Teknik Bilimler Meslek Yüksekokulu / Vocational School of Technical Sciences

Permanent URI for this collectionhttps://hdl.handle.net/11727/2031

Browse

Now showing 1 - 20 of 31

Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy
(,2013, 2013) Ankışhan, Haydar; Yılmaz, Derya
Snoring, which may be decisive for many diseases, is an important indicator especially for sleep disorders. In recent years, many studies have been performed on the snore related sounds (SRSs) due to producing useful results for detection of sleep apnea/hypopnea syndrome (SAHS). The first important step of these studies is the detection of snore from SRSs by using different time and frequency domain features. The SRSs have a complex nature that is originated from several physiological and physical conditions. The nonlinear characteristics of SRSs can be examined with chaos theory methods which are widely used to evaluate the biomedical signals and systems, recently. The aim of this study is to classify the SRSs as snore/breathing/silence by using the largest Lyapunov exponent (LLE) and entropy with multiclass support vector machines (SVMs) and adaptive network fuzzy inference system (ANFIS). Two different experiments were performed for different training and test data sets. Experimental results show that the multiclass SVMs can produce the better classification results than ANFIS with used nonlinear quantities. Additionally, these nonlinear features are carrying meaningful information for classifying SRSs and are able to be used for diagnosis of sleep disorders such as SAHS.
Optimization of Waiting and Journey Time in Group Elevator System Using Genetic Algorithm
(2014) Tartan, Emre Oner; Erdem, Hamit; Berkol, Ali
Efficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control system's performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm.
Timing studies of X Persei and the discovery of its transient quasi-periodic oscillation feature
(2014) Acuner, Z.; Inam, S.C.; Sahiner, S.; Serim, M.M.; Baykal, A.; Swank, J.
We present a timing analysis of X Persei (X Per) using observations made between 1998 and 2010 with the Proportional Counter Array (PCA) onboard the Rossi X-ray Timing Explorer (RXTE) and with the INTEGRAL Soft Gamma-Ray Imager (ISGRI). All pulse arrival times obtained from the RXTE-PCA observations are phase-connected and a timing solution is obtained using these arrival times. We update the long-term pulse frequency history of the source by measuring its pulse frequencies using RXTE-PCA and ISGRI data. From the RXTE-PCA data, the relation between the frequency derivative and X-ray flux suggests accretion via the companion's stellar wind. However, the detection of a transient quasi-periodic oscillation feature, peaking at similar to 0.2 Hz, suggests the existence of an accretion disc. We find that double-break models fit the average power spectra well, which suggests that the source has at least two different accretion flow components dominating the overall flow. From the power spectrum of frequency derivatives, we measure a power-law index of similar to-1, which implies that, on short time-scales, disc accretion dominates over noise, while on time-scales longer than the viscous time-scales, the noise dominates. From pulse profiles, we find a correlation between the pulse fraction and the count rate of the source.
A Novel Approach for Estimating Heat Transfer Coefficients of Ethylene Glycol-Water Mixtures
(2014) Bulut, Murat; Ankishan, Haydar; Demircioglu, Erdem; Ari, Seckin; Sengul, Orhan; https://orcid.org/0000-0002-6240-2545; AAH-4421-2019
Ethylene glycol-water mixtures (EGWM) are vital for cooling engines in automotive industry. Scarce information is available in the literature for estimating the heat transfer coefficients (HTC) of EGWM using knowledge-based estimation techniques such as adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN) which offer nonlinear input-output mapping. In this paper, the supervised learning methods of ANFIS and ANN are exploited for estimating the experimentally determined HTC. This original research fulfills the preceding modeling efforts on thermal properties of EGWM and HTC applications in the literature. An experimental test setup is designed to compute HTC of mixture over a small circular aluminum heater surface, 9.5 mm in diameter, placed at the bottom 40-mm-wide wall of a rectangular channel 3 mm x 40 mm in cross section. Measurement data are utilized as the train and test data sets of the estimation process. Prediction results have shown that ANFIS provide more accurate and reliable approximations compared to ANN. ANFIS present correlation factor of 98.81 %, whereas ANN estimate 87.83 % accuracy for test samples.
Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems
(2015) Demircioglu, Erdem; Yagli, Ahmet Fazil; Gulgonul, Senol; Ankishan, Haydar; Tartan, Emre Oner; Sazli, Murat H.; Imeci, Taha
This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs) using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and Differential Evolution (DE) are applied with ANN's conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking.
An OFDM throughput analysis for cognitive radio application in contiguous or noncontiguous TV white spaces
(2015) Ciftlikli, Cebrail; Tuncer, Ahmet Turgut; Ozturk, Yusuf
Radio frequency spectrum is a finite and scarce resource. Efficient use of the radio frequency spectrum is a fundamental research issue. Since a large portion of the assigned radio frequency spectrum is used only sporadically, the bands currently allocated to TV services can be opportunistically reassigned to support broadband networking services while continuing to provide broadcast TV. The fragmented and unused TV channels named white spaces have a considerable amount of bandwidth potential and long transmission ranges. Bandwidth scalability can be supported by bonding multiple contiguous or noncontiguous consecutive channels using orthogonal frequency division multiplexing (OFDM)-based cognitive radio. In this paper, an in-depth throughput analysis of OFDM fixed carrier spacing and fixed carrier number approaches has been done for various modulation schemes in TV white spaces. Signal propagation delay is studied under various channel conditions. An analytical model-based estimation of the throughput by taking into account channel bonding is presented.
A Simple Population Based Hybrid Harmonic Estimation Algorithm
(2016) Tartan, Emre Oner; Erdem, Hamit
This paper presents a new hybrid algorithm for harmonic estimation. The algorithm combines a simple fast population based search algorithm with Least Squares Method. It is based on the structural property of the harmonic estimation problem which implies that the signal model is linear in amplitude and nonlinear in phase. The hybrid algorithm uses the search algorithm for phase estimation and LS for amplitude estimation, iteratively. Exploiting the objective function defined according to the error of single harmonic's phase estimation, the proposed search algorithm distributes the population through equal intervals and simply narrows the search space sequentially in every generation. Unlike the other heuristic optimization algorithms that uses random distribution in initialization stage, the proposed method provides more robust convergence in the limits determined by the generation number. Simulation results show that the proposed hybrid algorithm not only gives accurate results but also significantly improves the computation time when compared with other heuristic optimization algorithms. Moreover this approach can be used to reduce the search duration when involved in other evolutionary optimization algorithms in a hybrid way and then can deal with frequency deviation and subharmonic estimation which are pitfalls for DFT based algorithms.
Reduced Graphene Oxide-GelMA Hybrid Hydrogels as Scaffolds for Cardiac Tissue Engineering
(2016) Shin, Su Ryon; Zihlmann, Claudio; Akbari, Mohsen; Assawes, Pribpandao; Cheung, Louis; Zhang, Kaizhen; Manoharan, Vijayan; Zhang, Yu Shrike; Yuksekkaya, Mehmet; Wan, Kai-tak; Nikkhah, Mehdi; Dokmeci, Mehmet R.; Tang, Xiaowu (Shirley); Khademhosseini, Ali; 0000-0002-2665-5799; 27254107; P-1760-2016
Biomaterials currently used in cardiac tissue engineering have certain limitations, such as lack of electrical conductivity and appropriate mechanical properties, which are two parameters playing a key role in regulating cardiac cell behavior. Here, the myocardial tissue constructs are engineered based on reduced graphene oxide (rGO)-incorporated gelatin methacryloyl (GelMA) hybrid hydrogels. The incorporation of rGO into the GelMA matrix significantly enhances the electrical conductivity and mechanical properties of the material. Moreover, cells cultured on composite rGO-GelMA scaffolds exhibit better biological activities such as cell viability, proliferation, and maturation compared to ones cultured on GelMA hydrogels. Cardiomyocytes show stronger contractility and faster spontaneous beating rate on rGO-GelMA hydrogel sheets compared to those on pristine GelMA hydrogels, as well as GO-GelMA hydrogel sheets with similar mechanical property and particle concentration. Our strategy of integrating rGO within a biocompatible hydrogel is expected to be broadly applicable for future biomaterial designs to improve tissue engineering outcomes. The engineered cardiac tissue constructs using rGO incorporated hybrid hydrogels can potentially provide high-fidelity tissue models for drug studies and the investigations of cardiac tissue development and/or disease processes in vitro.
The LOFT mission concept - A status update
(2016) Inam, S.C.
The Large Observatory For x-ray Timing (LOFT) is a mission concept which was proposed to ESA as M3 and M4 candidate in the framework of the Cosmic Vision 2015-2025 program. Thanks to the unprecedented combination of effective area and spectral resolution of its main instrument and the uniquely large field of view of its wide field monitor, LOFT will be able to study the behaviour of matter in extreme conditions such as the strong gravitational field in the innermost regions close to black holes and neutron stars and the supra-nuclear densities in the interiors of neutron stars. The science payload is based on a Large Area Detector (LAD, > 8m(2) effective area, 2-30 keV, 240 eV spectral resolution, 1 degree collimated field of view) and a Wide Field Monitor (WFM, 2-50 keV, 4 steradian field of view, 1 arcmin source location accuracy, 300 eV spectral resolution). The WFM is equipped with an on-board system for bright events (e. g., GRB) localization. The trigger time and position of these events are broadcast to the ground within 30 s from discovery. In this paper we present the current technical and programmatic status of the mission.
Discovery of a glitch in the accretion- powered pulsar SXP 1062
(2017) Imam, Sıtkı Cagdas; Serim, M.M.; Sahiner, S.; Cerri-Serim, D.; Baykal, A.; 0000-0003-0820-9186
We present timing analysis of the accretion-powered pulsar SXP 1062, based on the observations of Swift, XMM-Newton and Chandra satellites covering a time span of about 2 yr. We obtain a phase coherent timing solution that shows that SXP 1062 has been steadily spinning down with a rate-4.29(7) x10(-14) Hz s(-1) leading to a surface magnetic field estimate of about 1.5 x 10(14) G. We also resolve the binary orbital motion of the system from X-ray data that confirms an orbital period of 656(2) d. On MJD 56834.5, a sudden change in pulse frequency occurs with Delta v = 1.28(5) x 10(-6) Hz, which indicates a glitch event. The fractional size of the glitch is Delta v/v similar to 1.37(6) x 10(-3) and SXP 1062 continues to spin-down with a steady rate after the glitch. A short X-ray outburst 25 d prior to the glitch does not alter the spin-down of the source; therefore, the glitch should be associated with the internal structure of the neutron star. While glitch events are common for isolated pulsars, the glitch of SXP 1062 is the first confirmation of the observability of this type of events among accretion-powered pulsars. Furthermore, the value of the fractional change of pulse frequency ensures that we discover the largest glitch reported up to now.
A New Approach for Estimation of Heart Beat Rates from Speech Recordings
(2017) Ankishan, Haydar; Baysal, Ugur; 0000-0002-6240-2545; AAH-4421-2019; AAJ-5711-2020
Today, people are able to have information about their mental state, behavior, and health status in some issues from the features of the voices. The study involves calculating the heart rates of people using nonlinear equations with the help of the features of sound recordings. The model proposed for the study consists of the four inputs of the difference equation parameters which change with constant and variable sound features. When the experimental studies were examined, it was observed that the heart rate could be predicted with an accuracy of 89.76% by using 10s sound recordings. With the proposed equation, it is observed that the heart beat rate is related to the speech features, can be calculated these features with minimal error rate and also the nonlinear equation is presented in the literature.
Correlations between problem and solution domain measures of open source software
(2017) Ayyidiz, Tulin Ercelebi; Kocyigit, Altan; 0000-0002-7372-0223; AAE-1726-2021
Software size measurement and effort estimation methodologies in use today usually take the detailed requirements of software to be developed as the primary input and a certain amount of time and expertise is needed for size measurement. This paper analyzes the open source projects' correlations between the problem domain measures (the number of nouns and verbs) and solution domain measures (the number of software classes and methods). In this paper, 27 open source software projects are analyzed. Linear regression and cross validation techniques are applied to investigate the relation between the sizes of problem domain (i.e., conceptual) and solution domain (i.e., design) measures. The results reveal a strong correlation between the problem domain measures and the solution domain measures constituting the corresponding software. The results suggest that it is possible to use problem domain descriptions in the early stages of software development projects to make plausible predictions for the size and effort of the software.
A New Approach for the Acoustic Analysis of the Speech Pathology
(2017) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019
Voice disorders are a common physical problem that can be encountered today and can cause serious problems in the long term. It is necessary to analyze the voice and extract its characteristics correctly so that it can be treated. In some cases, due to their sound characteristics, they do not differ from each other characteristics exactly, and today's systems do not yet have the ability to make correct decisions. This study has taken into account those evident which from voice disturbances and tries to the analysis of these disorders by means of previously unused attributes with the help of classifier (SVMs). In this study, after the sounds are modeled with LPC and MFCC, disorder analysis is performed on the obtained signals. In the results obtained from experimental studies, it has been determined that 100% of the patients with four different diseases can be decomposed together with the used nonlinear features.
Max-Min Space Approach for Acoustic Signal Analysis
(2017) Ankishan, Haydar; Baysal, Ugur; 0000-0002-6240-2545; AAH-4421-2019; AAJ-5711-2020
Acoustic signals having pathological problem are difficult to discriminate from each other. Despite the presence of many features, the difficulties arise from the chaotic and nonlinear nature of these voices. Unlike the existing features, a new feature and feature space are emphasized in this study. Considering the maximum and minimum values of acoustic signals at certain time intervals, the relation between them is revealed and Max-Min space is created. Experimental studies have shown that the space distribution between pathological and normal sounds is completely separated from each other and that the space-scattering field sizes are different from each other. As a result of the studies, a time-based feature is introduced which allows the separation of chaotic and nonlinear acoustic signals in the literature.
A New Portable Device for the Snore/Non-Snore Classification
(2017) Ankishan, Haydar; Tuncer, A. Turgut; 0000-0002-6240-2545; AAH-4421-2019
Snoring is widely known as a disease. The aim of this paper is to introduce and validate our newly developed snoring detection device to identify automatically snore and non-snore sounds using a nonlinear analysis technique. The developed device can analyze chaotic features of a snore related sounds such as entropy, Largest Lyapunov Exponents (LLEs) and also has the data classification ability depending on the feature values. We report that the developed snoring detection device with proposed automatic classification method could achieve an accuracy of 94.38% for experiment I and 82.02 for experiment II when analyzing snore and non-snore sounds from 22 subjects. This study revealed the efficacy of our newly developed snoring detection device and indicated that it may be used at home an alternative to diagnose snore related sounds. It is anticipated that our findings will contribute to the development of an automated snore analysis system to be used in sleep studies.
Elevator Parking Approach in Nearest Car Method
(2018) Ciflikli, Cebrail; Tartan, Emre Oner
A fundamental factor that determines the system efficiency and the quality of service in elevator group control systems is the used elevator dispatching algorithm. Along with the elevator dispatching algorithm, using an elevator parking algorithm can provide improvements in the performance of an elevator group control system. In this study considering a system that uses Nearest Car Method as the elevator dispatching algorithm, average passenger waiting time is investigated under different traffic conditions using three parking algorithms and when no parking algorithm is used. For a more efficient elevator control system an adaptive park algorithm which is changing according to varying traffic conditions is proposed.
A New Approach for Discriminating the Acoustic Signals: Largest Area Parameter (LAP)
(2018) Ankishan, Haydar; Inam, S. Cagdas
Feature extraction of sound signals is essential for the performance of applications such as pattern and voice recognition etc. In this study, a method based on a novel feature is proposed to separate pathological human voice signals from healthy ones as well as to separate subgroups of pathological voices from each other. The voices are examined in time-frequency domain. Their differences obtained from the results of the proposed method are investigated and the mechanism of the method is demonstrated using experimental cases. It is concluded that the method succeeds to discriminate the voices marked "healthy" and "pathological".
An Android Application for Geolocation Based Health Monitoring, Consultancy And Alarm System
(2018) Tartan, Emre Oner; Ciflikli, Cebrail
In the last decade significant progress have been made in smart phone technology as well as in wireless wide area network technologies. Today among a wide population smart phones and mobile applications are considered as indispensable part of daily life. A field that mobile applications have great potential is health monitoring. Health monitoring covers various physiological signals. One of these signals is heart rate which is related to cardiovascular state of the body. Recently producers offer heart rate monitoring with the on board or wearable heart rate sensor. Although the main trend is for individual usage especially in sports, heart rate monitoring can also be benefited in an emergency alarm system for people who have potential risks while doing sports or elderly people. Such a distant monitoring system can be helpful to deliver first aid in emergency cases. Moreover an health expert can monitor states of the patients in real time. In this study we benefit the facilities provided by mobile technology and propose a geolocation-based heart rate monitoring system. The developed mobile application can send alarm message through notification, sms, mail and allows messaging with the health expert for consultancy. Hence if anomalies are observed in heart rate variability during the outdoor activities, emergency information can be delivered in the shortest time and the delays which have crucial affects can be prevented. The same framework can be extended to a more general system including different sensors for monitoring various physiological signals.
Classification of acoustic signals with new feature: Fibonacci space (FSp)
(2019) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019
In this study, a new feature and feature space (FSp) are introduced by using the approach of Fibonacci series formation. The results are presented as two experimental studies. The nine groups of acoustic signals and pathological human voices are investigated in the first and second experiments, respectively. Convolutional Neural Network (CNN) and Multi-Class Support Vector Machines (M-SVMs) are used to figure out the effect of the proposed feature and its FSp on the classification accuracy. It is observed that the proposed feature and its formed space yield significant results for the discrimination of those signals. Experimental studies show that the classification accuracy of test data is increased by 5.3% when the proposed feature is used with CNN and M-SVMs. In addition, each acoustic group is significantly discriminated in both experimental studies. It is concluded that the proposed feature and its space can be used as a temporal feature for different purposes such as automatic speech recognition, pattern recognition, and emotional voice discrimination etc. (C) 2018 Elsevier Ltd. All rights reserved.
A model for the visualization and analysis of elevator traffic
(2019) Ciflikli, Cebrail; Tartan, Emre Oner
Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.