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首页> 外文期刊>Journal of mechanics in medicine and biology >MATHEMATICAL IMPLEMENTATION OF HYBRID FAST FOURIER TRANSFORM AND DISCRETE WAVELET TRANSFORM FOR DEVELOPING GRAPHICAL USER INTERFACE USING VISUAL BASIC FOR SIGNAL PROCESSING APPLICATIONS
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MATHEMATICAL IMPLEMENTATION OF HYBRID FAST FOURIER TRANSFORM AND DISCRETE WAVELET TRANSFORM FOR DEVELOPING GRAPHICAL USER INTERFACE USING VISUAL BASIC FOR SIGNAL PROCESSING APPLICATIONS

机译:混合快速傅里叶变换和离散小波变换在图形用户界面上的开发及其在视觉处理中的应用

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摘要

In recent years, the application of discrete wavelet transform (DWT) on biosignal processing has made a significant impact on developing several applications. However, the existing user-friendly software based on graphical user interfaces (GUI) does not allow the freedom of saving the wavelet coefficients in .txt or .xls format and to analyze the frequency spectrum of wavelet coefficients at any desired wavelet decomposition level. This work describes the development of mathematical models for the implementation of DWT in a GUI environment. This proposed software based on GUI is developed under the visual basic (VB) platform. As a preliminary tool, the end user can perform "j" level of decomposition on a given input signal using the three most popular wavelet functions — Daubechies, Symlet, and Coiflet over "n" order. The end user can save the output of wavelet coefficients either in .txt or .xls file format for any further investigations. In addition, the users can gain insight into the most dominating frequency component of any given wavelet decomposition level through fast Fourier transform (FFT). This feature is highly essential in signal processing applications for the in-depth analysis on input signal components. Hence, this GUI has the hybrid features of FFT with DWT to derive the frequency spectrum of any level of wavelet coefficient. The novel feature of this software becomes more evident for any signal processing application. The proposed software is tested with three physiological signal — electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) — samples. Two statistical features such as mean and energy of wavelet coefficient are used as a performance measure for validating the proposed software over conventional software. The results of proposed software is compared and analyzed with MATLAB wavelet toolbox for performance verification. As a result, the proposed software gives the same results as the conventional toolbox and allows more freedom to the end user to investigate the input signal.
机译:近年来,离散小波变换(DWT)在生物信号处理中的应用对开发几种应用产生了重大影响。但是,现有的基于图形用户界面(GUI)的用户友好软件不允许自由地将小波系数保存为.txt或.xls格式,并且无法以任何所需的小波分解级别分析小波系数的频谱。这项工作描述了在GUI环境中实现DWT的数学模型的开发。该基于GUI的拟议软件是在Visual Basic(VB)平台下开发的。作为一种初步的工具,最终用户可以使用三种最受欢迎​​的小波函数-Daubechies,Symlet和Coiflet按“ n”阶对给定的输入信号执行“ j”级分解。最终用户可以将小波系数的输出保存为.txt或.xls文件格式,以进行进一步的研究。此外,用户可以通过快速傅里叶变换(FFT)了解任何给定小波分解级别中最主要的频率分量。此功能在信号处理应用中对输入信号分量进行深入分析时至关重要。因此,该GUI具有FFT与DWT的混合功能,可以得出任何级别的小波系数的频谱。该软件的新颖功能对于任何信号处理应用而言都更加明显。所建议的软件已通过三种生理信号-脑电图(EEG),心电图(ECG)和肌电图(EMG)-样本进行了测试。小波系数的均值和能量之类的两个统计特征被用作性能度量,用于验证所提出的软件优于传统软件。使用MATLAB小波工具箱对所提出软件的结果进行了比较和分析,以进行性能验证。结果,所提出的软件给出了与传统工具箱相同的结果,并为最终用户提供了更多自由来调查输入信号。

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