scholarly journals Construction of 40 Nanosecond Time Resolusion Step-Scan Ftir Difference Spectrometer

1999 ◽  
Vol 19 (1-4) ◽  
pp. 141-143
Author(s):  
Xuehua Hu ◽  
Thomas G. Spiro
Keyword(s):  
Fourier Transform ◽  
Time Resolution ◽  
Time Domain ◽  
Nd:Yag Laser ◽  
Transient Signals ◽  
Time Resolved ◽  
Pulsed Nd:Yag Laser ◽  
Scan Time ◽  
Ftir Spectrometer ◽  
The Time Domain

A step-scan time-resolved Fourier transform infrared (FTIR) spectrometer was constructed. Using 1064 nm radiation from a 9ns pulsed Nd:YAG laser as the IR source, we have obtained both the time-resolved spectra in the frequency domain and the change of the transient signals in the time domain. The latter allows us to measure the time resolution of the instrument to be about 40 nanoseconds.

scholarly journals Fourier-transform spatial modulation spectroscopy of single gold nanorods

Nanophotonics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 715-726 ◽  
Author(s):  
Heiko Kollmann ◽  
Martin Esmann ◽  
Julia Witt ◽  
Aleksandra Markovic ◽  
Vladimir Smirnov ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Gold Nanorods ◽  
Time Resolution ◽  
Time Domain ◽  
Signal To Noise Ratio ◽  
Spatial Modulation ◽  
High Signal ◽  
Extinction Cross Section ◽  
Modulation Spectroscopy ◽  
The Time Domain

AbstractSensing the scattered fields of single metallic nanostructures is a crucial step towards the applications of isolated plasmonic antennas, such as for the sensing of single molecules or nanoparticles. In the past, both near- and far-field spectroscopy methods have been applied to monitor single plasmonic resonances. So far, however, these spectral-domain techniques do not yet provide the femtosecond time resolution that is needed to probe the dynamics of plasmonic fields in the time domain. Here, we introduce a time-domain technique that combines broadband Fourier-transform spectroscopy and spatial modulation spectroscopy (FT-SMS) to quantitatively measure the extinction spectra of the isolated gold nanorods with a nominal footprint of 41×10 nm2. Using a phase-stable pulse pair for excitation, the technique is capable of rejecting off-resonant stray fields and providing absolute measurements of the extinction cross section. Our results indicate that the method is well suited for measuring the optical response of strongly coupled hybrid systems with high signal-to-noise ratio. It may form the basis for new approaches towards time-domain spectroscopy of single nanoantennas with few-cycle time resolution.

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

IDENTIFICATION OF INTERHARMONICS BASED ON WAVELET PACKET TRANSFORM

2021 ◽  
Vol 3 (1) ◽  
pp. 031-036
Author(s):  
S. A. GOROVOY ◽  
◽  
V. I. SKOROKHODOV ◽  
D. I. PLOTNIKOV ◽  
◽  
...  
Keyword(s):  
Fourier Transform ◽  
Simulation Model ◽  
Frequency Domain ◽  
Time Domain ◽  
Wavelet Packet ◽  
High Accuracy ◽  
Wavelet Packet Transform ◽  
Mathematical Apparatus ◽  
Nonlinear Load ◽  
The Time Domain

This paper deals with the analysis of interharmonics, which are due to the presence of a nonlinear load. The tool for the analysis was a mathematical apparatus - wavelet packet transform. Which has a number of advantages over the traditional Fourier transform. A simulation model was developed in Simulink to simulate a non-stationary non-sinusoidal mode. The use of the wavelet packet transform will allow to determine the mode parameters with high accuracy from the obtained wavelet coefficients. It also makes it possible to obtain information, both in the frequency domain of the signal and in the time domain.

A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements

2021 ◽  
Author(s):  
Yongzhi Qu ◽  
Gregory W. Vogl ◽  
Zechao Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Time Domain ◽  
Frequency Response Function ◽  
Frequency Component ◽  
Operating Conditions ◽  
Input Output ◽  
The Fourier Transform ◽  
The Time Domain

Abstract The frequency response function (FRF), defined as the ratio between the Fourier transform of the time-domain output and the Fourier transform of the time-domain input, is a common tool to analyze the relationships between inputs and outputs of a mechanical system. Learning the FRF for mechanical systems can facilitate system identification, condition-based health monitoring, and improve performance metrics, by providing an input-output model that describes the system dynamics. Existing FRF identification assumes there is a one-to-one mapping between each input frequency component and output frequency component. However, during dynamic operations, the FRF can present complex dependencies with frequency cross-correlations due to modulation effects, nonlinearities, and mechanical noise. Furthermore, existing FRFs assume linearity between input-output spectrums with varying mechanical loads, while in practice FRFs can depend on the operating conditions and show high nonlinearities. Outputs of existing neural networks are typically low-dimensional labels rather than real-time high-dimensional measurements. This paper proposes a vector regression method based on deep neural networks for the learning of runtime FRFs from measurement data under different operating conditions. More specifically, a neural network based on an encoder-decoder with a symmetric compression structure is proposed. The deep encoder-decoder network features simultaneous learning of the regression relationship between input and output embeddings, as well as a discriminative model for output spectrum classification under different operating conditions. The learning model is validated using experimental data from a high-pressure hydraulic test rig. The results show that the proposed model can learn the FRF between sensor measurements under different operating conditions with high accuracy and denoising capability. The learned FRF model provides an estimation for sensor measurements when a physical sensor is not feasible and can be used for operating condition recognition.

scholarly journals Radiofrequency Microwave Double Resonance Experiments in Fourier Transform Technique

1988 ◽  
Vol 43 (3) ◽  
pp. 280-282 ◽  
Author(s):  
N. Heineking ◽  
W. Stahl ◽  
H. Dreizler
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Double Resonance ◽  
Fourier Transform Spectroscopy ◽  
Microwave Spectroscopy ◽  
Valuable Method ◽  
The Time Domain

Abstract Radiofrequency microwave double resonance has proved as a valuable method in microwave spectroscopy in the frequency domain. We present comparable experiments in the time domain Fourier transform spectroscopy.

AN INNOVATIVE TECHNIQUE FOR QUALITY CONTROL OF GEAR PUMPS

2002 ◽  
Vol 09 (04) ◽  
pp. 383-392
Author(s):  
GIULIO FANTI ◽  
ROBERTO BASSO ◽  
MICHELE BERNARDI ◽  
UMBERTO VERZA
Keyword(s):  
Quality Control ◽  
Fourier Transform ◽  
Frequency Analysis ◽  
Time Domain ◽  
Mathematical Tool ◽  
Pressure Data ◽  
Innovative Technique ◽  
Gear Pumps ◽  
The Time Domain ◽  
Discharge Pressure

This paper describes an innovative technique for the quality control of gear pumps based on the frequency analysis of the oscillations of the discharge pressure using a new mathematical tool called "the spectrum of the power cepstrum." It consists in applying Fourier transform of the pressure data sampled in the time domain three times consecutively. The innovative technique makes it possible to obtain an acceptance mask for a set of gear pumps in the same series, in which the class and entity of a defect can be detected.

scholarly journals The Comparison of the Effect of Haimming Window and Blackman Window in the Time-Scaling and Pitch-Shifting Algorithms

2011 ◽  
Vol 1 ◽  
pp. 221-225
Author(s):  
Zhi Wei Lin ◽  
Li Da ◽  
Hao Wang ◽  
Wei Han ◽  
Fan Lin
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Method ◽  
Domain Type ◽  
Domain Method ◽  
Time Scaling ◽  
Window Functions ◽  
Inherent Frequency ◽  
The Time Domain

The real-time pitch shifting process is widely used in various types of music production. The pitch shifting technology can be divided into two major types, the time domain type and the frequency domain type. Compared with the time domain method, the frequency domain method has the advantage of large shifting scale, low total cost of computing and the more flexibility of the algorithm. However, the use of Fourier Transform in frequency domain processing leads to the inevitable inherent frequency leakage effects which decrease the accuracy of the pitch shifting effect. In order to restrain the side effect of Fourier Transform, window functions are used to fall down the spectrum-aliasing. In practical processing, Haimming Window and Blackman Window are frequently used. In this paper, we compare both the effect of the two window functions in the restraint of frequency leakage and the performance and accuracy in subjective based on the traditional phase vocoder[1]. Experiment shows that Haimming Window is generally better than Blackman Window in pitch shifting process.

An algorithm for Morlet wavelet transform based on generalized discrete Fourier transform

2019 ◽  
Vol 17 (05) ◽  
pp. 1950030
Author(s):  
Hua Yi ◽  
Peichang Ouyang ◽  
Tao Yu ◽  
Tao Zhang
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Discrete Fourier Transform ◽  
Time Domain ◽  
Morlet Wavelet ◽  
Wavelet Function ◽  
Linear Convolution ◽  
Calculation Results ◽  
Morlet Wavelet Transform ◽  
The Time Domain

Continuous wavelet transform (CWT) is a linear convolution of signal and wavelet function for a fixed scale. This paper studies the algorithm of CWT with Morlet wavelet as mother wavelet by using nonzero-padded linear convolution. The time domain filter, which is a non-causal filter, is the sample of wavelet function. By making generalized discrete Fourier transform (GDFT) and inverse transform for this filter, we can get a geometrically weighted periodic extension of the filter when evaluated outside its original support. From this extension of the time domain filter, we can get a causal filter. In this paper, GDFT-based algorithm for CWT, which has a more concise form than that of linear convolution proposed by Jorge Martinez, is constructed by using this causal filter. The analytic expression of the GDFT of this filter, which is essential for GDFT-based algorithm for CWT, is deduced in this paper. The numerical experiments show that the calculation results of GDFT-based algorithm are stable and reliable; the running speed of GDFT-based algorithm is faster than that of the other two algorithms studied in our previous work.

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