Volume 5, Issue 4, July 2017, Page: 29-50
Development of an Extended Improvement on the Simplified-Bluestein Algorithm
Amannah Constance Izuchukwu, Department of Computer Science, University of Nigeria, Nsukka, Nigeria
H. C. Inyiama, Department of Computer Science, University of Nigeria, Nsukka, Nigeria
Received: Dec. 21, 2017;       Accepted: Jan. 4, 2018;       Published: Jan. 31, 2018
DOI: 10.11648/j.com.20170504.11      View  1287      Downloads  46
This research was designed to develop an extended improvement on the simplified Bluestein algorithm (EISBA). The methodology adopted in this work was iterative and incremental development design. The major technologies used in this work are the numerical algorithms and the C++ programming technologies and the wave concept technology. The C++ served as a signal processing language simulator (SPLS). In the EISBA, the DSP input is encountered first. It is subjected to some numerical processing which included testing for efficiency on the C++ platform. This test platform provided the basis for comparison leading to the desired EISBA. The approach adopted in the study was the re-indexing, decomposing, and simplifying the default SBFFT algorithm. The computing speed of the default algorithms was tested on the C++ platform. The average execution time of the SBFFT was 3.50 seconds. Similarly, the average execution time of the EISBA was 1.74 ms. this result therefore shows that a version of algorithm with computing speed that is faster than that of SBFFT algorithm exist. 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 the EISBA provided they are interpreted in the digital wave type.
Development, Extended, Algorithm, Simplified, Bluestein, Fourier, Transform
To cite this article
Amannah Constance Izuchukwu, H. C. Inyiama, Development of an Extended Improvement on the Simplified-Bluestein Algorithm, Communications. Vol. 5, No. 4, 2017, pp. 29-50. doi: 10.11648/j.com.20170504.11
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Mathuranathan v. (2008). Channel Capacity & Shannon’s theorem – demystified. Retrieved 14/04/2017 from http://www.gaussianwaves.com/2008/04/channel-capacity/ Fast wavelet transformlink.springer.com/chapter/10.1007%2F978-3-319- 22075-8_7by J Gomes - ‎2015 - ‎Related articles.
Slot L. and Zur S. (2015). Shannon's Noisy-Channel Coding Theorem. Retrieved 12/03/2017 from http://homepages.cwi.nl/~schaffne/courses/infcom/2014/presentations/Luca s_Sebastian_NoisyChannelCoding.pdf.
Fraser, D. (1989). Interpolation by the FFT Revisited An Experimental Investigation, IEEE Transactions on Acoustics, Speech, and Signal Processing, (37) 5, pp. 665-675.
Matthew, P. D. (2000). Efficient Digital Filters, IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-.
Sanjit, K. M. (2001). Digital Signal Processing: A Computer Approach, McGraw-Hill, New York.
Yin, L. G., Agiieswari, K. R., (2005). Evaluation of DSP Based Numerical Relay for Over Current Protection Centre for Communication Service Convergence Technologies (CCSCT). Department of Electronics and Communication Engineering, Beijing China.
Areva, T. D. (1995). Network Protection and Automation Guide. Prentice Hall. New Jersey.
http://www.jstor.org/stable/3680137 Accessed: 30/07/2012 23:37.
Good, I. J. “The interaction algorithm and practical fourier analysis”. Journal of the royal statistical society, series B 20 (2): 361–372 JSTOR 2983896, 1958. (https://www.jstor.or/state/2983896). Addendum, ibid. 22 (2), 375 (1960).
Thomas, L. H. (1963). Using Computers to solve problems in Physics. Applications of Digital computers. Bostom: Ginn.
Pavan Kumar K. M., Priya Jain, Ravi Kiran S, Rohith N., Ramamani K. FFT Algorithm: A Survey. The International Journal of Engineering and Science (IJES) Volume 2 Issue 4 pages 22-26, 2013 ISSN (e): 2319-1813.
Standard 982. 1 – 1988. Piscataway, N. J: IEEE. Jessica Keyes software Engineering productivity handbook. Windcrest/MC.
Pfleeger, S. L. and C. McGowam 1990. Software metrics in the process maturity framework. Journal of Systems Software. 12:255-261.
https://www.researchgate.net/publication/221920249 Complex_Digital_Signal_Processing_in_T elecommunications [accessed May 29, 2017].
Godsill S. (2014). 3F3 Digital Signal Processing (DSP). Retrieved 27/05/2017 from https://www- sigproc.eng.cam.ac.uk/foswiki/pub/Main/3F3/3F3__Digital_Signal_Processing_(DSP)_2015_1 -49.pdf.
Drago D. (2012). Theory of Signal. Retrieved 14/06/2017 from http://www.informatics.buzdo.com/p030-signal- theory.htm Heckbert P. (1998). Fourier Transforms and the Fast Fourier Transform (FFT) Algorithm. Retrieved 02/05/2017 from https://www.cs.cmu.edu/afs/andrew/scs/cs/15-463/2001/pub/www/notes/fourier/fourier.pdf.
Vladimir, P. and Zlatka, N., Georgi, I., Miglen, O., (2011). Complex Digital Signal Processing in Telecommunications: Applications of Digital Signal Processing, Dr. Christian Cuadrado-Laborde (Ed.), 307-406.
Saeed, B. (2003). Interpolation in Digital Signal Processing and Numerical Analysis. New York: Springer-Verlag.
Browse journals by subject