Topic
Prime-factor FFT algorithm
About: Prime-factor FFT algorithm is a research topic. Over the lifetime, 2346 publications have been published within this topic receiving 65147 citations. The topic is also known as: Prime Factor Algorithm.
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01 Jan 1989
TL;DR: This paper describes the implementation of real and complex FFT algorithms on the Motorola DSP96002, a general purpose, dual-bus IEEE standard floating-point digital signal processor (DSP) that provides the basis for efficient implementation of FFTs and other fast transforms, such as the Discrete Walsh-Hadamard Transform (WHT).
Abstract: This paper describes the implementation of real and complex FFT algorithms on the Motorola DSP96002. The DSP96002 is a general purpose, dual-bus IEEE standard floating-point digital signal processor (DSP). At a 74 nanosecond instruction cycle, the DSP96002 implements a 1024 point real FFT in 0.905 milliseconds and a 1024 point complex FFT in 1.55 milliseconds. This performance is achieved by calculating up to three floating-point results in a single instruction cycle, or 40.5 MFLOPS peak. A radix-2 FFT butterfly is executed every four cycles, an average of 33.75 IEEE MFLOPS. The instruction set and architecture of the DSP96002 provide the basis for efficient implementation of FFTs and other fast transforms, such as the Discrete Walsh-Hadamard Transform (WHT), Discrete Cosine Transform (DCTj and Discrete Hartley Transform (DHT).
TL;DR: The effect of pitch period length and position with respect to the analysis frame is described, which may introduce distortions in spectra estimation in both pitch-synchronous and pitch-asynchronous analysis.
16 Aug 2002
TL;DR: An interactive PC based system has been developed, and, using this system, a speaker can be easily analyzed and the testing time is greatly reduced and the test data can be saved in the form of a graph of frequency versus % total harmonic distortion (THD).
Abstract: The paper discusses the analysis of a signal using fast Fourier transform (FFT). Fourier analysis is a mathematical technique based on decomposing signals into sinusoids. The discrete Fourier transform (DFT) is the technique used with periodic digitized signals. There are several ways to calculate the discrete Fourier transform, such as solving simultaneous linear equations, the correlation method and fast Fourier transform (FFT). While FFT produces the same result as the other approaches for calculating DFT, it is incredibly more efficient, often reducing the computation time by about three hundred times as compared to other DFT techniques. To demonstrate the practical aspects of fast Fourier transform, a speaker testing system is developed and different speakers are tested for buzzing. Buzzing is a common problem in speakers. To detect a speaker for buzzing, an interactive PC based system has been developed, and, using this system, a speaker can be easily analyzed. Also, by using this system, the testing time is greatly reduced and the test data can be saved in the form of a graph of frequency versus % total harmonic distortion (THD). Both the hardware and the software for this system are discussed.
TL;DR: In this paper, a new adaptive control algorithm in frequency domain was proposed, which corresponds to the ordinary filtered-x least mean square (LMS) algorithm in discrete time domain.
Abstract: A new adaptive control algorithm in frequency domain was proposed. This frequency domain approach corresponds to the ordinary filtered-x least mean square (LMS) algorithm in discrete time domain. In this approach, the fast Fourier transform (FFT) was used for converting signals between frequency domain and discrete time domain. Since the computaion processes in these two domains are almost independent with each other, this algorithm can be realized effectively by using two processors. Computer simulations were carried out for investigating convergence characteristics of this approach. As the result of these simulations, it was shown that this algorithm has faster convergence with less computation comparing to the ordinary filtered-x LMS algorithm.
09 Sep 2011
TL;DR: This chapter describes the use of Walsh–Hadamard transform (WHT) and Karhunen-Loeve transform (KLT) and the clustering analysis method was chosen by acclamation for 2-class and 3-class recognition of 2- FSK, 4-FSK and PSK signals.
Abstract: Automatic recognition of modulation is rapidly evolving area of signal analysis. In recent years, much interest by academic and military research institutes has focused around the research and development of recognition algorithms modulation. There are two mains reasons to know the correct modulation type of a signal: to preserve the signal information content and to decide the suitable counter action such as jamming (Nandi & Azzouz, 1998), (Grimaldi et al, 2007), (Park & Dae, 2006). From this viewpoint, considerable attention is being paid to the research and development of algorithms for the recognition of modulated signals. The need of practice made it necessary to solve the questions of automatic classification of samples of received signals with use of computers and available software. In this chapter, a new original configuration of subsystems for the automatic modulation recognition of digital signals is described. The signal recognizer being developed consists of five subsystems: (1) adaptive antenna arrays, (2) pre-processing of signals, (3) key features extraction, (4) modulation recognizer and (5) output stage. This chapter describes the use of Walsh–Hadamard transform (WHT) and Karhunen-Loeve transform (KLT) for the modulation recognition in high frequency (HF) and very high frequency (VHF) bands. The input real signal is pre-processed and converted to the “phase image”. The WHT and KLT is applied and the dimensionality reduction is implemented and the classifier recognized the signal. The clustering analysis method was chosen by acclamation for 2-class and 3-class recognition of 2-FSK, 4-FSK and PSK signals. The 2-class and 3-class minimum-distance modulation classifier was created in the MATLAB programme. The tests of designed algorithm were implemented on real signal patterns.