Abstract
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.
Highlights
BACKGROUND TO THE STUDYThe most popular digital filters are described and compared in this work
AIM OBJECTIVES OF THE STUDY The aim of the study is to develop an extended improvement on the simplified Bluestein algorithm
The result of this study shows that we can have faster numerical algorithms other than the Simplified Bluestein Fast Fourier Transforms (SBFFT) algorithm for the processing of digital signals
Summary
BACKGROUND TO THE STUDYThe most popular digital filters are described and compared in this work. To process analog signals digitally, an interface between the analog signal and a digital processor is required. This interface is known as analog-to-digital converter (ADC). The output of the analog-to-digital converter is a digital signal. This digital signal is appropriate for digital processor. Computer science and mathematics, a digital filter is a system that performs numerical operations on a sampled, discrete-time signal to reduce or enhance certain aspects of that signal. Numerical algorithms are used as filters to manipulate or process digital signals so that their operation times can be determined and compared . The wave concept technology is used to represent discrete data samples expressed in binary format. When the binary values are collected together, they can be further converted into numerical or decimal values
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