Sharif Digital Repository

Effective prevention and treatment management of spinal disorders can only be based on accurate estimation of muscle forces and spinal loads during various activities such as lifting. The infeasibility of experimental methods to measure muscle and spinal loads has prompted the use of biomechanical modeling techniques. A major shortcoming in many previous and current models is the consideration of equilibrium conditions only at a single cross section, rather than along the entire length of the spine, when attempting to compute muscle forces and spinal loads. The assumption of extensor global muscles with straight rather than curved paths and of the spinal segments as joints with no... 

The aim of this paper is to analyze the Farsi-English statistical machine translation systems as a useful communication tool. Improvement of the nation's communication increases the need of easier way of translating between different languages in front of expensive human translators. In this work, a statistical phrase-based system is run on Farsi - English pair languages and the effect of its parameters on the translation quality has been deeply studied. Using BLEU as a metric of translation accuracy, the system achieves an improvement of 1.84%, relative to the baseline accuracy, which is increment from 16.97% to 18.81% in the best case  

We study bargaining games between suppliers and manufacturers in a network context. Agents wish to enter into contracts in order to generate surplus which then must be divided among the participants. Potential contracts and their surplus are represented by weighted edges in our bipartite network. Each agent in the market is additionally limited by a capacity representing the number of contracts which he or she may undertake. When all agents are limited to just one contract each, prior research applied natural generalizations of the Nash bargaining solution to the networked setting, defined the new solution concepts of stable and balanced, and characterized the resulting bargaining outcomes.... 

This paper utilizes IJA stochastic learning automaton for detecting noise and tuning value of alpha parameter which is used for image sharpening via gas diffusion model. The method has been applied to gray-scale images in an automatic and adaptive fashion. It is shown that the IJA automaton detects noise and can reform it appropriately. It glides the image to find the pattern of noise and replace it by the relevant characteristics of neighborhood to carry out the local restoration. Then, the automaton makes the image sharp with gas diffusion model by learning alpha parameter. The IJA automaton calculates appropriate local value for each pixel. Finally, experiments are presented and... 

Reo is a connector language for the exogenous composition and orchestration of components in a software system. An operational semantics of Reo connectors can be given in terms of Büchi automata over a suitable alphabet of records, capturing both synchronization and context dependency requirements. In this paper, we define an action based linear time temporal logic for expressing properties of Reo connectors. Formulas can be synthesized into Büchi automata representing Reo connectors, thus leading to an automata based model checking algorithm. By generalizing standard automata based model checking algorithms for linear time temporal logic, we give an efficient on-the-fly algorithm for the... 

The cellular learning automaton (CLA), which is a combintion of cellular automaton (CA) and learning automaton (LA), is introduced recently. This model is superior to CA because of its ability to learn and is also superior to single LA because it is a collection of LAs which can interact with each other. The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. Recently, various types of CLA such as synchronous, asynchronous, and open CLAs have been introduced. In some applications such as cellular networks, we need to have a model of CLA for which multiple LAs reside in each cell. In this paper, we study a CLA model for which each cell has several LAs.... 

Cellular learning automata (CLA) which has been introduced recently, is a combination of cellular automata (CA) and learning automata (LA). A CLA is a CA in which a LA is assigned to its every cell. The LA residing in each cell determines the state of the cell on basis of its action probability vector. Like CA, there is a local rule that CLA operates under it. In this paper we introduce a new model of CLA in which each cell gets an external input vector from the environment in addition to reinforcement signal, so this model can work in non-stationary environments. Then two applications of the new model on image segmentation and clustering are given, and the results show that the proposed... 

This paper presents a new segmentation method for color images. It relies on soft and hard segmentation processes. In the soft segmentation process, a cellular learning automata analyzes the input image and closes together the pixels that are enclosed in each region to generate a soft segmented image. Adjacency and texture information are encountered in the soft segmentation stage. Soft segmented image is then fed to the hard segmentation process to generate the final segmentation result. As the proposed method is based on CLA it can adapt to its environment after some iterations. This adaptive behavior leads to a semi content-based segmentation process that performs well even in presence of... 

Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users' interactions. In this paper, we propose an algorithm to solve the web page recommendation problem. In our algorithm, we use distributed learning automata to learn the behavior of previous users' and recommend pages to the current user based on learned pattern. Our experiments on real data set show that the proposed algorithm performs better than the other algorithms that we compared to and, at the same time, it is... 

In this paper, two learning automata based adaptive limited fractional guard channel algorithms for cellular mobile networks are proposed. These algorithms try to minimize the blocking probability of new calls subject to the constraint on the dropping probability of the handoff calls. To evaluate the proposed algorithms, computer simulations are conducted. The simulation results show that the performance of the proposed algorithms are close to the performance of the limited fractional guard channel algorithm for which prior knowledge about traffic parameters are needed. The simulation results also show that the proposed algorithms outperforms the recently introduced dynamic guard channel... 

There is no method to determine the optimal topology for multi-layer neural networks for a given problem. Usually the designer selects a topology for the network and then trains it. Since determination of the optimal topology of neural networks belongs to class of NP-hard problems, most of the existing algorithms for determination of the topology are approximate. These algorithms could be classified into four main groups: pruning algorithms, constructive algorithms, hybrid algorithms and evolutionary algorithms. These algorithms can produce near optimal solutions. Most of these algorithms use hill-climbing method and may be stuck at local minima. In this article, we first introduce a... 

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin... 

The hierarchical structure of real-world problems has resulted in a focus on hierarchical frameworks in the reinforcement learning paradigm. Preparing mechanisms for automatic discovery of macro-actions has mainly concentrated on subgoal discovery methods. Among the proposed algorithms, those based on graph partitioning have achieved precise results. However, few methods have been shown to be successful both in performance and also efficiency in terms of time complexity of the algorithm. In this paper, we present a SCC-based subgoal discovery algorithm; a graph theoretic approach for automatic detection of subgoals in linear time. Meanwhile a parameter tuning method is proposed to find the... 

A solution to channel assignment problem in cellular networks is self-organizing channel assignment algorithm with distributed control. In this paper, we propose three cellular learning automata based dynamic channel assignment algorithms. In the first two algorithms, no information about the status of channels in the whole network will be used by cells for channel assignment whereas in the third algorithm, the additional information regarding status of channels may be gathered and then used by cells in order to allocate channels. The simulation results show that by using the proposed channel assignment algorithms the micro-cellular network can self-organize itself. The simulation results... 

Reo is a coordination language for modeling component connectors of component-based computing systems. Constraint automaton, as an extension of finite automaton, has been proposed as the operational semantics of Reo. In this paper, we introduce an extended definition of constraint automaton by which, every constraint automaton can be considered as a labeled transition system and each labeled transition system can be translated into a constraint automaton. We show that failure-based equivalences CFFD and NDFD are congruences with respect to composition of constraint automata using their join (production) and hiding operators. Based on these congruency results and by considering the temporal... 

In this paper, we propose a novel algorithm that combines color and texture information of skin with cellular learning automata to segment skin-like regions in color images. First, the presence of skin colors in an image is detected, using a committee structure, to make decision from several explicit boundary skin models. Detected skin-color regions are then fed to a color texture extractor that extracts the texture features of skin regions via their color statistical properties and maps them to a skin probability map. Cellular learning automatons use this map to make decision on skin-like regions. The proposed algorithm has demonstrated true positive rate of about 83.4% and false positive... 

Reo is an exogenous coordination language for component connectors extending data flow networks with synchronization and context-dependent behavior. We propose an operational model of Reo based on Büchi automata in which port synchronization is modeled by records labeling the transitions, whereas context dependencies are stored in the states. We provide a composition operator which models the joining of two connectors, and show that it can be obtained by using two standard operators: alphabet extension and automata product. Our semantics has the advantage over previous models in that it is based on standard automata theory, so that existing theories and tools can be easily reused. Moreover,... 

Reconfigurability for a component of a distributed component-based system means ability to replace the component with a new one, in presence of the other components. In this context, substitutability and compatibility of components should be analyzed. In this paper, we use I/O-Constraint Automata to model behavior of components and propose enough formalism for substitutability and compatibility analysis. We show that in the context of distributed non-blocking components, there is a relation weaker than usual simulation relation in automata theory for notion of substitutability. We illustrate the relation between substitutability and compatibility to reason about the reconfigurability of a... 

Constraint automata have been proposed as the operational semantics of Reo, a glue-code language for the exogenous composition and orchestration of components in a software system. In this paper we recast the theory of constraint automata into that of Büchi automata on infinite strings of records. We use records to express simultaneity constraints of I/O operations and show that every constraint automaton can be expressed as a Büchi automaton on an appropriate alphabet of records. Further, we give examples of component compositions that are expressible as Büchi automata but not as constraint automata. Finally, we show that the join composition operator for constraint automata and its... 

Reo is an exogenous coordination language for compositional construction of the coordinating subsystem of a component-based software. Constraint automaton is defined as the operational semantics of Reo. The main goal of this work is to prepare a model checking based verification environment for component-based systems, which their component connectors are modeled by Reo networks and Constraint Automata. We use compositional minimization and abstraction methods of model checking for verification of component-based systems and their component connectors modeled by Reo