Empirical Mode Decomposition Analysis Research Articles

Research on motion impedance cardiography de-noising method based on two-step spectral ensemble empirical mode decomposition and canonical correlation analysis

Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.

Long-term geomagnetic activities and stratospheric winter temperature

In this research, impact of long term solar forcing on stratospheric winter temperature is checked. The 11- year sunspot activity and geomagnetic indices (AE, Kp, Dst) are used as an indicator for solar forcing, as geomagnetic activity indices show good correlation with solar variability. To understand the impact of solar forcing through high latitude, stratospheric winter (November to March) time North Polar Region (60N–90N) temperature anomalies are considered. The findings showed that temperature changes in the stratosphere are significantly correlated with solar activity, as evidenced by a significant positive correlation between the 11-year moving mean of stratospheric (10 hPa) temperature anomalies and sunspot number. Approximately from 1970 to 2000, the North Polar Region saw positive anomalous stratospheric winter temperatures. During the same time, the geomagnetic activity also showed a substantial increase. The year-to-year correlation between stratospheric pole temperature and geomagnetic activity is significant (about 0.5). The Empirical Mode Decomposition analysis reveals a highly significant correlation (around 0.9) between the long-term component of stratospheric winter temperature (IMF-4) and the long-term component of geomagnetic activity (IMF-3 and IMF-4). One of the reasons for the increase in lower stratospheric temperature is an increase in ozone concentration during the same period when geomagnetic activity is higher. Empirical orthogonal function (EOF) and correlation analysis of stratospheric winter temperature with large-scale circulation patterns are also carried out. The spatial correlation is checked for stratospheric winter temperature at North Pole and lower atmospheric levels (250 hPa and 850 hPa) followed by pre-monsoon and monsoon season. This study includes statistical analysis, however, also highlights the necessity of in-depth dynamical analysis to improve our understanding of how solar activity impacts Earth's atmospheric layers, which may be helpful in predicting the weather and climate.

Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value.

Diesel Engine Turbocharger Monitoring by Processing Accelerometric Signals through Empirical Mode Decomposition and Independent Component Analysis

In this study, a method for the monitoring of internal combustion engine operation by vibration signals is proposed. The work falls within the context of the increasingly stringent standards relating to the environmental impact of engines and the development of monitoring and control techniques to ensure increased engine performance as well as fuel saving and reduction of pollutant emissions. Experimentation was performed on a turbocharged light-duty compression ignition direct-injection engine. Two monoaxial accelerometers were installed on the engine compressor case, the speed of which has been demonstrated to be closely related to the engine operation. Vibration measurements of the engine compressor case have been processed by combining the Empirical Mode Decomposition technique with Independent Component Analysis and Short Time Fourier Transform to indirectly estimate the turbocharger speed. The obtained traces have been compared to the direct turbocharger velocity measures during the stationary running of the engine (speed and load conditions varied in the complete engine’s range of operation). The results point out the potentiality of the methodology in algorithms devoted to identifying modifications of the combustion development regarding regular operation via indirect turbocharger speed monitoring.

A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention

The volatility of crude oil price can significantly impact the stability of the crude oil market and even the global economy. Effectively predicting global crude oil price and volatility provides a scientific basis for decision-making for market regulators and investors worldwide, promoting the sound development of the global economy. In this study, we integrate signal processing with deep learning methods to present an optimal forecasting strategy for global crude oil price and volatility. We select the daily price of the WTI crude oil market from April 4, 1983, to December 12, 2023, for calculating volatility. Subsequently, employing extreme-point symmetric empirical mode decomposition (ESMD), K-means clustering, and fast independent component analysis method, we decompose and reconstruct the forecasting data, obtaining independent components with non-Gaussian characteristics. These components serve as inputs to estimate the accuracy of various models, including BiLSTM, Attention, LSTM, SVR, RF, and their combinations, in predicting crude oil price and volatility from both a point prediction and interval prediction perspective. Empirical results demonstrate that data decomposition, reconstruction, and the BiLSTM-Attention model outperform other models in predicting crude oil price and volatility.

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

Effect of NO2 on SO2–SO42- conversion in atmosphere: Evidence from long-term precipitation

Sulfate aerosol (SO42−) is an important factor leading to acid rain and aggravating atmospheric particulate pollution. SO42− is mainly converted from gaseous SO2, in which the oxidant gas NO2 may play a key role. In real atmosphere with complexity and nonlinearity, the impact of NO2 on SO2–SO42- conversion exhibits scale dependence due to the combined effect of meteorology, atmospheric chemistry, and pollution emission. Here, based on the long-term atmospheric precipitation data from 2014 to 2021 of Nanchong City in the Sichuan Basin, the Extended Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) methods have been employed to analyze the nonlinear correlations between gaseous SO2 and SO42− in rainwater at different time scales. A multifractal parameter Δh has been described as the scaling features of SO2–SO42- conversion efficiency in the real atmosphere. Then, the impact of NO2 on SO2–SO42- conversion in cold and warm seasons has been quantified, using sliding window analysis and Pearson correlation analysis. The results show that NO2 significantly promoted SO2–SO42- conversion in the cold season, but had little effect on it in the warm season. As direct evidence from atmospheric precipitation analysis, this finding has provided a new theoretical basis for evaluating the impact of NO2 on secondary aerosol generation.

Nonlinear Characteristics of Radio Variability in Quasar 3C 446

Quasars are characterized by violent and large amplitude variability in all the observation wavelengths, and the analysis of optical variability is useful for developing theoretical models. Long-term optical variability data of quasar 3C 446 were collected from the University of Michigan Radio Astronomy Observatory database in the 4.8, 8.0, and 14.5 GHz radio bands from 1976 to 2012. Due to the complexity of the variability data, it's hard to study by the linear time series analysis methods. For learning more about non-linear characteristics of the temporal evolution of quasar variability, the Ensemble Empirical Mode Decomposition and nonlinear analysis are used to analyse chaotic dynamics, fractal properties, and periodicity. This paper focuses on whether there is a significant difference between the periodic and non-linear properties of the quasar variability before and after the removal of the periodic or chaotic components. It turns out that the variability of quasar 3C 446 in the radio bands consists of periodic, trend, and chaotic components, and the periodic and trend components are dominant. The periods of the variability after removing the chaotic and trend components are exactly the same as the periods of the original variability data, but the chaotic and fractal characteristics of the two are significantly different. The saturated correlation dimension indicates that the reconstruction of the dynamical system requires more independent parameters than the original optical variables after the removal of the periodic and trend components. The Kolmogorov entropy indicates that the loss of information is greater for the former than the latter, and the system is more chaotic and more complex. The Hurst value indicates that the self-similarity and long-range correlation are slightly stronger for the latter than the former.

EMD-based time-frequency analysis methods of audio signals

Using appropriate signal processing tools to analyze time series data accurately is essential for correctly interpreting the underlying processes. Commonly employed methods include kernel-based transforms that utilize base functions and modifications to depict time series data. This paper refers to the analysis of audio data using two such transforms: the Fourier transform and the wavelet transform, both based on assumptions regarding the signal's linearity and stationarity. However, in audio engineering, these assumptions often do not hold as the statistical characteristics of most audio signals vary over time, making them unsuitable for treatment as outputs from a Linear Time-Invariant (LTI) system. Consequently, more recent methods have shifted towards breaking down signals into various modes in an adaptive, data-specific manner, potentially offering benefits over traditional kernel-based methods. Techniques like empirical mode decomposition and Holo-Hilbert Spectral Analysis are examples of this. The effectiveness of these methods was tested through simulations using speech signals for both kernel-based and adaptive decomposition methods, demonstrating that these adaptive methods are effective for analyzing audio data that is both nonstationary and an output of the nonlinear system.

Research on features and localization of AE signals from the mechanical body of industrial robots based on WD-EMD

Abstract In order to reveal the propagation characteristics of acoustic emission (AE) signals in the body of industrial machinery, the characteristic frequencies and wave speeds of AE signals propagated on the interior and exterior surfaces (IS and ES) of the body were extracted. Subsequently, an algorithm utilizing characteristic frequency and time difference of arrival (TDOA) is proposed for the identification and localization of AE sources. Initially, an AE source is induced on the IS and ES of the body using the pencil-lead break method in accordance to ASTM standards, and the AE signal is captured by two piezoelectric sensors at a sampling frequency of 3 MHz. Then, to avoid the limitations of wavelet decomposition using self-selected wavelet scale and the problem that a single indicator cannot properly evaluate the correlation between independent mode functions (IMFs) with the original signal, this paper will conduct 4-layer wavelet decomposition of the original signal according to the response frequency range of the sensor, and select the wavelet details within the stable range of the response frequency of the sensor for preliminary reconstruction,and then the empirical mode decomposition (EMD) method is used to decompose the de-noised AE signal into 7 IMFs, and the AE waveform is reconstructed by the combined information of correlation coefficient and variance accounted for. Secondly, the reconstruction method combined with EMD analysis and a single index is compared with the proposed method in this paper to verify the reliability of the proposed method. In addition, the frequency domain characteristics of AE signal propagation process on the IS and ES of the body are extracted. Finally, based on the TDOA principle, the propagation speed of AE signal on the IS and ES of the body is calculated. Based on the geometric relationship between the AE source and two sensors, an algorithm for the location of the AE source is proposed. The results show that the proposed signal reconstruction method can effectively extract the features of AE signals, and the average positioning accuracy of the localization algorithm based on characteristic frequency and TDOA reaches 0.86%.

Detekcija klimatskih kolebanja Hilbert-Huangovom metodom na podacima stoljetnog opservatorija Zagreb-Grič, Hrvatska

Advanced statistical methods are applied on long-term data sets from Zagreb-Grič centennial observatory in Croatia to address recent climate change and variabilities; annual averages are used. Hilbert-Huang transform (HHT), consisting of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), is an empirically based data-analysis method for extracting periodic components embedded within generally nonlinear and non-stationary data. The EMD splits the original series into a so-called intrinsic mode functions (IMFs). First, using the EMD algorithm, intrinsic mode functions (IMFs) are obtained. The analysis of low frequency IMFs indicates significant influence of the North-Atlantic Oscillation on the air temperature, cloudiness and precipitation series in the part of northern Croatia. For the considered climatic elements, the analysis revealed mild deviations in natural fluctuations of the analyzed signal for the last 30 to 40 years, which are most likely caused by anthropogenic activities. Next, HSA is applied to each IMF obtained for the series of annual averages of sea level air pressure. In order to validate this approach and the results, an associated Hilbert spectrum (HS) is compared with the continuous wavelet analysis, i.e., this is another corresponding checkup. The comparison shows significantly improved time and frequency resolution in favor of HS. Moreover, HS provides a unique capability of displaying intra-wave frequency modulation, i.e., changes in frequency that occur within one cycle of oscillation.

Mid-to-long Term Wind Power Forecasting Based on ARIMA-BP Combined Model

Background: Increasing the accuracy of the output power forecasting for wind power is helpful to the improvement of the reliability of power dispatching. Objective: This study aimed to improve the forecasting accuracy of mid-to-long term wind power. Methods: A mid-to-long term wind power forecasting based on ARIMA-BP combined model was proposed. The Empirical Mode Decomposition (EMD) was used to decompose the historical wind power series and obtain the Intrinsic Mode Function (IMF) and residual components, thereby obtaining more regular components. Then, the optimum feature set was obtained based on the minimum Redundancy Maximum Relevance (mRAR) to improve the prediction accuracy for feature extraction. After that, the high-frequency components were predicted using the Back Propagation (BP) neural network model, while the low-frequency components were predicted using the Autoregressive Integrated Moving Average model (ARIMA). Finally, the predicted components obtained were superimposed to deduce the final mid- and long-term wind power prediction results. Results: An analysis was conducted according to the actual data from a typical wind farm. After comparison, it was found that, after empirical mode decomposition and feature extraction analysis, the error of the intelligent combination algorithm based on the ARIMA-BP combined model was smaller than that using only the BP neural network or only the ARIMA. Conclusion: By means of actual data analysis, the effectiveness of the method proposed by the study for mid- and long-term wind power prediction was verified.

The decomposition of guided waves overlapped in the time–frequency domain in composite materials

Compared with traditional bulk wave detection methods, guided wave detection has higher efficiency and accuracy, and therefore has a wider range of applications. However, the guided waves have two inherent properties, namely dispersion and multimodality. In propagation, the various modes of guided wave signals are usually overlapped in the time–frequency domain, which greatly limits the applicability of ultrasonic detection. In this paper, the generalized dispersive mode decomposition method is introduced to separate the modes overlapped in the time–frequency domain in the composite materials. In the method, the overlapped modes in the time–frequency domain can be separated by estimating the group delay of corresponding dispersive modes without any prior knowledge. When the estimated group delay is equal to the true group delay, the corresponding mode is completely separated from the overlapped modes. The dispersion curves of the carbon fiber composite laminate with a determined propagation direction were reconstructed by employing a combination of the generalized dispersive mode decomposition and zero-crossing techniques. And the accuracy and effectiveness were further validated through comparison with the results obtained from empirical mode decomposition and independent component analysis. The proposed method presents a useful guideline for the mode decomposition in the carbon composites and helps detect the mechanical properties or defects of the composite materials.

Non-stationary signal analysis utilizing VMD and EMD algorithm for multiple fault classification for industrial applications

The Hilbert spectrum images of intrinsic mode functions (IMF) of empirical mode decomposition (EMD) analysis and variational mode decomposition (VMD) analysis of faulty machine vibration signals are used in deep convolutional neural network (DCNN) for machine fault classification in which the DCNN automatically learns the features from spectral images using convolution layer. Though both EMD and VMD analysis suit well for non-stationary signal analysis, VMD has the merit of aliasing free IMFs. In this paper, the performance improvement of DCNN classification for a non-stationary vibration signal dataset using VMD is brought out. The numerical experiment uses the Hilbert spectrum images of 4 EMD-IMFs and 4 VMD-IMFs in DCNN to classify 10 different faults of the Case Western Reserve University (CWRU) bearing dataset. The confusion matrices are obtained and the plot of model accuracies in terms of epochs for the DCNN is analysed. It is shown that the spectrum images of one of the four EMD-IMFs, IMF0, give a validation accuracy of 100% and in the case of VMD the spectrum images of two of the four VMD-IMFs, IMF0, and IMF1 give a validation accuracy of 100%. This reveals that non-aliasing IMFs of VMD are better at classifying bearing faults. Further to bring out the merits of VMD analysis for non-stationary signals the numerical experiment is conducted using VMD analysis for binary fault classification of the milling dataset which is more non-stationary than the bearing dataset which is proved by plotting the statistical parameters of both datasets against time. It is found that the DCNN classification is 100% accurate for IMF3 of VMD analysis which is much better than the 81% accuracy provided by EMD analysis as per existing literature. The performance comparison highlights the merits of VMD analysis over EMD analysis and other state-of-the-art methods and ensemble learning methods.

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.

Secular changes in the tropical stratospheric water vapour entry induced by the Indo-Pacific warm pool warming

A decreasing trend in the tropical (30°S ∼ 30°N) stratospheric water vapour (SWV) entry in recent four decades (from 1984 to 2020) is detected based on the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) measurements and the ERA5 reanalysis dataset using linear regression and Ensemble Empirical Mode Decomposition (EEMD) analysis. With the concurrent warming of the sea surface temperature (SST), the Indo-Pacific warm pool (IPWP) appears to be the most significant region among the tropical oceans based on correlation analysis. >43% of the decreasing tropical lower SWV trend is likely to be related to the IPWP SST warming. To validate this relationship, two groups of idealized runs are carried out with version 4 of the Whole Atmosphere Community Climate Model (WACCM4) and version 5 of the Community Atmosphere Model (CAM5). Both simulations agree with the observational-based linkage. The IPWP-SST-warming forced simulations show that the temperature in the tropical tropopause has decreased at the rate of around 0.318 K per decade in the coldest point region, as the tropical convection over the IPWP has become more vigorous and excited stronger equatorial waves to produce adiabatic cooling around tropopause. This cooling tropical tropopause leads to a dehydrating tropical lower stratosphere at the rate of 0.025 ppmv per decade, as expected by the freeze-drying mechanism. These results imply the substantial warming trend of IPWP is an important factor for the long-term trend of the tropical SWV entry under climate change, and a better representation of this relationship in the model is critical for the SWV projection under future climate scenarios.

Analysing the Effects of Atmospheric Teleconnections on Streamflow Regime in the Eastern Black Sea Basin in Türkiye

Analysing the variations in hydrological cycle components is essential for water resources planning and management. In this study, the relationship between the streamflow data belonging to five discharge gauging stations in the Eastern Black Sea Basin in Türkiye and the Arctic Oscillation (AO), East Atlantic-Western Russia (EAWR), North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP) was investigated. For this purpose, Spearman’s correlation test, ensemble empirical mode decomposition (EEMD) and relative importance analysis were used. Accordingly, Spearman’s correlation coefficients were calculated between raw streamflow data, decomposed streamflow data via EEMD and atmospheric teleconnections. Then, the relative importance analysis was applied to determine the atmospheric teleconnections’ influences on streamflow data. The findings showed that the relationship between raw streamflow data and atmospheric teleconnections is generally more significant and negative in the winter and spring. Furthermore, it was observed that the linkage between the decomposed streamflow data and atmospheric teleconnections could differentiate. Although no significant correlation between atmospheric teleconnections and raw streamflow data was detected in some months, significant correlations were detected between atmospheric teleconnections and decomposed streamflow data. This reveals the importance of examining the relationship between atmospheric teleconnections and streamflow data for different periods. The relative importance analysis revealed that the influence of atmospheric teleconnections on streamflow data could change from station to station and from component to component. This study showed that investigating the effects of atmospheric teleconnections on streamflow data for different components and periods is important.

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.

Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.

A PAMGuard catalog for comparing detection and classification performance of two mode decomposition algorithms

The ONR project “Application of an Empirical Mode Decomposition (EMD) detection and classification process to environments for Naval monitoring and detection” created empirical and variational mode decomposition analysis workflows to detect and cluster diverse underwater signals across four distinct datasets. Twenty-five call types from the Bering and Chukchi Seas, Aeon’s North Atlantic sites, CTBTO hydroacoustic stations, and the Chambal River were detected and clustered with varying levels of success. The final precision, recall, and accuracy results of these call types will be presented. Evaluating the performance of our developed detection and classification algorithms involved a comparison with the widely used open-source software, PAMGuard. However, obtaining information about the parameters used to construct detectors for different call types in PAMGuard proved challenging due to limited literature availability. To the best of our knowledge, some call types had never been processed in PAMGuard, and for those that had, not all parameters required for adapting the detectors to our datasets were consistently published. To facilitate the use of our PAMGuard detectors by other researchers for their own datasets, we will present a user-friendly guide that will outline the features that yielded optimal PAMGuard performance for each call type.

Retina images classification based on 2D empirical mode decomposition and multifractal analysis

Diabetic retinopathy is an ocular disease caused by long-term damage to the retina due to high blood sugar levels. Elevated blood sugar can impair the microvasculature in the retina, leading to vascular abnormalities and the formation of abnormal new blood vessels. These changes can manifest in the retina as hemorrhages, leaks, vessel dilation, retinal edema, and retinal detachment. The retinas of individuals with diabetes exhibit different morphologies compared to those without the condition. Most histological images cannot be accurately described using traditional geometric shapes or methods. Therefore, this study aims to evaluate and classify the morphology of retinas with varying degrees of severity using multifractal geometry. In the initial experiments, two-dimensional empirical mode decomposition was employed to extract high-frequency detailed features, and the classification process was based on the most relevant features in the multifractal spectrum associated with disease factors. To eliminate less significant features, the random forest algorithm was utilized. The proposed method achieved an accuracy of 96%, sensitivity of 96%, and specificity of 95%.