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- MakaleYayıncıda açık erişim2023Scopus SJRQ3Optimization Approach in Window Function Design for Real-Time Filter Applications
Serbet, Fatmanur; Kaya, Turgay
Eliminating the Gibbs oscillations that occur during the Finite Impulse Response (FIR) digital filter design with the Fourier Series method will ensure correct filtering. For this reason, the development of the window improves the performance of the filter and, therefore, the system. In this study, the cosh window function is designed using Particle Swarm Optimization, which is a preferred optimization method in many areas. Thus, alternatives to the standard results obtained from the existing traditional calculations will be produced, and different windows that perform the same function will be obtained. In addition, exponential and cosh window functions were designed in LabVIEW environment, which is a graphical programming language-based program, and the designed windows were analyzed at different parameter values. LabVIEW provides a fast and easy programming environment, and it provides the opportunity to realize real-Time applications with its external hardware. Utilizing this feature, the amplitude spectrum of cosh window designed in LabVIEW is displayed in real time for different window parameter values. As a result, FIR digital filters were designed using cosh window based on optimization and the cosh window designed in LabVIEW, and the distorted EEG signal was filtered using these filters and displayed in real time.
AtıfScopus8WoS5OpenAlex5
İncele - BildiriYalnız künye2020Performance Evaluation of Different Window Functions for Audio Fingerprint Based Audio Search Algorithm
Uyanik, Hakan; Koseoglu, Murat
Window functions are structures that are frequently used to increase the quality of the transformation when processing the signal in the time-frequency domain, especially when creating the spectrogram of the signal. In calculations for frequency information, Fourier transforms of short-time samples of the signal are transformed into spectrogram data with the help of window functions, and then operations are performed by using these data. In this study, the performance of three different window functions, which are used in obtaining spectrogram data in the algorithm developed for audio fingerprint-based audio recognition, have been tested under different artificial noise conditions. The results of these three different window functions were compared graphically, and a truth table was obtained based on the analyzed database. As a result of the simulations carried out in MATLAB, it has been observed that the hanning window function gives better spectral results for different noise levels, and accordingly it produces better matching results compared to the other two window functions as reported in former studies on different applications. In addition, some improvements about window length have been added into algorithm to obtain better results.
AtıfScopus12OpenAlex10İncele