فائزه میرزایی

In this research, evaluating the effect of design appeal, perceived quality, subjective norm and brand popularity on repurchase intention with moderating role of brand reputation (Case Study: Huawei Smartphones) is conducted. This research is an applied research, the method is descriptive-survey, it is also considered as a correlation research. The statistical population of the research is including all customers of Huawei smartphones in Iran Mobile Market in Tehran. Sampling method is simple-random method and by using Jersey-Morgan table, the study sample is obtained 384 people. To collect data, standard questionnaire from Filieri & Lin (2017), article is used that was extracted, translated and slightly modified adapt to the case study. Cronbach's alpha test is used to determine the reliability of the questionnaire and for inferential analysis, SPSS and LISREL software is used to confirm or reject hypotheses. The results of data analysis indicate that design appeal has positive and significant impact on brand reputation and customer repurchase intention. Also, perceived quality has positive and significant impact on brand reputation and customer repurchase intention. The positive and significant impact of subjective norm on brand reputation and customer repurchase intention has been approved. The results also showed that brand popularity has positive and significant impact on brand reputation and customer repurchases intention. Finally, the results showed brand reputation has positive and significant impact on customer repurchase intention.

Swarm robotic (SR) draws inspiration from nature and is emerged as synthesis of swarm intelligence and robotic. Due to special properties like feasibility, robustness and scalability, swarm robotic has several advantages in comparison to other multi robot systems. In this paper, firstly, SR source of inspiration is introduced, and a precise definition along with desirable properties of swarm robotic is presented. Secondly, SR application domains, well-known projects and simulation environments are reviewed. Thirdly, an overview of various modelling approaches and algorithms is reported. The presented algorithms are divided into task-specific categories. So some of common tasks that could be profitably tackled using swarm robotic can be understood. Finally SR open problems and challenges are discussed. We expect this paper can give a comprehensive view of swarm robotic to researchers.

Due to difficulties in formal implementation of swarm robotic systems, controlling software of such systems is developed in an ad-hoc manner and with trial and error. So, it is hard to reuse these systems for other similar problems. Moreover, testing, analyzing and verifying the correctness of the controller are difficult too. There is no guarantee that the implementation matches the specifications. To address these problems, supervisory control theory as a formal approach is suggested. In this paper, probabilistic and timed supervisory control theory (ptSCT) is implemented on ARGoS platform in swarm robotic. The proposed approach automatically calculates ptSCT, and then generates the equivalent controlling software codes. The generated controlling software can be used for both simulation and running on real robots without any changes. For comparison purposes, two tasks namely obstacle avoidance and synchronization of robots are designed using both SCT and proposed ptSCT. The approach is successfully validated in both tasks using up to 64 E-Puck robots. The experimental results show the advantages of the ptSCT, in terms of simplicity, reusability, and automatic code generation.

In today’s modern world, there is no place for passwords and security has the highest priority in current systems. Biometrics like face and voice can be circumvented by fraudulent methods but fingerprint has a high security in this aspect. In this paper a new fingerprint matching approach is introduced. The proposed method with various modifications has reached an accuracy equal or higher than the best existent methods. This method uses triangular relation of minutiaes. Experimental results show high accuracy and robustness to image transition and rotation. The time consuming part of the system are determined and then paralleled by CPU and GPU depend on dataor process complexity. GPU parallelization is performed by CUDA platform. Successful results of paralleled system show that the speed and accuracy of our system are suitable for real-time applications. We have managed to achieve up to 9x speed-up in the system run-time. The proposedsystem accuracy on FVC2002 database is higher than 99%.

Fingerprint as a biometric has the most applications in verification and identification systems, because of its specific properties. In identification systems, input image is compared with all of images stored in the database. In huge databases, the comparison will take large amounts of time; Consider FBI databases, for instance. Image classification is one of the approved methods to increase the identification speed. Only one class is assigned to each fingerprint in tradition absolute classification. Various reasons like noise or lack of all the singularity points in captured region, cause the problem in determination of an absolute class for all the images. In this article, a new method based on probabilistic classification is presented. In the proposed approach, a set of classes are considered for each input image with a specific probability. These classes are searched in order of their probabilities priority in matching stage. Experiments on well-known FVC2002 database, exhibit the effect of probable classification clearly. Using only the second and third classes assigned by the proposed method, the identification system achieves about 18% increase in accuracy and 2-3 times speedup in compared to the traditional methods.