Frequency Content Research Articles

Spectral graph model for fMRI: A biophysical, connectivity-based generative model for the analysis of frequency-resolved resting-state fMRI

Abstract Resting-state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain’s functional organization and to examine whether it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being underutilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysics-informed signal generation model followed by graph spectral (i.e., eigen) decomposition. We call this model a spectral graph model (SGM) for fMRI, using which we can not only quantify the structure–function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal’s spectral and spatial features into a small number of biophysically interpretable parameters. We expect this model-based analysis of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure–function relationships in a variety of brain disorders.

Detection of infrasound sources using three-years array data in 2019-2021 deployed at the Lützow-Holm Bay region, Antarctica

Time-space variations of infrasound source locations for three years, 2019-2021, were studied byusing a combination of two local arrays in the Lützow-Holm Bay region (LHB), Antarctica. The local arrays deployed at two coastal outcrops clearly detected temporal variations in signal frequency content as well as propagating directions during the three years. A large number of infrasound sources were detected with many located to the north and north-west directions from the arrays. These events were generated within the Southern Indian Ocean and the northern part of LHB with frequency-content of a few seconds; these “microbaroms” are believed to originate from oceanic swells. From austral summer to fall additional infrasound sources are determined to be located to the north-east. These sources might be related to the effects of katabatic winds across the continental coastal area. Furthermore, several impulsive infrasound events during the winter had higher predominant frequencies of a few Hz, higher than the microbaroms. On the basis of a comparison of source locations with sea-ice and glacier distribution form MODIS satellite images, these high-frequency sporadic sources may be cryo-seismic signals associated with cryosphere dynamics near the local arrays. These results suggest that infrasound waves can be used to monitor surface environments in the coastal area of Antarctica.

Dynamic response and vibration signature assessment of SDOF steel system using RISAM shaking table

Civil engineering structures face major challenges, particularly earthquakes, which are random phenomena in their intensity and frequency content. The seismic response of structures depends largely on the intensity of an earthquake and especially on its frequency content. Structural Health Monitoring (SHM) is a relevant tool for assessing the dynamic behavior of structures subjected to seismic excitations. This is achieved by using experimental measurements of structural dynamic parameters. The main objective of this study is to identify the vibration signature of a single-degree-of-freedom SDOF steel system. Based on the laws of similarity, a 1:6 reduced-scale model is developed. In this context, dynamic experiments using the shaking table of the RISAM laboratory (Risk Assessment and Management: University of Tlemcen) are carried out to determine the structural dynamic parameters of this steel system. This is achieved through experimental determination of natural frequency and damping of the reduced model. Acceleration and displacement measurements are also established. The experimental estimation of damping is established using the logarithmic decrement method. Several dynamic analyses are carried out based on the finite elements model of the reduced steel system model. The obtained results show that the approach used to determine the dynamic parameters of this reduced model leads to realistic results. On the other hand, a perfect concordance between the numerical and experimental results has been approved.

Reverse-time migration of mobile marine vibrator data

Marine vibrators are viable alternatives to seismic air guns in ocean-bottom acquisition due to their less adverse impact on the environment, their ability to generate lower frequency content, and their suitability for simultaneous acquisition. However, mobile marine vibrators introduce a unique set of processing and imaging challenges for ocean-bottom acquisition, the main examples of which are the Doppler effect and the time-dependent source-receiver offsets, which are not features of conventional marine acquisitions. Standard seismic data processing and imaging techniques assume stationary sources and are not fully suitable for mobile marine vibrator data without modifications. Not accounting for source motion in seismic imaging introduces phase change and mispositioning of imaged structures. Further, ignoring source-motion effects in imaging workflow may lead to the estimation of inaccurate migration velocity models or imply the use of incorrect migration velocity, e.g., in seismic-to-well ties. We develop a reverse-time migration (RTM) approach that accounts for the source-motion effects and is capable of producing accurate subsurface images, closely matching stationary acquisition and imaging results. Synthetic examples illustrate the ability of the proposed method to construct accurate subsurface RTM images even for source velocities higher than those commonly used in typical marine acquisitions. Our approach is not limited to imaging mobile marine vibrator data in ocean-bottom acquisition, but is also applicable to mobile streamer data using marine vibrators or seismic air-gun sources.

Effects of ground motion characteristics on liquefaction response of earth dams using a non-Darcy hydro-mechanical model

During earthquake-induced excitations, significant pore pressure gradients can develop in earth dams, limiting the applicability of Darcy’s law for fluid flow. This study investigates the behavior of a liquefied earth dam subjected to various seismic excitations with differing frequency content, aiming to enhance insights into the effects of dynamic loading on fluid–structure interactions. To accurately capture the soil’s response, the Pastor-Zienkiewicz generalized plasticity model is employed to represent realistic soil behavior. Additionally, a modified Forchheimer equation is used to address varying permeability through a non-Darcy flow law. Numerical simulations, performed using a finite element code developed in Fortran, are validated through comparative analyses with previous studies. The results show that in low-permeability regions, changes in earthquake frequency content have minimal impact on discrepancies between the predicted results of Darcy and non-Darcy models. However, the non-Darcy model generally estimates horizontal displacements to be around 4% higher on average in these regions. This difference increases as earthquake frequency content decreases, reaching up to 6%. In contrast, in permeable regions, the non-Darcy model predicts significantly higher horizontal displacements, with an average increase of approximately 20% for high-frequency seismic events and 8% for low-frequency ones. Additionally, in high-permeability regions, the non-Darcy model deviates notably from the Darcy model as peak ground velocity increases, particularly affecting the upstream shell and clay core of the dam. This disparity becomes more pronounced as the input motion’s frequency content decreases, resulting in elevated pore pressures and slightly reduced vertical displacements in these regions.

An Anechoic, High-Fidelity, Multidirectional Speech Corpus.

We currently lack speech testing materials faithful to broader aspects of real-world auditory scenes such as speech directivity and extended high frequency (EHF; > 8 kHz) content that have demonstrable effects on speech perception. Here, we describe the development of a multidirectional, high-fidelity speech corpus using multichannel anechoic recordings that can be used for future studies of speech perception in complex environments by diverse listeners. Fifteen male and 15 female talkers (21.3-60.5 years) recorded Bamford-Kowal-Bench (BKB) Standard Sentence Test lists, digits 0-10, and a 2.5-min unscripted narrative. Recordings were made in an anechoic chamber with 17 free-field condenser microphones spanning 0°-180° azimuth angle around the talker using a 48 kHz sampling rate. Recordings resulted in a large corpus containing four BKB lists, 10 digits, and narratives produced by 30 talkers, and an additional 17 BKB lists (21 total) produced by a subset of six talkers. The goal of this study was to create an anechoic, high-fidelity, multidirectional speech corpus using standard speech materials. More naturalistic narratives, useful for the creation of babble noise and speech maskers, were also recorded. A large group of 30 talkers permits testers to select speech materials based on talker characteristics relevant to a specific task. The resulting speech corpus allows for more diverse and precise speech recognition testing, including testing effects of speech directivity and EHF content. Recordings are publicly available.

Eye Movement Classification using Feature Engineering and Ensemble Machine Learning

This paper explores the classification of gaze direction using electrooculography (EOG) signals, integrating signal processing, deep learning, and ensemble learning techniques to enhance accuracy and reliability. A complex technique is proposed in which several feature types are derived from EOG data. Spectral properties generated from power spectral density analysis augment basic statistical characteristics such as mean and standard deviation, revealing the frequency content of the signal. Skewness, kurtosis, and cross-channel correlations are also used to represent intricate nonlinear dynamics and inter-channel interactions. These characteristics are then reformatted into a two-dimensional array imitating picture data, enabling the use of the pre-trained ResNet50 model to extract deep and high-level characteristics. Using these deep features, an ensemble of bagging-trained decision trees classifies gaze directions, lowering model variance and increasing prediction accuracy. The results show that the ensemble deep learning model obtained outstanding performance metrics, with accuracy and sensitivity ratings exceeding 97% and F1-score of 98%. These results not only confirm the effectiveness of the proposed approach in managing challenging EOG signal classification tasks but also imply important consequences for the improvement of Human-Computer Interaction (HCI) systems, especially in assistive technologies where accurate gaze tracking is fundamental.

A new approach for assessing the contribution of Ground Motions Frequency Content and Soil-Structure Interaction to the Seismic Response of Reinforced Concrete Moment Frames

One of the key steps in the seismic resilience assessment of buildings is gaining insight into their seismic response. The frequency content of ground motions and the dynamic characteristics of the building contribute mutually to the seismic response of the entire system (soil + structure). This study aims to present a new approach to assess the effects of the soil structure interaction (SSI) and of the frequency content of the ground motion. A case study of reinforced concrete moment frames (RCMFs) is herein presented by applying 280 ground motion time history records, which were registered on site classes A and B (based on ASCE 7–16). To this end, initially the characteristics of the input motions (e.g. amplitude and frequency content) were calculated, and then the time histories were partitioned into three clusters based on two optimal clustering features, namely Peak Ground Acceleration to Peak Ground Velocity ratio (PGA/PGV) and F5 (representing the frequency associated with 5% of total power in cumulative power spectral density, PSD). The clustering were done by implementing an unsupervised machine learning (ML) algorithm (i.e. K-means clustering), also the number of clusters was achieved using a well-known method in ML literature. Several time histories from each of the clusters were randomly selected to perform incremental dynamic analysis (IDA) on a 6- and a 10-story RCMF. Two different base conditions were considered: fixed-based (SSI neglected) and flexible-base (considering SSI effects). The findings demonstrate the contribution of frequency content and SSI to the seismic response of RCMFs, and also present an approach for considering frequency content in seismic analysis.

Unsupervised data-driven response regime exploration and identification for dynamical systems.

Data-Driven Response Regime Exploration and Identification (DR2EI) is a novel and fully data-driven method for identifying and classifying response regimes of a dynamical system without requiring human intervention. This approach is a valuable tool for exploring and discovering response regimes in complex dynamical systems, especially when the governing equations and the number of distinct response regimes are unknown, and the system is expensive to sample. Additionally, the method is useful for order reduction, as it can be used to identify the most dominant response regimes of a given dynamical system. DR2EI utilizes unsupervised learning algorithms to transform the system's response into an embedding space that facilitates regime classification. An active sequential sampling approach based on Gaussian Process Regression is used to efficiently sample the parameter space, quantify uncertainty, and provide optimal trade-offs between exploration and exploitation. The performance of the DR2EI method was evaluated by analyzing three established dynamical systems: the mathematical pendulum, the Lorenz system, and the Duffing oscillator, and its robustness to noise was validated across a range of noise magnitudes. The method was shown to effectively identify a variety of response regimes with both similar and distinct topological features and frequency content, demonstrating its versatility in capturing a wide range of behaviors. While it may not be possible to guarantee that all possible regimes will be identified, the method provides an automated and efficient means for exploring the parameter space of a dynamical system and identifying its underlying "sufficiently dominant" response regimes without prior knowledge of the system's equations or behavior.

Thickness monitoring of threshing mixture on the oscillating plate of corn grain harvester

Cleaning is a critical operation in the corn grain harvesting process, and the operation is seriously affected by the uneven distribution of the threshing mixture on the oscillating plate. To monitor the thickness of the threshing mixture on the oscillating plate of the corn grain harvester, in this study, we adopted the lever principle to design a corn threshing mixture thickness monitoring device based on the piezoresistive effect and theoretically analyzed the lever impact mechanics. The factors affecting the force on the sensor of the monitoring device are the vibration frequency of the oscillating plate, the thickness of the threshing mixture, and grain moisture content, which were obtained through theoretical analysis. The operational performance of the corn threshing mixture thickness monitoring device was assessed through a set of single-factor experiments, revealing the influence laws of three factors on the impact voltage: vibration frequency, thickness of the threshing mixture, and grain moisture content. A back-propagation (BP)11Back-propagation (BP) neural network prediction model was established using an orthogonal experiment with impact voltage, vibration frequency, and grain moisture content as input layers and the thickness of the corn threshing mixture as the output layer. An STM32 microcontroller was used as the primary control unit to build a thickness monitoring data acquisition and processing system that could process and operate the impact voltage collected by the pressure sensor and the vibration frequency signal collected by a Hall sensor. The data were wirelessly transmitted to a PC to predict the thickness of the threshing mixture by using the trained BP neural network. The experimental results of monitoring the variation in corn threshing mixture thickness demonstrated that the device could effectively monitor changes in thickness. The monitoring accuracy of the monitoring device was found to be over 90%, and the system can be used for monitoring the threshing mixture thickness on the oscillating plate of the corn grain harvester.

Linear prediction on Cent scale for fundamental frequency analysis

Understanding the fundamental frequency and harmonic content of audio signals is crucial for many applications in music analysis, including music transcription, audio synthesis, and genre identification. This study formulates a signal processing approach combining Linear Prediction (LP) analysis and the Cent scale to accurately characterize the pitch and harmonic structure of the audio signals. Pitch tracking on the LP spectrum in the Cent scale provides more accurate and reliable pitch estimation, especially in the presence of noise or overlapping harmonics. The Cent scale aligns the harmonics of different notes more closely, making it easier to discern the correct pitch.

Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis

ABSTRACT This paper presents a finite element model for the dynamic vibration of continuous-welded rails (CWR) under different longitudinal stresses and base conditions. The model supports the physical principles of a vibration-based nondestructive testing (NDT) technique aimed at inferring the lateral and vertical strength, and the neutral temperature of CWR. The model consists of a ~ 5 meters-long rail periodically restrained by distributed translational springs that simulates the lateral and vertical resistance provided by baseplates, fasteners and spikes. Vibration is triggered by a force function equivalent to the mechanical impact of a hammer. The frequency content of the vibrations below 700 Hz and across a range of different longitudinal stress and support conditions is computed using the power spectral density, which constitutes the input matrix of a machine learning algorithm able to learn the complex relationship among frequencies, axial stress, and support conditions. Finally, the model is used to quantify any differences in excitation and detection points to optimize the setup of the proposed NDT technique.

Evaluating the dynamic response and phase angle behavior of SBS-modified asphalt mixtures for enhanced pavement performance

Bitumen exhibits viscoelastic properties, showcasing both viscous and elastic behaviors, which are characterized by the phase angle and dynamic modulus. Issues like early fatigue fractures, rutting, and permanent deformations in bituminous asphalt pavements arise due to moisture susceptibility, high-temperature deformation, low-temperature cracking, and overloading. These distresses result in potholes, alligator cracks, and specific deformations that lead to early pavement failure, increasing rehabilitation and maintenance costs. To address these issues, this study examines the dynamic modulus and phase angle behavior of Styrene Butadiene Styrene (SBS) modified and unmodified asphalt mixtures. SBS was incorporated in various proportions, ranging from 2 to 7% by the weight of bitumen. The asphalt mixture performance tester (AMPT) was utilized to measure the dynamic modulus at temperatures of 4.4, 21.1, 37.8, and 54.4 °C, and frequencies of 0.1, 0.5, 1, 5, 10, and 25 Hz. The study found significant correlations between dynamic modulus, temperature, loading frequency, and SBS content. Additionally, Multi Expression Programming (MEPX) and regression modeling were employed to estimate the dynamic modulus of SBS-modified HMA. Results indicated that increasing SBS content up to 7% decreased penetration and ductility values by up to 46% and 56%, respectively, while raising the softening point by 63% due to increased stiffness. The blend with 6% SBS by weight of bitumen exhibited superior performance compared to other mixtures. Phase angle initially increased with rising temperature, peaking at 37.8 °C at lower frequencies, and continued to increase at higher frequencies. Isothermal and isochronal plots showed that the 0% SBS mix had a higher phase angle due to increased bitumen content. Overall, the HMA mix with 6% SBS provided the best outcomes.

Application of the Beating Method to Define the Electroencephalogram α−Rhythm Frequency Content

Among plenty of the brain oscillation electrical patterns, the spindle oscillations in the alpharhythm band has main functional significance, in particular in emotional, memory, motivation and attention processes. At the same time, signal processing techniques well developed in radio engineering are not used in electroencephalogram (EEG) processing. For the first time the beating method was applied to electroencephalograms. It is shown that the beating method allows the determination of α−rhythm frequencies, which are not always resoluted using Furrier transform. EEG is non-stationary process that’s why instantaneous frequency is a very important characteristics of EEG. The calculation of instantaneous frequency values was carried out based on the Hilbert transform and intersection detector method. A comparative analysis of electroencephalograms frequency content obtained by Furrier transform, Hilbert transform and Zero-level intersection detector method was carried out.

A Hybrid Approach to Enhanced Signal Denoising Using Data-Driven Multiresolution Analysis with Detrended-Fluctuation-Analysis-Based Thresholding and Stationary Wavelet Transform

In this work, a new method for denoising signals is developed that is based on variational mode decomposition (VMD) and a novel metric using detrended fluctuation analysis (DFA). The proposed method first decomposes the signal into band-limited intrinsic mode functions (BLIMFs) using VMD. Then, a DFA-based developed metric is employed to identify the ‘noisy’ BLIMFs (based on their DFA-based scaling exponent and frequency content). The existing DFA-based methods use a single-slope threshold to detect noise, assuming all signals have the same noise pattern and ignoring their unique characteristics. In contrast, the proposed DFA-based metric sets adaptive thresholds for each mode based on their specific frequency and correlation properties, making it more effective for diverse signals and noise types. These predominantly noisy BLIMFs are then denoised using shrinkage techniques in the framework of stationary wavelet transform (SWT). This step allows efficient denoising of components, mainly the noisy BLIMFs identified by the adaptive threshold, without losing important signal details. Extensive computer simulations have been carried out for both synthetic and real electrocardiogram (ECG) signals. It is demonstrated that the proposed method outperforms the state-of-the-art denoising methods and with a comparable computational complexity.

Deep Learning Model Size Performance Evaluation for Lightning Whistler Detection on Arase Satellite Dataset

The plasmasphere within Earth’s magnetosphere plays a crucial role in space physics, with its electron density distribution being pivotal and strongly influenced by solar activity. Very Low Frequency (VLF) waves, including whistlers, provide valuable insights into this distribution, making the study of their propagation through the plasmasphere essential for predicting space weather impacts on various technologies. In this study, we evaluate the performance of different deep learning model sizes for lightning whistler detection using the YOLO (You Only Look Once) architecture. To achieve this, we transformed the entirety of raw data from the Arase (ERG) Satellite for August 2017 into 2736 images, which were then used to train the models. Our approach involves exposing the models to spectrogram diagrams—visual representations of the frequency content of signals—derived from the Arase Satellite’s WFC (WaveForm Capture) subsystem, with a focus on analyzing whistler-mode plasma waves. We experimented with various model sizes, adjusting epochs, and conducted performance analysis using a partial set of labeled data. The testing phase confirmed the effectiveness of the models, with YOLOv5n emerging as the optimal choice due to its compact size (3.7 MB) and impressive detection speed, making it suitable for resource-constrained applications. Despite challenges such as image quality and the detection of smaller whistlers, YOLOv5n demonstrated commendable accuracy in identifying scenarios with simple shapes, thereby contributing to a deeper understanding of whistlers’ impact on Earth’s magnetosphere and fulfilling the core objectives of this study.

Experimental investigation on the restart characterization of shale oil-water flow in a horizontal pipe

ABSTRACT With the dwindling conventional oil resources and increasing energy demand, shale oil development has gradually become a hot spot in the global energy industry. Moreover, the shale oil pipeline also inevitably experiences abnormal operations, such as shutdown and restart during regular maintenance or emergency accident repairs. However, previous restart research primarily focused on conventional crude oil pipelines, and there is still a research gap on shale oil-water flow. Therefore, this work conducted the shale oil-water restart flow experiment in the multi-test section pipe flow loop, considering the temperature (20–50℃), starting frequency (15–25 hz), and water content (0–80%). Based on the restart pressure and volume flow rate change, the response characteristics of start pressure are studied. Moreover, the changing rules of restart pressure are explored with starting frequency, temperature, and water content. Eventually, the variation trend of the restart pressure absolute errors between the calculated value and the experimental data with the starting frequency is analyzed. The results show that the restart pressure reaches the maximum around 10 s after the pump starts, and the maximum restart pressure is approximately 1.5 times the stabilized restart pressure. Moreover, the restart pressure increases linearly with the starting frequency and decays exponentially with the temperature and water content (after the inversion point). With the starting frequency reduced from 45 hz to 15 hz, the restart pressure reduces from 24.6 kPa to 10.0 kPa at 20°C. Furthermore, the empirical formulas can predict the starting pressure at low frequencies. This research lays a theoretical foundation for the safe restart of shale oil-water flow.

Investigation of the Effective Numerical Model for Seismic Response Analysis of Concrete-Faced Rockfill Dam on Deep Overburden

The construction of high rockfill dams on deep overburden in seismically active regions poses significant challenges. Currently, there are no standardized guidelines for defining the computational domain range in seismic analysis, necessitating the establishment of a universally applicable computational domain range that optimizes the balance between computational accuracy and efficiency. This has critical engineering implications for the seismic analysis of rockfill dams on deep overburden. This study employed the seismic wave input method to consider the dynamic interaction between the dam, overburden, and infinite domain. A systematic investigation was conducted on a concrete-faced rockfill dam (CFRD) constructed on deep overburden, considering the influences of overburden thickness, dam height, overburden properties, soil layer configuration, ground motion intensity, and the frequency content of the seismic waves. The acceleration response and seismic deformation of the dam were analyzed. Subsequently, the computational domain range corresponding to various levels of acceptable engineering precision was established. The results indicated that the lateral boundary length should extend a minimum distance equal to the sum of 3 times the overburden depth and 1.2 times the maximum dam height. Additionally, the depth below the overburden–bedrock interface should extend at least 1.2 times the maximum dam height. This study provides a crucial foundation for determining the optimal computational domain range in the seismic analysis of rockfill dams constructed on deep overburden.

Numerical Investigation of Unsteady Flow Characteristics of Planar Plug Nozzle in Over-Expanded Conditions

Abstract Plug nozzles are frequently regarded as a strong contender for futuristic single-stage-to-orbit space missions due to their inherent altitude-compensating properties. Optimizing the design of these nozzles to achieve peak performance during the initial ascent phase is of preeminent importance. The current research aims to deepen our comprehension of the unsteady behavior of plug nozzles during this ascent phase, commonly referred to as the overexpanded flow regime. It utilizes a full-spike plug nozzle and employs a finite volume-based solver within the OpenFOAM framework to investigate flow features across Nozzle Pressure Ratios (NPRs) ranging from 3 to 8. Additionally, the Ffowcs Williams Hawkings (FW-H) analogy is employed to assess the influence of acoustics in the nozzle's flow field at different NPRs.Spectral analysis and flow visualizations are employed to study the frequency content and the respective flow features of the system. It is observed that the flow exhibits separation beyond NPR 4 and is highly unsteady at NPR 6, which is inferred from the prominence of the recirculation bubble. However, beyond NPR 6, the flow tends to reattach the plug surface, dampening the disturbance.

Acoustic emission monitoring of damage modes in reinforced concrete beams by using narrow partial power bands

This work presents an acoustic emission (AE) based method, named a series of narrow partial power bands (SN2PB), to monitor the damage mechanisms within reinforced concrete beams during quasi-static bending tests. Unlike conventional time-domain methods, which give a global view of the involved cracking modes, SN2PB has the advantage of obtaining information on cracking modes within each AE hit in reduced frequency bands. SN2PB is applied by dividing the frequency content of each AE signal into narrow bands. Results show that the same AE signal can contain shear and tension cracking signatures at the lower and upper frequency bands, respectively. This work shows also the presence of a transitional domain between the two distinct bands. Changes and fluctuations corresponding to the involved mechanisms during the entire mechanical tests are therefore followed and visualized in the form of a heatmap. Moreover, the densities of the shear and tensile mechanisms at an instant are also determined using a Gaussian Mixture Model. Results show that the separation obtained using the SN2PB method is more advantageous than that of the conventional method based on the average frequency and the RA parameter.