Fast Fourier Transform Analysis Research Articles

Employing Siamese Networks as Quantitative Biomarker for Assessing the Effect of Dolphin-Assisted Therapy on Pediatric Cerebral Palsy.

This study explores the potential of using a Siamese Network as a biomarker for assessing the effectiveness of Dolphin-Assisted Therapy (DAT) in children with Spastic Cerebral Palsy (SCP). The problem statement revolves around the need for objective measures to evaluate the impact of DAT on patients with SCP, considering the subjective nature of traditional assessment methods. The methodology involves training a Siamese network, a type of neural network designed to compare similarities between inputs, using data collected from SCP patients undergoing DAT sessions. The study employed Event-Related Potential (ERP) and Fast Fourier Transform (FFT) analyses to examine cerebral activity and brain rhythms, proposing the use of SNN to compare electroencephalographic (EEG) signals of children with cerebral palsy before and after Dolphin-Assisted Therapy. Testing on samples from four children yielded a high average similarity index of 0.9150, indicating consistent similarity metrics before and after therapy. The network is trained to learn patterns and similarities between pre- and post-therapy evaluations, in order to identify biomarkers indicative of therapy effectiveness. Notably, the Siamese Network's architecture ensures that comparisons are made within the same feature space, allowing for more accurate assessments. The results of the study demonstrate promising findings, indicating different patterns in the output of the Siamese Network that correlate with improvements in symptoms of SCP post-DAT. Confirming these observations will require large, longitudinal studies but such findings would suggest that the Siamese Network could have utility as a biomarker in monitoring treatment responses for children with SCP who undergo DAT and offer them more objective as well as quantifiable manners of assessing therapeutic interventions. Great discrepancies in neuronal voltage perturbations, 7.9825 dB on average at the specific samples compared to the whole dataset (6.2838 dB), imply a noted deviation from resting activity. These findings indicate that Dolphin-Assisted Therapy activates particular brain regions specifically during the intervention.

Artificial intelligence and electrocardiography: A modern approach to heart rate monitoring

The integration of Artificial Intelligence (AI) in Electrocardiography (ECG) and Photoplethysmography (PPG) signifies AI's profound influence on heart rate monitoring and analysis. ECG traditionally offers critical insights into cardiac health, necessitating expert interpretation. This study introduces an AI framework with Fast Fourier Transformation Analysis for swift, human-like interpretation of complex ECG signals. A multilayer AI Network accurately detects intricate features, enhancing ECG analysis precision. Leveraging comprehensive datasets, AI models proficiently identify heart dysfunctions like atrial fibrillation and hypertrophic cardiomyopathy, and can estimate age, sex, and race. The proliferation of mobile ECG technologies has spurred AI-based ECG phenotyping, impacting clinical and population health. This research explores AI's role in enhancing cardiac health assessment and clinical decision-making using MATLAB, acknowledging its transformative potential and inherent limitations.

Effects of air gap eccentricity on different rotor structures for PMSM in electric vehicles

In addition to research related to static eccentricity and dynamic eccentricity examining fault and diagnostic methods in various types of motors, there are also studies conducted for motors with non-uniform air gaps similar to the condition. In these studies, improvements and changes in motor performance for such types of motors can be observed. In this study, the effects of the rotor structures determined in the study and the air gap shape, which is effective by changing the eccentricity ratio, on the motor performance of the interior permanent magnet synchronous motor designed for electric vehicle applications are examined. Besides the elliptical rotor structure, which is widely researched in dynamic eccentricity studies, these eccentric motor structures also include novel rectangular and polygonal eccentric rotor structures. Motor models with non-uniform air gap structures are designed in two dimensions and subjected to simulation and analysis studies using the finite element method. At the end of the study, along with the parameters identified for motor performance, the harmonic components of the stator current, radial air gap flux density and the output torque obtained through Fast Fourier Transform analysis have been provided.

Time-series analysis of radon concentrations for Bengaluru's atmosphere.

The Fast Fourier Transform (FFT) analysis of the activity concentration of radon and selected meteorological parameters was carried out at Department of Physics, Bangalore University, Bengaluru (12056'44"N, 77030'25″E, 840m above MSL). All of the measured parameters, with the exception of pressure, showed a clear diurnal trend, which can be explained by the presence of typical atmospheric processes such as temperature inversion in the morning and greater vertical mixing in the afternoon. Radon's time series has a latent memory of sub-diurnal cycles, as shown via FFT analysis. The monthly average radon has higher levels of activity during winter months compared with monsoon and summer months. Days during the monsoon season had the lowest radon activity, which may be ascribed to the fact that less radon was being exhaled from the soil as a result of the rain. Radon was recorded at 8.06±0.56Bq/m3, temperature at 28.9°C, humidity at 55.2% and pressure at 918mbar.

Analysis of spatiotemporal variation in land surface temperature on the Tibetan Plateau from 2001 to 2020

ABSTRACT With global climate warming intensifying, the Land Surface Temperature (LST) variation on the Tibetan Plateau (TP) has become a hot topic in environmental studies. This letter employs spatial analysis to identify LST distribution characteristics, the Pettitt test to detect abrupt changes, and Fast Fourier Transform (FFT) to analyse periodic variations. Analysing mean annual and seasonal LST from 2001 to 2020, the results indicate the following: (1) Over the past 20 years, the TP’s LST increased slowly at 0.08°C per decade, with significant seasonal variations. Warming trends dominate in spring, summer, and autumn, shifting from the northern to the southwestern plateau. Autumn shows the most significant warming at 0.31°C per decade, while winter shows a cooling trend at −0.46°C per decade. (2) The Pettitt test reveals no statistically significant abrupt changes in mean annual and seasonal LST, though potential change points were detected in 2013 for mean annual LST, and in 2005, 2006, 2014, and 2017 for summer, spring, autumn, and winter, respectively. This indicates relative stability in interannual temperature changes, with some asynchronous seasonal variations. FFT analysis indicates dominant short-term variability with a period of 3.33 years and secondary cycles of 10 and 5 years.

A method to design a fast chaotic oscillator using CCTA

This article introduces a novel method for designing a fast chaotic oscillator using a CCTA (Current Conveyor Transconductance Amplifier) based on Chua's circuit. The proposed method uses innovative configurations and advanced simulation techniques to overcome challenges in high-speed operation, nonlinear dynamics, and Analog Building Block (ABB) selection. The design begins with nonlinear negative resistance, essential for Chua's diode characteristics, including two negative resistances, NR1 and NR2. The circuit integrates one CCTA block, two grounded capacitors, two fixed resistors, one inductor, and one potentiometer. It is simulated using PSPICE with IC (Integrated Circuit) macro-models and 180nm CMOS (Complementary Metal Oxide Semiconductor) technology. Various chaotic waveforms and attractors are produced, validating the theoretical and mathematical predictions. By varying the resistance values (1450Ω, 1650Ω, 1800Ω, 1950Ω), the circuit exhibits different chaotic behaviors, such as large limit cycles, double-scroll attractors, Rossler-type attractors, and I-periodic attractors. FFT (Fast Fourier Transform) analysis confirms the highest dominant operating frequency of 37.5MHz. A Monte Carlo simulation with 100 runs shows maximum voltage variations in the chaotic waveforms of 5.21 % and 4.61 % across the capacitors, demonstrating robustness and reliability. This design offers significant advancements in implementing high-frequency chaotic oscillators, with potential applications in various fields requiring chaotic signal generation.•A novel design of Chua's diode and Chua's chaotic oscillator using only one CCTA block is presented in this paper.•The proposed chaotic oscillator achieves the highest operating frequency of 37.5MHz.•The proposed circuit is simulated using commercially available ICs (MAX435 and AD844) and CMOS 180nm technology in PSPICE to confirm its workability.

A neuro-fuzzy based prediction modeling and mechanical characterization of multiwalled-carbon nanotubes filled Luffa cylindrica hybrid core and isotropic skin-based sandwich plate

The study focuses on mechanical characterization and adaptive neuro-fuzzy inference system (ANFIS)-based prediction modeling of MWCNT-filled Luffa cylindrica hybrid core-based sandwich plates. Experiments determine natural frequencies using modal impact hammer tests with a Fast Fourier Transform (FFT) analyzer. Elastic properties from tensile tests utilized for numerical simulations of natural frequencies in ABAQUS, showing high consistency with experimental results. SEM analysis characterizes the fiber-matrix bond in the hybrid core. ANFIS modeling establishes input–output relationships for unseen data sets. Comparisons of numerical simulations with ANFIS predictions demonstrate an accuracy within a 5% margin of error, providing insights into dynamic behavior and potential applications.

Large-Eddy Simulation of Low-Frequency Flow Oscillations for NACA0012 and Dynarig Sail at Large Attack Angles

Unmanned sailboats, harnessing wind for propulsion, offer great potential for extended marine research due to their virtually unlimited endurance. The sails typically operate at high attack angles, which contrasts with aircraft that maintain small angles to prevent stalling. Despite the reduction in lift during stalling, the resultant increase in drag contributes significantly to the sail’s thrust. However, the sail often experiences vortex shedding due to high attack angles, leading to low-frequency oscillations and erratic navigation. This study employs large-eddy simulations (LESs) on a 3D NACA0012 sail at a Reynolds number of 3.6 × 105, which is validated by experimental data. It observes the lift and drag coefficients across attack angles from 5 to 90 degrees and compares these with a Dynarig sail. The findings reveal that higher attack angles amplify fluctuations in lift and drag coefficients. Vortex shedding, resulting from flow separation, creates pressure changes and oscillations in aerodynamic forces. Fast Fourier transformation (FFT) analysis identifies dominant frequencies between 0.5 and 10 Hz, indicating low-frequency oscillations. The study’s insights into the impact of attack angle and sail type on the oscillation frequency are favorable for the design of unmanned sailboats, aiding in the prediction of wind-induced frequencies and optimal attack angle determination.

Beyond life: Exploring hemodynamic patterns in postmortem mice brains.

We utilize Laser Speckle Contrast Imaging (LSCI) for visualizing cerebral blood flow in mice during and post-cardiac arrest. Analyzing LSCI images, we noted temporal blood flow variations across the brain surface for hours postmortem. Fast Fourier Transform (FFT) analysis depicted blood flow and microcirculation decay post-death. Continuous Wavelet Transform (CWT) identified potential cerebral hemodynamic synchronization patterns. Additionally, non-negative matrix factorization (NMF) with four components segmented LSCI images, revealing structural subcomponent alterations over time. This integrated approach of LSCI, FFT, CWT, and NMF offers a comprehensive tool for studying cerebral blood flow dynamics, metaphorically capturing the 'end of the tunnel' experience. Results showed primary postmortem hemodynamic activity in the olfactory bulbs, followed by blood microflow relocations between somatosensory and visual cortical regions via the superior sagittal sinus. This method opens new avenues for exploring these phenomena, potentially linking neuroscientific insights with mysteries surrounding consciousness and perception at life's end.

Analysis of Properties and Macroscopic Defects of Metallic Bars, Pipes, and Strands through the Spectrum of Low-Frequency Excitations.

It is demonstrated that the application of piezoelectric sensors to metallic bars and strands can enable determining the status of the integrity of these elements through the spectrum of their acoustic excitations. The voltage output of the piezo, secured to metal bars or strands, is fed to the input of a Fast Fourier Transform analyzer, which allows displaying the spectrum of the excitations from which information on the length, overall quality of the metal, and the presence of defects can be obtained. We show that the analysis, performed on several materials and strands of different lengths, could be useful for cases in which visible inspection and/or direct access to the entire body of the metallic elements is not possible. Applications of our study for testing metallic structures embedded in concrete foundations are reported for construction sites.

Experimental investigation of acoustic moiré effect controlled by twisted bilayer gratings

Recently, the moiré pattern has attracted lots of attention by superimposing two planar structures of regular geometries, such as two sets of metasurfaces or gratings. Here, we show the experimental investigation of acoustic moiré effect by using twisted bilayer gratings (i.e., one grating twisted with respect to the other). We observed the guided resonance that occurred when the incident ultrasound beam was coupled with the guiding modes in a meta-grating, significantly influencing the reflection and transmission. Tunable guided resonances from the moiré effect with complete ultrasound reflection at different frequencies were further demonstrated in experiments. Combining the measurements of transmission spectra and the Fast Fourier Transform analyses, we reveal the guided resonance frequencies of moiré ultrasonic metasurface can be effectively controlled by adjusting the twisting angle of the bilayer gratings. Our results can be explained in a simplified model based on the band folding theory, providing a reliable prediction on the precise control of ultrasound reflection via the twisting angle adjustment. Our work extends the moiré metasurface from optics into acoustics, which shows more possibilities for the ultrasound beam engineering from the moiré effect and enables the exploration of functional acoustic devices for ultrasound imaging, treatment and diagnosis.

Using the Hilbert Transform to Evaluate the Effects of Functional Foods on Autonomic Nervous System Activity: A Comparison with the Fast Fourier Transform.

Evaluating the autonomic nervous system (ANS) via heart rate variability (HRV) to investigate the effects of food on human health has attracted attention. However, using a conventional HRV analysis via the fast Fourier transform (FFT), it is difficult to remove artifacts such as body movements and/or abnormal physiological responses (unexpected events) from the HRV analysis results. In this study, an analysis combining bandpass filters and the Hilbert transform was applied to HRV data on functional food intake to compare with FFT analysis. HRV data were obtained from six males by recording electrocardiograms on functional food, γ-aminobutyric acid, intake. HRV indices were calculated by both analysis. In the Hilbert analysis, all HRV indices were obtained for the same number of sampling points as the HRV data. The standard errors of all HRV indices tended to be smaller in the Hilbert analysis than in the FFT analysis. In conclusion, the Hilbert analysis was more suitable than FFT analysis for evaluating ANS via HRV on functional foods intake.

Unveiling Acoustic Cavitation Characterization in Opaque Chambers through a Low-Cost Piezoelectric Sensor Approach

This study investigates the characterization of acoustic cavitation in a water-filled, opaque chamber induced by ultrasonic waves at 20 kHz. It examines the effect of different acoustic radiator geometries on cavitation generation across varying electrical power levels. A cost-effective piezoelectric sensor, precisely positioned, quantifies cavitation under assorted power settings. Two acoustic radiator shape configurations, one with holes and another solid, were examined. The piezoelectric sensor demonstrated efficacy, corroborating with existing literature, in measuring acoustic cavitation. This was achieved through the Fast Fourier Transform (FFT) analysis of voltage data, specifically targeting sub-harmonic patterns, thereby providing a robust method for cavitation detection. Results demonstrate that perforated geometries enhance cavitation intensity at lower power levels, while solid shapes predominantly affect cavitation axially, exhibiting decreased activity at minimal power. The findings recommend using two different shape geometries on the acoustic radiator for efficient cavitation detection, highlighting intense cavitation on radial walls and cavitation generation on the bottom. Due to the stochastic nature of cavitation, averaging data is critical. The spatial limitation of the sensor necessitates prioritizing specific areas over complete coverage, with multiple sensors recommended for comprehensive cavitation pattern analysis.

Numerical and experimental investigations in a cracked wind turbine blade using natural frequencies

In this study, the vibration behavior of a wind turbine blade under influence of the transverse crack was studied. A mathematical model based on free vibration of the blade with induced transverse crack was prepared to estimate the natural frequencies. Wind turbine blade was modeled considering double tapered beam with linearly varying width and thickness keeping constant aspect ratio. A Matlab program was constructed to obtain the frequencies from mathematical model. Modal analysis of wind turbine blade was performed simulating the induced transverse crack of different depths at various locations to estimate the natural frequencies using Fast Fourier Transform (FFT) analyzer. To verify the results obtained from mathematical model and modal analysis, finite element analysis was performed. Same methodology of FEA was implemented on another blade with different aspect ratio and blade profile to verify the obtained results. It was observed that the methods adopted are in correlation with one another depicting reduction in natural frequencies as crack approaches to the free end of blade as well as for increased depth of crack signifying reduction in stiffness of the blade.

The Effect of Damages on Modal Frequencies of Flat Glass Fiber Reinforced Polyurethane Foam Sandwich Structures

The Effect of Damages on Modal Frequencies of Flat Glass Fiber Reinforced Polyurethane Foam Sandwich Structures

Analysis of Unique Motility of the Unicellular Green Alga Chlamydomonas reinhardtii at Low Temperatures down to -8 °C.

Previous studies of motility at low temperatures in Chlamydomonas reinhardtii have been conducted at temperatures of up to 15 °C. In this study, we report that C. reinhardtii exhibits unique motility at a lower temperature range (-8.7 to 1.7 °C). Cell motility was recorded using four low-cost, easy-to-operate observation systems. Fast Fourier transform (FFT) analysis at room temperature (20-27 °C) showed that the main peak frequency of oscillations ranged from 44 to 61 Hz, which is consistent with the 60 Hz beat frequency of flagella. At lower temperatures, swimming velocity decreased with decreasing temperature. The results of the FFT analysis showed that the major peak shifted to the 5-18 Hz range, suggesting that the flagellar beat frequency was decreasing. The FFT spectra had distinct major peaks in both temperature ranges, indicating that the oscillations were regular. This was not affected by the wavelength of the observation light source (white, red, green or blue LED) or the environmental spatial scale of the cells. In contrast, cells in a highly viscous (3.5 mPa·s) culture at room temperature showed numerous peaks in the 0-200 Hz frequency band, indicating that the oscillations were irregular. These findings contribute to a better understanding of motility under lower-temperature conditions in C. reinhardtii.

Emissions generated by electric vehicles in the 9-500 kHz band: Characterization, propagation, and interaction

Electric Vehicle Charging Processes (EVCPs), due to the involved power electronics with high-switching frequencies, generate high-amplitude emissions that could have a negative impact on Power Quality (PQ) and Narrowband Power Line Communications (NB-PLC) systems. In this context, this paper deals, first, with the frequency and time characterization of the disturbances generated by four EVCPs in the 9-500 kHz frequency range. The study is based on measurements carried out in a controlled Low Voltage (LV) grid. For this purpose, a novel procedure for the frequency and time characterization is proposed. The frequency characterization is based on the calculation of a set of parameters, which allows evaluating not only the amplitude of the disturbances in the frequency band under analysis, but also their spectral distribution. The time variability is characterized by means of a Fast Fourier Transform (FFT) analysis that leads to a simplified model to evaluate the time-dependent behavior of the disturbances, which shows a sub-cycle periodic pattern of the emissions in the frequency band of interest. Second, the propagation of the previously characterized emissions is analyzed, concluding that they propagate several meters through the LV grid. Although in most cases the disturbances are attenuated with distance, there might be resonances that lead to higher amplitudes at an electrical point distant from the source of the emissions. Finally, the influence of the simultaneous charging of several EVs is studied. The results show that, in general, the amplitudes correspond to the superposition of the individual disturbances, in addition to intermodulation products due to the switching frequencies of the inverters.As a conclusion, the high amplitude time-varying emissions, together with their capacity for propagation and interaction, are a matter to be analyzed due to their influence on PQ and their potential degradation of the performance of PLC in the frequency band from 9 kHz to 500 kHz.

Viscoelasticity of a carbon nanotube-laden air-water interface.

The viscoelasticity of a carbon nanotube (CNT)-laden air-water interface was characterized using two different experimental methods. The first experimental method used a Langmuir-Pockels (LP) trough coupled with a pair of oscillating barriers. The second method is termed the Bicone-Trough (BT) method, where a LP trough was custom-built and fit onto a rheometer equipped with a bicone fixture to standardize the preparation and conditioning of a particle-laden interface especially at high particle coverages. The performance of both methods was evaluated by performing Fast Fourier Transform (FFT) analysis to calculate the signal-to-noise ratios (SNR). Overall, the rheometer-based BT method offered better strain control and considerably higher SNRs compared to the Oscillatory Barriers (OB) method that oscillated barriers with relatively limited positional and speed control. For a CNT surface coverage of 165mg/m2 and a frequency of 100mHz, the interfacial shear modulus obtained from the OB method increased from 39 to 57mN/m as the normal strain amplitude increased from 1 to 3%. No linear viscoelastic regime was experimentally observed for a normal strain as small as 0.5%. In the BT method, a linear regime was observed below a shear strain of 0.1%. The interfacial shear modulus decreased significantly from 96 to 2mN/m as the shear strain amplitude increased from 0.025 to 10%.

The impact of operating temperatures on the fluctuating flow field and precessing vortex core in cyclone separator using large-eddy simulations

The present study evaluates numerically the impact of the operating temperature of gas on the cyclone performance viz. the pressure drop, collection efficiency, and flow field details at an inlet velocity, Uin=15 m/s. The gas temperature in a range of 273–1073 K is considered to significantly vary the fluid density and viscosity. For an in-depth analysis, we use advanced closure large-eddy simulation (LES) with the standard Smagorinsky model for treating the unresolved scales. LES can accurately provide additional details on the precessing vortex core phenomena that give rise to enhanced fluctuations in the core region of the cyclone. Apart from the traditional fast Fourier transformation analysis to evaluate the periodicity in the signal, we also perform continuous wavelet transformation and empirical mode decomposition operation on the temporal velocity signals for a better understanding of the flow instabilities—the signals reveal variations of frequency components with time, indicating a non-stationary behavior. It has been observed that an increase in the gas temperature causes lateral contraction of the inner vortex followed by the reduction in its precessional frequency about the cyclone axis with a significantly increased level of noise in the spectra. Furthermore, both pressure losses and collection efficiency largely reduce due to the weakening of swirling strength and enhancement in the fluctuating velocity components with an increase in the gas temperature.

Performance evaluation of machine learning algorithms and impact of activation functions in artificial neural network classifier for bearing fault diagnosis

Machine learning algorithms are used to identify the bearing condition. In this work, different machine learning techniques such as decision tree, logistic regression, support vector machine (SVM), and artificial neural network (ANN) are used and compared to find healthy and faulty conditions of the bearing. The identification of the condition of the bearing is based on vibrations recorded using Fast Fourier Transform Analyzer. The vibration data recorded for the bearings have been used to categorize the condition of the bearing as healthy or faulty by applying machine learning techniques. The dataset of healthy and faulty bearings is collected using a four-channel Fast Fourier Transform Analyzer (FFT) analyzer. However, the statistical feature extraction technique has been used to evaluate the accuracy and performance of artificial neural network, support vector machine, logistic regression, and decision tree algorithms based on their classification accuracy and total costs. The result of the work reveals that the performance of the activation function based artificial neural network (ANN-AF) and SVM algorithm is better than logistic regression and decision tree models. However, it is observed that the use of appropriate activation functions within the ANN-AF technique improves the accuracy of the machine learning model.