Serdar Kockanat

The parameter extraction of the thermally annealed Schottky barrier diode using the modified artificial bee colony

In this paper, a new method based on the modified artificial bee colony (MABC) algorithm to determine the main characteristic parameters of the Schottky barrier diode such as barrier height, ideality factor and series resistance. For this model, the Ni/n-GaAs/In Schottky barrier diode was produced and annealed at different temperature in a laboratory. The performance of the modified ABC method was compared to that of the basic artificial bee colony (ABC), particle swarm optimization (PSO), differential evolution (DE), genetic algorithm (GA) and simulated annealing (SA). From the results, it is concluded that the modified ABC algorithm is more flexible and effective for the parameter determination than the other algorithms.

Image denoising with 2-D FIR filter by using artificial bee colony algorithm

In this paper, a new design approach that has employed the artificial bee colony algorithm for image denoising using two dimensional finite impulse response digital filter, is discussed. Four different images have been used for testing. The white Gaussian noise has been added to each of the images and the two dimensional finite impulse response digital filter removes the noise from the noisy images. The original images have been compared with the restored images.

A novel 2D-ABC adaptive filter algorithm

Recently, two dimensional (2D) adaptive filter, which can self-adjust the filter coefficients by using an optimization algorithm driven by an error function, has attracted much attention by researchers and practitioners, because 2D adaptive filtering can be employed in many image processing applications, such as image denoising, enhancement and deconvolution. In this paper, a novel 2D artificial bee colony (2D-ABC) adaptive filter algorithm was firstly proposed and to the best of our knowledge, there is no study describing 2D adaptive filter algorithm based on metaheuristic algorithms in the literature. At the first stage, in order to analyze the performance and computational efficiency of the novel 2D-ABC adaptive filter algorithm, it was used in the 2D adaptive noise cancellation (ANC) as recommend in literature. For a fair comparison, the competitor 2D adaptive filter algorithms were applied to the same 2D-ANC setup under same condition, such as same Gaussian noise, same filter order or same test images. The results of the novel 2D-ABC adaptive filter algorithm were compared with those of the 2D affine projection algorithms (APA), 2D normalized least mean square (NLMS) and 2D least mean square (LMS) adaptive filter algorithms. At the second stage, to demonstrate the robustness of the novel 2D-ABC adaptive filter algorithm, it was implemented for speckle noise filtering on noisy clinical ultrasound images. The results show that the novel 2D-ABC adaptive filter algorithm has a better performance than the other classical adaptive filter algorithms and its denoising efficiency is quite well on noisy images with different characteristics. A novel 2D-ABC adaptive filter algorithm has been proposed.The results show that the 2D-ABC adaptive filter algorithm has a better performance than the others.The proposed adaptive filter algorithm has been applied to image denoising problem.There is no study describing 2D adaptive filter algorithm based on metaheuristic algorithms in the literature.

The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature

Recently, the design of the two-dimensional digital filter has become a subject of interest in the field of two-dimensional signal processing. The two-dimensional digital filter has been applied in many important areas such as image processing, television systems and seismic signal processing. In digital filter design, there are several indispensable aims such as stability, reduced computational complexity and computational time. Thus, researchers and practitioners have investigated various advanced methods based on metaheuristic optimization algorithms for the design of the two-dimensional digital filter. Metaheuristic optimization algorithms have been applied to solve different complicated problems in various fields and they have also been successfully used in digital filter design. This paper presents a review of the design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms such as the genetic algorithm, differential evolution and particle swarm optimization. By comparing the proposed design approaches based on metaheuristic optimization algorithms, it is observed that the genetic algorithm is the most preferred algorithm and emerging novel algorithms using metaheuristic optimization algorithms have better performance in terms of computational complexity and computational time. It is hoped that this review will be helpful for researchers and practitioners studying the design of two-dimensional digital filters.

Parameter determination of the Schottky barrier diode using by artificial bee colony algorithm

In this paper, a new method based on the Artificial Bee Colony (ABC) for determining the Schottky barrier height (Φb), ideality factor (n) and series resistance (RS) of a Schottky barrier diode (SBD) model using forward current-voltage (I-V) characteristics, is described. For this SBD model, the Ni/n-GaAs/In Schottky barrier diode was produced in a laboratory and the I-V characteristics of the SBD were measured. The real parameters (Φb, n, RS) and obtained parameters from the ABC of the SBD model were compared to determine the models accuracy.

Parameter tuning of artificial bee colony algorithm for Gaussian noise elimination on digital images

In this paper, the control parameters of the artificial bee colony algorithm were examined to determine for the best performance of the noise elimination problem on gray level digital images. In order to eliminate a noise, a two dimensional finite impulse response digital filter was designed and the artificial bee colony algorithm was used to adjust its coefficient matrix. For the best selected control parameters, the designed two dimensional finite impulse response digital filter was used to eliminate the Gaussian noise on the gray level digital images at different noise densities and the performance of the designed filter was compared in terms of the noise tolerance.

Aort valve Doppler signal noise elimination using IIR filter designed with ABC algorithm

The heart valves are most important parts of the heart. The biomedical signals such as aortic valve Doppler signals are sensitive to noise and interference. In this paper, aortic valve Doppler noise is eliminated with an infinite impulse response (IIR) filter. Filter coefficients were determined with sampled aortic signal using a swarm intelligence algorithm, artificial bee colony, and the designed filters eliminate noise on aortic cycle signal. 2nd and 4th degree infinite impulse response filters were designed with artificial bee colony algorithm.

A new approach based on differential evolution algorithm for harmonic estimation problems

Electrical harmonic is an important issue affecting power quality of electrical systems. Estimation of power harmonics are essential to design filters to be utilized in electrical power systems for reducing harmonics and their effects on the electrical power systems. In this study, a harmonic estimator based on differential evolution algorithm is proposed for harmonic estimation problems shown in power systems. Analyses of designed estimator are performed for two different experiments. In addition, performances of proposed estimator are compared with available results of the literature. The achieved results of this study showed that the proposed harmonic estimator in this paper is superior among previously reported ones.

A differential search algorithm application for solving harmonic estimation problems

This paper presents an optimization approach based differential search algorithm (DSA) for amplitude and phase estimation of time varying power signal in electrical power systems. Proposed method has been tested for two well-known literature problems that include fundamental, sub- and inter-harmonic estimation cases. The performance of the suggested DSA method has been compared with those of the Genetic Algorithm-Least Square (GA-LS), Particle swarm optimization with passive congregation least square (PSOPC-LS), Bacterial foraging optimization (BFO), Fuzzy bacterial foraging-least square (F-BFO-LS) and Bacterial foraging optimization-recursive least square (BFO-RLS). The results show that the proposed DSA method has an efficient, fast and accurate estimation behavior among previously reported ones in the case of time varying harmonic power signals. In addition, the estimated amplitude and phase values using DSA method can provide possibility to design suitable harmonic reducing filter for improving power quality.

Harmonic estimator design using hybrid particle swarm optimization

The demand for electrical energy is increasing day by day and achieving high quality energy is becoming very important subject. Especially, harmonics occurred in power systems not only reduce the quality of the energy but also cause dangerous situations such as heating and burning. For this reason, the estimation of the amplitude and phase of harmonics plays an important role in the design of electrical systems that will reduce the negative effects of harmonics. In this study, a novel harmonic estimator design for the harmonic estimation problem, which is frequently proposed in the literature, is proposed using hybrid particle swarm optimization (HPSO). The obtained results are compared with those of the proposed studies in literature and the success of the designed HPSO based harmonic estimator is analyzed.