Noise Components Research Articles

Vibration and noise reduction on a cover for power electronics for electric vehicles with vibroacoustic metamaterials

Electric vehicles represent a particular challenge in terms of noise, vibration and harshness, since there is no masking of the combustion engine anymore. For consumer acceptance, long distances need to be covered with electric vehicle, which makes lightweight structures necessary. Consequently, vibration problems occur during operation, as lightweight structures are more susceptible to vibrations. Common measures for vibration and noise reduction add a considerable amount of mass to the vehicle, counteracting the lightweight efforts. Vibroacoustic metamaterials as a novel approach promise greater vibration reductions than common measures like Alubutyl or bitumen and are accompanied by a lower weight. This study deals with the design of vibroacoustic metamaterials for the noise and vibration reduction of a cover for power electronics of an electric vehicle. The design process is demonstrated for two different kinds of vibroacoustic metamaterials made from sheet metal and a plastic film. The structural dynamic behavior of both approaches is experimentally investigated and compared with a cover equipped with Alubutyl. The vibroacoustic metamaterial, which is made from sheet metal, is furthermore tested in a vehicle under driving condition. A reduction of tonal noise components by 11 dB is achieved within the passenger compartment.

Prediction and evaluation of the low-frequency noise impact from industrial noise sources

German legislation requires an assessment of low-frequency noise in addition to the regular noise assessment for industrial plants. The detailed prediction and assessment of low-frequency noise immissions is carried out according to the administrative regulation for noise TA Lärm and the German standard DIN 45680. However, a standardised procedure for predictions of low-frequency noise does not exist. For this reason, different approaches for the prediction of low-frequency noise immissions have been developed over the last two decades. Typically, the method according to DIN ISO 9613-2 for sound propagation outside is extended heuristically to the lower frequency range. The predicted low-frequency noise level outside the room is taken as a basis for determining the noise immission level inside the room. The level of knowledge about the quality of sound propagation modeling of low-frequency noise is still debatable. A forecast method of low-frequency noise components should be on the "safe side" in terms of noise control; however, the scope of the required measures to meet the noise requirements must be comprehensible and proportionate. We present an overview of the building blocks of a forecasting process and the challenges that arise. The topic is illustrated by a practical example.

Comprehensive investigations on spectral and temporal features of GX 5−1 using AstroSat observations

ABSTRACT Comprehensive spectrotemporal analyses of the Z-type neutron star low-mass X-ray binary GX 5−1 were performed using 10 broad-band observations from AstroSat/Soft X-ray Telescope and Large Area X-ray Proportional Counter (LAXPC) instruments. The LAXPC-20 hardness–intensity diagram showed horizontal and normal branches (HBs and NBs) of the Z track which exhibited secular motion. The time-averaged spectra in the energy range 0.7–25.0 keV could be fitted with the model combination – $\tt {constant}\, \times \, \tt {tbabs}\, \times \, \tt {edge}\, \times \, \tt {edge}\, \times \, \tt {thcomp}\, \times \, \tt {diskbb}$. This yielded $\Gamma \, \sim$ 2, $kT_{\mathrm{ e}}\, \sim$ 3.3 keV, and $F_{\mathrm{ disc}}$/$F_{\mathrm{ total}}\, \sim$ 0.8 indicating the soft/intermediate spectral state of the source during the observations. Flux-resolved spectral analysis revealed a positive correlation between $kT_{\mathrm{ in}}$ and $F_{\mathrm{ bol}}$. However, a negative correlation was observed between them in one of the NBs. Time-averaged temporal analysis revealed multiple HB oscillations (HBOs) and NB oscillations (NBOs), and peaked noise components in the $\sim$5–50 Hz range. Furthermore, flux-resolved temporal analysis showed that the frequency of the HBOs correlates positively whereas the strength of HBOs correlates negatively with $F_{\mathrm{ bol}}$, indicating their probable origin from the accretion disc. In contrast, the frequency and strength of NBOs remain fairly constant with $F_{\mathrm{ bol}}$, suggesting that they originate from a different region in the system. Using the relativistic precession model along with highest frequency of the HBO, the upper limits of the magnetic dipole moment ($\mu$) and field strength (B) at the poles of the neutron star in the system were found to be 25.60$\times \, 10^{25}$ G cm3 and 3.64$\times \, 10^{8}$ G, respectively, for $k_{\mathrm{ A}}$ = 1.

WPConvNet: An Interpretable Wavelet Packet Kernel-Constrained Convolutional Network for Noise-Robust Fault Diagnosis.

Deep learning (DL) has present great diagnostic results in fault diagnosis field. However, the poor interpretability and noise robustness of DL-based methods are still the main factors limiting their wide application in industry. To address these issues, an interpretable wavelet packet kernel-constrained convolutional network (WPConvNet) is proposed for noise-robust fault diagnosis, which combines the feature extraction ability of wavelet bases and the learning ability of convolutional kernels together. First, the wavelet packet convolutional (WPConv) layer is proposed, and constraints are imposed to convolutional kernels, so that each convolution layer is a learnable discrete wavelet transform. Second, a soft threshold activation is proposed to reduce the noise component in feature maps, whose threshold is adaptively learned by estimating the standard deviation of noise. Third, we link the cascaded convolutional structure of convolutional neutral network (CNN) with wavelet packet decomposition and reconstruction using Mallat algorithm, which is interpretable in model architecture. Extensive experiments are carried out on two bearing fault datasets, and the results show that the proposed architecture outperforms other diagnosis models in terms of interpretability and noise robustness.

Enhancing visual seismocardiography in noisy environments with adaptive bidirectional filtering for Cardiac Health Monitoring

BackgroundWearable sensors have revolutionized cardiac health monitoring, with Seismocardiography (SCG) at the forefront due to its non-invasive nature. However, the substantial motion artefacts have hindered the translation of SCG-based medical applications, primarily induced by walking. In contrast, our innovative technique, Adaptive Bidirectional Filtering (ABF), surpasses these challenges by refining SCG signals more effectively than any motion-induced noise. ABF leverages a noise-cancellation algorithm, operating on the benefits of the Redundant Multi-Scale Wavelet Decomposition (RMWD) and the bidirectional filtering framework, to achieve optimal signal quality.MethodologyThe ABF technique is a two-stage process that diminishes the artefacts emanating from motion. The first step by RMWD is the identification of the heart-associated signals and the isolating samples with those related frequencies. Subsequently, the adaptive bidirectional filter operates in two dimensions: it uses Time-Frequency masking that eliminates temporal noise while engaging in non-negative matrix Decomposition to ensure spatial correlation and dorsoventral vibration reduction jointly. The main component that is altered from the other filters is the recursive structure that changes to the motion-adapted filter, which uses vertical axis accelerometer data to differentiate better between accurate SCG signals and motion artefacts.OutcomeOur empirical tests demonstrate exceptional signal improvement with the application of our ABF approach. The accuracy in heart rate estimation reached an impressive r-squared value of 0.95 at − 20 dB SNR, significantly outperforming the baseline value, which ranged from 0.1 to 0.85. The effectiveness of the motion-artifact-reduction methodology is also notable at an SNR of − 22 dB. Consequently, ECG inputs are not required. This method can be seamlessly integrated into noisy environments, enhancing ECG filtering, automatic beat detection, and rhythm interpretation processes, even in highly variable conditions. The ABF method effectively filters out up to 97% of motion-related noise components within the SCG signal from implantable devices. This advancement is poised to become an integral part of routine patient monitoring.

NICER Observes the Full Z-track in GX 13+1

We present the temporal analysis of the persistent neutron star low-mass X-ray binary GX 13+1 using NICER data. Classification of this source has been ambiguous so far. We investigate the evolution of the source in its hardness–intensity diagram (HID) and power density spectra (PDS) of the 0.5–10 keV NICER archival data. For the first time, we detect the source tracing out the entire Z-track, distinctly identifying the horizontal branch (HB), normal branch (NB) and flaring branch (FB). We also detect a peaked noise component in the PDS at ∼5.4 Hz, which appears to be present when the source is either in the NB or FB. We note a positive slope of the HB in the HID which could be due to either the high intrinsic absorption of the source or the stronger contribution of the soft spectral components in the soft energy domain.

Effect of Ferrite Core Modification on Electromagnetic Force Considering Spatial Harmonics in an Induction Cooktop

This study investigates the influence of ferrite shape modifications on the performance and noise characteristics of an induction cooktop. The goal is to optimize the air gap dimensions between ferrites and cookware, enhancing efficiency while managing noise levels. Using finite element method (FEM) simulations, we analyze the spatial distribution of magnetic forces and their harmonics. Eight ferrite shape models were examined, focusing on both outer and inner air gaps. Model #8 (reduced outer air gap) and Model #9 (reduced inner air gap) were experimentally validated. Noise measurements indicated that Model #8 reduced 120 Hz harmonic noise components, while Model #9 increased them due to enhanced excitation forces. Current measurements confirmed that Model #9 achieved higher efficiency, with RMS current reduced to 94.54% of the base model. The study reveals a trade-off between performance and noise: inner air gap reduction significantly boosts efficiency but raises noise levels, whereas outer air gap reduction offers balanced improvements. These findings provide insights for optimizing induction cooktop designs, aiming for quieter operation without compromising efficiency.

Research on Signal Noise Reduction and Leakage Localization in Urban Water Supply Pipelines Based on Northern Goshawk Optimization.

In order to enhance the accuracy and adaptability of urban water supply pipeline leak localization, based on the Northern Goshawk Optimization, a novel joint denoising method is proposed in this paper to reduce noise in negative pressure wave signals caused by leaks. Firstly, the Northern Goshawk Optimization optimizes the decomposition levels and penalty factors of Variational Mode Decomposition, and obtains their optimal combination. Subsequently, the optimized parameters are used to decompose the pressure signals into modal components, and the effective components and noise components are distinguished according to the correlation coefficients. Then, an optimized wavelet thresholding method is applied to the selected effective components for secondary denoising. Finally, the signal components that have been denoised twice are reconstructed with the effective signal components, and the denoised negative pressure wave signals are obtained. Simulation experiments demonstrate that compared to wavelet transforms and Empirical Mode Decomposition, our method achieves the highest signal-to-noise ratio improvement of 12.23 dB and normalized cross correlation of 0.991. It effectively preserves useful leak information in the signal while suppressing noise, laying a solid foundation for improving leak localization accuracy. After several leak simulation tests on the leakage simulation test platform, the test results verify the effectiveness of the proposed method. The minimum relative error of the leakage localization is 0.29%, and an average relative error is 1.64%, achieving accurate leakage localization.

Time series decomposition of land surface temperature for long-term trend forecasting and impact on nesting sea turtle habitats in the Arabian Gulf

Abstract Improving Land Surface Temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as on important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats' long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, SEATS, and the Seasonal and Trend decomposition using Loess (STL) approach. The long-term trends in LST data are driven by climate change rather than seasonal fluctuations. We employed Neural Network Auto Regression (NNAR), BaggedETS, Exponential Smoothing models, and STL method to project future LST values. We also explored advanced forecasting models like Dynamic Harmonic Regression, TBATS, and SARIMA for comparative performance analysis. Extended warm periods were identified for Abu Ali Island between 2017 and 2018 through several decomposition methods, likely linked to the 2015-2016 El Niño event. We also conducted a Marine Heat Wave (MHW) analysis from 2010-2020, establishing a pronounced impact of the 2015-2016 El Niño on the Arabian Gulf. In nesting beach environments with high LST, marine heatwaves could have a significant impact on sea turtle populations without human intervention such as artificially cooling the nest temperature. SARIMA model showed higher forecasting precision for in-situ weather data while NNAR model demonstrated superior performance with remotely sensed data.

A novel optimal resonance band selection method for wheelset-bearing fault diagnosis based on tunable-Q wavelet transform

The detection of faults in the wheelset-bearing system is crucial for guaranteeing the safety of train operations. The core is to extract the optimal resonance band (ORB) and repetitive transient impact signals from the collected axle box acceleration signals. Accordingly, a novel automatic fault detection method called the bidirectional iterative merging multi-Q tunable-Q wavelet transform (BIMMQTQWT) is proposed to address the issue that existing methods are vulnerable to background noise and irrelevant components. First, a series of band-pass filters with almost constant bandwidth are constructed by the improved multi-Q tunable-Q wavelet transform (IMQTQWT) derived from the fault characteristics. Second, the fault information contained in each sub-band coefficient is preliminarily estimated using the correlative envelope comprehensive indicator (CECI). Third, the ORBs are automatically selected using the maximum CECI based on a strategy called bidirectionally merging adjacent frequency bands (BIMFBs). Finally, Envelope demodulation based on the ORB is executed followed by identifying bearing faults. The effectiveness in detecting multiple wheelset-bearing faults of the proposed method is validated through simulation and bench experiment signals. And the superior performance of the proposed method is exhibited compared with the existing average infogram and resonance sparse decomposition.

An impulsive noise filter for rail vibration measurements using a laser Doppler vibrometer on a moving platform

This paper proposes a new impulsive noise (i.e., IN) filter for non-contact rail vibration measurements performed by a laser Doppler vibrometer (i.e., LDV) placed on a moving rail car. The development of the filter is motivated by the fact that such measurements can be contaminated by IN, which is caused by a high level of speckle noise due to the movement of the LDV. Consequently, some portions of the LDV signals can be distorted, leading to missing measurements from some rail segments. The proposed filter consists of two main steps: (i) detecting the signal segments containing the IN (i.e., detection step), and (ii) replacing the detected segments to estimate their IN-free values (i.e., estimation step). The performance of the filter was evaluated on a synthetic signal, which was created by combining a rail vibration signal in the vertical direction (i.e., vibration component) with a speckle noise signal of an LDV placed on a moving platform (i.e., noise component). The reason to use a synthetic signal was to know the vibration and noise components separately. This allowed for the examination of IN’s behavior, which showed that IN manifests itself as artificial peaks as well as high-frequency noise in the proximity of artificial peaks (i.e., artificial fluctuations). Accordingly, the proposed filter was designed to detect and replace both artificial peaks and fluctuations. Qualitative and quantitative evaluations of the filtered synthetic signal showed that the proposed filter exhibited good performance in mitigating the IN.

Wiener Filter with Convolutional Neural Network for Noise Removal in API-Based AI Models

This research aims to develop a robust Application Program Interface (API)-Based Artificial Intelligence (AI) system for effective noise removal from audio signals, enhancing speech quality and intelligibility in noisy environments to be fed into different AI models to assess the applicant interview. The proposed methodology combines sophisticated signal processing techniques and noise reduction algorithms with AI models trained on clean voice data and noise patterns. To achieve this goal, we leverage two key components: the Wiener filter and a Convolutional Neural Network (CNN). The Wiener filter serves as the foundational noise reduction technique, exploiting statistical properties of the signal and the noise to suppress unwanted noise components effectively. Concurrently, CNN is integrated to classify the clean and noisy audio. In this research, the best optimizers selected, including Adam, SGD, RMSprop, Adagrad, and Adadelta are evaluated to identify the most suitable classification. The optimizers evaluated through cross-validation and hold-out validation in the same batch size (25) and epoch (25) were used. The study demonstrates that the Adam optimizer yields the best results. The epoch was optimized to 35, 75, 105, and 125 and epoch of 105 was selected with accuracy of 99.52%, Recall of 100%, F1-Score of 99.50%, and ROC_AUC of 99.99% for cross-validation while Accuracy of 98.79%, Recall of 99.21%, F1-Score of 98.81%, and ROC_AUC of 99.54% for hold-out validation, significantly improving AI model performance. Lastly, we ensured the batch size parameter was suitable for our model by tuning it with different settings (25, 50, 75, and 125) using the optimized optimizer and epoch. The batch size of 25 yielded the best accuracy. The modeled CNN also included kernel regularization L2 to avoid overfitting.

High responsivity and detectivity β-Ga2O3 solar-blind photodetectors optimized by oxygen vacancy engineering

Solar-blind photodetectors (SBPDs) are core essential components for many critical applications such as precision guidance, fire warning, and space communications. Ultra-wide bandgap semiconductor β-Ga2O3 is considered to be an ideal material for the fabrication of SBPDs. However, synthetizing β-Ga2O3 with high quality factor while simultaneously in situ modulation of electronic and optoelectronic properties to enhance performance has been challenging. Here, pulsed laser deposition (PLD) technology is used to synthesize high-quality β-Ga2O3 thin films on a sapphire substrate. The oxygen vacancy engineered β-Ga2O3 films can achieve in situ precise control of their surface morphology, optical parameters, and optoelectronic properties by simply adjusting the oxygen pressure. Meanwhile, the optimal thickness of the β-Ga2O3 film for the developing high-performance SBPD is ∼221 nm, determined by fitting and analyzing the optical parameters measured by the ellipsometry. Subsequently, the influence of oxygen pressure on the performance of β-Ga2O3 SBPD is thoroughly explored, considering the optimization of electrode size and deposition time. When the oxygen pressure is set to 15 Pa, the β-Ga2O3-based SBPD achieves highly competitive responsivity (R) and detectivity (D*) at 250 nm, with values of 1080 A·W−1 and 1.4 × 1016 cm·W−1·Hz1/2, respectively. Additionally, the noise component of the β-Ga2O3 SBPD is further studied to calibrated the traditional device performance results. This work introduces a simple and straightforward approach to in situ tuning of the optoelectronic properties of β-Ga2O3, which is important for advancing β-Ga2O3 film growth technology and fabricating high-performance photodetectors.

Early warning signals of flashover in compartment fires

There have been efforts to predict the occurrence of flashover. Due to its sudden development and the difficulty in discerning warning signs during the induction phase, the approach to flashover prediction is still under investigation. This research tests a new approach for detecting flashover events within a compartment through single-signal processing of the heat release rate (HRR). A set of ordinary differential equations aligned with a two-zone model are formulated and transformed into stochastic differential equations, subsequently solved through a numerical method. Based on the noisy HRR readings, a dynamical marker is constructed as a product of two quantities: the smoothed HRR and the standard deviation of the noise component. The dynamical marker was found to increase prior to a rise in the HRR signal on its own, confirming its superiority as a sign of flashover detection. To assess the practical applicability of the dynamical marker, we computed it to detect flashover incidents using HRR obtained from an FDS simulation and the fire calorimetry database provided by NIST; the dynamical marker exhibited a significant rise before the transition to flashover, confirming its potential as an early warning signal.

Estimation of the error in determining the motion parameters of maneuvering objects

The study contains the definition of errors in estimating the motion parameters of maneuvering objects that arise due to the discrepancy between the real model and the model used to build the filter. In the real model, along with noise and stochastic uncertainties, non-stochastic uncertainties act. The solution to the problem of estimation errors is based on the use of convex polyhedra. The statement of the research problem is given. It is shown that since the observer uses the motion model and the measurement equation, the source of estimation errors is not only the above, but also the fact that along with the random component of the measurement noise, there is also a non-stochastic component that is not taken into account by the observer. At the same time, the statistical characteristics of the latter do not exist or are unknown. It is shown that the use of a filter, for example, Kalman, will lead to errors. The conditions that determine the average filtering error and the total standard error of the estimation are described. It is noted that the obtained calculation results allow us to estimate the maximum permissible values used in the design of the filter for the most unfavorable case of evaluation. The relationship between the filtration quality and the parameters of the effects is determined. Expressions are obtained regarding the filtration quality for the worst and the best for object maintenance. The asymptotic properties of filtering errors are considered. It is shown that the "convergence" of the filter means its final "memory", since errors at the next step do not depend on errors from all previous steps, but only on steps from a certain value. The conditions under which the potential accuracy of the filter is achieved are also determined. The solution of extreme problems is described. You can determine the maximum filtering quality by iterating over the values on a set of corner points or using extremum conditions. Numerical simulation of extreme values of filtering errors is given. The asymptotic properties of filtering errors are considered. It is shown that the "convergence" of the filter means its final "memory", since errors at the next step do not depend on errors from all previous steps, but only on steps from a certain value. The conditions under which the potential accuracy of the filter is achieved are also determined. The solution of extreme problems is described. You can determine the maximum filtering quality by iterating over the values on a set of corner points or using extremum conditions. Numerical simulation of extreme values of filtering errors is given. Expressions for the set of average values of filtering errors are found, asymptotic and extreme properties of errors and sets are considered. It is shown that the conducted analysis of the accuracy of the filtering algorithm is evaluative in nature and is designed for the worst case. If the accuracy is not satisfactory, it is necessary to either change the model or synthesize a minimax-stochastic filter.

Experimental and Simulation Study on Flow-Induced Vibration of Underwater Vehicle

At high speeds, flow-induced vibration noise is the main component of underwater vehicle noise. The turbulent fluctuating pressure is the main excitation source of this noise. It can cause vibration of the underwater vehicle’s shell and eventually radiate noise outward. Therefore, by reducing the turbulent pressure fluctuation or controlling the vibration of the underwater vehicle’s shell, the radiation noise of the underwater vehicle can be effectively reduced. This study designs a cone–column–sphere composite structure. Firstly, the effect of fluid–structure coupling on pulsating pressure is studied. Next, a machine learning method is used to predict the turbulent pressure fluctuations and the fluid-induced vibration response of the structure at different speeds. The results were compared with experimental and numerical simulation results. The results show that the deformation of the structure will affect the flow field distribution and pulsating pressure of the cylindrical section. The machine learning method based on the BP (back propagation) neural network model can quickly predict the pulsating pressure and vibration response of the cone–cylinder–sphere composite structure under different Reynolds numbers. Compared with the experimental results, the error of the machine learning prediction results is less than 7%. The research method proposed in this paper provides a new solution for the rapid prediction and control of hydrodynamic vibration noise of underwater vehicles.

Multi-timescale attention residual shrinkage network with adaptive global-local denoising for rolling-bearing fault diagnosis

In actual engineering scenarios, bearing fault signals are inevitably overwhelmed by strong background noise from various sources. However, most deep-learning-based diagnostic models tend to broaden the feature extraction scale to extract rich fault features for bearing-fault identification under noise interference, with little attention paid to multi-timescale discriminative feature mining with adaptive noise rejection, which affects the diagnostic performance. Thus, a multi-timescale attention residual shrinkage network with adaptive global-local denoising (AMARSN) was proposed for rolling-bearing fault diagnosis by learning discriminative multi-timescale fault features from signals and fully eliminating noise components in the multi-timescale fault features. First, a multi-timescale attention learning module (MALMod) was developed to capture multi-timescale fault features and enhance their discriminability under noise interference. Subsequently, an adaptive global-local denoising module (AGDMod) was constructed to fully eliminate noise in multiscale fault features by constructing specific global-local denoising thresholds and designing an adaptive smooth soft thresholding function. Finally, end-to-end bearing fault diagnosis tasks were realized using a softmax classifier located at the end of the AMARSN. The AMARSN was validated using two bearing datasets. The extensive results demonstrated that the AMARSN can mine more effective fault features from signals and achieve average diagnostic accuracies of 85.24% and 80.09% under different noise with different levels.

Temporal convolutional network with soft threshold and contractile self-attention mechanism for remaining useful life prediction of rolling bearings

Abstract RUL prediction is an effective approach to prevent system failures and cut maintenance expenditures. Due to the wide receptive field and the avoidance of future information leakage, temporal convolutional neural network (TCN) is widely applied for RUL estimation of bearings. However, the predictive performance of TCN is limited by the loss of degradation features and the breakdown of continuity of timing information. To overcome the above defects, hybrid temporal convolutional neural network with soft threshold and contractile self-attention mechanism (HTCN-SC) is presented. Firstly, the adaptive threshold is determined by the contraction self-attention mechanism with higher interpretability, which captures the contribution of different features to the estimation of RUL. Then, the soft threshold is employed to activate the degraded features. On the one hand, the degeneracy features endowed by the dilated causal convolution with obvious negative values are fully preserved. On the other hand, the noise components that are given low weights are completely suppressed compared to the original TCN. Finally, parallel branch composed of one-dimensional convolutional networks are used to supplement the continuity of time series. Degradation signals from different working conditions and bearings are employed to verify the performance of the HTCN-SC. The results indicate that HTCN-SC with accurate RUL estimation and generalization ability is an effective tool for rolling bearing health monitoring.

Patterns in the Chaos: The Moving Hurst Indicator and Its Role in Indian Market Volatility

Estimating the impact of volatility in financial markets is challenging due to complex dynamics, including random fluctuations involving white noise and trend components involving brown noise. In this study, we explore the potential of leveraging the chaotic properties of time series data for improved accuracy. Specifically, we introduce a novel trading strategy based on a technical indicator, Moving Hurst (MH). MH utilizes the Hurst exponent which characterizes the chaotic properties of time series. We hypothesize and then prove empirically that MH outperforms traditional indicators like Moving Averages (MA) in analyzing Indian equity indices and capturing profitable trading opportunities while mitigating the impact of volatility.

Research on SNR enhancement method of underwater airgun impulse signal

Abstract Seismic airgun blasting is nowadays widely employed for ocean exploration and marine geophysical study. Being powerful acoustic source showing excellent repeatability and consistency, it’s potentially applicable for long-distance underwater acoustic communication. Given the sound generating mechanism of airguns, the generated signals cannot be modulated compared with sources signals that are more commonly used for communications purposes. Thus the use of time delay shift coding system, which is both necessary and advantageous under such circumstances. The detection of the impulsive signal from airgun blasting and accurate estimation of its arrival time at the receiving end is paramount for carrying out a time delay shift coding communication. Consequentially, augmentation of the signal-to-noise ratio (SNR) of received signal through enhancement is the most direct and effective approach. This manuscript presents an enhancement method of the airgun blasting signals: A recursive wavelet modulus maxima denoising method is introduced, by separating the raw signals into noise components and signal components iteratively, details information that is erroneously erased by conventional wavelet modulus maxima denoising method can thus been preserved. Coupled with the proposed use of a loop coefficient of determination, automatic balancing between denoising effectiveness and distortion of valid signals is acrhieved.