Fast Fourier Transform Spectrum Research Articles

Scanning ex situ solid-state magnetic resonance imaging on polymeric films using a static magnetic field gradient by an electromagnet.

A new technique for ex situ solid-state nuclear magnetic resonance imaging of polymeric films has been developed. This method uses the static magnetic field gradient generated by a water-cooled copper electromagnet. The imaging process involves scanning the sample in the plane of the film under a static magnetic field gradient. Two-dimensional (2D) 19F MRI measurements are attempted, where the fast Fourier transform (FFT) spectra of the second half of a Hahn echo peak is used for the depth (X-axis) direction, and the sample film is mechanically moved for the Y-axis, which is in-plane with respect to the film surface and normal to the inter-magnetic-pole line (Z-axis). The sample is a poly(tetrafluoroethylene) [PTFE] film with carved stripes on its surface. Furthermore, three-dimensional (3D) measurements of the film surface and thickness orientations were also performed for an analogous PTFE film. For 2D profiling on the film surface, 2D sample-moving was used. For depth profiling, FFT spectrum analysis was undertaken for each voxel situated at a specific (Y, Z) coordinate within the film. As a result, a 3D image of the sample and its corresponding geometry were obtained, although the scanning area was restricted to a part of the sample. Finally, numerical simulations of the spatial distribution of the static magnetic field were performed to confirm the validity of the present method.

In vivo brain MRS at a 1.5T clinical scanner: Optimized derivative fast Fourier transform for high-resolution spectra from time signals encoded with and without water suppression

AbstractWe study single-voxel in vivo proton magnetic resonance spectroscopy (MRS) of white matter in the brain of a 25 year old healthy male volunteer. The free induction decay (FID) data of short length (0.5KB) are encoded at a long echo time (272 ms) with and without water suppression at a clinical scanner of a weak magnetic field (1.5T). For these FIDs, the fast Fourier transform (FFT) gives sparse, rough and metabolically uninformative spectra. In such spectra, resolution and signal to noise ratio (SNR) are poor. Exponential or Gaussian filters applied to the FIDs can improve SNR in the FFT spectra, but only at the expense of the worsened resolution. This impacts adversely on in vivo MRS for which both resolution and SNR of spectra need to be very good or excellent, without necessarily resorting to stronger magnetic fields. Such a long sought goal is at last within reach by means of the optimized derivative fast Fourier transform (dFFT), which dramatically outperforms the FFT in every facet of signal estimations. The optimized dFFT simultaneously improves resolution and SNR in derivative spectra. They are presently shown to be of comparably high quality irrespective of whether water is suppressed or not in the course of FID encodings. The ensuing benefits of utmost relevance in the clinic include a substantial shortening of the patient examination time. The implied significantly better cost-effectiveness should make in vivo MRS at low-field clinical scanners (1.5T) more affordable to ever larger circles of hospitals worldwide.

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.

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

Speech Emotion Recognition Based on Voice Fundamental Frequency

The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.

Prediction of gastroesophageal reflux episodes by smooth muscle electromyography: A translational study in rats and adolescents

AimsOur aim was to measure the myoelectric modifications during gastric acid secretion along with the gastric pH in a rat model and to detect the gastrointestinal (GI) myoelectric changes in adolescents suffering from gastroesophageal reflux disease (GERD) along with the esophageal pH measurement. Main methodsIn anesthetized rats, gastric acid secretion was initiated with intragastric histamine (50 mg/kg), and gastric pH, GI myoelectric activity and mechanical GI contractions were measured with intragastric pH electrode, subcutaneously implanted smooth muscle electromyography (SMEMG) electrodes and organ implanted strain gauges, respectively. In the clinical study, esophageal pH and GI myoelectric activity were measured in adolescents suffering from GERD with intraesophageal pH electrode and SMEMG electrodes placed on the abdominal surface, respectively. The SMEMG records were analyzed by fast Fourier transformation (FFT) and power spectrum density maximum (PsDmax) values were calculated for the GI segments. Key findingsIn rats, histamine initiated an immediate increase in gastric PsDmax, which preceded the significant reduction in gastric pH by 75 min. The myoelectric change was independent of mechanical GI contractions. In adolescents, the GERD episodes were preceded by a significant increase in gastric PsDmax 45 min earlier. These changes were independent of motion or meals. SignificanceIncreased gastric myoelectric activity during histamine stimulation or GERD might be linked to the enhanced activity of the gastric proton pump, indicating a link between gastric acid secretion and GERD episodes. It is supposed that SMEMG might be a tool for predicting forthcoming reflux episodes in GERD.

Research on spectral method of lost circulation layer located by transient pressure wave

AbstractLeakage in the wellbore annulus during drilling operations can affect normal production operations, resulting in a severe waste of resources and economic loss, so it is crucial to adopt a fast and effective leak identification method for subsequent plugging operations. For the complex problem of judging the location of the leakage layer, we proposed the method of excitation pressure wave to identify the location of the leakage layer. By analyzing the transfer of pressure waves within the annular pipe system and the pressure head response spectrum, the leak's location is identified based on the location of the resonance point and the change in resonance amplitude. The pressure wave signal contains too much noise. The variational mode decomposition (VMD) algorithm and the Hilbert joint spectrum were used to extract the main frequency components to reconstruct the signal to achieve the denoising effect. On this basis, the reconstructed signal is processed by fast Fourier transform (FFT) to obtain the pressure wave response spectrum, analyze the frequency domain features, and then determine the location of the leakage layer. The experimental results verify that: ① When a leak occurs in the wellbore annulus, the pressure wave will generate additional resonance points in the frequency domain due to the presence of the leak point. ② The combination of the VMD algorithm, FFT, and Hilbert joint spectrum can effectively remove the noise of the pressure wave signal. ③ The method effectively avoids the difficulty of identifying negative pressure waves in the time domain analysis of pressure wave signals. It can effectively locate the leaky layer in the frequency domain analysis. It is concluded that the principle of the method is feasible and has practical significance for field application.

Studying the Effect of Initial Conditions and System Parameters on the Behavior of a Chaotic Duffing System

This work presents a five-period chaotic system called the Duffing system, in which the effect of changing the initial conditions and system parameters d, g and w, on the behavior of the chaotic system, is studied. This work provides a complete analysis of system properties such as time series, attractors, and Fast Fourier Transformation Spectrum (FFT). The system shows periodic behavior when the initial conditions xi and yi equal 0.8 and 0, respectively, then the system becomes quasi-chaotic when the initial conditions xi and yi equal 0 and 0, and when the system parameters d, g and w equal 0.02, 8 and 0.09. Finally, the system exhibits hyperchaotic behavior at the first two conditions, 0 and 0, and the bandwidth of the chaotic signal becomes wider (2 a.u.) than in the first case. So, this system can be used in many physical applications, such as encrypting confidential information.

Influence of Operationally Consumed Propellers on Multirotor UAVs Airworthiness: Finite Element and Experimental Approach

Unmanned aerial vehicles (UAVs) are widely used in many applications due to the current technological trend. In these applications, understanding the concept of the operating frequency range is vital to ensure UAV safety. Although most current studies are focused on health monitoring and fault diagnostic methodologies, none of them has included a comprehensive knowledge of the behavior of the natural frequency behavior. This research examines the effectiveness of computer-aided design (CAD) and computer-aided engineering (CAE) in advancing finite element modal analysis. Theoretical results were further evaluated on an experimentally operated drone to understand the interference concept of natural and operational frequencies. Extensive, high-quality, and comprehensive soft-labeled data was acquired in the time-domain from the selected hovering quad-copter equipped with an accelerometer sensor connected to a data acquisition system and a PC platform. Healthy propellers and operationally consumed propellers vibration signal datasets were recorded. The collected datasets were transformed to frequency-domain using Fast Fourier Transform (FFT) spectrum analysis. In summary, the finite element modal analysis provided a thorough behavior understanding of the faulty operating drone status by comparing the natural frequencies to those acquired experimentally based on a detailed discussion. The relationship between natural and operational frequencies is then investigated and presented.

Phononic origin of strain-controlled friction force

Employing fast Fourier transform (FFT) of instantaneous friction and interfacial phonon spectrum, phononic origin of friction between graphene layers, in which one layer suffering different biaxial strain, is disclosed. FFT spectrum indicates washboard frequencies make major contribution to friction. Peak value in FFT spectrum decreases with increase of strain, leading to drop of friction. Density of states for interface atoms proves energy exchange between tip and substrate is conditional on coupling of interfacial vibration frequencies. There is unified phonon spectrum on relative sliding surfaces at zero strain, which can establish effective energy dissipation channels. Once a strain is applied, unified phonon modes are no longer formed, which destroys conditions for establishment of energy dissipation channels and thus hinders energy dissipation.

Anomalous magnetotransport behavior in single-crystal VSb2

We report the anomalous magnetotransport properties in high-quality single-crystal ${\mathrm{VSb}}_{2}$ without V atom vacancies. ${\mathrm{VSb}}_{2}$ shows typical metallic behavior with convex-upward characteristics, which can be explained using a parallel-resistor model. The three-dimensional weak antilocalization effect leads to a large saturation magnetoresistance (MR) up to $595%$, which disappears when the magnetic field is along the $ab$ plane. Instead, a clear Shubnikov--de Haas oscillation at low temperatures is observed, based on which the fast Fourier transformation spectrum analyses reveal three fundamental frequencies. The two-carrier fitting of Hall resistivity suggests that the saturation MR originates from the giant magnitude difference of the two carrier concentrations. Moreover, the angle-resolved magnetoresistance is measured by rotating the sample around its $c$ axis and $ab$ plane. A transition from twofold symmetry to isotropy is observed.

Fast and Enhanced MMW Imaging System Using a Simple Row Detector Circuit with GDDs as Sensor Elements and an FFT-Based Signal Acquisition System.

The relatively high atmospheric propagation of millimeter-waves (MMW) was found to be one of the most critical reasons for the development of reliable sensors for MMW detection. According to previous research works, it has been already shown that incident MMW radiation on a glow discharge detector (GDD) can increase the discharge current. Hence, the electrical mode of detection can be employed to detect the presence of MMW radiation. In this article, a new design of a row detector using GDDs as pixel elements, and the influence of MMW incidence on GDD's discharge current, were acquired using an elementary data acquisition (DAQ) platform. The DAQ system computes the averaged Fast Fourier Transform (FFT) spectrum of the time signal and returns the FFT results as magnitude based on the level of detection. An FFT-based signal acquisition proved to be a better alternative to the lock-in detection that was commonly used in MMW detection systems. This improved detection circuit provides enhanced noise filtering, thereby resulting in better MMW images within a short time. The overhead expense of the entire system is very low, as it can avoid lock-in amplifier stages that were previously used for signal enhancement. A scanning mechanism using a motorized translation stage (step motor) is involved to place and align the row detector in the image plane. The scanning can be carried out vertically to perform the imaging, by configuring the step motor after selecting the desired step size and position. A simplified version of the MMW detection circuit with a dedicated over-voltage protection facility is presented here. This made the detection system more stable and reliable during its operation. The MMW detection circuit demonstrated in this work was found to be a milestone to develop larger focal plane arrays (FPA) with very inexpensive sensor elements.

Steady-State Free Precession sequences for high and low field NMR spectroscopy in solution: Challenges and opportunities

The receptivity of NMR spectroscopy is low when compared to other techniques. Historically, increasing the strength of the static magnetic field has been the major approach to increase NMR sensitivity. In recent years several polarization transfer protocols have been used to enhance the signal-to-noise ratio (SNR), although they require special accessories and/or sample preparation. In this paper, we consider both the challenges and opportunities of steady-state free precession (SSFP) pulse sequences as a simple and efficient alternative to enhance SNR, in standard high-resolution and benchtop low-resolution NMR spectrometers. The maximum gain in these sequences is obtained with the shortest time between the pulses (Tp). However, when Tp<T2, the SSFP signal contains FID and echo components which lead to phase, intensity, and truncation artifacts on spectra obtained by Fast Fourier transform (FT). Several phase alternation SSFP sequences were used to cancel the echo component and minimize these problems in the FT spectra. Krylov base diagonalization method (KBDM) was used to eliminate the phase and truncation problems in spectra acquired by SSFP pulse sequences and can be a viable alternative to FT. The experiments were performed in high and low resolution (bench top) NMR spectrometers and significant enhancements in SNR of low receptivity nuclei such as 13C and 15N could be achieved. The SSFP sequences were also shown to enhance SNR in nuclei with high receptivity such as 19F and 31P, in very dilute samples, as is common in environmental and biological samples.

An adaptive neuro fuzzy inference system model for studying free in plane and out of plane vibration behavior of curved beams

The current study examines the free vibration behavior of curved beams in detail. Basic, lightweight curved beams are used in many different engineering projects. Therefore, it is very important to study their dynamic behavior extensively. Hence, in this study, numerical analysis on free in-plane and out-of-plane vibration of curved beams is carried out using finite element modeling software ANSYS, and experimental analysis was conducted using Fast Fourier Transform (FFT) spectrum analyzer for validation of numerical results. Adaptive neuro-fuzzy inference system (ANFIS) models are developed using 384 numbers of datasets that are procured from numerical investigations. The input parameters considered are cross-sectional area, radius of curvature, curvature angle, support conditions, and mode of vibration. An empirical equation is derived for estimating natural frequencies. Different statistical indices, such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), is used to evaluate the performance of the input parameters. Sensitivity analysis is used to spot the input parameters that have the greatest influence on the natural frequency of curved beams and rank them in importance.

Prediction of the Equivalent Steering Angle of a Front-Wheel, High-Clearance Paddy Field Management Machine

To solve the problem of poor steering consistency for each steering wheel of a four-wheel, independent-steering, high-clearance paddy field management machine, given that the true steering angle of the front wheel cannot be directly obtained through the left and right front wheels steering angle value, a BP (Back Propagation) neural network equivalent steering angle prediction method based on signal feature extraction is proposed in this paper, which can be used to obtain the equivalent steering angle of the front wheel. First, the kinematics model of the paddy field management machine was constructed with the prediction of the steering angle of the management machine as the object. The body was set up in two application environments of cement ground and paddy field, two moving speeds (25 cm/s, 50 cm/s) and three preset steering angles (0°, ±10°) to form six motion modes. The steering angle of the front wheel of the body, the three-axis acceleration of the body, and the angular velocity of the z-axis under twelve conditions were collected. Combining the collected data with the actual trajectory data of the differential BeiDou, the feasibility analysis of the equivalent steering angle prediction was carried out. With the aim of determining an appropriate combination of input factors for optimal equivalent steering angle prediction by the BP neural network, we used FFT (Fast Fourier Transform) and power spectrum conversion to extract and analyze the signal features of the airframe attitude data and obtain the frequency characteristics of the peak power point of the power spectrum, respectively. Through a factor combination test, the optimal BP neural network training factors were determined and, finally, we confirmed that the rotation angles of the front wheels and the z-axis angular velocity may be used as effective training factors. In our investigation on the test set, the mean square error of the equivalent steering angle was found to be less than 0.66°, demonstrating that our approach is effective for obtaining the true steering angle of the front wheel.

Study of RSSI Accuracy for Outdoor Sensor Localization

The movement characteristics that nodes exhibit will bring serious problems in a Wireless Sensor Network (WSN) because most of the protocols do not adapt to node movement. This requires a mobility-aware evaluation technique to locate nodes, forecast communication quality, trace the movement trajectory and formulate thresholds to initiate future handover. One off-the-shelf technology using the Received Signal Strength Indicator (RSSI) of wireless radio is extensively adopted because of the small expense. A node can compute the relative distance between it and its partner by using RSSI readings. However, the measurement results of RSSI fluctuate significantly due to the effects of shadowing and fading. This paper is oriented to investigate the localization accuracy of RSSI in an outdoor ambient condition. To do this, some dynamic and stationary tests are carried out, a calibration line displaying the one-to-one match between RSSI and distance is mapped, five filtering approaches are developed to alleviate the fluctuation of movement curves, and the alleviation effect is measured by calculating the Root Mean Square Error (RMSE) values and by converting the time-domain filtering results into the Fast Fourier Transform (FFT) spectrum. Although the optimized RMSE is reduced to 0.86 and the noise FFT amplitude is less than 1[Formula: see text]dBm, it may still have a single RSSI value corresponding to more than one distance. Moreover, these distances can differ as much as 2.8[Formula: see text]m. Since the evaluation error is not acceptable for most cases, it is inaccurate for a mobile node to measure the distance from its partner rely on RSSI readings under outdoor scenarios.

Fast and efficient approach for multi-component quantum wells analysis based on FFT

Elaboration of the technology of the multicomponent Quantum Wells (QWs) based devices requires repeated time consuming diffraction curves analysis proceeded for each of the grown structures to determine their basis structural parameters. Optimization of the diagnostic procedures and making them more efficient is the key point from the manufacturer point of view. In this paper we present the use of Fast Fourier Transform (FFT) employed to fast determination of the thickness of epitaxial layers. The presented method combines the approach using the Rocking Curves (RC) measured by X-ray Diffraction (XRD) together with the analysis of the FFT spectrum. In this work GaInNAs/GaAs MQWs structures grown by atmospheric pressure-metalorganic vapour phase epitaxy (AP-MOVPE) were investigated. In the case of the series of the defined type samples, this method allows replacement of the time consuming simulation method used for diagnostic of the thickness of deposited layers in favor of FFT spectrum analysis.

Fabry Perot Cavity Length Demodulation Method Based on Continuous Thinning Algorithm of Fast Fourier Transform Spectrum

Optical fiber Fabry Perot sensors are widely used in industrial production and life. It is of great significance to improve the performance of sensor demodulation algorithm. Based on the analysis of the principle and error source of the cavity length demodulated by the Fast Fourier Transform (FFT) algorithm, the FFT-FT algorithm with continuous refinement of FFT spectrum is introduced. By using the feature that FFT-FT algorithm can refine at the peak of discrete spectrum, the length of F-P cavity demodulated by FFT algorithm is optimized to improve the accuracy of the demodulation. Through simulation experiments, the F-P cavity length of FFT demodulation algorithm and FFT-FT demodulation algorithm under the same light source signal are compared and analyzed, and the goodness of fit of the fitting curve of continuous demodulation is analyzed. The simulation results show that the error of FFT demodulation algorithm is more than 100 times that of FFT-FT algorithm when it is refined 100 times near the peak value of FFT discrete spectrum. The goodness of fit of the demodulation fitting curve calculated by FFT-FT algorithm is more than 2 times that of FFT algorithm, which proves that the improved algorithm demodulation F-P cavity length value has better consistency with the real value.

Evaluation of nature and intensity of fire concrete spalling by frequency analysis of sound records

The paper presents a new method for identifying the form and intensity of spalling using an analysis of the acoustic signal emitted by concrete. To assess the intensity and monitor the course of concrete spalling during fire exposure, the analysis of the recorded sound from the furnace chamber is used. The spalling intensity was determined based on signal amplitude analysis. Investigation of sound records showed that the character of spalling can be categorized by a well-defined frequency value. Based on the analysis of the Fast Fourier Transform (FFT) spectrum, the authors have distinguished the following phenomena that are the source of the signal by assigning the corresponding frequency of the sound wave vibration: explosive spalling, aggregate spalling and destructive spalling.

Depth Determ\u0131nat\u0131on From Grav\u0131ty Data Us\u0131ng Rad\u0131al Ampl\u0131tude Spectrum: The Dikili (\u0130zmir) Geothermal Area

The Dikili geothermal area, in the northern part of Izmir province, is one of the best known geothermal areas in Western Anatolia. This study attempts to analyze and interpret Bouguer gravity data to determine average structural depth values and assess geothermal resources using the Radial Amplitude Spectrum Method (RASM), based on 2D Fast Fourier Transform (FFT) analysis. We selected four different areas to apply the method. The greatest advantage of this method over the conventional power spectrum method is that it can determine the mean depth from 2D FFT spectra, not using a single cross-section taken in one direction. Thus, the user can select an area rather than a direction and average depth can be determined more accurately. The results show that average depth values of the top of the reservoir vary between 314 and 640 m in the region.