M. Dogan Elbi

Pamukkale University · Department of Electrical and Electronic Engineering (Graduate)

The Fourier Decomposition Method (FDM) is an advanced tool to gather information about signals from nonlinear and/or non-stationary systems. It decomposes a signal into a finite set of zero-mean bandlimited oscillation modes, so-called analytic Fourier intrinsic band functions (AFIBFs). Owing to its amplitude and frequency modulation properties, ea...

The Fourier Decomposition Method (FDM) is an advanced tool to gather information about signals from nonlinear and/or non-stationary systems. It decomposes a signal into a finite set of zero-mean band-limited oscillation modes, so-called analytic Fourier intrinsic band functions (AFIBFs). Owing to its amplitude and frequency modulation properties, e...

There are two main methods for measuring the efficiency of decision-making units (DMUs): data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Each of these methods has advantages and disadvantages. DEA is more popular in the literature due to its simplicity, as it does not require any pre-assumption and can be used for measuring...

Fourier decomposition method (FDM) is an adaptive data-driven time-frequency analysis tool developed recently for nonlinear and non-stationary time series. This method intrinsically decomposes any signal into a small number of band-limited zero-mean orthogonal functions called as Fourier intrinsic band functions (FIBFs) via zero-phase ideal band-pa...

The Fourier decomposition method (FDM) is the newest time-frequency-energy analysis tool for signals. Using the FDM, any signal can be represented by the sum of a small number of band-limited orthogonal functions termed Fourier intrinsic band functions (FIBFs). In its present form, however, the FDM is not based on an optimality criterion. The lack...

Empirical mode decomposition (EMD) is a favorite tool for analyzing nonlinear and non-stationary signals. It decomposes any signal into a finite set of oscillation modes consisting of intrinsic mode functions and a residual function. Superimposing all these modes reconstructs the signal without any information loss. In addition to satisfying the pe...

Empirical mode decomposition (EMD) is a tool developed for analyzing nonlinear and non-stationary signals. It is capable of splitting any signal into a set of oscillation modes known as intrinsic mode functions and a residual function. Although the EMD satisfies the perfect signal reconstruction property by superimposing all the oscillation modes,...

Bipolar disorder is a disease that is mostly encountered in medical literature. This psychiatric illness with hypomanic and depressive mood can be modelled as limit cycle oscillator. Artificial sweetener like Aspartame (ASP), which has neurologic and behavioral effects on animals, induces the bipolar disorder-like behavioral properties on rats. In...

Without having any information of original signal, estimating the desired signal from noisy measurements is a challenging problem. In this paper, the denoising problem of signals corrupted by additive white Gaussian noise (AWGN) is considered in the empirical mode decomposition (EMD) framework, and five different noise suppression scenarios based o...

The reconstruction problem of a high-resolution (HR) signal from a set of its noise-corrupted low-resolution (LR) versions is considered. As a part of this problem, a hybrid method that consists of four operation units is proposed. The first unit applies noise reduction based on the empirical mode decomposition interval-thresholding to the noisy LR...

High-resolution signal reconstruction from a set of its noisy low-resolution measurements is considered. As an alternative solution to this problem, a method employing the empirical mode decomposition (EMD) based denoising approach is proposed. In the framework of the proposed method, iterative EMD interval-thresholding based denoising procedure is...

In this study, a hybrid method is proposed for the reconstruction of a high-resolution (HR) signal from a set of its noise corrupted low-resolution (LR) versions. In this hybrid method, noise reduction based on the empirical mode decomposition and Savitzky-Golay filtering is applied to the LR observations. Afterwards, zero-interpolated HR signals a...

The problem of reconstructing a known high-resolution signal from a set of its low-resolution parts exposed to additive white Gaussian noise is addressed in this paper from the perspective of statistical multirate signal processing. To enhance the performance of the existing high-resolution signal reconstruction procedure that is based on using a s...

In this study, the noise cancellation problem on noise corrupted low-frequency signals by using the Empirical Mode Decomposition (EMD) method is considered. For this aim, the Intrinsic Mode (IM) functions of the low-frequency signal corrupted by white Gaussian noise are obtained by applying EMD on this signal. Savitzky-Golay filter and Least Square...