Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/45

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Citation Count: 0
Amplitude and Frequency Modulations with Cellular Neural Networks
(Springer, 2015) Tander, Baran; 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.
Citation Count: 11
Derivation of the optical constants of spin coated CeO2-TiO2-ZrO2 thin films prepared by sol-gel route
(Pergamon-Elsevier Science Ltd, 2011) Ghodsi, Farhad E.; Tepehan, Fatma Zehra; Tepehan, Galip Gültekin
Ternary thin films of cerium titanium zirconium mixed oxide were prepared by the sol-gel process and deposited by a spin coating technique at different spin speeds (1000-4000 rpm). Ceric ammonium nitrate ce(NO3)(6)(NH4)(2) titanium butoxide Ti[O(CH2)(3)CH3](4) and zirconium propoxide Zr(OCH2CH2CH3)(4) were used as starting materials. Differential calorimetric analysis (DSC) and thermogravimetric analysis (TGA) were carried out on the CeO2-TiO2-ZrO2 gel to study the decomposition and phase transition of the gel. For molecular structural elemental and morphological characterization of the films Fourier Transform Infrared (FTIR) spectral analysis X-ray diffraction (XRD) energy dispersive X-ray spectroscopy (EDS) cross-sectional scanning electron microscopy (SEM) and atomic force microscopy (AFM) were carried out. All the ternary oxide thin films were amorphous. The optical constants (refractive index extinction coefficient band gap) and thickness of the films were determined in the 350-1000 nm wavelength range by using an nkd spectrophotometer. The refractive index extinction coefficient and thickness of the films were changed by varying the spin speed. The oscillator and dispersion energies were obtained using the Wemple-DiDomenico dispersion relationship. The optical band gap is independent of the spin speed and has a value of about E-g approximate to 2.82 +/- 0.04 eV for indirect transition. (C) 2011 Published by Elsevier Ltd.
Citation Count: 58
Energy Aware Multi-Hop Routing Protocol for WSNs
(IEEE, 2018) Dağ, Tamer; Dağ, Tamer
In this paper we propose an energy-efficient multi-hop routing protocol for wireless sensor networks (WSNs). The nature of sensor nodes with limited batteries and inefficient protocols are the key limiting factors of the sensor network lifetime. We aim to provide for a green routing protocol that can be implemented in a wireless sensor network. Our proposed protocol's most significant achievement is the reduction of the excessive overhead typically seen in most of the routing protocols by employing fixed clustering and reducing the number of cluster head changes. The performance analysis indicates that overhead reduction significantly improves the lifetime as energy consumption in the sensor nodes can be reduced through an energy-efficient protocol. In addition the implementation of the relay nodes allows the transmission of collected cluster data through inter cluster transmissions. As a result the scalability of a wireless sensor network can be increased. The usage of relay nodes also has a positive impact on the energy dissipation in the network.
Citation Count: 3
In silico identification of critical proteins associated with learning process and immune system for Down syndrome
(Public Library Science, 2019) Dağ, Tamer; Dağ, Tamer
Understanding expression levels of proteins and their interactions is a key factor to diagnose and explain the Down syndrome which can be considered as the most prevalent reason of intellectual disability in human beings. In the previous studies the expression levels of 77 proteins obtained from normal genotype control mice and from trisomic Ts65Dn mice have been analyzed after training in contextual fear conditioning with and without injection of the memantine drug using statistical methods and machine learning techniques. Recent studies have also pointed out that there may be a linkage between the Down syndrome and the immune system. Thus the research presented in this paper aim at in silico identification of proteins which are significant to the learning process and the immune system and to derive the most accurate model for classification of mice. In this paper the features are selected by implementing forward feature selection method after preprocessing step of the dataset. Later deep neural network gradient boosting tree support vector machine and random forest classification methods are implemented to identify the accuracy. It is observed that the selected feature subsets not only yield higher accuracy classification results but also are composed of protein responses which are important for the learning and memory process and the immune system.
Citation Count: 0
Machine Learning Approaches for Predicting Protein Complex Similarity
(Mary Ann Liebert Inc Publ, 2017) Farhoodi, Roshanak; Akbal-Delibas, Bahar; Haspel, Nurit
Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals electrostatic and desolvation forces) and the similarity of a conformation to its native structure the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and calibrate their weights by using a training set to evaluate and rank candidate complexes. Despite improvements in the predictive power of recent docking methods producing a large number of false positives by even state-of-the-art methods often leads to failure in predicting the correct binding of many complexes. With the aid of machine learning methods we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation. We trained a two-layer neural network a multilayer neural network and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock. We validated these methods with a set of refinement candidate structures. We were able to predict the root mean squared deviations (RMSDs) of protein complexes with a very small often less than 1.5 angstrom error margin when trained with structures that have RMSD values of up to 7 angstrom. In our most recent experiments with the protein samples having RMSD values up to 27 angstrom the average prediction error was still relatively small attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.
Citation Count: 12
Optimal input design for the detection of changes towards unknown hypotheses
(Taylor & Francis Ltd, 2004) Kerestecioğlu, Feza; Cetin, I
The effects of auxiliary input signals on detecting changes in ARMAX processes via statistical tests are discussed. Two extensions to the Cumulative Sum Test are considered. The first is applicable when the direction of the change in the parameter space is known but its magnitude is unknown. The second is applicable when neither is known. The performance criteria for the design of stationary stochastic inputs are based on the asymptotic properties of the tests. It is shown that power-constrained optimal inputs have discrete spectra and a suitably chosen input can greatly improve the detection performance.
Citation Count: 1
Optimal Power Allocation Between Training and Data for MIMO Two-Way Relay Channels
(IEEE-INST Electrical Electronics Engineers Inc, 2015) Şenol, Habib; Tepedelenlioğlu, Cihan; Şenol, Habib
Power allocation between training and data in MIMO two-way relay systems is proposed which takes into consideration both the symmetric and asymmetric cases of the two sources. For the former we present a closed form for the optimal ratio of data energy to total energy which is suitable for the single antenna case as well and can be simplified when the number of antennas is large. We also show that the achievable rate is a monotonically increasing function of the data time. Concerning the asymmetric case we prove that the difference of the two SNRs is either a concave or convex function of the energy ratio depending on the imbalance between the two sources. Using this the minimum SNR between the two sources is maximized.
Citation Count: 29
Received signal strength based least squares lateration algorithm for indoor localization
(Pergamon-Elsevier Science Ltd, 2018) Arsan, Taner; Dağ, Tamer
Following the success of accurate location estimation for outdoor environments locating targets in indoor environments has become an important research area. Accurate location estimation of targets for indoor environments has the potential for the development of many different applications such as public safety social networking information and mapping services. However the GPS (Global Positioning System) technology used for outdoor environments is not applicable to indoor environments making accurate location estimation a challenging issue for indoor environments. In this paper we propose a received signal strength based least squares lateration algorithm which uses the existing infrastructure. By employing redundancy in the number of access points and applying least squares approximations to the received signal strength values the lateration algorithm increases the accuracy of location estimations. The usage of the existing infrastructure makes the proposed algorithm low cost when compared to other positioning algorithms which need very precise high cost components. (C) 2017 Elsevier Ltd. All rights reserved.
Citation Count: 2
Transmitter source location estimation using crowd data
(Pergamon-Elsevier Science Ltd, 2018) Arsan, Taner; Arsan, Taner
The problem of transmitter source localization in a dense urban area has been investigated where a supervised learning approach utilizing neural networks has been adopted. The cellular phone network cells and signals have been used as the test bed where data are collected by means of a smart phone. Location and signal strength data are obtained by random navigation and this information is used to develop a learning system for cells with known base station location. The model is applied to data collected in other cells to predict their base station locations. Results are consistent and indicating a potential for effective use of this methodology. The performance increases by increasing the training set size. Several shortcomings and future research topics are discussed. (C) 2017 Elsevier Ltd. All rights reserved.
Citation Count: 18
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.

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