نشریه محاسبات نرم
سال سوم شماره 1 (پیاپی 5، بهار و تابستان1393)

Service-oriented software engineering (SOSE) enables rapid and cost-efficient development of distributed software even in heterogeneous environments where deliver its solutions as services. SOSE aims at providing methods and tools to enhance the quality of products and make software development as beneficial as possible. A challenging issue in SOSE is providing services with the required quality which contributes in cost-efficient software development. To this end, the quantitative measurement of quality is crucial. There are several valuable works in literature for quantifying different quality aspects of service-oriented solutions. However, each of the existing works has some strengths and weaknesses. Here we try to survey the literature on the quantitative measurement of the various aspects of service's quality in SOSE. To do so, we propose a model for quality assessment of services. Based on the proposed model, we categorize existing methods and analyze their strengths, weaknesses, and applications. The results of our analysis and comparisons make it possible to select the most appropriate method for quantifying each quality aspect of services which, to the best of our knowledge, is not available in the literature.

Due to the growth of digital images require efficient methods to annotate the images is sense. In this paper, a semi-supervised spectral clustering with relevance feedback is used to annotate digital photos which is overcome the local minima problem on clustering methods by using some labeled information given by users. Performance of the proposed method is tested on Corel 5K dataset and the results demonstrate the efficiency and accuracy of the proposed method compared with other clustering methods.

In recent years, advanced cars had been equipped by intelligent parking system and in future, other cars will be done. Many methods were proposed for this system, like fuzzy controllers. The fuzzy controller is simple and has high speed calculations, but for this system have not favorite stability and performance. In this paper, fuzzy sliding mode controller is designed in discrete time for parallel and garage parking of car to enhance stability and improvement of performance and robustness of the system. Simulation of this system in MATLAB software verifies mentioned favorite aims.

In this paper, a new structure for image encryption using hybrid cellular automata is presented. The image encryption is done in two steps. At the first step, each pixel is encrypted by rules of hybrid cellular automata. In the next step, each pixel converted to a binary number and each bit is encrypted by rules of cellular automata. The rules are made by a function. Due to reversibility of cellular automata, the decryption of the image is possible by doing the steps reversely. The experimental results show that this algorithm has satisfactory performance in terms of quantitative assessment.

People re-identification is one of the most important and fundamental processes in video surveillance systems. The accuracy and efficiency of this task influence the effectiveness of the subsequent processes. Event detection and behavior analysis are instances of such subsequent processes that are classified in semantic levels. In people re-identification, having an image or video of an individual in a specific camera, we want to infer whether the person has been seen previously in the other cameras. Various changes in people’s appearance, recorded by different cameras, can lead to many difficulties in people re-identification. These changes may occur due to hardware limitations such as low-resolution, different color responses of cameras, lighting changes in different camera locations and person’s angle relative to the camera. In this paper we will present a review on the existing approaches in people re-identification, then, a new classification of these works based on their functionalities is provided. Our studies shows that the problem of changing the camera angles has not been seriously considered in the existing approaches and future research could provide models that able to resolve these problems.

Knowing of the water table around the region and access to its contour maps is one of the most important planning tools for withdrawal underground aquifers and implementing civil projects. Generally, by using the piezometric wells in the region and different methods of estimation, the water table determined. Limitation of these methods is the inability to estimate water table on different days of the year. In this study, by using artificial neural network and time of the measurements of the water table as one of the inputs, the network is trained to estimate contour maps of water table on different days of the year. For this purpose, the water table data in Behbahan plain for the years 1370 to 1385 were used to training the network. Correlation coefficient 0.9906 between actual values and estimated values of the trained network indicates that the estimation is very good. Finally based on this network, contour map of water table in Behbahan plain is plotted for four different days in 1384.