scholarly journals Improved Carrier Frequency-Shifting Algorithm Based on 2-FFT for Phase Wrap Reduction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhouqiang Zhang ◽  
Feilei Wang ◽  
Guangshen Xu ◽  
Jiangtao Jia ◽  
Xuejing Liu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Shift ◽  
Frequency Domain ◽  
Rational Number ◽  
Carrier Frequency ◽  
Integer Number ◽  
Shift Method ◽  
Frequency Shifting

The number of phase wraps that result from the carrier component can be completely eliminated or reduced by first applying a fast Fourier transform (FFT) to the image and then shifting the spectrum to the origin. However, because the spectrum can only be shifted by an integer number, the phase wraps of the carrier component cannot be completely reduced. In this paper, an improved carrier frequency-shifting algorithm based on 2-FFT for phase wrap reduction is proposed which allows the spectrum to be shifted by a rational number. Firstly, the phase wraps are reduced by the conventional FFT frequency shift method. Secondly, the wrapped phase with residual carrier components is filtered and magnified sequentially; the amplified phase is transformed into the frequency domain using an FFT, and then, the wrapped phase with the residual carrier components can be further reduced by shifting the spectrum by a rational number. Simulations and experiments were conducted to validate the efficiency of the proposed method.

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.

Acquisition Algorithm Based on Circular Correlation for GPS L2C CM Code Signal and the Software Implementation

2014 ◽  
Vol 543-547 ◽  
pp. 2341-2344
Author(s):  
Xue Fen Zhu ◽  
Yang Yang ◽  
Dong Rui Yang ◽  
Fei Shen ◽  
Xi Yuan Chen
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Carrier Frequency ◽  
Initial Phase ◽  
Input Data ◽  
Software Implementation ◽  
Front End ◽  
Circular Correlation ◽  
Satellite Signal ◽  
Local Code

L2C is a new civilians signal launched by the modernized GPS Block IIR-M satellite. This paper studies L2C acquisition algorithms with the implementations on MATLAB. Circular correlation is utilized to implement the acquisition algorithm. The input satellite signal is collected by hardware front-end and the local code then simulated by software. The input data after frequency reduction processing and the local simulated code are converted into the frequency domain by means of FFT (Fast Fourier Transform). After performing circular correlation, the initial phase of the CM code is attained and the carrier frequency is found with the resolution of 50Hz.The effectiveness of the acquisition algorithm is finally verified through the actual satellite experiments.

scholarly journals High Performing Sentiment Analysis based on Fast Fourier Transform over Temporal Intuitionistic Fuzzy Value

2021 ◽  
Author(s):  
Basanti Pal Nandi ◽  
Amita Jain ◽  
Devendra Kumar Tayal ◽  
Poonam Ahuja Narang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Sentiment Analysis ◽  
Frequency Domain ◽  
Fuzzy Set ◽  
Opinion Mining ◽  
New Technology ◽  
Intuitionistic Fuzzy Set ◽  
Sentiment Classification ◽  
Intuitionistic Fuzzy

Abstract Sentiment analysis or opinion mining has an extensive area in the field of research. Today we consider the huge amount of structured and unstructured data available in the web for a particular subject to get an opinion. The surplus data handling termed as big data requires some new technology to deal with. This paper considers the requirement of sentiment analysis of such huge data for fast processing. Based on Fast Fourier Transform on Temporal Intuitionistic fuzzy set generated from text, this algorithm (FFT-TIFS) expedites the sentiment classification. Fourier analysis converts a signal from its time domain to its representation in frequency domain. Such frequency domain algorithm on Temporal Intuitionistic fuzzy set is used in Sentiment analysis for the first time. This algorithm is useful for short twitter text, document level as well as sentence level binary sentiment classification. It is tested on aclImdb, Polarity, MR, Sentiment140 and CR dataset which gives an average of 80% accuracy. The proposed method shows significant improvement in required time complexity where the method achieves 17 times faster processing in comparison to sequential Fuzzy C Means(FCM) method and again it is at least 7 times faster than distributed FCM method present in literature. The method presented in this paper has a novel approach towards fastest processing time and suitability of various sizes of the text sentiment analysis.

scholarly journals Periodic Noise Removal in Strain and Natural Images Using 2-D Fast Fourier Transform

2013 ◽  
pp. 90-94
Author(s):  
Mandeep Kaur ◽  
Dinesh Kumar ◽  
Ekta Walia ◽  
Manjit Sandhu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Domain ◽  
Noise Removal ◽  
Natural Images ◽  
Periodic Pattern ◽  
Periodic Noise ◽  
Mean Filter ◽  
The Mean

This paper presents a 2-D FFT removal algorithm for reducing the periodic noise in natural and strain images. For the periodic pattern of the artifacts, we apply the 2-D FFT on the strain and natural images to extract and remove the peaks which are corresponding to periodic noise in the frequency domain. Further the mean filter applied to get more effective results. The performance of the proposed method is tested on both natural and strain images. The results of proposed method is compared with the mean filter based periodic noise removal and found that the proposed method significantly improved for the noise removal.

scholarly journals Quality factor estimation based on the peak frequency shift method using a robust Fourier transform to VSP data

2019 ◽  
Vol 16 (6) ◽  
pp. 1061-1070 ◽  
Author(s):  
Rómulo Sandoval ◽  
José L Paredes ◽  
Flor A Vivas
Keyword(s):  
Fourier Transform ◽  
Frequency Shift ◽  
Quality Factor ◽  
Peak Frequency ◽  
Estimation Methods ◽  
Signal Spectrum ◽  
Q Factor ◽  
Shift Method ◽  
Vsp Data ◽  
Factor Estimation

Abstract Quality factor estimation (Q estimation) of vertical seismic profile (VSP) data are necessary for the process referred to as inverse Q-filtering, which is used, in turn, to improve the resolution of seismic signals. In general, the performances of Q estimation methods, based on the standard Fourier transform, are severely degraded in the presence of heavy-tailed distributed noise. In particular, these methods require a bandwidth detection which is difficult to estimate due to instabilities caused by outliers or gross errors, leading to an incorrect Q estimation. In this paper, an improvement of the Q-factor estimation based on the peak frequency shift method is proposed, where the signal spectrum is obtained using a robust transform algorithm. More precisely, the robust transform method assumes that the perturbations that contaminate the signal of interest can be characterized as random samples following a zero-mean Laplacian distribution, leading to the weighted median as the optimal operator for determining each transform coefficient. The proposed method is validated on synthetic datasets using different levels of noise and its performance is compared to those yielded by various methods based on the standard Fourier transform. Furthermore, a non-Gaussianity test is performed in order to characterize the noise distribution in real data. From the non-Gaussianity test, it can be observed that the underlying noise is better characterized using a Laplacian statistical model, and therefore, the proposed method is a suitable approach for computing the Q factor. Finally, the proposed methodology is applied to estimate the Q factors of real VSP data.

Establishing the State of Operation on Vibration Behavior of the Industrial Robot

2012 ◽  
Vol 186 ◽  
pp. 247-253 ◽  
Author(s):  
Dan Niculescu ◽  
Marek Vagaš ◽  
Adrian Olaru ◽  
Mikuláš Hajduk ◽  
Adrian Ghionea
Keyword(s):  
Fourier Transform ◽  
Life Cycle ◽  
Fast Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Preventive Maintenance ◽  
Industrial Robot ◽  
Maintenance Management ◽  
Automatic Diagnosis ◽  
Made In

Diagnosis measurement of vibration and noise, should allow monitoring of equipment defects, through a system of preventive maintenance, predictive. Automatic diagnosis of machinery and equipment was made in order to ensure a higher reliability of these and how to obtain a more extended life cycle without the occurrence of defects. Vibrations are always measured in analog format (time domain) and must be transformed into the frequency domain. Therefore Fast Fourier Transform (FFT) method is used to evaluate vibration Almega AX-V6 robot. The application of preventive and predictive maintenance management supports enterprise, because it proves effective, the information you provide in making decisions.

scholarly journals Problem Pengolahan Data Gaya Berat Mikro antar Waktu

2016 ◽  
Vol 3 (02) ◽  
pp. 130
Author(s):  
Supriyadi S
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Groundwater Level ◽  
Time Lapse ◽  
Density Change ◽  
Gravity Survey ◽  
Micro Gravity

<span>At time lapse microgravity survey will be got data in place for difference period. The Anomaly <span>caused by subsidence and density change under surface which related to groundwater level <span>change. This matter become problem when will take one of the anomaly sources to processed <span>is furthermore. Reduction one of anomaly source cannot be done direct but must be done with <span>filtering process. Process filtering done by using FFT (Fast Fourier Transform), its principal is <span>to move data from time domain to frequency domain. At frequency domain this is mathematics <span>process conducted. On subsidence case study in Semarang by using this technique indicate that <span>subsidence value from time lapse micro gravity survey have tendency is equal to result from <span>geodesy survey.</span></span></span></span></span></span></span></span><br /></span>

scholarly journals IMPLEMENTASI TRANSFORMASI FOURIER UNTUK TRANSFORMASI DOMAIN WAKTU KE DOMAIN FREKUENSI PADA LUARAN PURWARUPA ALAT PENDETEKSIAN GULA DARAH SECARA NON-INVASIF

2020 ◽  
Vol 4 (2) ◽  
pp. 234-244
Author(s):  
Umam Hidayaturrohman ◽  
Erfiani Erfiani ◽  
Farit M Afendi
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Domain ◽  
Blood Sugar ◽  
Processing Speed ◽  
Fourier Transformation ◽  
Dft Method ◽  
The Body ◽  
Non Invasive ◽  
The Time Domain

Diabetes mellitus is the result of changes in the body caused by a decrease of insulin performance which is characterized by an increase of blood sugar level. Detection of blood sugar can be done with Invasive methods or non-invasive methods. However, non-invasive methods are considered better because they can check early, faster and accurate. The prototype output is values of intensity in the time domain, thus fourier transformation is very much needed to transform into the frequency domain. In this study, Fourier transformation methods used are Discrete Fourier Transform (DFT), Fast Fourier Transform Radix-2, and Fast Fourier Transform Radix-4. Evaluation for the best method is done by comparing the processing speed of each method. The FFT Radix-4 method is more effective to perform the transformation into the frequency domain. The average processing speed with the FFT Radix-4 method reaches 2.67×105 nanoseconds, and this is much faster 5.06×106 nanoseconds than the FFT Radix-2 method and 2.40×107 nanoseconds faster than the DFT method.

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