Özmen, Atilla
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Özmen, Atilla
A.,Özmen
A. Özmen
Atilla, Özmen
Ozmen, Atilla
A.,Ozmen
A. Ozmen
Atilla, Ozmen
Özmen, A.
Ozmen, A.
Özmen, Atilla
A.,Özmen
A. Özmen
Atilla, Özmen
Ozmen, Atilla
A.,Ozmen
A. Ozmen
Atilla, Ozmen
Özmen, A.
Ozmen, A.
Özmen, Atilla
Job Title
Dr. Öğr. Üyesi
Email Address
Main Affiliation
Electrical-Electronics Engineering
Electrical-Electronics Engineering
05. Faculty of Engineering and Natural Sciences
01. Kadir Has University
Electrical-Electronics Engineering
05. Faculty of Engineering and Natural Sciences
01. Kadir Has University
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
3
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4QUALITY EDUCATION
0
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
1
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
2
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
1
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14LIFE BELOW WATER
0
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15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
1
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This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
46
Articles
13
Views / Downloads
351/5416
Supervised MSc Theses
8
Supervised PhD Theses
1
WoS Citation Count
146
Scopus Citation Count
206
Patents
0
Projects
0
WoS Citations per Publication
3.17
Scopus Citations per Publication
4.48
Open Access Source
22
Supervised Theses
9
| Journal | Count |
|---|---|
| 2012 International Symposium on Innovations in Intelligent Systems and Applications | 2 |
| 2007 IEEE 15th Signal Processing and Communications Applications, SIU | 1 |
| 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 | 1 |
| 2015 9th International Conference on Electrical and Electronics Engineering (ELECO) | 1 |
| 2018 26th Signal Processing and Communications Applications Conference (SIU) | 1 |
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46 results
Scholarly Output Search Results
Now showing 1 - 10 of 46
Conference Object A Numerical Method for Frequency Determination in the Astable Cellular Neural Networks With Opposite-Sign Templates(IEEE, 2006) Özmen, Atilla; Tander, BaranIn 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.Article Citation - WoS: 11Citation - Scopus: 12Application of Deep Neural Network (dnn) for Experimental Liquid-Liquid Equilibrium Data of Water + Butyric Acid + 5-Methyl Ternary Systems(Elsevier B.V., 2021) Bekri, Sezin; Dilek, Özmen; Aykut, Türkmenoğlu; Atilla, Özmen; Özmen, Atilla; Türkmenoğlu, Aykut; Özmen, DilekLLE data are important for simulation and design of extraction equipment. In this study, deep neural network (DNN) structure was proposed for modelling of the ternary liquid-liquid equilibrium (LLE). LLE data of (water + butyric acid + 5-methyl-2-hexanone) ternaries defined at three different temperatures of 298.2, 308.2, and 318.2 K and P = 101.3 kPa, were obtained experimentally and then correlated with nonrandom two-liquid (NRTL) and universal quasi-chemical (UNIQUAC) models. The performance of the proposed DNN model was compared with that of NRTL and UNIQUAC in terms of the root mean square errors (RMSE). RMSE values were obtained between 0.02-0.06 for NRTL and UNIQUAC, respectively. For DNN, the error values were obtained between 0.00005-0.01 for all temperatures. According to the calculated RMSE values, it was shown that proposed DNN structure can be better choice for the modelling of LLE system. Othmer-Tobias and Hand correlations were also used for the experimental tie-lines. Distribution coefficient and separation factors were calculated from the experimental data.Conference Object Liquid-Liquid Equilibria of Water+propionic Acid+solvent Ternaries at 298.2 K(2006) Özmen, Dilek; Özmen, Atilla(Liquid + liquid) equilibrium (LLE) data for (water + propionic acid + solvent) were measured at T= 298.2 K and atmospheric pressure. The solvents were methyl isoamyl ketone (5-methyl-2-hexanone) and diisobutyl ketone. The tie-line data were predicted by the UNIFAC method. A comparison of the extracting capabilities of the solvents was made with respect to distribution coefficients separation factors and solvent free selectivity bases.Conference Object Citation - Scopus: 4Smart Stethoscope(IEEE, 2020) Türker, Mehmet Nasuhcan; Çagan, Yagiz Can; Yıldırım, Batuhan; Demirel, Mücahit; Özmen, Atilla; Tander, Baran; Çevik, MesutIn this study, a device named smart stethoscope that uses digital sensor technology for sound capture, active acoustics for noise cancellation and artificial intelligence (AI) for diagnosis of heart and lung diseases is developed to help the health workers to make accurate diagnoses. Furthermore, the respiratory diseases are classified by using Deep Learning and Long Short-Term Memory (LSTM) techniques whereas the probability of these diseases are obtained.Conference Object Analytical Approaches for the Amplitude and Frequency Computations in the Astable Cellular Neural Networks With Opposite Sign Templates(IEEE, 2007) Tander, Baran; Özmen, AtillaIn 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.Master Thesis Caching Algorithm Implementation for Edge Computing in Iot Network(Kadir Has Üniversitesi, 2020) Abduljabbar, Mohammed; Özmen, Atilla; Öğrenci, Arif SelçukThe developing IoT concept brings new challenges to the service providers. The architecture of the networks changes to satisfy the needs arising by the large number of connected devices. Edge computing is the new architectural solution that will be used in the IoT networks. This architecture is more dynamic than the cloud computing network where the data can be quickly processed in the different layers of the network without going to the cloud. This will remove the problems faced by cloud computing: increase in data traffic and increase in latency of provided services. Research on edge computing in IoT networks encompass information-centric networks, use of 5G, and improving the hardware devices however a suitable solution for all the IoT use cases is not available yet. In this thesis, use of caching among IoT nodes is proposed as a solution to increase the efficiency of edge computing. Caching is an old but effective solution for dealing with data because it improves the real-time response of the system and can be used in IoT use cases. It will also not cause an extra hardware cost. In this research, two commonly used caching algorithms, LRU (Least Recently Used) and FIFO (First in First Out), are investigated and compared for their performance in sample IoT scenarios. Reductions in data processing time are observed where CPU and RAM utilizations are enhanced.Conference Object Citation - Scopus: 1Channel Equalization With Cellular Neural Networks(IEEE, 2010) Özmen, Atilla; Tander, BaranIn 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.Conference Object Citation - Scopus: 1The Effect of Data Augmentation on Adhd Diagnostic Model Using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2019) Cicek, G.; Ozmen, A.; Akan, A.Attention Deficit Hyperactivity Disorder (ADHD) is a neuro-behavioral hyperactivity disorder. It is frequently seen in childhood and youth, and lasts a lifetime unless treated. The ADHD classification model should be objective and robust. Correct diagnosis usually depends on the knowledge and experience of health professionals. In this respect, an automated method to be developed for the ADHD classification model is of great importance for clinicians. In this study, the effect of data augmentation on ADHD classification model with deep learning was investigated. For this purpose, magnetic resonance images were taken from NPIstanbul NeuroPsychiatry Hospital and ADHD-200 database. Since the images were not sufficient in terms of training, data augmentation methods were applied and by convolutional neural network (CNN) architecture, these data were classified and tried to reveal the diagnosis of the disease independently from the non-objective experiences of the health professionals. © 2019 IEEE.Conference Object Citation - WoS: 1Citation - Scopus: 1Age Classification by WGAN Brain MR Image Augmentation(IEEE, 2024) Yaman, Batuhan; Yilmaz, Ozge Zeynep; Darici, Muazzez Buket; Ozmen, AtillaMedical image augmentation plays a crucial role in enhancing the performance of Artificial Intelligence (AI) applications in medical sciences. Augmenting medical images is important for solving data scarcity, increasing data diversity, enhancing robustness and reliability of model and improving training and test results that can be done in medical sciences. In this work we show that Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) can be used for increasing the performance of data classification. To achieve that, we have augmented healthy brain MR images by using WGAN and updated the dataset. The results give that when dataset augmented by WGAN-GP is used as input for CNN-based model to solve age classification problem, accuracy of this model increases to 98,37% from 95,14%. It can be concluded that the purposed WGAN-based brain MR image augmentation method enhances the performance of image classification.Conference Object Citation - Scopus: 3Detection of Trojans in Integrated Circuits(IEEE, 2012) Baktır, Selçuk; Güçlüoğlu, Tansal; Özmen, Atilla; Alsan, Hüseyin Fuat; Macit, Mustafa CanThis paper presents several signal processing approaches in Trojan detection problem in very large scale integrated circuits. Specifically wavelet transforms spectrograms and neural networks are used to analyze power side-channel signals. Trojans in integrated circuits can try to hide themselves and become almost invisible due to process and measurement noises. We demonstrate that our initial results with these techniques are promising in successful detection. Discrete wavelet transforms and spectrograms can provide clear visual assistance in detecting Trojans by catching the time-scale differences and time-frequency activities introduced by the Trojans. Furthermore neural networks with sufficient training are also used and simulation results show that correct decisions are possible with a very high success rate. © 2012 IEEE.
