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 A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

Elimination of minimal FFT grid-size limitations

2002 ◽  
Vol 35 (4) ◽  
pp. 505-505 ◽  
Author(s):  
David A. Langs
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Input Data ◽  
Complex Structure ◽  
Grid Size ◽  
Structure Factors ◽  
The Fourier Transform

The fast Fourier transform (FFT) algorithm as normally formulated allows one to compute the Fourier transform of up toNcomplex structure factors,F(h),N/2 ≥h> −N/2, if the transform ρ(r) is computed on anN-point grid. Most crystallographic FFT programs test the ranges of the Miller indices of the input data to ensure that the total number of grid divisions in thex,yandzdirections of the cell is sufficiently large enough to perform the FFT. This note calls attention to a simple remedy whereby an FFT can be used to compute the transform on as coarse a grid as one desires without loss of precision.

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 Unified reconfigurable commutation scheme of FFT

2019 ◽  
pp. 50-56
Author(s):  
P. S. Poperechny ◽  
I. Yu. Poperechnaya
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Time Scale ◽  
Software Implementation ◽  
Tree Level ◽  
Frequency Scale ◽  
Traditional Scheme ◽  
Communication Scheme ◽  
Different Order ◽  
Additional Hardware

In the traditional scheme for calculating the fast Fourier transform (FFT), the input arguments for the butterfly computation are switched in a different order depending on the computation stage, which leads to additional resource expenditures in hardware or software implementation. The article offers the method for FFT calculation by means of unified communication scheme stageby‑stage. There is an iterating equation for hardware and software implementation. The equation consists of two‑level loops rather than tree level loop for the traditional scheme. According to the iterating equation the unified communication scheme FFT is provided for both time scale and frequency scale. Also the reconfiguration of schemes by different samples number is provided too. The rotating multipliers are the same like in non‑reconfigurable (fixed) communication scheme. So the offered approach does not required additional hardware or software resources.

scholarly journals Efficient Software Implementation of the Nearly Optimal Sparse Fast Fourier Transform for the Noisy Case

2015 ◽  
Vol 11 (22) ◽  
pp. 73-94 ◽  
Author(s):  
Alexander López-Parrado ◽  
Jaime Velasco Medina
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Direct Calculation ◽  
Software Implementation ◽  
Original Version ◽  
Execution Times ◽  
Sparse Fast Fourier Transform ◽  
Modified Algorithm ◽  
Efficient Software

In this paper we present an optimized software implementation (sFFT-4.0)of the recently developed Nearly Optimal Sparse Fast Fourier Transform (sFFT) algorithm for the noisy case. First, we developed a modified versionof the Nearly Optimal sFFT algorithm for the noisy case, this modified algorithm solves the accuracy issues of the original version by modifying theflat window and the procedures; and second, we implemented the modifiedalgorithm on a multicore platform composed of eight cores. The experi-mental results on the cluster indicate that the developed implementation isfaster than direct calculation using FFTW library under certain conditions of sparseness and signal size, and it improves the execution times of previous implementations like sFFT-2.0. To the best knowledge ofthe authors,the developed implementation is the first one of the Nearly Optimal sFFT algorithm for the noisy case.

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