Tander, Baran

Tander, Baran

Name Variants

Tander, Baran
B.,Tander
B. Tander
Baran, Tander
Tander, Baran
B.,Tander
B. Tander
Baran, Tander
Tander, B

Job Title

Dr. Öğr. Üyesi

Email Address

Tander@khas.edu.tr

Full item page

Scholarly Output

12

Articles

3

Citation Count

0

Supervised Theses

1 Scholarly Output Search Results

Now showing 1 - 10 of 12

A Numerical Method for Frequency Determination in the Astable Cellular Neural Networks With Opposite-Sign Templates
(IEEE, 2006) Özmen, Atilla; Özmen, Atilla; Tander, Baran; Tander, Baran
In this study a numerical method is proposed to determine the oscillation frequencies in the astable cellular neural networks with opposite-sign templates [1]. This method depends on the training of a multilayer perceptron that uses various template coefficients and the correspondant frequency values as inputs and outputs. First of all a frequency surface is obtained from templates and then training samples are picked from this surface in order to apply to multilayer perceptron. The effects of the template coefficients to the oscillation frequencies are also investigated. Furthermore an oscillator design is carried out for simulation and the performance as well as the advantages of the proposed method are evaluated.
Analytical Approaches for the Amplitude and Frequency Computations in the Astable Cellular Neural Networks With Opposite Sign Templates
(IEEE, 2007) Tander, Baran; Tander, Baran; Özmen, Atilla; Özmen, Atilla
In this paper, by using surface fitting methods, analytical approaches for amplitudes and frequencies of the x(1,2)(t) "States" in a simple dynamical neural network called "Cellular Neural Network with Opposite Sign Templates" which was proposed by Zou and Nossek [1], are obtained under oscillation conditions. The mentioned explicit expressions are employed in a cellular neural network based, amplitude and frequency tuneable oscillator design.
Hücresel Sinir Ağları için Gerilim Kaynaklı Hücre Modelleri
(AVES YAYINCILIK, 2001) Tander, Baran; Tander, Baran; Ün, Mahmut
Bu makalede, bağımsız ve bağımlı gerilim kaynağı tabanlı yeni bir Hücresel Sinir Ağı hücre devresi önerilmiştir. Bu modelde akım kaynaklı Chua ve Yang ‘ın klasik hücre devresinin aksine hücreler için denge noktaları dinamik birimdeki Rx ve Cx’ den bağımsızdırlar. Tam bir hücre devresi tasarlanıp kararlı ve kararsız durumlar için benzetimleri yapılmıstır. Önerilen modelin avantaj ve dezavantajları sonuçlar bölümünde tartışılmıştır.
Design and Implementation of a Cellular Neural Network Based Oscillator Circuit
(World Scientific and Engineering Acad and Soc, 2009) Tander, Baran; Tander, Baran; Özmen, Atilla; Özmen, Atilla; Özçelep, Yasin
In this paper, a novel inductorless oscillator circuit with negative feedbacks, based on a simple version of a "Cellular Neural Network" (CNN) called "CNN with an Opposite Sign Template" (CNN-OST) is designed and implemented. The system is capable of generating quasi-sine oscillations with tuneable amplitude and frequency which can't be provided at the same time in the conventional oscillator circuits.
Design and Implementation of a Negative Feedback Oscillator Circuit Based on a Cellular Neural Network With an Opposite Sign Template
(2010) Tander, Baran; Özmen, Atilla; Özmen, Atilla; Tander, Baran; Özçelep, Yasin
In this paper explicit amplitude and frequency expressions for a Cellular Neural Network with an Opposite-Sign Template (CNN-OST) under oscillation condition are derived and a novel inductorless oscillator circuit with negative feedbacks based on this simple structure is designed and implemented. The system is capable of generating quasi-sine signals with tuneable amplitude and frequency which can't be provided at the same time in the classical oscillator circuits.
Estimation of Energy Production in Biogas Plants
(Kadir Has Üniversitesi, 2023) YURTEKİN, ŞEVVAL AYŞE; Tander, Baran; Özmen, Atilla; Tander, Baran
The importance of renewable energy sources is getting more and more significant day by day. Renewable energies are required since the world's energy consumption is rising in tandem with the human population. The gas produced from wastes such as biogas, agricultural waste, and animal dung is an example of biomass energy, a renewable energy source. Machine learning is a branch of computer science that tries to improve its performance with the data it accumulates over time by simulating a human learning mechanism. This research begins with a discussion of biogas generation and investigations. The discussion then shifts to artificial intelligence, machine learning, and neural networks. In the application portion, an application of feature selection utilizing data, wastes, and biogas production from the Pales biogas plant is developed, as well as an application that forecasts biogas output using regression and an artificial neural network model. In the Python-based model, machine learning and deep learning libraries are utilized, and the data is preprocessed to make it compatible with the model. In this 22-featured model, the elements that contribute to the model, specifically biogas generation, were chosen using feature selection algorithms. The regression model and neural network model were created with the selected features as Dairy cow manure, Wheat Juice, Potato peel, Potato whole, Mixed vegie, Weak vinasse and Poultry manure. There are three distinct digesters included in the data. Three separate analyses were performed on three distinct digesters, and the findings were compared to the cumulative data. In the study, 20% of the data were set aside as test data and 80% were used for training. In terms of model performance, the data feature selection approach was effective for regression models, but negatively effect of variable reduction in neural networks. The R2 score was 52% in the neural network model trained without variable selection, and the mean of regression models trained with feature selection was 49%.
Neural Network Design for the Recurrence Prediction of Post-Operative Non-Metastatic Kidney Cancer Patients
(Institute of Electrical and Electronics Engineers Inc., 2016) Tander, Baran; Tander, Baran; Özmen, Atilla; Özmen, Atilla; Özden, Ender
In this paper various post-operative recurrence estimation models called nomograms for the kidney cancer patients without any metastates are introduced and novel systems based on a Multilayer Perceptron Neural Network are designed to simplify and integrate the mentioned techniques which is believed to ease the physician' s post-operative follow up procedures. The parameters effecting the recurrence are the TNM stage tumor size and nuclear (Fuhrman) grade the existance of necrosis and vascular invasion. Independent systems for two of the individual prediction methods as well as a system that combines these are designed and performance analyses are carried out to verify the reliability. © 2015 Chamber of Electrical Engineers of Turkey.
Amplitude and Frequency Modulations With Cellular Neural Networks
(Springer, 2015) Tander, Baran; Tander, Baran; Özmen, Atilla; Özmen, Atilla
Amplitude and frequency modulations are still the most popular modulation techniques in data transmission at telecommunication systems such as radio and television broadcasting gsm etc. However the architectures of these individual systems are totally different. In this paper it is shown that a cellular neural network with an opposite-sign template can behave either as an amplitude or a frequency modulator. Firstly a brief information about these networks is given and then the amplitude and frequency surfaces of the generated quasi-sine oscillations are sketched with respect to various values of their cloning templates. Secondly it is proved that any of these types of modulations can be performed by only varying the template components without ever changing their structure. Finally a circuit is designed simulations are presented and performance of the proposed system is evaluated. The main contribution of this work is to show that both amplitude and frequency modulations can be realized under the same architecture with a simple technique specifically by treating the input signals as template components.
Mobile Application Development for the Estimation of Recurrence in Post-Operative Kidney Cancer Cases
(IEEE, 2018) Tander, Baran; Tander, Baran; Özmen, Atilla; Özmen, Atilla; Ozden, Ender
In this paper a post-operative recurrence estimation tool called Sorbellini's nomogram for the kidney cancer patients showing no metastates is introduced and a novel application for mobile devices based on this model is developed for the physician's follow up procedures. The TNM stage tumor size nuclear (Fuhrman) grade the existance of necrosis and vascular invasion are employed as the input parameters for this software to predict the recurrence probability in mentioned patients. Finaly the performance analyses are carried out to verify the reliability of the application.
Channel Equalization With Cellular Neural Networks
(IEEE, 2010) Özmen, Atilla; Özmen, Atilla; Tander, Baran; Tander, Baran
In this paper a dynamic neural network structure called Cellular Neural Network (CNN) is employed for the equalization in digital communication. It is shown that this nonlinear system is capable of suppressing the effect of intersymbol interference (ISI) and the noise at the channel. The architecture is a small-scaled simple CNN containing 9 neurons thus having only 19 weight coefficients. Proposed system is compared with linear transversal filters as well as with a Multilayer Perceptron (MLP) based equalizer.