A hybrid time and frequency domain digital implementation of flickermeter based on mirror image extension

The basic principles of an efficient new methodology for the calculation of the nonsinusoidal periodic steady state in power systems with nonlinear and time-varying components are described. All linear parts, including the network and part of the loads, are represented in the frequency domain, while nonlinear and time-varying components, mainly loads, are represented in the time domain. This hybrid process is iterative, with periodic, nonsinusoidal, bus voltages as inputs for both frequency domain solutions and time domain simulations: a current mismatch is calculated at each bus and used to update the voltages until convergence is reached. Thus the process, but not the solution, is decoupled for the individual harmonics. Its efficiency is enhanced by the use of Newton type algorithms for fast convergence to the periodic steady state in the time domain simulations. Potential applications of this methodology are in the computation of harmonic power flow and in the steady state initialization needed in the calculation of electromagnetic transients.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher’s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher’s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

The time-resolved reflectance of photons from a homogeneous tissue was modeled using a Monte Carlo simulation. The data was then converted by fast Fourier transform (FFT) into the frequency domain. In the frequency domain, the phase, (Phi) , and modulation, M, of collected light from a frequency-modulated light source was determined. A comparison of Monte Carlo and diffusion theory was made for various separation distances between the source and collector on the tissue surface. The results showed that Monte Carlo and diffusion theory agreed in the time domain only for times larger than 500 ps after injection of an impulse of photons. In the frequency domain, Monte Carlo and diffusion theory agreed only if the probe separation, r, was at least 2 cm apart for (mu) s' equals (mu) s(1 - g) equals 5 cm-1, or in dimension less units r(mu) s' > 10. The effect of buried absorbed is also tested in the time and frequency domains. A semi-infinite volume of absorber is placed at 0, 3 mm, 6 mm, or (infinity) from the surface of a nonabsorbing tissue. The presence of a deep absorber on the time and frequency domain data show that attenuation of longer pathlength photons causes the phase of collected photons to reduce and the modulation of collected photons to increase. Both effects are indicative of the net shorter pathlength of the ensemble of collected photons.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The algorithm for an indirect time domain (TD) analysis using arbitrary frequency domain method is presented.In previous work, an optimized version of the indirect timedomain method based on adaptive sampling iterative algorithm used in combination with inverse Fast Fourier Transform (FFT) was presented. In this paper, the algorithm is further improved by circumventing the use of inverse FFT altogether, and using the simple formula based on the analytical inverse Fourier transform. The proposed approach is simpler and more convenient for use with the underlying iterative optimization method, as only a part of the frequency domain (FD) signal has to be transformed to the time domain during each iteration procedure, the frequency domain discretisation does not have to be uniform, and the time domain discretisation does not have to be performed. These advantages can also lead to the reduced computational time cost of the frequency to time domain transformation when compared with the standard inverse FFT.

Tradicionalmente, la detección de faltas en máquinas eléctricas se basa en el uso de la Transformada Rápida de Fourier ya que la mayoría de las faltas pueden ser diagnosticadas con ella con seguridad si las máquinas operan en condiciones de régimen estacionario durante un intervalo de tiempo razonable.&#13;\nSin embargo, para aplicaciones en las que las máquinas operan en condiciones de carga y velocidad fluctuantes (condiciones no estacionarias) como por ejemplo los aerogeneradores, el uso de la Transformada Rápida de Fourier debe ser reemplazado por otras técnicas.&#13;\nLa presente tesis desarrolla una nueva metodología para el diagnóstico de máquinas de inducción de rotor de jaula y rotor bobinado operando en condiciones no estacionarias, basada en el análisis de las componentes de falta de las corrientes en el plano deslizamiento frecuencia. La técnica es aplicada al diagnóstico de asimetrías estatóricas, rotóricas y también para la falta de excentricidad mixta.&#13;\nEl diagnóstico de las máquinas eléctricas en el dominio deslizamiento-frecuencia confiere un carácter universal a la metodología ya que puede diagnosticar máquinas eléctricas independientemente de sus características, del modo en el que la velocidad de la máquina varía y de su modo de funcionamiento (motor o generador).&#13;\nEl desarrollo de la metodología conlleva las siguientes etapas:&#13;\n(i) Caracterización de las evoluciones de las componentes de falta de asimetría estatórica, rotórica y excentricidad mixta para las máquinas de inducción de rotores de jaula y bobinados en función de la velocidad (deslizamiento) y la frecuencia de alimentación de la red a la que está conectada la máquina.&#13;\n(ii) Debido a la importancia del procesado de la señal, se realiza una introducción a los conceptos básicos del procesado de señal antes de centrarse en las técnicas actuales de procesado de señal para el diagnóstico de máquinas eléctricas.&#13;\n(iii) La extracción de las componentes de falta se lleva a cabo a través de tres técnicas de filtrado diferentes: filtros basados en la Transformada Discreta Wavelet, en la Transformada Wavelet Packet y con una nueva técnica de filtrado propuesta en esta tesis, el Filtrado Espectral. Las dos primeras técnicas de filtrado extraen las componentes de falta en el dominio del tiempo mientras que la nueva técnica de filtrado realiza la extracción en el dominio de la frecuencia.&#13;\n(iv) La extracción de las componentes de falta, en algunos casos, conlleva el desplazamiento de la frecuencia de las componentes de falta. El desplazamiento de la frecuencia se realiza a través de dos técnicas: el Teorema del Desplazamiento de la Frecuencia y la Transformada Hilbert.&#13;\n(v) A diferencia de otras técnicas ya desarrolladas, la metodología propuesta no se basa exclusivamente en el cálculo de la energía de la componente de falta sino que también estudia la evolución de la frecuencia instantánea de ellas, calculándola a través de dos técnicas diferentes (la Transformada Hilbert y el operador Teager-Kaiser), frente al deslizamiento. La representación de la frecuencia instantánea frente al deslizamiento elimina la posibilidad de diagnósticos falsos positivos mejorando la precisión y la calidad del diagnóstico. Además, la representación de la frecuencia instantánea frente al deslizamiento permite realizar diagnósticos cualitativos que son rápidos y requieren bajos requisitos computacionales.&#13;\n(vi) Finalmente, debido a la importancia de la automatización de los procesos industriales y para evitar la posible divergencia presente en el diagnóstico cualitativo, tres parámetros objetivos de diagnóstico son desarrollados: el parámetro de la energía, el coeficiente de similitud y los parámetros de regresión. El parámetro de la energía cuantifica la severidad de la falta según su valor y es calculado en el dominio del tiempo y en el dominio de la frecuencia (consecuencia de la extracción de las componentes de falta en el dominio de la frecuencia). El coeficiente de similitud y los parámetros de regresión son parámetros objetivos que permiten descartar diagnósticos falsos positivos aumentando la robustez de la metodología propuesta.&#13;\nLa metodología de diagnóstico propuesta se valida experimentalmente para las faltas de asimetría estatórica y rotórica y para el fallo de excentricidad mixta en máquinas de inducción de rotor de jaula y rotor bobinado alimentadas desde la red eléctrica y desde convertidores de frecuencia en condiciones no estacionarias estocásticas.

초록 In this study, the scattered far-field due to a cavity embedded in infinite media subjected to the incident SH-wave was calculated by the boundary element method. The effects of cavity shape and di stance between internal cavity and internal point in infinite media were considered. The scattered far-field of the frequency domain was transformed into the signal of the time domain by using the Inverse Fast Fourier Transform(IFFT). It was found that the amplitude of scattered signal in time domain decreased with the increase of the distance between the detecting points of ultrasonic scattered field and the center of internal cavity in media. In addition, the time delay was clearly found in time domain waveform as the distance between the detecting points of ultrasonic scattered field and the center of internal cavity was gradually increased

Applications like fuel cells, batteries or food analysis are typical fields of interest for electrical impedance spectroscopy (EIS).Another field with promising results are EIS measurements in semiconductor (sc) gas sensor applications [1].Due to their high sensitivity and low cost, sc gas sensors are ideally suited for different applications, especially in the field of safety and security.The major drawbacks of sc gas sensors, i.e. poor selectivity and sensor baseline drift, can be overcome by using temperature cycled operation (TCO) with DC measurement [2] or EIS, both in combination with intelligent signal processing.Currently, EIS for sc gas sensors is only possible in laboratory environments using commercial impedance analyzers, which are characterized by high cost, poor mobility and long acquisition time.However, for efficient development of application specific sc gas sensor systems and/or a self-monitoring strategy [3] extensive field tests are necessary.Therefore, a new hardware concept for EIS of sc gas sensors based on FPGA-technology was developed.This concept also allows a combination of EIS with TCO for the first time.Additionally, state-of-the-art feature extraction methods are applied to sc gas sensor data and new features were tested [4]. MotivationDue to their high sensitivity and low cost, semiconductor gas sensors are ideally suited for safety and security applications like fire and leakage detection [5,6].The major drawbacks of this type of sensor are the poor selectivity, the sensor baseline drift and sensor poisoning.Especially for safety and security applications these drawbacks must be overcome to allow a proper function of the system.Temperature cycled operation (TCO) and electrical impedance spectroscopy (EIS) -both in combination with intelligent signal processing -are the main approaches for improving both selectivity and stability.In TCO mode the sensor temperature is changed periodically [2] and the DC resistance of the sensor is read-out continually during this cycle.For EIS the sensor temperature is usually kept constant and the sensor impedance is measured over a wide frequency range, e.g. during a frequency sweep [1,7,8].To detect a sensor malfunction due to sensor poisoning a self-monitoring strategy can be realized by combining TCO und EIS (fig. 1) [3].This strategy is based on redundant information from both modes which are evaluated separately and comparing both results.Additional information can be used for selectivity enhancement.

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

Fast Fourier Transform (FFT)/Inverse Fast Fourier Transform (IFFT) are the key computation block in Orthogonal Frequency Division Multiplexing (OFDM) system. They dominate most of the areas and power dissipation in implementation therefore efficient low-power and high-throughput implementation of FFT processor is essential for successful deployment of OFDM based system. High-throughput and Low-power 128-point FFT Processor is designed using Radix-25 algorithm to reduce computation complexity and the feedforward pipeline architecture [called Multipath Delay Commutator (MDC)] with four data path for providing higher throughput. This architecture is more hardware-efficient than Multipath parallel feedback (MDF) design, which makes them attractive for the computation of FFT in most demanding applications. The proposed FFT processor uses improved Digit slice multiplier for complex multiplication. This proposed FFT processor is designed using Verilog and implemented using 180 nm CMOS Technology with supply voltage of 1.8v. The result shows significant reduction in power consumption in comparison with existing design and increasing throughput with area trade off.

Heart period variability has been considered for clinical assessment of autonomic function, determining the presence of haemorrhage or disease states, and for predicting mortality from traumatic injury. However, for heart period variability to be clinically useful, a number of important methodological issues should be addressed, including the minimum number of R-R intervals (RRI) required for accurate derivation, and the reproducibility of these metrics. ECGs were recorded for > or =10 min in 18 resting, supine subjects (12 M/6 F; 19-55 years). Heart period variability analyses included 21 time, frequency and complexity domain metrics. For assessment of minimum RRIs required, measurements were made from ECG recordings of 5 min down to 30 s for time and frequency domain metrics, and from 800 RRIs down to 100 RRIs for complexity metrics, by methodical truncation of the data set. Inter-subject variability was assessed by calculating the range and co-efficient of variation (%CV) across all subjects. Two independent 30 s or 100 RRI ECG segments were used to assess intra-subject variability via calculation of %CV in each subject. Six time and frequency domain metrics were robust down to 30 s of data, while five complexity metrics were robust down to 100 RRIs. All time and frequency domain metrics (except for RRI) exhibited high inter-subject variability (CVs > or = 30.0%), while five of eleven complexity metrics displayed low inter-subject variability (CVs < or = 8.5%). In the assessment of intra-subject variability in metrics valid with 30 s or 100 RRIs of ECG, only one time domain and four complexity metrics had CVs < 10%. Metrics that are highly reproducible and require few RRIs are advantageous for patient monitoring as less time is required to assess physiological status and initiate early interventions. Based on our analyses from healthy, resting humans, we have identified a select cohort of heart period variability metrics that performed well in regards to these two criteria.

Fast Fourier Transform (FFT) is computationally an efficient algorithm that transforms a function in time domain to function in frequency domain. All 3G and 4G wireless technologies use OFDM and FFT is an integral block of transceiver loop. In this paper three FFT architectures are designed that fall within the Cooley-Tukey class of algorithms. These FFT designs are targeted for OFDM applications especially in future generations of Software Defined Radio (SDR) systems. The FFT is performed on 64-point complex valued input samples with 16-bit precision. Each core is optimized for a combination of area, power, and speed. The design is targeted for a partially reconfigurable FPGA platform such as Xilinx Virtex-4 or above. The FFT cores have been simulated in Model Sim and synthesized using Xilinx ISE software. The cores can be readily downloaded on to target FPGA board to check for run time configurability. The performance evaluation of these cores show that they run at clock frequency of about 80MHz.

We describe the main properties of the EQUATOR airborne geophysical system and how the system can be employed to solve some engineering and environmental problems. To date, the EQUATOR system has been used for groundwater search in 2014 and 2015 in Siberia. The name “EQUATOR” originates from the intention to EQUATE, or to balance time and frequency domain approaches in airborne electromagnetics, which is why the system was originally designed. To the best of our knowledge, EQUATOR is the only system to collect electromagnetic data both in time and in frequency domains. We further explain the difference between the two methods on some numerical and field examples. Also we describe some advantages of their coupling. In particular, by virtue of our approach, a numerically more robust solution of the one-dimensional electromagnetic inverse problem may be achieved. The recorded full-time measurements are further converted to in-phase, quadrature and off-time responses, as opposed to standard frequency or time domain solutions, which have been widely adapted within the industry. When inverting the data, our main focus is within the top 100 m of the Earth, which are the most relevant for engineering and environmental studies. We emphasize that a combined use of frequency and time domain data gives more detailed information about geological structures in the near-surface. As an example, we present some case studies obtained over a platform (Siberia) and a crystalline shield (Western Africa) setting. We further suggest using these results in such near-surface studies as sediment thickness estimation, soil state analysis, groundwater search, and geological structural analysis.

Intra-speaker variations due to perceptually induced stress or emotion adversely affect speech recognition system performance. In this paper, we combine auditory based (Mel frequency cepstral coefficients and linear predictive cepstral coefficients) features and articulatory based (voicedness) features for robust speech recognition. Voicedness features are derived using linear and teager energy operator (TEO) based nonlinear fast Fourier transform (FFT) spectra. Nonlinear properties are analyzed in both the time and frequency domains. In addition, we investigate the sensitivity of all these FFT spectra to stress and observe the performance of individual FFT spectra. The system is tested using stressed speech data from the speech under simulated and actual stress (SUSAS) database. The results show that articulatory based features help to improve the system performance. Furthermore, significant performance improvement has been observed when using the FFT spectrum which is less sensitive to stress

The position of loose part can be located by analyzing the impact signal generated by the steel plate and the metal part which strikes the plate or falls to it from a certain height. A new method utilizing Fast Fourier transform (FFT) and Support Vector Machine (SVM) is proposed in this paper to locate positions of loose parts. FFT changes signals into frequency domain from time domain. While trained by the eigenvalues (i.e. Feature values) of impact signals from simulant device, SVM can distinguish locations of loose parts in an acceptable bias. Experiments proved that the above mentioned method is feasible.

In this paper, we investigate the propagation of an ultra‐wideband electromagnetic pulse in a homogeneous, cold plasma which is used to represent a simplified model of the atmosphere. The standard procedure for the computation of the corresponding transient field involves the application of a fast Fourier transform (FFT) to a well‐known, analytical, frequency‐domain solution. However, because of the long tails in both the time and frequency domains, a large number of sample points are required to compute the transient response using this FFT approach. In this paper, we introduce a new asymptotic extraction technique which dramatically reduces the number of sample points required by the FFT. First, we review the recently derived closed‐form expression for a double‐exponential pulse propagating in a homogeneous, collisionless, cold plasma. Since the high‐frequency behavior does not depend on the electron collision frequency, an analytical frequency‐domain expression, which is similar in form to the one encountered for the collisionless, cold plasma and encompasses this high‐frequency behavior, can be subtracted from the exact expression for the plasma with a nonzero collision frequency. The extracted term is evaluated analytically. The remaining expression, which can be transformed to the time domain with a FFT, requires only a modest number of sample points. This dramatically improves the numerical efficiency of the approach. We find that the extracted analytical term provides a very good approximation for the early‐time behavior of the transient pulse.