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In this paper, we used a shape matching algorithm to recognize Farsi digits. For each sampled point on the contour of a shape, we obtain a descriptor showing the distribution of the other points of the contour, with respect to this point. Based on these descriptors, we find the corresponding points of the two contours and take the sum of their distances as a dissimilarity measure between two shapes. Then we define a geometric transformation that maps the sampled points of the one shape to the corresponding points of the other shape. The bending energy of this transform is taken as the second dissimilarity measure between two shapes. We optimized the parameters of the matching algorithm for the recognition of Farsi digits and used the method of minimum distance from the class prototypes for the recognition. In a test on a set of 1288 digits, we obtained a recognition rate of 89.9%. This result was obtained without any post processing

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In some mobile systems, intelligent antennas are used to increase efficiency in a wide frequency band and different environmental and electromagnetic conditions. Environmental factors, such as position of nearby objects, antenna mobility and even user type, can vary the antenna input impedance appreciably. Therefore, it is necessary to incorporate an adaptive impedance matching circuit between the antenna array elements and the transceiver to maximize the antenna radiation power. In this research note, the design of adaptive matching circuit for intelligent quadrifilar helical antenna in the UHF band (for GSM) is presented. The GA algorithm is used to optimize the results. The designed circuit can decrease the VSWR from 20 to less than 2 at any frequency within the GSM bandwidth.

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Abstract This paper scrutinizes the lexical feature and syntactic position of the so-called obligatory adverbs in Persian. Previous studies have viewed Persian adverbs under two classifications, namely obligatory adjuncts and optional adjuncts, with no account of their syntactic position. Within Minimalist theory, following the specifier treatment of adverbs, we propose that obligatory adverbs, in sharp contrast with other adverbs, occupy no specifier position of any dedicated functional projections, rather they are base generated in the specifier position of Verb Phrase.    

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In this paper, we propose a novel method for fully automatic detection and tracking of human heads and faces in video sequences. The proposed algorithm consists of two modules: a face detection module and a face tracking module. The Detection module, detects the face region and approximates it with an ellipse at the first frame using a modified version of AdaBoost cascaded classifier. The detection module is capable of considering the 2-D head pose rotation. The tracking module utiliyes a combination of deformable mesh energy minimization and feature matching approaches. In order to track a face, features are extracted in the face region to tessellate the human face with triangular unstructured meshes. For tracking a mesh, it is necessary to define mesh energies including internal and external energies. We have used new energy definitions for both the internal and the external energies which can accurately track rigid and non-rigid motions of a face and facial features at subsequent frames. We tested the proposed method with different video samples like cluttered backgrounds, partial illumination variations, put on glasses, and 2-D and/or 3-D rotating and translating heads. The experimental results showed that the algorithm is rotation insensitive and has high accuracy, stability and also has convergence for face detection and tracking.

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This paper has been presented a new method for optimum design of multi-stage axial flow compressor blades considering overall performance and stage matching. This tool is made up of an optimization algorithm, a flow solver and a parametric geometry generation system. A two-dimensional streamline curvature code has been improved to evalute the compressor performance parameters and flow field. Design parameters consist of geometrical parameters and aerodynamic performance parameters include the minimum loss of blade and allowable range of incidence. The efficiency increment of a 10-stage compressor has been investigated to evaluate the proposed method. The geometry of three front stages of compressor is fixed, the geometry of three middle stages is optimized and four rear stages have been re-staggered. At the first, compressor is optimized by re-designing middle stages and second, it is optimized by re-designing middle stages and re-staggering rear stages. In best case, compressor efficiency has been improved by 1.18 in nominal speed and 1.83 percent in 95% of nominal speed.

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This paper deals with the design of an unknown input observer (UIO) with the assumption that the well-known observer matching condition is not satisfied. The proposed method can be used for fault detection problems with the use of residual vector. The basis of method is to compensate the unmatched uncertainties with the use of a set of auxiliary outputs. The introduced auxiliary outputs are obtained from successive integration of the system measurements and known inputs. Then, an unknown input observer is proposed which estimates exponentially the outputs. Therefore, the residual vector, generated from the estimated outputs and the actual outputs, will be obtained which insensitive to the unmatched disturbances. At the same time, the sensitivity of the proposed residual vector to the fault in sensors is investigated. The generated residual vector will be more robust against the presence of noise in the measurements. It is shown through numerical simulations that the proposed residual vector is sensitive to the presence of fault in sensors while it is insensitive to the presence of the unknown input. In addition, a comparison with a derivative based method is presented.

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This paper is devoted to sensor fault detection in linear systems with observer-based approach. It is assumed that the system has linear dynamics with the presence of uncertainties. The uncertainties are modeled as unknown input (disturbance), while it is assumed that the well-known observer matching condition is not necessarily satisfied. To decouple the unknown-input effects, and distinguish their effects from the fault effects, an equivalent dynamic system is proposed which is independent from the unknown input. The equivalent system is constructed by the use of a unique integral filter. The introduced integral-filter, which is called buffer-based integral filter in this paper, has frequency response similar to the low-pass filter. Hence, the capability of noise filtration will also be provided. The construction of the equivalent dynamic system is achieved from the use of multiple successive buffer-based integrators and the number of successive filters is related to relative degree between the unknown input and the sensor output. Then, an unknown input observer is proposed for the equivalent system, and therefore, a disturbance-decoupled and fault-sensitive with exponential-convergent toward-zero residual vector will be generated. Although, the generated residual vector can be used for sensor and actuator fault diagnosis problems; however, the focus of this paper will be on the sensor fault detection. Finally, the applicability of the proposed method will be investigated via simulation of a simple inverted-pendulum on a horizontal-moving cart.

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The various types of unemployment have been key subjects of economics from the past periods. Sometimes, unemployment is the result of improper distribution of labor force. According to the statistical data, medical labor market in Iran is faced with such a problem. The density of medical labor in metropolitan areas is high. As a result, this market is of supply surplus. On the other hand, Iran’s towns and suburbs experience demand surplus. This type of unemployment indicates a kind of market failure, which needs to design a market as a solution. Market design as a subset of game theory requires developing through collaborative efforts of a wide range of specialists. In this article, we analyze medical labor market using the matching theory as a subset of market design. This theory is a main subset of market design theory. We apply a hypothetical market; provide a model and present suggestions to amend the medical labor market in Iran.  

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Secure outsourcing is essential to growth of cloud usage. There are some protocols allowing any functionality to be outsourced. However, specific constructions are necessary in order to do so in an efficient way. In this paper, we consider the problem of secure outsourced pattern matching. Our solution is based on Bit-Parallel Shift-ADD algorithm. The properties of this insecure algorithm allow our construction to search in an outsourced text, without revealing any non-trivial information to the computing server. We achieve a round optimal protocol that allows us to search for patterns with wildcards and handles the Hamming distance computation. Since the protocol has no leakage to the server, it cannot be optimal considering communication complexity; however, we suggest efficient techniques to achieve communication optimality through outsourcing of decryption as well. The security of our protocol is proved in the semi-honest setting. Then, in order to retain the efficiency of the protocol, we omit the correctness property in the malicious setting and prove that the scheme remains private in the presence of malicious adversaries.

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Simultaneous localization and mapping (SLAM) is a fundamental problem in autonomous robotic. Many algorithms have been exploited to solve this problem, among these algorithms, FastSLAM is one of the most widely used and Unscented FastSLAM is one of the newest. Although in several scientific researches it is stated that Unscented FastSLAM outperforms FastSLAM, there are still unexamined potentials regarding Unscented FastSLAM. Therefore, this paper seeks to improve the overall performance of Unscented FastSLAM. Map accuracy and quality directly depend on the accuracy of localization and observations. In SLAM algorithms, robot pose is predicted using motion model, and then corrected using the difference between map features and recently observed features. Accuracy of pose estimation may improve by comparing two sequential observations and modifying robot pose to result in best match between them. This method is called scan matching and has been successfully combined with FastSLAM algorithm and some other SLAM algorithms not including Unscented FastSLAM. Therefore, this paper seeks to investigate the performance of Unscented FastSLAM combined with scan matching. Simulation results show that combining Unscented FastSLAM with scan match significantly improves accuracy of localization and mapping.

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In the real world, dispersion of suppliers and demanders prevents market formation. Kidney market is a true example of such markets that is not formed due to thin features. To tackle the problem, one needs to design a mechanism that can both revive the market and ensure the most efficient supply-demand matching. A matching is efficient when no other matching can allocate agents to a better place, or cannot strongly allocate an agent to a better place. To this end, the mechanism should be designed to allocate agents to their highest preferences, so that agents cannot make another better choice. In this study, information on 20 patient-donor pairs was recorded at a clearing house in Hamedan province in 2016. After processing data, the patients’ preferences were ordered in terms of matching blood group, illness period, donor’s age, kinship relationship with donor, and gender of donor. Then, a mechanism was designed to match 17 patients with the most efficient kidneys. When patient i^th matches donor i^th and registers at the clearing house, the mechanism can offer her/his the most efficient kidney. If a given patient finds a donor better than his/her peer donor, then he/she will leave his/her peer donor. Otherwise, he/she matches her paired donor.
 

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Structural damage not only changes the dynamic characteristics of the structure, but also it may lead to complete destruction of the structure in some cases. Since early identification of damage can prevent such catastrophic events, structural health monitoring and damage detection has absorbed the attention of the civil, mechanical and aerospace engineers in the last decades. An effective health monitoring methodology not only can provide information about the global serviceability of the monitored structure, but also it can help the engineers to prepare cost-effective rehabilitation programs based on the obtained details about the health of the structure and its members. Different methods have been proposed for structural damage identification and estimation. Vibration-based methods consider the changes in the structural modal parameters, like natural frequencies and associated mode shapes, and/or their derivatives, like modal flexibility and residual force vector, for damage identification and quantification. Considering their acceptable sensitivity to wide-range of structural damages, vibration-based methods are considered as one of the most practical approaches for structural fault prognosis. Employing vibration parameters to define the damage detection problem as a model updating problem, is one of the well-known strategies that can return both the damage location and extent in different types of engineering structures. Such methods can be solved with optimization algorithms to find and report the structural damage in terms of the global extremums of a damage-sensitive objective function.
In this paper a new model updating approach for health monitoring and damage localization and quantification in engineering structures is presented. At first, a damage-sensitive objective function, which is based on the error function between the modal data of the monitored structure and its analytical model, is proposed. This objective function is formulated by means of the point-by-point matching strategy to minimize the difference between two models. Modal natural frequencies and the associated mode shape vectors are directly fed to the objective function and this can result in an easy assessment methodology to check the convergence rate of the function. Moreover, in such a case, the objective function uses the sensitivity of both these parameters for damage identification. The proposed inverse problem is solved using Moth-Flame Optimization (MFO) algorithm which has been inspired form spiral convergence of moths toward artificial lights. From mathematical point of view, updating the position of the moths with respect to the flames –which are the best solutions obtained during iterations–, reduces the probability of being trapped in the local extremum points and also, ensures the convergence of the algorithm to its global optimal solution. The applicability of the method was evaluated by studying different damage patterns on three numerical examples of engineering structures: a seven-story shear frame, a simple beam with 10 elements, and a planar truss with 29 elements. In all these studies, damages were simulated as reduction in the stiffness matrix of the damaged elements. Different issues, like noise effects, were considered and their impacts on the performance of the proposed method were investigated. Furthermore, comparative studies were carried out to discuss the advantages and drawbacks of the introduced method as well as the employed techniques. The obtained results indicate that the method is an effective strategy for vibration-based damage detection and localization in engineering structures.

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Regulation is one of the important socioeconomic tasks of governments. Regulation literature focuses on government intervention in the markets and determination of structure and the rules governing it in order to regulate the production and distribution of commodities. The commodity exchange is one of the leading regulatory bodies, which conventionally results in regulating commodities market by providing a convenient, transparent, and observable trading system. The advantage of commodities exchange is the presence of regulatory and supervisory bodies by which all producers, consumers, and traders enjoy the benefits of commodity regulation, but the institutional barriers to competitiveness can, however, prevent the regulation from being effective on prices. This study aims to test the effects of regulating the listed commodities in Iran Mercantile Exchange on commodity price fluctuations. To this end, price movements of 20 commodities traded on Iran Mercantile Exchange are compared with 20 ones not listed in Iran Mercantile Exchange using their coefficient of variation (CV) and Propensity Score Matching method. According to the results, the supply of commodities on the Iran Mercantile Exchange has led to an increase in their price fluctuations compared to similar off-exchange ones. In other words, trading commodities on Iran Mercantile Exchange has not prevented price fluctuations because the institutional barriers to competitiveness affect regulatory facilitation and cause its ineffectiveness.