Performance Analysis of Surface Reconstruction Algorithms in Vertical Scanning Interferometry Based on Coherence Envelope Detection.

Central Java Province is a potential area that has an important role in national food production. El Nino and La Nina events have a very large impact if they occur in Indonesia, especially in Central Java Province. This study aims to analyze the impact of El Nino and La Nina events specifically in Central Java Province where the areas more dominantly affected by El Nino and La Nina. The research location uses 3 rainfall stations, namely the Tunggal Wulung Meteorological Station, Tegal Maritime Meteorological Station, and Tanjung Emas Maritime Meteorological Station. The method used is the Fast Fourier Transform (FFT) method and the long periodogram. The results showed that the periods of El Nino and La Nina phenomena recurring using the FFT method are forecasts for the periods 2.2428, 2.8036, and 3.7381 years. While using the lom periodogram method, namely in the periods of 2,480, 2,621, and 3:16 years. The results of the analysis of the FFT method obtained results that varied in the height of rainfall at the 3 stations, while the results of the analysis using the periodogram log showed that the Tunggal Wulung rain station had the highest peak rainfall. This study concludes that there are variations in the peak incidence using either the slow periodogram or the FFT method. However, both of these methods and based on the BMKG and TRMM, the Tunggal Wulung Meteorological Station has the highest peak between Tegal Station and Tanjung Emas Station.

The Fast Fourier Transform (FFT) method for solving the 1-D heat diffusion equation offers an efficient approach for resolving partial differential equations (PDEs) with various time steps . FFT is used to transform the 1-D heat diffusion equation into the frequency domain and back to the time domain through inverse FFT. Using mathematical modeling with initial and Dirichlet boundary conditions, the numerical solutions produced by FFT are compared with analytical solutions. The accuracy of the method is validated using MAE and MSE calculated in Matlab. At several time intervals , the obtained MAE and MSE values indicate a good agreement between the numerical and analytical solutions, with very small errors. Numerical stability analysis confirms the reliability of the FFT method across various The variation in time step has a significant impact on the accuracy and stability of the solution. Smaller time steps improve accuracy and stability but require longer computation times. The optimal time step selected in this study is Increasing the number of discretization points also enhances accuracy but implies an increase in computational load and memory usage. The FFT method demonstrates good numerical consistency with increasing

Effect of aggregate shapes on local fields in 3D mesoscale simulations of the concrete creep behavior

Maximum Entropy Spectral Analysis (MESA) Method is a powerful alternative to the traditionally used Blackman Tukey (BT) and Fast Fourier Transform (FFT) Methods in hydrologic and climatologic time series analysis. Maximum entropy spectra of annual rainfall time series measured at Mangalore and Waltair and monthly flow time series of Godavari and Krishna Rivers in India are presented and compared with the spectra obtained by using BT and FFT methods. A recently developed Bayesian decision rule is applied to determine the length of filters in MESA. Sensitivity of the maximum entropy spectra to the length of filter used is also discussed.

Interferometric instruments have the following serious weak points: (1) the necessity of doing a Fourier transform that involves a vast amount of calculation; (2) the lack of knowledge of suitable measuring conditions until the Fourier transform is finished; and (3) the spectral resolution of the conventional Fourier-based techniques is significantly affected by the sampling rate, data length, and noise in signal processing. In this paper, an enhanced spectrometer is proposed using the modified forward-backward linear prediction method (MFBLP) with a search algorithm. To document the advantage of the method presented, a computer simulation for multiple-wavenumber estimation is investigated. The MFBLP method is truly superior to the fast Fourier transform (FFT) method. In general, the spectral resolution using the FFT method is proportional to the data length. In this paper, however, it is shown that excellent results can also be obtained from only 60 sample points using the FFT method. Moreover, from experimental results, we also conclude that the sampling rate must be consistent with the condition 632<f/sub p//spl middot/t/spl middot/D<3164, where f/sub p/, represents the value of the pulse generator frequency in Hz, t the observation time, and D the decimation factor.

Chapter 6 - Fast Fourier transform methods

The fast Fourier transform (FFT) algorithm is widely used as a standard tool to carry out spectral analysis because of its computational efficiency. However, the presence of multiple tones frequently requires a fine frequency resolution to achieve sufficient accuracy, which imposes the use of a large number of FFT points that results in large area and power overheads. In this paper, an FFT method is proposed for on-chip spectral analysis of multi-tone signals with particular harmonic and intermodulation components. This accurate FFT analysis approach is based on coherent sampling, but it requires a significantly smaller number of points to make the FFT realization more suitable for on-chip built-in testing and calibration applications that require area and power efficiency. The technique was assessed by comparing the simulation results from the proposed method of single and multiple tones with the simulation results obtained from the FFT of coherently sampled tones. The results indicate that the proper selection of test tone frequencies can avoid spectral leakage even with multiple narrowly spaced tones. When low-frequency signals are captured with an analog-to-digital converter (ADC) for on-chip analysis, the overall accuracy is limited by the ADC's resolution, linearity, noise, and bandwidth limitations. Post-layout simulations of a 16-point FFT showed that third-order intermodulation (IM3) testing with two tones can be performed with 1.5-dB accuracy for IM3 levels of up to 50 dB below the fundamental tones that are quantized with a 10-bit resolution. In a 45-nm CMOS technology, the layout area of the 16-point FFT for on-chip built-in testing is 0.073 mm2, and its estimated power consumption is 6.47 mW.

To apply both autoregressive (AR) and fast Fourier transform (FFT) spectral analysis at rest, during two different dynamic exercise intensities and in recovery from maximal exercise and to compare raw and normalized powers obtained with both methods. Sixteen participants (age 22.3 +/- 4.3 year) performed resting, submaximal and maximal protocols. The submaximal protocol consisted of two 5-min walks at 4 km h(-1) at treadmill grades of 0 and 7.5%. Beat-to-beat R-R series were recorded. FFT and AR analyses were preformed on the same R-R series. Compared to AR, FFT provided higher total power (TP) and raw high-frequency (HF) power at rest and exercise. Furthermore, FFT LF/HF ratio was lower than with the AR, except under resting conditions. Both methods showed reductions in TP, raw HF and LF powers during exercise and recovery. Only the AR revealed a significant reduction for normalized HF power and increase for normalized LF power in transition from rest to exercise conditions. AR and FFT methods are not interchangeable at rest or during dynamic exercise conditions. The AR method is more sensitive to the effects of exercise on the normalized power spectra of heart rate variability (HRV) than FFT. Finally, as both approaches are equally insensitive to the increase of exercise relative intensity, there is no practical advantage of performing HRV spectral analyses by the AR or FFT at higher workloads.

This article presents a FFT method in statistic quality control. Most previous statistic quality control tools can not analysis the quality data in both time and frequency. In this paper, the FFT method is used to transforming our view of the quality signal from time-based to frequency-based, to provide a basis for current decision during production as to when to hunt for cause of variation and take action intend to correct them, and when to leave a process alone. In spite of the apparent simplicity of the using FFT, the enterprise involved in trying to manage and improve quality find that it use calls for an entirely new point of view. The article conclusion proved by a practical example.

This paper explores the possibility of using a combination of the empirical mode decomposition (EMD) and the Hilbert transform (HT), termed the Hilbert-Huang transform (HHT) method, to identify the modal damping ratios of the structure with closely spaced modal frequencies. The principle of the HHT method and the procedure of using the HHT method for modal damping ratio identification are briefly introduced first. The dynamic response of a two-degrees-of-freedom (2DOF) system under an impact load is then computed for a wide range of dynamic properties from well-separated modal frequencies to very closely spaced modal frequencies. The natural frequencies and modal damping ratios identified by the HHT method are compared with the theoretical values and those identified using the fast Fourier transform (FFT) method. The results show that the HHT method is superior to the FFT method in the identification of modal damping ratios of the structure with closely spaced modes of vibration. Finally, a 36-storey shear building with a 4-storey light appendage, having closely spaced modal frequencies and subjected to an ambient ground motion, is analyzed. The modal damping ratios identified by the HHT method in conjunction with the random decrement technique (RDT) are much better than those obtained by the FFT method. The HHT method performing in the frequency-time domain seems to be a promising tool for system identification of civil engineering structures.

In passive narrow-band or broadband target detection sonars, line arrays are used to enhance performance. In large towed arrays, wave-number-frequency (k-ω) data analysis methods have been used on an experimental basis to detect and identify the presence of noise generators. The noise sources are composed of acoustically radiating sources located in the ocean, vibrational energy within the array, and pressure disturbances along the array. In this paper, a hybrid autoregressive (AR) technique is presented that will generate k-ω solutions having better spatial resolution than is currently possible with methods that make use of a two-dimensional fast Fourier transform (FFT). The hybrid AR method is implemented by applying a one-dimensional FFT on the sampled time series data and using this result to obtain an averaged array cross-spectral density matrix. The AR method is now applied to the spatial cross spectral density matrix to obtain a wave-number-frequency spectrum. For the hybrid AR method, equations are presented for calculating the spatial wave-number resolution as a function of signal-to-noise ratio (SNR), array size, and time-bandwidth product (T-BW). The equations for resolution and array gain are useful for determining when to apply the hybrid AR method. In addition to the hybrid AR development, a modification of the FFT–FFT method is introduced that provides efficient and rapid computation of a standard wave-number-frequency spectrum. Comparisons for various SNR, AR order, and T-BW conditions using computer simulated, and actual at-sea data are made between the k-ω solutions obtained using this modified FFT method and our proposed hybrid AR technique.

In this paper, we analyzed a spectrum of the SMPS (Switching Mode Power Supply) which uses DPWM (Digital Pulse Width Modulation) and adopts SSC (Spread Spectrum Clocking) circuit to reduce EMI (Electro-Magnetic Interference) noise. Generally, SSC circuits are used in order to reduce noise peaks by spreading switching frequency spectrum. Such noise peak reduction is verified with a general FFT (Fast Fourier Transform) method through sufficiently long time which includes many switching cycles. However, effects of drastic changes of power supply voltage such as voltage dropping, onetime noise or bounce in power distribution network are apparently not revealed in the spectrum because their effects are averaged down in the general FFT method. These ripples in power distribution become sources of EMI failures. For accurate spectrum analysis in the transition period, FFT should be performed separately with respect to each transient section. Therefore, we carried out short-time Fourier transform (STFT) with respect to several local window sections as well as general Fourier transform with respect to the overall interval. The separated local windows are the overshoot section and the undershoot section.

Series of thermodiffusion experiments using optical digital interferometry (ODI) have been conducted onboard the International Space Station. Conventionally, the two-dimensional (2D) fast Fourier transform (FFT) fringe analysis technique has been applied as a fast and reliable technique to extract data. In this study, for the first time, the windowed Fourier transform (WFT) method is used to analyse the same experiments. In this method, a Fourier transformation is applied on the fringes at two different stages: initially, during the filtration of the non-zero peaks and then on the wrapped phase image. We provide a detailed comparison between FFT and WFT results of binary and ternary mixtures for ODI thermodiffusion experiments. The substantial enhancements of this method are presented and discussed for different experiments conducted for both binary and ternary mixtures. We show that while disturbances in the phase fringe pattern can cause significant error in FFT techniques, if the windowed Fourier filtration (WFF) parameters are properly chosen this type of noise can be eliminated during WFF analysis. The importance of replacing the FFT method becomes more pronounced for the ternary system, as this method fails to reconcile reliable concentration profiles. The results of this chapter can show that the application of the windowed Fourier transform in optical digital interferometry investigations show improved results over the same experiments analysed using FFT methods, especially for experiments involving very small heat and mass fluxes such as the Soret effect in multicomponent mixtures.

Abstract. Several magnetic measurements and theoretical developments from different research groups have shown certain relationships with worldwide geological processes. Secular variation in geomagnetic cutoff rigidity, magnetic frequencies, or magnetic anomalies have been linked with spatial properties of active convergent tectonic margins or earthquake occurrences during recent years. These include the rise in similar fundamental frequencies in the range of microhertz before the Maule 2010, Tōhoku 2011, and Sumatra–Andaman 2004 earthquakes and the dramatic rise in the cumulative number of magnetic anomalous peaks before several earthquakes such as Nepal 2015 and Mexico (Puebla) 2017. Currently, all of these measurements have been physically explained by the microcrack generation due to uniaxial stress change in rock experiments. The basic physics of these experiments have been used to describe the lithospheric behavior in the context of the seismo-electromagnetic theory. Due to the dramatic increase in experimental evidence, physical mechanisms, and the theoretical framework, this paper analyzes vertical magnetic behavior close to the three latest main earthquakes in Chile: Maule 2010 (Mw 8.8), Iquique 2014 (Mw 8.2), and Illapel 2015 (Mw 8.3). The fast Fourier transform (FFT), wavelet transform, and daily cumulative number of anomalies methods were used during quiet space weather time during 1 year before and after each earthquake in order to filter space influence. The FFT method confirms the rise in the power spectral density in the millihertz range 1 month before each earthquake, which decreases to lower values some months after earthquake occurrence. The cumulative anomaly method exhibited an increase prior to each Chilean earthquake (50–90 d prior to earthquakes) similar to those found for Nepal 2015 and Mexico 2017. The wavelet analyses also show similar properties to FFT analysis. However, the lack of physics-based constraints in the wavelet analysis does not allow conclusions that are as strong as those made by FFT and cumulative methods. By using these results and previous research, it could be stated that these magnetic features could give seismic information about impending events. Additionally, these results could be related to the lithosphere–atmosphere–ionosphere coupling (LAIC effect) and the growth of microcracks and electrification in rocks described by the seismo-electromagnetic theory.

Doppler picture velocimetry (DPV) is a tool for visualizing and measuring the flow velocity distribution of tracer particles in a laser light sheet. A frequency sensitive Michelson interferometer, tuned for detecting the velocity distribution by the Doppler effect, visualizes the velocity information of tracer particles crossing an illuminating laser light sheet as interference fringe patterns. Many efforts have been done to evaluate best these DPV patterns, in order to obtain the frequency distribution and, by applying the Doppler formula, the velocity profile of the tracers. The first processing method, developed in 1982, relied on manual processing of the pictures by the user, due to the unavailability of suitable high performance picture processing algorithms. This drawback made DPV being considered as a rather time-consuming measurement technique with limited accuracy, compared to existing commercial velocity measurement systems (e.g. PIV). This is no more the state of the art: The new DPV analysis software, presented in this paper, allows automated processing of the interference fringe samples obtained by two images, a reference picture without frequency shift and a Doppler picture containing the frequency shift, using single beam velocimetry. Based on Fast Fourier transformation (FFT), the presented algorithm determines the corresponding velocity profile (in pseudo colours) within only a few seconds on a standard personal computer without user intervention.