Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 1Citation - Scopus: 1Comparison of the Predicted and the Observed Wave Spectral Parameters During the Storms at Filyos Coasts, the Southwestern Black Sea(Elsevier, 2022) Öztunalı Özbahçeci, Bergüzar; Güler, MuzafferIn-situ wave measurement data are mainly used to validate the bulk wave parameters predicted by numerical models. Although the frequently used third-generation wave models are spectral models, determination of various spectral parameters and validation with the observed data are not common. This study covers the spectral analysis of selected storm records of a nearshore wave measurement campaign carried out at Filyos coasts with the complex bottom topography in Turkey, Southwestern Black Sea. The bulk wave and the spectral parameters are also calculated by a third-generation nearshore wave model, SWAN (Simulating Waves Nearshore), forced by the ERA5 offshore wave data, which is the newest re-analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) for the selected storms. Before using ERA5 offshore wave data, they are calibrated by the wave data of the satellite radar altimeter. In-situ measured bathymetry data are used in the SWAN model. Observed and predicted bulk wave and spectral parameters are compared, and the statistical error measures are calculated not only for the significant wave height, the peak period, and the peak wave direction but also for the three different spectral periods, three different frequency width parameters, a directional width and, a spectral peakedness parameter for the first time. Low values of statistical error measures show that the current wave predictions have a good agreement with the observed ones in terms of the significant wave height, Hs, and the peak period, Tp. However, the SWAN model predicts a slightly narrower frequency and directional spectrum with higher peaks, although the error measures are low. Moreover, SWAN can not predict the wide range of spectral shape occurrences that the observed spectra have. The development of the various spectral parameters during the storms is also investigated for the first time. It is found that the frequency and directional spreading of the observed spectra become wider and unsharpened in the late stages of the storm compared to the early stages. However, the same tendency is not observed clearly in the predicted directional spreadingArticle Citation - WoS: 9Citation - Scopus: 11Test Input Generation From Cause-Effect Graphs(Springer, 2021) Kavzak Ufuktepe, Deniz; Ayav, Tolga; Belli, FevziCause-effect graphing is a well-known requirement-based and systematic testing method with a heuristic approach. Since it was introduced by Myers in 1979, there have not been any sufficiently comprehensive studies to generate test inputs from these graphs. However, there exist several methods for test input generation from Boolean expressions. Cause-effect graphs can be more convenient for a wide variety of users compared to Boolean expressions. Moreover, they can be used to enforce common constraints and rules on the system variables of different expressions of the system. This study proposes a new mutant-based test input generation method, Spectral Testing for Boolean specification models based on spectral analysis of Boolean expressions using mutations of the original expression. Unlike Myers' method, Spectral Testing is an algorithmic and deterministic method, in which we model the possible faults systematically. Furthermore, the conversion of cause-effect graphs between Boolean expressions is explored so that the existing test input generation methods for Boolean expressions can be exploited for cause-effect graphing. A software is developed as an open-source extendable tool for generating test inputs from cause-effect graphs by using different methods and performing mutation analysis for quantitative evaluation on these methods for further analysis and comparison. Selected methods, MI, MAX-A, MUTP, MNFP, CUTPNFP, MUMCUT, Unique MC/DC, and Masking MC/DC are implemented together with Myers' technique and the proposed Spectral Testing in the developed tool. For mutation testing, 9 common fault types of Boolean expressions are modeled, implemented, and generated in the tool. An XML-based standard on top of GraphML representing a cause-effect graph is proposed and is used as the input type to the approach. An empirical study is performed by a case study on 5 different systems with various requirements, including the benchmark set from the TCAS-II system. Our results show that the proposed XML-based cause-effect graph model can be used to represent system requirements. The developed tool can be used for test input generation from proposed cause-effect graph models and can perform mutation analysis to distinguish between the methods with respect to the effectiveness of test inputs and their mutant kill scores. The proposed Spectral Testing method outperforms the state-of-the-art methods in the context of critical systems, regarding both the effectiveness and mutant kill scores of the generated test inputs, and increasing the chances of revealing faults in the system and reducing the cost of testing. Moreover, the proposed method can be used as a separate or complementary method to other well-performing test input generation methods for covering specific fault types.Article Citation - WoS: 67Citation - Scopus: 78Chirp Group Delay Analysis of Speech Signals(Elsevier, 2007) Bozkurt, Barış; Couvreur, Laurent; Dutoit, ThierryThis study proposes new group delay estimation techniques that can be used for analyzing resonance patterns of short-term discrete-time signals and more specifically speech signals. Phase processing or equivalently group delay processing of speech signals are known to be difficult due to large spikes in the phase/group delay functions that mask the formant structure. In this study, we first analyze in detail the z-transform zero patterns of short-term speech signals in the z-plane and discuss the sources of spikes on group delay functions, namely the zeros closely located to the unit circle. We show that windowing largely influences these patterns, therefore short-term phase processing. Through a systematic study, we then show that reliable phase/group delay estimation for speech signals can be achieved by appropriate windowing and group delay functions can reveal formant information as well as some of the characteristics of the glottal flow component in speech signals. However, such phase estimation is highly sensitive to noise and robust extraction of group delay based parameters remains difficult in real acoustic conditions even with appropriate windowing. As an alternative, we propose processing of chirp group delay functions, i.e. group delay functions computed on a circle other than the unit circle in z-plane, which can be guaranteed to be spike-free. We finally present one application in feature extraction for automatic speech recognition (ASR). We show that chirp group delay representations are potentially useful for improving ASR performance. (c) 2007 Elsevier B.V. All rights reserved.Article Citation - WoS: 4Citation - Scopus: 8Prioritizing Mcdc Test Cases by Spectral Analysis of Boolean Functions(John Wiley and Sons Inc., 2017) Ayav, TolgaTest case prioritization aims at scheduling test cases in an order that improves some performance goal. One performance goal is a measure of how quickly faults are detected. Such prioritization can be performed by exploiting the Fault Exposing Potential (FEP) parameters associated to the test cases. FEP is usually approximated by mutation analysis under certain fault assumptions. Although this technique is effective, it could be relatively expensive compared to the other prioritization techniques. This study proposes a cost-effective FEP approximation for prioritizing Modified Condition Decision Coverage (MCDC) test cases. A strict negative correlation between the FEP of a MCDC test case and the influence value of the associated input condition allows to order the test cases easily without the need of an extensive mutation analysis. The method is entirely based on mathematics and it provides useful insight into how spectral analysis of Boolean functions can benefit software testing.