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In this study, the geometric process (GP) model is considered in order to calculate the debugging and testing costs of a software product. Under the assumption of the GP model, the debugging and testing costs of the software product are obtained depending on the first and second moment functions of the GP. It is observed that the values of the first and second moment functions of the process must be known in order to calculate the debugging and testing costs. At the same time, the calculation of moment functions also depends on both the distribution of the first interarrival time of the GP and the estimates of the model and distribution parameters. In this study, the proposed debugging and testing costs are calculated for the data set containing 136 failure times of a real-time command and control system. For this dataset, it has been shown in previous studies that the GP with gamma distribution can be proposed as a model. Under gamma distribution assumption, the maximum likelihood estimates of the model parameters are obtained. Using the estimates of the model parameters, the first and second moment functions of the GP are calculated with the help of the numerical methods proposed for these functions. Finally, the debugging and testing costs are obtained for the data set. [ABSTRACT FROM AUTHOR]

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The main idea of brain-computer interfaces (BCIs) is to facilitate the lives of patients having difficulties to move their muscles due to a disorder of their motor nervous systems but healthy cognitive functions. BCIs are usually electroencephalography (EEG)-based, and the success of the BCIs relies on the precision of signal preprocessing, detection of distinctive features, usage of suitable classifiers and selection of effective channels. In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imagery/movement tasks. In the first stage of the channel selection, the channels were combined according to the neurophysiological information about brain functions acquired from the literature, then averaged and a single channel was formed. In the second stage, selective channels were specified with the common spatial pattern-linear discriminant analysis (CSP-LDA)-based sequential channel removal. After the channel selection phase, the feature extraction was carried out with local transformation-based methods (LTBM): local centroid pattern (LCP), one-dimensional-local gradient pattern (1D-LGP), local neighborhood descriptive pattern (LNDP) and one-dimensional-local ternary pattern (1D-LTP). The distinctions and deficiencies of these methods were compared with other methods in the literature and the classification performances of the k-nearest neighbor (k-NN) and the support vector machines (SVM) were evaluated. As a result, the proposed methods yielded the highest average classification accuracies as 99.34%, 95.95%, 98.66% and 99.90% with the LCP, 1D-LGP, LNDP and 1D-LTP when using k-NN, respectively. The two-stage channel selection and 1D-LTP method showed promising results for recognition of motor tasks. The LTBM will contribute to the development of EEG-based BCIs with the advantages of high classification accuracy, easy implementation and low computational complexity.