Teknik Bilimler Meslek Yüksekokulu / Vocational School of Technical Sciences
Permanent URI for this collectionhttps://hdl.handle.net/11727/2031
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Item A New Approach for Discriminating the Acoustic Signals: Largest Area Parameter (LAP)(2018) Ankishan, Haydar; Inam, S. CagdasFeature 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".Item Max-Min Space Approach for Acoustic Signal Analysis(2017) Ankishan, Haydar; Baysal, Ugur; 0000-0002-6240-2545; AAH-4421-2019; AAJ-5711-2020Acoustic 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.Item 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-5711-2020Today, 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.Item Elevator Parking Approach in Nearest Car Method(2018) Ciflikli, Cebrail; Tartan, Emre OnerA 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.Item Correlations between problem and solution domain measures of open source software(2017) Ayyidiz, Tulin Ercelebi; Kocyigit, Altan; 0000-0002-7372-0223; AAE-1726-2021Software 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.Item Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy(,2013, 2013) Ankışhan, Haydar; Yılmaz, DeryaSnoring, 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.