Frequency domain discrete fourier transform spread generalized multi-carrier system and its performance analysis

2009 ◽  
Vol 32 (3) ◽  
pp. 456-464 ◽  
Author(s):  
Yun Rui ◽  
Honglin Hu ◽  
Huiyue Yi ◽  
Hsiao-Hwa Chen ◽  
Yueh-Min Huang
Keyword(s):  
Fourier Transform ◽  
Performance Analysis ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Carrier System

scholarly journals Application of two-dimensional fast Fourier transform algorithm, analog of the Cooley-Tukey algorithm, for 4k fixed format digital image of satellite data in frequency domain processing

2020 ◽  
Vol 149 ◽  
pp. 02010 ◽  
Author(s):  
Mikhail Noskov ◽  
Valeriy Tutatchikov
Keyword(s):  
Fourier Transform ◽  
Numerical Experiment ◽  
Fast Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Satellite Data ◽  
Digital Image ◽  
Digital Images ◽  
Two Dimensional ◽  
Fast Fourier Transform Algorithm

Currently, digital images in the format Full HD (1920 * 1080 pixels) and 4K (4096 * 3072) are widespread. This article will consider the option of processing a similar image in the frequency domain. As an example, take a snapshot of the earth's surface. The discrete Fourier transform will be computed using a two-dimensional analogue of the Cooley-Tukey algorithm and in a standard way by rows and columns. Let us compare the required number of operations and the results of a numerical experiment. Consider the examples of image filtering.

scholarly journals Water uptake analysis in Japanese mustard spinach: A numerical approach

2019 ◽  
Author(s):  
Aoi Shimada ◽  
Takuya Seki ◽  
Musubu Ichikawa
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Water Uptake ◽  
Experimental System ◽  
Time Lapse ◽  
Numerical Approach ◽  
Frequency Domain Analysis ◽  
Plant Dynamics ◽  
Image Difference

AbstractWe constructed an experimental system consisting of a syringe pump and water level feedback control to reveal the dynamics of water uptake in Japanese mustard spinach. We also analyzed differences in time-lapse images to quantify turgor movement in these plants. By performing harmonic analysis using the discrete Fourier transform on these dynamics, the frequency domain was obtained. These methods enabled the detailed visualization of plant dynamics.HighlightIn this study, the water uptake dynamics of Japanese mustard spinaches were measured in detail and image difference analysis was performed to investigate the plants’ turgor movements. Frequency domain analysis of the plant dynamics was carried out using a discrete Fourier transform.

scholarly journals Enhanced CORDIC Based Rotator Design for Sinusoidal Transforms

2020 ◽  
Vol 9 (3) ◽  
pp. 1001-1004
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Mean Square Error ◽  
Time Domain ◽  
Basis Function ◽  
The Other ◽  
Mean Square ◽  
Circular Rotation ◽  
The Mean

Transforms play an important role in conversion of information from one domain to the other. To be more specific transforms like Discrete Fourier transform (DFT) and Discrete Cosine transform (DCT) helps us to migrate from one time domain to frequency domain based on the basis function selected. The basis function of the every sinusoidal transform carries out a circular rotation to convert information from one domain to the other. There are applications related to communication which requires this rotation into the hyperbolic trajectory as well. Multiplierless algorithm like CORDIC improves the latency of the transforms by eliminating the number of multipliers in the basis function. In this paper we have designed and implemented enhanced version of CORDIC based Rotator design. The Enhanced version is simulated for order 1 to order 36 to emphasize on the results of the proposed algorithm. Results shows that the enhanced CORDIC rotator design surpasses the Mean square error after the order 18 compared to standard CORDIC. Unified CORDIC also can be implemented using the said algorithm to implement different three trajectories.

Synthesis and Simplification of Fractal Profile

2011 ◽  
Vol 279 ◽  
pp. 262-265
Author(s):  
Chao Zhou ◽  
Cheng Hui Gao ◽  
Jie Chen ◽  
Lian Feng Lai
Keyword(s):  
Finite Element ◽  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
High Frequency ◽  
Space Domain ◽  
Finite Element Software ◽  
Redundant Data ◽  
Frequency Components

In order to import a synthesized fractal profile into finite element software, the profile synthesized by discrete Fourier transform was studied. The synthesized profile was filtered in frequency domain in order to filter its high frequency components and to make it smooth, and then the chord deviation algorithm was used to reduce its redundant data in space domain. It was found that: after filtering, the profile is smooth but with lots of redundant data; the chord deviation algorithm can simplify the profile which is redundant in space domain; the time needed in the process of importing a profile into finite element software can be reduced greatly after profile simplification.

scholarly journals DFT21: Discrete Fourier Transform in the 21st century

2021 ◽  
Author(s):  
Prasanta Pal ◽  
Shataneek Banerjee ◽  
Amardip Ghosh ◽  
David R. Vago ◽  
Judson Brewer
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Real World ◽  
Data Science ◽  
Digital Data ◽  
Series Data ◽  
Real World Data ◽  
World Data

<div> <div> <div> <p>Knowingly or unknowingly, digital-data is an integral part of our day-to-day lives. Realistically, there is probably not a single day when we do not encounter some form of digital-data. Typically, data originates from diverse sources in various formats out of which time-series is a special kind of data that captures the information about the time-evolution of a system under observation. How- ever, capturing the temporal-information in the context of data-analysis is a highly non-trivial challenge. Discrete Fourier-Transform is one of the most widely used methods that capture the very essence of time-series data. While this nearly 200-year-old mathematical transform, survived the test of time, however, the nature of real-world data sources violates some of the intrinsic properties presumed to be present to be able to be processed by DFT. Adhoc noise and outliers fundamentally alter the true signature of the frequency domain behavior of the signal of interest and as a result, the frequency-domain representation gets corrupted as well. We demonstrate that the application of traditional digital filters as is, may not often reveal an accurate description of the pristine time-series characteristics of the system under study. In this work, we analyze the issues of DFT with real-world data as well as propose a method to address it by taking advantage of insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that a dramatic, never-before-seen improvement is possible by re-imagining DFT in the context of real-world data with appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital world in terms of accuracy, reliability, and information retrievability from raw-data. </p> </div> </div> </div>

scholarly journals DFT21: Discrete Fourier Transform in the 21st century

2021 ◽  
Author(s):  
Prasanta Pal ◽  
Shataneek Banerjee ◽  
Amardip Ghosh ◽  
David R. Vago ◽  
Judson Brewer
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Real World ◽  
Data Science ◽  
Digital Data ◽  
Series Data ◽  
Real World Data ◽  
World Data

<div> <div> <div> <p>Knowingly or unknowingly, digital-data is an integral part of our day-to-day lives. Realistically, there is probably not a single day when we do not encounter some form of digital-data. Typically, data originates from diverse sources in various formats out of which time-series is a special kind of data that captures the information about the time-evolution of a system under observation. How- ever, capturing the temporal-information in the context of data-analysis is a highly non-trivial challenge. Discrete Fourier-Transform is one of the most widely used methods that capture the very essence of time-series data. While this nearly 200-year-old mathematical transform, survived the test of time, however, the nature of real-world data sources violates some of the intrinsic properties presumed to be present to be able to be processed by DFT. Adhoc noise and outliers fundamentally alter the true signature of the frequency domain behavior of the signal of interest and as a result, the frequency-domain representation gets corrupted as well. We demonstrate that the application of traditional digital filters as is, may not often reveal an accurate description of the pristine time-series characteristics of the system under study. In this work, we analyze the issues of DFT with real-world data as well as propose a method to address it by taking advantage of insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that a dramatic, never-before-seen improvement is possible by re-imagining DFT in the context of real-world data with appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital world in terms of accuracy, reliability, and information retrievability from raw-data. </p> </div> </div> </div>

Induction Motor Fault Diagnosis Using Voltage Spectrum of Auxiliary Winding and Lissajous Curve of its Park Components

2013 ◽  
Vol 805-806 ◽  
pp. 963-979 ◽  
Author(s):  
Lamiaâ El Menzhi ◽  
Abdallah Saad
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Induction Motor ◽  
Discrete Fourier Transform ◽  
Time Domain ◽  
New Method ◽  
Spectrum Simulation ◽  
Simulation Results ◽  
The Time Domain

In this paper, a new method for induction motor fault diagnosis is presented. It is based on the so-called an auxiliary winding voltage and its Park components. The auxiliary winding is a small coil inserted between two of the stator phases. Expressions of the inserted winding voltage and its Park components are presented. After that, discrete Fourier transform analyzer is required for converting the signals from the time domain to the frequency domain. A Lissajous curve formed of the two Park components is associated to the spectrum. Simulation results curried out for non defected and defected motor show the effectiveness of the proposed method.

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