Computer Engineering

Abstract This paper describes architecture for multi agent mobile robot systems in market-based task allocation systems. Constructing architecture for multi-agent mobile robots system is heavily studied in the literature. Also market-based task allocation has been taken considerable attention in the literature. But combining these two areas is a new subject for the researchers. In this paper, we developed architecture for market-based task allocation systems and applied to an example with three mobile robots.

Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher's Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.

Purpose – The main purpose of the paper is the validation of a broad range of RANS turbulence models, for the prediction of flow and heat transfer, for a broad range of boundary conditions and geometrical configurations, for this class of problems. Design/methodology/approach – Two- and three-dimensional computations are performed using a general-purpose CFD code based on a finite volume

It is natural and almost inevitable to obtain images that are overly smooth and noisier than possible best out of image sensors. Blurring is generally noticeable in the regions containing edges and appears as softening effect by averaging. It is important to restore and enhance blurred and noisy images in many imaging applications. In this study, an image quality measure

Modified subgradient method has been employed to solve super-resolution restoration problem. The technique uses augmented Lagrangians for nonconvex minimization problems with equality constraints. The subgradient of the constructed dual function is ...