the modal properties, such as natural frequencies and mode shapes. The numerical examples indicate that the proposed approach is fast and accurate. The inverse problems deal with damage parameter quantification: damage with different severity in two sets of different locations can produce identical changes of the natural frequencies. In this dissertation, it is proposed to solve the inverse problem of damage quantification with the aid of the Haar wavelets and machine learning. Two datasets and two machine learning methods are ex- amined with the aim to highlight the most promising approach. The first dataset is based on the natural frequency parameters. For the second dataset, the mode shapes are transformed into the Haar wavelet transform coefficients. Back propa- gation neural networks and random forests are trained to predict the location and severity of the induced damage. The comparison of the approaches shows that the Haar wavelet transformation is more sensitive to the damage localisation, while the natural frequency parameters are more sensitive to the damage severity quan- tification. In most simulation cases of the damaged beams, the neural networks produce as precise predictions of the damage characteristics as the random forests; however, the random forests are easier to adjust and train.
The results presented in the dissertation can help understand behaviour of more complex structures under similar conditions, provide apparent influence on design concepts of structures, and enable new possibilities for operational and maintenance concepts.

Scale invariance is a widely used concept to analyze real-world data from many different applications and multifractal analysis has become the standard corresponding signal processing tool. It characterizes data by describing globally and geometrically the fluctuations of local regularity, usually measured by means of the Hölder exponent. A major limitation of the current procedure is that it applies only to locally bounded functions or signals, i.e., to signals with positive regularity. The present contribution proposes to characterize local regularity with a new quantity, the p-exponent, that permits negative regularity in data, a widely observed property in real-world data. Relations to Hölder exponents are detailed and a corresponding p-leader multifractal formalism is devised and shown at work on synthetic multifractal processes, representative of a class of models often used in applications. We formulate a conjecture regarding the equivalence between Hölder and p-exponents ...

We present a new image reconstruction method for Electrical Capacitance Tomography (ECT) by exploiting the sparsity of reconstructed images. ECT image reconstruction is generally ill-posed because the number of measurements is small whereas the image dimensions are large. Inspired by recent developments in Compressive Sensing (CS), given the sparsity of the signal (image), our idea is to apply an efficient and stable algorithm through L1 regularization to recover the sparse signal with sufficient measurements that have cardinality comparable to the sparsity of the signal. In this paper, we apply an efficient GPSR (Gradient Projection for Sparse Reconstruction) algorithm to reconstruct the sparse signal under DCT basis (GPSR-DCT). Our results on real data show that the proposed GPSR-DCT algorithm can better preserve object boundary and shape, as compared to a representative state-of-the-art ECT image reconstruction algorithm, Projected Landweber Iteration with Linear Back Projection ...

Targeting at the problem of finding the best demodulation band when applying envelope analysis in rolling bearing fault diagnosis, this paper proposes a novel Fast Kurtogram Demodulation Method (FKDM) to solve the problem. FKDM is established based on the theory of spectrum kurtosis and the short-time Fourier Transform. It determines the best demodulation band firstly, which is also known as the central frequency and frequency resolution. Then, the fault signals can be demodulated in the obtained frequency band by using envelope demodulation algorithm. The FKDM method ensures the fault diagnosis correction by solving the problem of demodulation band selection. Applied FKDM in rolling bearing fault diagnosis and compared with conventional envelope analysis, the results demonstrate FKDM can achieve a better performance.

Until recently, knowledge discovery would be restricted to a static analysis, disregarding any temporal or sequential relations within the data. In the last decade, temporal data mining developed to be a hot topic of research, looking for those temporal dependencies, unveiling new insights in various areas of interest, including bioinformatics. Sequential pattern mining tries to achieve such goals, by finding frequent patterns within a population and returning them to the user. However, its application as a basis for a direct classification problem with clinical data was never studied, to our knowledge. Hence, this work uses discovered sequential patterns as features for standard classifiers, using a clinical dataset obtained from Amyotrophic Lateral Sclerosis (ALS) patients. The preliminary results are very promising, achieving a prediction accuracy over 83% with a very reduced set of features, both from original data and sequential patterns. Future work includes advancing from a classification problem to prognosis prediction.

Foremost, gratitude goes to Dr. Stanley Reisman who served as my thesis advisor, and also as teacher throughout all my years at NJIT as a biomedical engineering student. His combined skepticism and faith in my abilities pushed me farther than I was able to recognize. Thanks are given to Dr. Gail Forrest fir lending me her knowledge, technical advice as well as feedback in the middle of a stressful workload. Her insights were not only contextual, but practical as well. Dr. Xiaouri Tang was instrumental in providing guidance, by asking all the right questions, and keeping me on track. Appreciation goes to Dr. Richard Foulds whose exuberant support of my love of sharing knowledge gave me great hope.

Background. Development of the methods for identification and assessment of early signs of heart disorders makes it possible to catch the sight of disease at its initial stage. The article considers the methods of early diagnosis of the cardiovascular system using electrocardiographic markers of cardiac electrical instability. Objective. The aim of the study is to identify low-amplitude components, which are inaccessible to standard procedures of electrocardiogram (ECG) evaluation by means of modern methods of registration, digital processing of electrocardiosignals and high resolution electrocardiography. Methods. For detection of diagnostic symptoms associated with cardiac electrical instability, changes in real and simulated electrocardiosignals have been studied using different types of analysis: in time and frequency domains, scattergrams, cluster analysis, wavelet analysis and principal component analysis. Results. The developed combined methods for analysis of low-amplitude components of electrocardiosignals allowed us to perform detection of late potentials, as well as T wave alternans, reflecting cardiac electrical instability. Conclusions. Identification and evaluation of subtle manifestations of cardiac electrical activity are carried out. The use of the proposed method made it possible to distinguish the bursts of late potentials from the noise and to determine the temporal area of their localization.

We identified 147 contiguous United States warm-season (May, June, July, and August) derechos between 1995 and 2024. This results in an average of approximately five derechos each warm season. Using the compiled database, trends in derecho occurrence over the last 30 y were evaluated. The frequency of derecho-producing convection has increased over the past 30 y, most notably during the last 10 y, although the degree to which inconsistencies in storm reporting over the decades and multi-decadal climate variability contribute to this trend is unclear. The number of high-end derechos occurring every year has also increased with time. Spatiotemporal distributions and derecho series are identified, and distributions of severe wind reports across derecho events are presented for the 1995-2024 period. Spatial trends in derecho-path locations emulate the (warm) seasonal shift of mesoscale convective systems and elevated mixed layers across the US, with a poleward shift as the warm season progresses. In addition to the derecho-trend analysis, we also look at associations between annual and monthly derechos with seasonal and sub-seasonal climate-forcing mechanisms. The most notable association is Niño-4 sea-surface temperature anomalies with warming in the central Pacific.

This book presents the essentials of the Finite Volume Method as applied to fluid flows. In order to aid the student to learn computational methods progressively, the book first introduces the foundation numerical methods of Potential Field (Flow Net) and Finite Difference Methods, with practical Examples. Thus, the book is presented in four Sections:
• Behaviour of partial differential equations, covering mathematical and physical behaviour, and solution conditions.
• Numerical methods, involving foundation methods and Finite Volume Method, as well as interpolation techniques.
• Solution methods, for transport equations of both scalar property and momentum, as well as accuracy of the solution methods.
• Applications to turbulent flows, involving incorporation of k-£ turbulence model, and Examples of Computations of some practical turbulent flow cases.
The book provides a concise treatment of these topics, and contains materials that will aid good understanding of CFD for undergraduate and graduate students and practicing engineers.

Estonian astronomers have suffered a painful loss. One of our most talented and prospective young astronomers, Veikko Saar, died in a tragic accident in Japan, on the coast of the Pacific Ocean on September 12, 1998. Veikko was born in the family of the well-known astrophysicist Enn Saar. Life at the Observatory brought Veikko into close contact with computers when he was yet a teenager. Veikko became a skilled programmer soon, being at the same time interested in the studies of the vast Universe. And so it happened that a 16-year-old schoolboy was a co-author of a scientific paper devoted to cosmology. Veikko studied at the University of Tartu. Being a student in the Department of Physics, he succeeded in obtaining a one-year