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Bolt loosening in bolted lap-joint connections can result in catastrophic failures in industrial settings. Therefore, it is crucial to investigate these connections thoroughly and implement preventative measures. However, examining such connections requires considering multiple influencing parameters. In this study, numerical simulations were conducted using three-dimensional finite element analysis in Abaqus software to evaluate these parameters in detail. The study employed a guided wave-based structural health monitoring approach and a precise ultrasonic-guided Lamb wave propagation method. Time signals were analyzed using phase shift and transmission coefficient (TC) factors as suitable indicators. Additionally, the S0 mode of the captured waves provided a highly informative wave packet for analysis due to its specific order. Initially, various simulations were carried out to determine the optimal length of the overlapping region that would result in the most effective transmission of waves. Increasing the bolt torque and the contact friction coefficient between the two plates in the overlapping region reduces the time for waves to reach the sensor and increases the TC of the passing waves. However, the saturation state does not cause any change in the received signals. Increasing the bolt torque is more effective in facilitating wave transmission through the connections than increasing the contact friction coefficient. Furthermore, variations in excitation voltage do not affect the arrival time or TC of the waves. The continuous wavelet transformation enabled a more detailed time-frequency analysis. The results demonstrate that changes in bolt torque do not impact the wave frequencies reaching the sensors, making the frequency insensitive to bolt loosening. Ultimately, the numerical simulation results were validated through experiments on a table-top laboratory sample. This research enhances researchers' understanding of monitoring the health status of bolted lap-joint connections by investigating the impact of various parameters on guided Lamb wave transmission through these connections. [ABSTRACT FROM AUTHOR]

AbstractThe study investigated GVC and exchange rate comovements from 1990 to 2018. The study employed a continuous wavelet coherence procedure to investigate the time-frequency dependence of global value chain (GVC) participation and exchange rates relationship in Africa. We find from the study that there is interdependency between exchange rates and global value chain participation only in the very short term for all selected countries. The study finds interdependent and well-governed networks continue to experience muted effects of exchange rate effects on GVC participation while independent firms in countries participating in GVC forward integration are negatively impacted.

The presence of outliers in signals collected by structural health monitoring systems, caused by sensor failure, equipment malfunction, or transmission interruption, can lead to misjudgments of a structure's working status and damage degree. This study proposes a novel, fast, accurate, and automatic method which are capable of reconstructing signals according to adjacent channels, detecting outliers by amplifying and sorting reconstructing errors, and recovering normal values to the corresponding locations. A parallel reservoir computing-based reconstructor with a decomposition module which purifies frequency components of input for each sub-network is utilized for improved precision. In addition, the adopted local outlier factor algorithm simplifies outlier detection work as simplex threshold comparison. The proposed method is analyzed for its effectiveness in detecting various types of outliers, such as spikes, abnormal segments, external trends, shifting, and baseline drift, using an acceleration dataset from the Shanghai Tower.

The study investigated GVC and exchange rate comovements from 1990 to 2018. The study employed a continuous wavelet coherence procedure to investigate the time-frequency dependence of global value chain (GVC) participation and exchange rates relationship in Africa. We find from the study that there is interdependency between exchange rates and global value chain participation only in the very short term for all selected countries. The study finds interdependent and well-governed networks continue to experience muted effects of exchange rate effects on GVC participation while independent firms in countries participating in GVC forward integration are negatively impacted. Impact statement: The use of continuous wavelet coherence technique to monitor global value chain (GVC) participation levels amidst extreme market volatility offers unprecedented insights for both countries and firms. By enabling the measurement of interdependency between exchange rates and African economies' participation in GVCs, this approach facilitates informed decision-making and risk management strategies. It empowers stakeholders to gauge the impact of macroeconomic variables, particularly exchange rates, on GVC involvement, thereby fostering adaptability and resilience in an ever-changing economic landscape. Ultimately, this innovation enhances the ability of nations and businesses to navigate volatile market conditions and optimize their engagement in global value chains. [ABSTRACT FROM AUTHOR]

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, bluegreen (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R²c, R²p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R²c, R²p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas. [ABSTRACT FROM AUTHOR]

This study attempts to find out the possible opportunities for the investors of Pakistan Islamic market to maximize the benefits of divergence. Dow Jones Islamic market returns (DJIMR) of four different regions named as Dow Jones Islamic Americas, Dow Jones Islamic European, Dow Jones Islamic Asian Pacific and Dow Jones Islamic Middle East and North Africa Regions across the Pakistan Karachi Meezan Index-30 have been used to capture the approach of diversification benefits. According to objectives, Multivariate GARCH--Dynamic Conditional Correlation (DCC) model is applied and it is suggested that local investors of Pakistan should prefer to invest in Asian pacific region, then to Americas region and further Middle East and North Africa region and least preference should be given to European region to maximize benefits from diversification. In order to capture the change in diversification benefits during various stock holding horizons, Continuous wavelet transformation-CWT is applied and results have indicated insignificant relationship/correlation between two markets (local and international markets of all regions) for short period of holdings rather medium and long term holdings period. Indeed, this study has focused on advance concept of incorporating the role of stock holding horizons while determining the benefits of diversification which has greater implications in the field of portfolio diversification. [ABSTRACT FROM AUTHOR]

Lamb waves occur in thin-walled structures in two wave modes—the symmetric and the antisymmetric mode. Their oscillation on the structures‘ surfaces is either in phase (symmetric) or shifted by a phase angle of π (antisymmetric). In this work, a method is developed by which to compare the surfaces’ oscillation phase relation to answer the question of whether fiber metal laminates show the same surface oscillation behavior as described for metals. The evaluation of time signals regarding the instantaneous phase angle is performed by using the continuous wavelet transformation and the short-time Fourier transformation. Numerical simulations utilizing the finite element method provide time signals from the top and bottom surface of different thin-walled structures of different material settings and configurations. The numerically obtained time signals are evaluated by the developed methods with respect to the oscillation phase. Subsequently, the oscillation phase is evaluated experimentally for the wave propagation in a fiber metal laminate. It is shown that the method based on the continuous wavelet transformation is suitable for the evaluation of oscillation phase relations in time signals. Additionally, it is proven that fiber metal laminates show only two phase relations, which indicates the occurrence of Lamb waves.

Metasurfaces have proved to possess extraordinary properties and propelled the development of versatile functional devices in the terahertz domain, but the intrinsic dispersion severely impedes their broadband applications. Sporadic studies on achromatic terahertz metasurfaces are either subject to specific polarization operation or challenged in the fabrication process for extended bandwidth and numerical aperture. Herein a novel meta‐atom scheme made up of a silicon–silica–silicon sandwich‐shaped structure is presented that enlarges the propagation phase as well as improves the dispersion engineering capability at low losses. As a classic case, an achromatic metalens is constituted by these meta‐atoms and experimentally demonstrated which shows a remarkable achromatic focusing performance from 0.5 to 1.1 THz with a numerical aperture of 0.47 and an average efficiency as high as 43.1%. Continuous wavelet transformation analysis further proves the metalens has negligible group delay dispersion. This study not only demonstrates an outstanding terahertz achromatic metalens but also provides a novel meta‐atom paradigm for constructing achromatic metasurfaces such as holographic plate, beam deflector, and Bessel axicon, further not limited to THz bands. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) genome architecture is critical for numerous cellular processes, including transcription, while certain conformation-driven structural alterations are frequently oncogenic. Inferring 3D chromatin configurations has been advanced by the emergence of chromatin conformation capture assays, notably Hi-C, and attendant 3D reconstruction algorithms. These have enhanced understanding of chromatin spatial organization and afforded numerous downstream biological insights. Until recently, comparisons of 3D reconstructions between conditions and/or cell types were limited to prescribed structural features. However, multiMDS , a pioneering approach developed by Rieber and Mahony (2019). that performs joint reconstruction and alignment, enables quantification of all locus-specific differences between paired Hi-C data sets. By subsequently mapping these differences to the linear (1D) genome the identification of relocalization regions is facilitated through the use of peak calling in conjunction with continuous wavelet transformation. Here, we seek to refine this approach by performing the search for significant relocalization regions in terms of the 3D structures themselves, thereby retaining the benefits of 3D reconstruction and avoiding limitations associated with the 1D perspective. The search for (extreme) relocalization regions is conducted using the patient rule induction method (PRIM). Considerations surrounding orienting structures with respect to compartmental and principal component axes are discussed, as are approaches to inference and reconstruction accuracy assessment. The illustration makes recourse to comparisons between four different cell types. [ABSTRACT FROM AUTHOR]

Lamb waves occur in thin-walled structures in two wave modes—the symmetric and the antisymmetric mode. Their oscillation on the structures' surfaces is either in phase (symmetric) or shifted by a phase angle of π (antisymmetric). In this work, a method is developed by which to compare the surfaces' oscillation phase relation to answer the question of whether fiber metal laminates show the same surface oscillation behavior as described for metals. The evaluation of time signals regarding the instantaneous phase angle is performed by using the continuous wavelet transformation and the short-time Fourier transformation. Numerical simulations utilizing the finite element method provide time signals from the top and bottom surface of different thin-walled structures of different material settings and configurations. The numerically obtained time signals are evaluated by the developed methods with respect to the oscillation phase. Subsequently, the oscillation phase is evaluated experimentally for the wave propagation in a fiber metal laminate. It is shown that the method based on the continuous wavelet transformation is suitable for the evaluation of oscillation phase relations in time signals. Additionally, it is proven that fiber metal laminates show only two phase relations, which indicates the occurrence of Lamb waves. [ABSTRACT FROM AUTHOR]

With the development of automated and integrated large-scale industrial systems, accurate and effective fault diagnosis methods are required to ensure the security and reliability of running mechanical equipment. Due to the time consumption and poor generalization performance of conventional machine learning-based methods, deep learning (DL)-based methods have wider application prospects due to their end-to-end architectural properties. However, in the DL models, problems such as a large number of trainable parameters, complicated hyperparameter tuning, and initialization instability increase the difficulty of model training and limit higher performance. To address these disadvantages of the DL method, we proposed a novel DL framework by applying convolutional neural networks (CNNs) based on the optimization of transfer learning (TL). TL can help the model achieve higher precision with less computational cost by transferring low-level features and fine-tuning high-level layers. In addition, data processing was implemented using continuous wavelet transformation (CWT) to convert vibration signals into 2-D images, and support vector machines (SVM) were employed to replace the fully connected layers for better classification. As a result, the proposed method was superior to the classical deep architecture trained from scratch. The performance of the proposed method is analyzed by presenting testing reports, convergence curves, and confusion matrixes. Moreover, experiments comprised of cross-domain diagnosis, simulated composite fault detection, and performance comparison on seven mechanical datasets, including bearings, gearboxes, and rotors, are presented. Based on these results, it can be observed that our method achieved the highest accuracy under various conditions.

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of v max = 56.3 m / s . The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions. [ABSTRACT FROM AUTHOR]

Structural health monitoring systems for composite laminates using guided ultrasonic waves become more versatile with the structural integration of sensors. However, the data generated within these sensors have to be transmitted from the laminate to the outside, where polyimide-based printed circuit boards play a major role. This study investigates, to what extent integrated polyimide inlays with applied sensor bodies influence the guided ultrasonic wave propagation in glass fiber-reinforced polymer specimens. For reasons of resource efficiency, narrow specimens are used. Numerical simulations of a damping-free specimen indicate reflections of the S 0 -mode at the integrated inlay. This is validated experimentally with an air-coupled ultrasonic technique and a 3D laser Doppler vibrometry measurement. The experimental data are evaluated with a method including temporal and spatial continuous wavelet transformations to clearly identify periodically occurring wave packages as edge reflections and distinguish them from possible inlay reflections. However, even when separating in-plane and out-of-plane movements using the 3D measurement, no reflections at the inlays are detected. This leads to the conclusion that polyimide inlays are well suited as substrates for printed circuit boards integrated into fiber-reinforced polymer structures for structural health monitoring, since they do not significantly influence the wave propagation. [ABSTRACT FROM AUTHOR]

For diseases that affect brain function, such as strokes, post-onset rehabilitation plays a critical role in the wellbeing of patients. MEG is a technique with high temporal and spatial resolution that measures brain functions non-invasively, and it is widely used for clinical applications. Without the ability to concurrently monitor patient brain activity in real-time, the most effective rehabilitation cannot occur. To address this problem, it is necessary to develop a neurofeedback system that can aid rehabilitation in real time; however, doing so requires an analysis method that is quick (less processing time means the patient can better connect the feedback to their mental state), encourages brain-injured patients towards task-necessary neural oscillations, and allows for the spatial location of those oscillation patterns to change over the course of the rehabilitation. As preliminary work to establish such an analysis method, we compared three decomposition methods for their speed and accuracy in detecting event-related synchronization (ERS) and desynchronization (ERD) in a healthy brain during a finger movement task. We investigated FastICA with 10 components, FastICA with 20 components, and spatio-spectral decomposition (SSD). The results showed that FastICA with 10 components was the most suitable for real-time monitoring due to its combination of accuracy and analysis time. [ABSTRACT FROM AUTHOR]

Accurate frequency measurement plays an important role in many industrial and robotic systems. However, different influences from the application's environment cause signal noises, which complicate frequency measurement. In rough environments, small signals are intensively disturbed by noises. Thus, even negative Signal-to-Noise Ratios (SNR) are possible in practice. Thus, frequency measuring methods, which can be used for low SNR signals, are in great demand. In previous work, the method of cross-correlation spectrum has been developed as an alternative to Fast Fourier-Transformation or Continuous Wavelet Transformation. It is able to determine the frequencies of a signal under strong noise and is not affected by Heisenberg's uncertainty principle. However, in its current version, its creation is computationally very intensive. Thus, its application to real-time operations is limited. In this article, a new way to create the cross-correlation spectrum is presented. It is capable of reducing the calculation time by 89% without significant accuracy loss. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with an SNR of −14 dB and a signal length of 2000 data points. When applied to "self-mixing"-interferometry signals, the method can reach a normalized root-mean-square error of 0.21% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended. [ABSTRACT FROM AUTHOR]

This paper proposes an acoustic emission (AE) based automated crack characterization method for reinforced concrete (RC) beams using a memory efficient lightweight convolutional neural network named SqueezeNet. The proposed method also includes a signal-to-image technique, which is continuous wavelet transformation (CWT) that decomposes the AE signals over time-frequency scales and extracts the crack/fracture information in both the time and frequency domains. First, AE signals for two types of cracks (minor and severe), along with the normal condition (no crack), are collected from the experimental test bed. Second, the previously mentioned CWT based signal-to-image technique is applied to generate two-dimensional time-frequency images that are then converted to gray scale images for faster computation. These images are supplied to the SqueezeNet for classification of the concrete crack types. We extensively modified the fire module of the SqueezeNet (SQN-MF) by introducing depth-wise convolutional kernels and channel shuffling operations. Not only does the proposed method utilize deep learning-based techniques for crack classification of concrete beams for the first time, but also the CWT-based imaging technique has not yet been explored in this field either. Additionally, this method does not follow the typical AE burst feature (features like AE counts, peak-amplitude, rise time, decay time, etc.) based methods, and as a result, we no longer require extensive human intervention and expertise to get deep understanding of the crack types. SQN-MF achieves AlexNet-level accuracy with fifty times fewer parameters and has an implementable memory size for the field programmable gate array boards. Overall, the method achieves 100% accuracy. It is 20.8% higher than the typical feature extraction and traditional machine learning based methods. We observed a 4% accuracy increase for the proposed SQN-MF compared to the typical SqueezeNet with bypass connections.

The relationship between economic growth and inflation is one of the long-standing issues in macroeconomics, which is theoretically and politically confronted with many controversies. This is especially important for the Iran's economy, which seeks to achieve price stability and accelerate economic growth. In this regard, the present study has used continuous wavelet transformation to provide a new insight into the relationship between economic growth and inflation with time-frequency analysis in 1369:2–1397:2. The results show that in the long run (more than 4 years), an increase (decrease) in economic growth is accompanied by a decrease (increase) in inflation. In addition, the increase in economic growth has caused inflationary pressures in limited form and in short run (1380-1383). Therefore, it is recommended that policy makers focus more on economic growth in the long run.

• Mental fatigue did not alter perceived effort during low-intensity cycling. • Wavelet analysis reveals EEG changes from mental fatigue during cycling. • Increased beta desynchronization and resynchronization at Cz due to mental fatigue. • Low-intensity cycling reduced perceived mental fatigue. This study explored how mental fatigue affects brain activity during a low-intensity bike task utilising a continuous wavelet transformation in electroencephalography (EEG) analysis. The aim was to examine changes in brain activity potentially linked to central motor commands and to investigate their relationship with ratings of perceived exertion (RPE). In this study, sixteen participants (age: 21 ± 6 y, 7 females, 9 males) underwent one familiarization and two experimental trials in a randomised, blinded, cross-over study design. Participants executed a low-intensity bike task (9 min; 45 rpm; intensity (W): 10 % below aerobic threshold) after performing a mentally fatiguing (individualized 60-min Stroop task) or a control (documentary) task. Physiological (heart rate, EEG) and subjective measures (self-reported feeling of mental fatigue, RPE, cognitive load, motivation) were assessed prior, during and after the bike task. Post-Stroop, self-reported feeling of mental fatigue was higher in the intervention group (EXP) (74 ± 16) than in the control group (CON) (37 ± 17; p < 0.001). No significant differences in RPE during the bike task were observed between conditions. EEG analysis revealed significant differences (p < 0.05) in beta frequency (13–30 Hz) during the bike task, with EXP exhibiting more desynchronization during the pedal push phase and synchronization during the pedal release phase. These results suggest that mental fatigue, confirmed by both subjective and neurophysiological markers, did not significantly impact RPE during the bike task, possibly due to the use of the CR100 scale or absence of a performance outcome. However, EEG data did reveal significant beta band alterations during the task, indicating increased neural effort under mental fatigue. These findings reveal, for the first time, how motor-related brain activity at the motor cortex is impacted during a low-intensity bike task when mentally fatigued. [ABSTRACT FROM AUTHOR]

Abstract Dengue fever, a disease caused by a vector‐borne flavivirus, is endemic to tropical countries, but its occurrence has been reported worldwide. This study aimed to understand important factors contributing to the spatial and temporal patterns of dengue occurrence in São Paulo, the largest municipality of Brazil. The temporal assessment of dengue occurrence covered the 2011–2016 time period and was based on climatological data, such as the El Niño indices and time series statistical tools such as the continuous wavelet transformation. The spatial assessment used Landsat 8 data for years 2014–2016 to estimate land surface temperature and normalized indices for vegetation, urban areas, and leaf water. Results from a cross correlation for the temporal analysis found a relationship between the sea surface temperature anomalies index and the number of reported dengue cases in São Paulo (r = 0.5) with a lag of +29 (weeks) between the climatic event and the response on the dengue incidence. This relationship, initially nonlinear, became linear after correcting for the lag period. For the spatial assessment, the linear stepwise regression model detected a low relationship between dengue incidence and minimum surface temperature (r = 0.357) and no relationship with other environmental parameters. The poor relationship might be due to confounding effects of socioeconomic factors as these seem to influence the spatial dynamics of dengue incidence. More testing is needed to validate these methods in other locations. Nevertheless, we presented possible tools to be used for the improvement of dengue control programs.

Abstract Understanding the relationship between macroeconomic variables and the stock market is important because macroeconomic variables have a systematic effect on stock market returns. This study uses monthly data from India for the period from April 1994 to July 2018 to examine the long-run relationship between the stock market and macroeconomic variables. The empirical findings suggest that standard cointegration tests fail to identify any relationship among these variables. However, a transformation that extracts the actual functional relationship between these variables using the alternating conditional expectations algorithm of (J Am Stat Assoc 80:580–598, 1985) identifies strong evidence of cointegration and indicates nonlinearity in the long-run relationship. Further, the continuous partial wavelet coherency model identifies strong coherency at a lower frequency for the transformed variables, establishing the fact that the long-run relationship between stock prices and macroeconomic variables in India is nonlinear and time-varying. This evidence has far-reaching implications for understanding the dynamic relationships between the stock market and macroeconomic variables.

The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time–frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time–frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.

Precise frequency measurement plays an essential role in many industrial and robotic systems. However, different effects in the application’s environment cause signal noises, which make frequency measurement more difficult. In small signals or rough environments, even negative Signal-to-Noise Ratios (SNRs) are possible. Thus, frequency measuring methods, which are suited for low SNR signals, are in great demand. While denoising methods such as autocorrelation do not suffice for small signal with low SNR, frequency measurement methods such as Fast-Fourier Transformation or Continuous Wavelet Transformation suffer from Heisenberg’s uncertainty principle, which makes simultaneous high frequency and time resolutions impossible. In this paper, the cross-correlation spectrum is presented as a new frequency measuring method. It can be used in any frequency domain, and provides greater denoising than autocorrelation. Furthermore, frequency and time resolutions are independent from one another, and can be set separately by the user. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with a SNR of −10 dB and a signal length of 1000 data points. When applied to “self-mixing”-interferometry signals, the method can reach a normalized root-mean square error of 0.2% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended.

Dengue fever, a disease caused by a vector‐borne flavivirus, is endemic to tropical countries, but its occurrence has been reported worldwide. This study aimed to understand important factors contributing to the spatial and temporal patterns of dengue occurrence in São Paulo, the largest municipality of Brazil. The temporal assessment of dengue occurrence covered the 2011–2016 time period and was based on climatological data, such as the El Niño indices and time series statistical tools such as the continuous wavelet transformation. The spatial assessment used Landsat 8 data for years 2014–2016 to estimate land surface temperature and normalized indices for vegetation, urban areas, and leaf water. Results from a cross correlation for the temporal analysis found a relationship between the sea surface temperature anomalies index and the number of reported dengue cases in São Paulo (r = 0.5) with a lag of +29 (weeks) between the climatic event and the response on the dengue incidence. This relationship, initially nonlinear, became linear after correcting for the lag period. For the spatial assessment, the linear stepwise regression model detected a low relationship between dengue incidence and minimum surface temperature (r = 0.357) and no relationship with other environmental parameters. The poor relationship might be due to confounding effects of socioeconomic factors as these seem to influence the spatial dynamics of dengue incidence. More testing is needed to validate these methods in other locations. Nevertheless, we presented possible tools to be used for the improvement of dengue control programs. Key Points: The temporal analysis found a relationship between the sea surface temperature anomalies (SSTA) index and dengue incidence in São PauloThe spatial assessment detected a low relationship between dengue incidence and temperature but not with other environmental parametersThe low relationship between the physical environment and dengue incidence may be explained by confounding effects of socioeconomic factors [ABSTRACT FROM AUTHOR]

• Method to segment multi-peak FBG spectrum by wavelet transformation is proposed. • Segmented length of spectrum can be adjusted that is better than traditional method. • As segmented length increases, the following fitting accuracy of FBG peak increases. • The amount of calculation cost did not increase much. A method to segment multi-peak FBG reflection spectrum based on continuous wavelet transformation (CWT) is proposed and experimentally demonstrated. First order Gaussian or Mexican hat wavelet basis with the suitable scale is selected in CWT processing. The pairs of extremum points in the processed signal are used to segment original signal. Multi-peak FBG spectrum is simulated for the two cases, with no-overlap peaks and noise, with partial overlapped peaks and noise. An experiment is conducted on four FBGs connected together in series. The simulating and experimental results show it has advantages to segment the original signal by CWT in adjusting suitable segmented length and obtaining more accurate central wavelength, compared to traditional methods. [ABSTRACT FROM AUTHOR]

Continuous wavelet transform approach has been applied to the pseudo-rapidity distribution of shower tracks produced in 1 6 O–AgBr interactions at 60 AGeV and 3 2 S–AgBr interactions at 200 AGeV. Multiscale analysis of wavelet pseudo-rapidity spectra has been performed in order to find out the overabundance of produced tracks at some preferred pseudo-rapidity values, i.e., production of particle clusters. Presence of ring-like correlation is not confirmed from the analysis in pseudo-rapidity space only. The clusterization effect may be attributed to the presence of Bose–Einstein correlation among the produced tracks. Comparison of experimental results with that obtained from analyzing events generated by FRITIOF and UrQMD codes is not reproduced. [ABSTRACT FROM AUTHOR]

• Novel method for the condition monitoring of gas turbines using acoustic emissions. • Wavelet analysis used to transform acoustic emission signals into images. • Deep convolutional neural networks used to classify turbine condition. • Network trained on experimental data with two healthy and two faulty conditions. Compared to vibration monitoring, acoustic emission (AE) monitoring in gas turbines is highly sensitive to changes that do not involve whole-body motion, such as wear, rubbing, and fluid-induced faults. AE signals captured by suitably mounted sensors can potentially provide early indications of abnormal turbine operation before such abnormalities manifest in structural vibration or emitted airborne noise. However, developing an online fault detection system requires extensive real-time data treatment to extract appropriate features and indicators from raw AE records. To build such a system for industrial turbines, researchers need to understand the AE-generating mechanisms associated with turbine operation and the sources of background noise. In this study, we aim to develop such an understanding using a small-scale turbine whose operational conditions can be modified safely to reflect both normal and faulty conditions. Our signal processing approach involves first extracting a time-series envelope using an averaging time selected to enhance major features and eliminate irrelevant noise. We then generate time–frequency features using a continuous wavelet transform, which are used to train a deep convolutional neural network to classify gas turbine conditions. The resulting model demonstrates high accuracy in classifying two normal running conditions and two faulty conditions at various turbine speeds. Overall, the proposed methodology offers a powerful tool for gas turbine condition monitoring, and we make all associated data available in open-source format to facilitate further research in this field.4 [ABSTRACT FROM AUTHOR]

The phenomenon of knock is an abnormal combustion occurring in spark-ignition (SI) engines and forms a barrier that prevents an increase in thermal efficiency while simultaneously reducing CO2 emissions. Since knocking combustion is highly stochastic, a cyclic analysis of in-cylinder pressure is necessary. In this study we propose an approach for efficient and robust detection and identification of knocking combustion in three different internal combustion engines. The proposed methodology includes a signal processing technique, called continuous wavelet transformation (CWT), which provides a simultaneous analysis of the in-cylinder pressure traces in the time and frequency domains with coefficients. These coefficients serve as input for a convolutional neural network (CNN) which extracts distinctive features and performs an image recognition task in order to distinguish between non-knock and knock. The results revealed the following: (i) The CWT delivered a stable and effective feature space with the coefficients that represents the unique time-frequency pattern of each individual in-cylinder pressure cycle; (ii) the proposed approach was superior to the state-of-the-art threshold value exceeded (TVE) method with a maximum amplitude pressure oscillation (MAPO) criterion improving the overall accuracy by 6.15 percentage points (up to 92.62%); and (iii) The CWT + CNN method does not require calibrating threshold values for different engines or operating conditions as long as enough and diverse data is used to train the neural network.

Despite the imitability of the signatures due to presence of numerous image processing programs, online verification systems could provide sufficient security for e-signatures. Recent developments in touchscreen technology and android programming also lead to utilization of hidden interfaces stealthily collecting the unique characteristics and storing the key features aside from geometrics. Therefore, we initially designed a signing interface for touchscreens which stealthily collects the precise coordinates while an individual is signing on the screen by fingertips. Even if the coordinate data is extracted as a matrix consisting of x and y values with corresponding time, the speed array is consequently calculated to investigate the higher frequency regions. The speed data processed by continuous wavelet transformations (CWT) to reveal the frequency information of the signing speed with respect to time information. The grayscale spectrograms created by wavelet transforms are converted into arrays for subsequent training session performed by support vector machines (SVM). The trained network successfully classified further attempts of the real and fake signatures with 1.67% false negative (FNR), 3.33% false positive rates (FPR) and 3.41% equal error rate (EER) for 120 signatures, even though the signature is totally public. For understanding the validity of the CWT and SVM running consecutively, the experiments are re-conducted for the signatures taken from SVC2004 and SUSIG public databases. [ABSTRACT FROM AUTHOR]

Streamflow is an essential component of the hydrologic cycle in the regional and global scale and the main source of fresh water supply. It is highly associated with natural disasters, such as droughts and floods. Therefore, accurate streamflow forecasting is essential. Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing. Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables. This study proposes multigene genetic programming (MGGP) as a selection tool. After the CWT analysis, it selects important scales to be imposed into the artificial neural network (ANN). A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model. One month ahead downstream flow is used as output, and downstream flow, upstream, rainfall, temperature, and potential evapotranspiration with associated lags are used as inputs. Before modeling, wavelet coherence transformation (WCT) analysis was conducted to analyze the relationship between variables in the time-frequency domain. Several combinations were developed to investigate the effect of the variables on streamflow forecasting. The results indicated a high localized correlation between the streamflow and other variables, especially the upstream. In the models of the standalone layout where the data were entered to ANN and MGGP without CWT, the performance is found poor. In the best-scale layout, where the best scale of the CWT identified as the highest correlated scale is chosen and enters to ANN and MGGP, the performance increased slightly. Using the proposed model, the performance improved dramatically particularly in forecasting the peak values because of the inclusion of several scales in which seasonality and irregularity can be captured. Using hydrological and meteorological variables also improved the ability to forecast the streamflow. [ABSTRACT FROM AUTHOR]

Underwater wireless communication is a prominent research field owing to its vast range of applications such as underwater sensor networks, remotely operated vehicles (ROVs) and tsunami warning systems. The acoustic signals used in the underwater wireless systems are 105 times slower than light which makes it band-limited. Communication under water is affected not only by natural activities but also by shipping noises, all of which degrade the performance of underwater systems. Hence appropriate de-noising algorithms have to be developed to improve the performance of the band-limited underwater systems. The algorithm described in this paper was developed using linear frequency modulation (FM) waveform as input and using the continuous wavelet transformation (CWT) based on fast Fourier Transform (FFT) method with Morlet wavelet. The proposed algorithm provides an SNR improvement of around 12 dB when compared with the algorithm developed using the chirp signal as input and using Morlet wavelet with wavelet packet decomposition (WPD) technique. [ABSTRACT FROM AUTHOR]