In this study computer vision and robot arm are used together to design a smart robot arm system which can identify objects from images automatically and perform given tasks. A serving robot application, in which specific tableware can be identified and lifted from a table, is presented in this work. A new database was created by using images of objects used in serving a meal. This study consists of two phases: First phase includes recognition of the objects through computer vision algorithms and determining the specified objects' coordinates. Second phase is the realization of the robot arm's movement to the given coordinates. Artificial neural network is used for object recognition in this system. 98.30 % overall accuracy of recognition is achieved. Robot arm's joint angles were calculated by using coordinate dictionary for moving the arm to desired coordinates and the robot arm's movement was performed.

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In this paper a parallel image processing and frame rate stabilization approach is proposed. This approach works on a regular PC with a multi-core CPU. It is implemented under .NET Framework and tested on Microsoft Windows 7 operating system, performing several experiments. It is also applied to a face recognition application to increase its image processing performance successfully. Results show that, handled workload when 4 physical cores are used is approximately 5.25 times the workload handled with one core. It is also shown that the approach successfully distributes the workload on CPU cores and produces output at a stable frame rate under both steady and unsteady workloads. This approach can be used for various signal processing or multimedia applications to parallelize their tasks to increase the performance on multi-core CPUs.

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Özetçe-Bu çalışmada duyguların sınıflandırılması için seslerden elde edilen çok sayıda öznitelik, temel bileşenler analizi ve Fisher ayrışım analizi ile farklı sayılarda temel bileşenler seçilerek farklı uzaylara taşınmıştır. Basit Bayes sınıflandırıcısı kullanılarak yeni uzaylarda sınıflandırma yapılmış ve elde edilen sonuçlar karşılaştırılmıştır. Sınıflandırmalar sonunda temel bileşenler uzayında elde edilen en yüksek sınıflandırma başarısı 48.02% olurken, Fisher uzayında en yüksek başarı 57.87% olarak hesaplanmıştır. Anahtar Kelimeler -temel bileşenler analizi; fisher doğrusal ayrışım analizi; duygu tanımaAbstract-In this study, a large number of features that were obtained to classify speech emotions were projected into different spaces, selecting different numbers of principal components in principal component analysis and Fisher's discriminant analysis. Classifications were performed in those spaces using Naïve-Bayes classifier and obtained results were compared. While the highest accuracy obtained in the Fisher space was 57.87%, it was calculated as 48.02% in the principal component space.