Abstract
This study was designed to compare the computing efficiency of FC-TNADSP and the FBNADSP.to ascertain a faster numerical algorithm necessary for the processing of digital signals. The faster numerical algorithm established in this study is abbreviated with RCFC-TNADSP (Compared Resultant-TNADSP). The methodology adopted in this work was comparative analysis development design. The major technologies used in this work are the FC-TNADSP and FBNADSPwhich were hitherto simulated on the c++ programming technologies. The c++ served as a signal processing language simulator (SPLS). The execution times of the fast Cooley-Tukey and the fast Bluestein algorithms were 1.70 seconds and 1.74 seconds respectively. On comparing the speeds of the fast Cooley–Tukey and the fast Bluestein algorithms we observed that the fast Cooley-Tukey algorithm has 0.04 seconds speed improvement over the fast Bluestein algorithm. In line with this outcome, we concluded that the fast Cooley-Tukey algorithm (FC-TNADSP) is faster than the fast Bluestein algorithm (FBNADSP). In the same vein the fast Cooley-Tukey algorithm (FC-TNADSP algodsp-2) is therefore the fastest DSP algorithm. This is however faster than the spectrum of FFT algorithms of O(nlogn) computing speed, a speed considered to be the fastest hitherto. The result of this study shows we can have faster numerical algorithms other than the traditional spectrum of FFT algorithms of O(nlogn) computing speed. The algorithms were tested on input block of width 1000 units, and above, and can be implemented on input size of 100 000, and 1000 000 000 without the challenge of storage overflow. The input samples tested in this work was the discretized pulse wave form with undulating shape out of which the binary equivalents were extracted. Other forms of signals may also be tested in this fast algorithm provided they are interpreted in the digital wave type. In order to optimized the advantage of the developed algorithms, the frequency index, K should be as defined in this study, that is
Highlights
Signals play an important role in our daily life
On comparing the speeds of the fast Cooley–Tukey and the fast Bluestein algorithms we observed that the fast Cooley-Tukey algorithm has 0.04 seconds speed improvement over the fast Bluestein algorithm
On comparing the speeds of the fast Cooley–Tukey and the fast Bluestein algorithms we observed that the fast Cooley-Tukey algorithm has 0.04 seconds speed improvement over the fast Bluestein algorithm.In line with this outcome, we conclude that the fast Cooley-Tukey algorithm (FC-TNADSP) is faster than the fast Bluestein algorithm (FBNADSP)
Summary
Signals play an important role in our daily life. A signal is the variable parameter that contains information and by which information is transmitted in an electronic system or circuit Dictionary of Science. The majority of the signals found in science are analog in nature In analog signals, both dependent variable and independent variables are continuous. Both dependent variable and independent variables are continuous Such signals may be processed directly by analog systems (i.e., analog filters) for the purpose of changing their characteristics or extracting some desired information. As a matter of fact, the techniques and applications of Digital Signal Processing (DSP) are as old as Newton and Gauss and as new as today’s digital computers and Integrated Circuits (ICs). This rapid development of Digital Signal Processing (DSP) has been a result of the significant advances in digital computer technology and IC fabrication techniques. The information contained in the signal can be extracted either in the original domain or in the transformed domain
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