Fast Fourier Transform Analysis Research Articles

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.

Comparative analysis of harmonic sensitivity for stator fault diagnosis in induction motors

Induction motors play a vital role in various industrial applications due to their commendable efficiency and reliability. However, their susceptibility to faults, especially in challenging industrial environments, highlights the need for vigilant fault detection to prevent unforeseen downtimes and reduce subsequent repair costs. Early fault diagnosis is crucial in this context. A systematic approach to diagnosing faults in asynchronous motors involves the application of signal processing techniques, particularly utilizing the Fast Fourier Transform (FFT). The FFT, as a mathematical tool, enables a comprehensive analysis of signals, facilitating the identification and isolation of their frequency components. Monitoring the frequency components within a motor's signal provides a means to determine the existence and severity of faults. FFT analysis allows for the monitoring of four distinct categories of harmonics: time harmonics (TH), rotor slot harmonics (RSH), rotor bar fault harmonics (RBFH), and eccentricity fault harmonics (EFH). Each type of harmonic offers valuable insights into specific fault categories. Empirical evidence, drawn from experimental results, emphasizes the heightened sensitivity of rotor slot harmonics (RSH) in detecting stator faults. Continuous monitoring of the RSH frequency component enables the prompt detection and localization of stator faults, along with an assessment of their severity. Additionally, this diagnostic methodology proves effective in identifying micro short-circuits between stator coils, allowing for a proactive strategy in predictive maintenance. This proactive approach enables anticipatory part replacement before degradation progresses to the point of causing comprehensive failure in the production chain. The combination of FFT-based signal processing and harmonic analysis establishes a robust framework for the early detection and localization of faults in asynchronous motors within industrial settings. This contributes to enhanced operational reliability and efficiency, ultimately ensuring smoother industrial processes.

CRACK EFFECT ON THE NATURAL FREQUENCY OF COMPOSITE BEAM: FINITE ELEMENT ANALYSIS WITH EXPERIMENTAL STUDY

Cracks in structural elements can significantly affect their dynamic behaviour and lead to detrimental consequences. This study investigates the influence of cracks on the natural frequency of a laminated beam under free vibration, employing a combination of finite element method (FEM) analysis and experimental work. The numerical computation is done using ABAQUS simulation software by developing a comprehensive FEM model that accurately represents the laminated beam's mechanical behaviour, considering material anisotropy, geometry and crack propagation. The finite element model is validated through experimental tests conducted on both intact and cracked laminated beam specimens. The experimental setup utilizes Fast Fourier Transform (FFT) analyzer high-precision measurement tool to obtain accurate natural frequency values. The parametric study is conducted to analyze the impact of crack location, size, and orientation on the laminated beam's natural frequency. The FEM simulations reveal that the presence of cracks leads to a reduction in natural frequency, indicating a decrease in structural stiffness. The extent of frequency reduction is found to be influenced by crack characteristics and their proximity to critical locations within the beam. The experimental investigations corroborate the FEM findings, highlighting the consistency between numerical predictions and actual measurements.

Pratuokng: The Borneo bamboo zither of Bidayuh Sarawak

The pratuokng is an idiochord tube zither, which is a musical instrument. The Bidayuh Biatah pratuokng bamboo sound radiators of Annah Rais in Padawan, Sarawak, Malaysia are made up of seven strings and one drum. The sound reproduction capabilities are unique and innovative. This study intends to highlight the importance of sound studies in the studio setting. The frequency spectrum was evaluated using a Fast Fourier Transform (FFT) analysis via PicoScope oscilloscopes, and the time frequency analysis (TFA) using Adobe Audition that produced the spectrograms. The observations and perceptions offered by the actual meaning of this bamboo tube zither instruments showed that the notes of the pratuokng strings are E, D, F, Ab, B, A#, and B, respectively. The drum note is A. The 1st, 2nd, 3rd(A), 3rd(B), 4th(A), 4th(B), and 5th strings are 320 Hz (E4 = 329.63), 300 Hz (D4 = 293.67), 350 Hz (F4 = 349), 410 Hz (A4b = 415), 490 Hz (B4 = 493), 470 Hz (A4# = 466), and 500 Hz (B4 = 493), respectively, i.e., with note E, D, F, Ab, B, A#, and B, respectively. The drum is 210 Hz (A3 = 220). The signal in the frequency spectrum showed both distinct fundamental and higher partial frequencies. In particular, the first octave are present except for string 4B and 5. The inconsistent number of partials in each string is due to the inconsistent thickness of the raised fiber from the bamboo tube.

Combustion noise characterization and optimization of PCCI combustion using response surface methodology

The current work aims at characterizing the premixed charge compression ignition (PCCI) combustion with regression-based approach using response surface methodology. PCCI operating parameters such as load, pilot injection timing, main injection timing, pilot injection quantity, exhaust gas recirculation and injection pressure are considered as input variables. Engine performance indicators such as brake thermal efficiency, brake specific fuel consumption, carbon monoxide (CO), hydrocarbon (HC), oxide of nitrogen (NOx), smoke emissions, combustion phasing and combustion noise metric ringing intensity are considered as output responses. Experimental results validate the optimal solution from response surface methodology approach, and good agreement is found between mathematical models and experimental results. Comparative examination of optimized PCCI combustion versus conventional combustion showed a 66% and 44% decrement in NOx and smoke emissions. Except for CO and HC emissions, the percentage penalty of other responses with PCCI combustion is less than 10%. In addition to ringing intensity another combustion noise metric combustion noise level is computed from Fast Fourier Transform (FFT) analysis of cylinder pressure trace. A combustion noise level of 73.26 dB is obtained at optimized conditions.

The application of trajectory analysis method and Fast Fourier Transform analysis method in the division of flow instability influence regions under ocean conditions

For offshore nuclear power platforms, complex ocean conditions may lead to flow instability and decrease the safety of the reactor. In this study, the trajectory analysis method is used to assess the stability of the system. The Fast Fourier Transform analysis method is applied to analyze the evolution process of flow fluctuations, and an analysis process is developed for the division of affected regions by flow instability under ocean conditions. The diagram of system operating condition can be quantitatively divided into different regions. The research results indicate that as the power increases, the system trajectory diagram composed of inlet and outlet flows undergoes an evolution process from a convergence loop to a self-sustained loop, and ultimately to a divergence loop. According to the flow fluctuation characteristics of flow instability evolution under ocean conditions, the diagram of system operating condition can be divided into ocean condition control region, joint control region, and flow instability control region. Additionally, the impact of maximum heaving acceleration on the region division is investigated. When the maximum heaving acceleration is relatively small, the ocean condition control region is smaller while the joint control region is larger. This study shows that the triggering mechanism of flow fluctuations under ocean conditions and provides guidance for the design and safe operation of systems in such environments.

Improved assessment sensitivity of time-varying cavitation events based on wavelet analysis

Ultrasonic cavitation, characterized by the oscillation or abrupt collapse of cavitation nuclei in response to ultrasound stimulation, plays a significant role in various applications within both industrial and biomedical sectors. In particular, inertial cavitation (IC) has garnered considerable attention due to the resulting mechanical, chemical, and thermal effects. Passive cavitation detection (PCD) has emerged as a valuable technique for monitoring this procedure. While the fast Fourier transform (FFT) is a widely used algorithm to analyze IC-induced broadband noise detected by PCD system, it may not adequately capture the time-varying instability of cavitation due to potential nuclei collapse during ultrasound irradiation. In contrast, the continuous wavelet transform offers a more flexible approach, enabling more sensitive analysis of signals with varying frequencies over time. In this study, nanodiamond (ND) and its derivative, nitro-doped nanodiamond (N-AND), known to possess cavitation potential from previous research, were chosen as the source of cavitation nuclei. The cavitation signals detected by PCD were subjected to both FFT and wavelet analyses, with their results comprehensively compared. This research showcased the feasibility of employing wavelet analysis for effective inertial cavitation evaluation. It provided the advantage of monitoring the temporal evolution of cavitation events in real-time, enhancing sensitivity to weak and unstable cavitation signals, especially those in higher order components (3rd and 4th order). Additionally, it yielded a higher level of precision in determining IC thresholds and doses. Furthermore, the inclusion of time information through wavelet analysis offered insights into the limitations of low-cycle ultrasound in inducing IC. This study introduces a novel perspective for more sensitive and precise cavitation assessment, leveraging time and frequency data from wavelet analysis, and holds promise for effective utilization of cavitation effects while minimizing losses and damages resulting from unintended cavitation events.

Flow boiling heat transfer and pressure fluctuation characteristics in a divergent channel with inclined downward-facing heater

The core catcher cooling system is used in nuclear power plant to mitigate the core meltdown accident through flow boiling heat transfer. In order to better understand the flow boiling characteristics in the system, experimental research was conducted on an inclined channel. The flow channel is oriented 15° with a divergent downward-heating surface. The two-phase flow behavior and pressure fluctuations were capture by a high-speed camera and pressure transducer. The inlet mass flux and subcooling effects on flow boiling characteristics were deeply investigated. The inlet mass flux and subcooling conditions are within 100–400 kg/m2s and 1–30 K, respectively. The results showed that the boiling curves shift to the higher wall superheat with the increase of mass flux, and the effect of subcooling is weak. The bubble dynamic images by the high-speed camera showed that the bubbles continue to grow and coalesce during the sliding process along the wall. Large bubble blankets are formed periodically upon the boiling surface beyond a heat flux threshold, and they would undergo a rapid condensation process after departing the heating area, which leads to the liquid reversal and accompanied by strong pressure fluctuations. Further, beyond a certain subcooling and heat flux, two-phase flow instability phenomenon appears, which is characterized by complete condensation of bubble blanket accompanied by pressure shocks, meanwhile, the heat transfer coefficient decreases and the wall superheat increases sharply. The bubble blanket frequency could be obtained by the Fast Fourier Transform analysis of pressure, which is positively correlated with the heat flux and is negatively correlated with the mass flux and subcooling. Furthermore, a dimensionless correlation of reduction factor (RF) was proposed, which directly related the bubble blanket characteristics to the thermal–hydraulic parameters.

Standard resting state electroencephalographic power density computed from posterior bipolar channel characterizes patients with Alzheimer’s disease

AbstractBackgroundAbnormalities in cortical sources of resting‐state eyes closed electroencephalographic (rsEEG) rhythms recorded by hospital settings (10‐20 montage) with 19 scalp electrodes characterized Alzheimer’s disease (AD) from preclinical to dementia stages. An intriguing rsEEG application is the monitoring and evaluating of AD progression in large populations with few electrodes in low‐cost devices. Here we assessed whether the above‐mentioned abnormalities could be observed from fewer scalp electrodes in patients with mild cognitive impairment (ADMCI) or dementia (ADD) due to AD.MethodClinical and rsEEG data acquired in hospital settings (10‐20 montage) from 88 ADD and 75 ADMCI participants and 70 age‐, education‐, and sex‐matched normal elderly controls (Nold) were available in an Italian‐Turkish archive (PDWAVES Consortium; www.pdwaves.eu). Standard spectral Fast Fourier Transform (FFT) analysis of rsEEG data for individual delta, theta, and alpha frequency bands was computed from 6 monopolar scalp electrodes to derive bipolar C3‐P3, C4‐P4, P3‐O1, and P4‐O2 markers.ResultThe ADMCI and ADD groups showed increased delta/alpha and theta/alpha power density at the C3‐P3, C4‐P4, P3‐O1, and P4‐O2 bipolar channels compared to the Nold group (Figure 1). The best classification accuracy (area under the receiver operating characteristic curve) between the ADD and ADMCI vs Nold individuals was reached using alpha2/theta power density computed at the C3‐P3 bipolar channel (AUROC for ADMCI vs Nold = 81%; AUROC for ADD vs Nold = 90%; Figure 2).ConclusionStandard rsEEG power density computed from six posterior bipolar channels characterized AD status from preclinical to dementia stages. These results may pave the way toward diffuse clinical applications in the health monitoring of dementia using low‐cost EEG systems with a strict number of electrodes in lower‐ and middle‐income countries.