scholarly journals Rancang Bangun Spectrum Analyzer Menggunakan Fast Fouier Transform Pada Single Board Computer

Author(s):  
Afandi Nur Aziz Thohari ◽  
Agfianto Eko Putra

Spectrum analyzer is an instrument device to measure the magnitude of the frequency and the power of signal. It has many benefits, such as used for testing telecommunication devices, determining the allocation of unused frequencies and also for practicum in schools or universities. However, because of these many benefits, the price of this signal measuring equipment soared in the market.As an alternative, a device that can serve as spectrum analyzer yet has an affordable price is invented in the form of the prototype of spectrum analyzer built using a single board computer by applying a fast Fourier transform algorithm. Feedback from the prototype is in the form of radio signal captured using RTL-SDR.The test results showed that the range of frequencies that can be displayed by the prototype is 24 MHz to 1.769 MHz. Then the test results of fast Fourier transform computing on N points showed that the prototype can work smoothly using the N from 512 to 32.768 points. The use of N more than 32.768 points will cause CPU and disk memory overloaded and lead to a slow performance. Finally, comparison of the levels of spectrum was performed using spectrum analyzer Anritsu MS2720T. As a result, it is known that prototype can be used to show the location of the frequency spectrum of the radio signal appropriately.

1980 ◽  
Vol 17 (3) ◽  
pp. 284-284
Author(s):  
Robert J. Meir ◽  
Sathyanarayan S. Rao

This paper presents a full and well-developed view of the Fast Fourier Transform (FFT). It is intended for the reader who wishes to learn and develop his own fast Fourier algorithm. The approach presented here utilizes the matrix description of fast Fourier transforms. This approach leads to a systematic method for greatly reducing the complexity and the space required by variety of signal flow graph descriptions. This reduced form is called SNOCRAFT. From this representation, it is then shown how one can derive all possible fast Fourier transform algorithms, including the Weinograd Fourier transform algorithm. It is also shown from the SNOCRAFT representation that one can easily compute the number of multiplications and additions required to perform a specified fast Fourier transform algorithm. After an elementary introduction to matrix representation of fast Fourier transform algorithm, the method of generating all possible fast Fourier transform algorithms is presented in detail and is given in three sections. The first section discusses the Generation of SNOCRAFT and the second section illustrates how Operations on SNOCRAFT are made. These operations include inversion and rotation. The last section deals with the FFT Analysis. In this section, examples are provided to illustrate how one counts the number of multiplications and additions involved in performing the transform that one has developed.


2020 ◽  
Vol 149 ◽  
pp. 02010 ◽  
Author(s):  
Mikhail Noskov ◽  
Valeriy Tutatchikov

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.


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