سال:1394 | دوره: | شماره: |تعداد مقالات:10

A typical Radio frequency identification system (RFID) includes one reader and a number of tags. The reader transmit to commands to the tags with interrogation radio signals, and tags receive the command and then respond with their identification. Readers with interrogation signals can read tags’ responses. In RFID systems, when multiple tags respond to one reader simultaneously, tag collision can occur. In addition, multiple reader to tag collision occurs when more than one reader wants to read one tag at the same time. RFID collision problems are a group of the open research topics. In this paper, a high-throughput reader collision avoidance protocol based on code division multiple access (CDMA) has been proposed to solve the reader collision problem with the help of RFID network systems based on orthogonal codes, which allow all readers to read the tags at the same time and frequency. Comparison of this protocol with the pulse protocol shows great improvement on the average read throughput.

Wireless sensor network includes a large number of nodes which are distributed in a geographical location. The essential fact about WSN is that energy of nodes is limited. Therefore, presenting proper solutions as an optimized routing is crucial to equally use energy of all nodes. In this paper we propose a method which performs routing in WSNs using greedy approach. It is able to choose optimum rout based on energy level and distance. Since our method tries to equally utilize energy of different nodes, it will eventually result in lifetime increase. In addition to modifying energy consumption, simulation results show that proposed algorithm achieves considerable improvement in reduction of end-to-end delay and increase in packet delivery rate.

The successful key of trading in the forex market is the selection of correct exchange in proper time based on an exact prediction of future exchange rate. Foreign exchange rates are affected by many correlated economic, political and even psychological factors. Therefore, in order to achieve a profitable trade these factors should be considered. The application of intelligent techniques for forecasting has been proved extremely successful in recent years. Previous studies have mainly focused on the historical prices and the trading volume of one market only. In this paper, we have used Artificial Neural Networks (ANN) to predict the exchange rate with respect to three external factors including gold, petroleum prices and FTSE 100 index. The result of forecasts is compared with the ANNs without external factors. The empirical results demonstrate that the proposed model can be an effective way of forecasting. For the experimental analysis phase, the data of exchange rate of GBP/USD is used.

Moving towards nanometer scales, Quantum-dot Cellular Automata (QCA) technology emerged as a novel solution, which can be a suitable replacement for complementary metal-oxide-semiconductor (CMOS) technology. The 3-input majority function and inverter gate are fundamental gates in the QCA technology, which all logical functions are produced based on them. Like CMOS technology, making the basic computational element such as an adder with QCA technology, is considered as one of the most important issues that extensive research have been done about it. In this paper, a new QCA full-adder based on coupled majority-minority and 5-input majority gates is introduced which its novel structure, appropriate design technique selection and its arrangement make it very suitable. The experimental results showed that the proposed QCA full-adder makes only 48 cells and the first output is obtained in the 0.05clock. Therefore, the presented QCA full-adder improves the number of cells and gains a speedup rate of 33% in comparison with the best previous robust QCA full-adders. In addition, temperature analysis of the QCA full-adders shows that our design is more robust compared with other suggested QCA full-adders.

Control of robotic systems is an interesting subject due to their wide spectrum applications in medicine, aerospace and other industries. This paper proposes a novel continuous control mechanism for tracking problem of a 5-DOF upper-limb exoskeleton robot. The proposed method is a combination of a recently developed robust integral of the sign of the error (RISE) feedback and neural network (NN) feed-forward terms. The feed-forward NN learns nonlinear dynamics of the system and compensates for uncertainties while the NN approximation error and nonlinear bounded disturbances are overcome by the RISE term. Typical NN-based controllers generally result in uniformly ultimately bounded (UUB) stability due to the NN reconstruction error. In this paper to eliminate this error and achieve asymptotic tracking, the RISE feedback term is integrated into the NN compensator. Finally, a comparative study on the system performance is conducted between the proposed control strategy and two other conventional control methods. Simulation results illustrate the effectiveness of the proposed method.

Extension of inter-turn fault in windings of PMSM can damage all parts of electrical systems, and in some cases in sensitive applications may lead to irreparable events. Identification of such small faults at incipient steps can be so helpful to protect entire part of electrical system. In this paper, intelligent protection system is designed which is made by two major parts. In the first part of intelligent protection system K-Nearest Neighbor classifier is used as a detecting system to discriminate inter-turn fault from normal condition, phase to phase fault and open circuit condition and also to detect faulty phase, simultaneity. After that if inter-turn fault is happened, second part of proposed system which is based on an ANN Trained with Improved Gravitational Search Algorithm determines the amount of fault. IGSA is presented to improve the performance of the proposed protection system in this paper. Obtained results show that both part of intelligent proposed and intelligent protection system can do their best performance. It can successfully detect inter-turn fault and follow it and predict amount of this fault.

The increasing of software’s quality is a significant issue that attracts the attention of many researchers in this field. They are looking for a modeling that meets the users trust and reliance on software. Therefore, the demand for high reliability software development has been increased as well. Having suitable design, increasing reliability is required. Coupling and cohesion are of among divide and conquer designs that by having useful choices, the complexity of software can be reduced. In this paper, the reliability, as an important quality parameter, will be evaluated with applying an approach based on fuzzy computing of cohesion and coupling. Of among other approaches, coupling and cohesion fuzzy computing are more accurate and focus on Myers Model. Since the rate of coupling and cohesion has impact on any non-Functional factors, the approach helps software engineering to calculate quality parameters with metrics and coefficient of accuracy. The policy system of premium of disable people, as the case study, has been chosen to show in object oriented system, how coupling and cohesion relations can be analyzed.

Filtering of web pages with inappropriate contents is one of the major issues in the field of intelligent network'‎s‎ security. ‎Having a good intelligent filtering method with high accuracy and speed is needed for any country in order to control users‎'‎ access to the web. ‎So‎, ‎it has been considered by many researchers. Presenting web pages in an understandable way by machines is one of the most important preprocessing steps. ‎Thus, offering a way to describe web pages with lower dimensions would be very effective, especially in determining the nature of web pages with respect to whether they should be filtered out or not. ‎In this paper, we propose an automatic method to detect forbidden keywords from web pages. ‎Next, we define a new representation of web pages in vector form which consists of weighted sum and frequency of forbidden keywords in different parts of web pages named ‎‎RWSF‎‎‎. ‎For this, a ranking dictionary of keywords including forbidden keywords is used. To evaluate the proposed method, 2643 pages consisting of 1311 normal pages and 1332 forbidden pages were used. Among these, 1851 pages were used to train the system and 792 pages were used for system evaluation. The system has been assessed using various classifiers such as: k-Nearest Neighbor, Support Vector Machines, Decision Tree and Artificial Neural Networks. Evaluation results indicate the high efficiency and accuracy of the proposed method in all classifiers.‎‎‎

The Emergency Medical Centers are very helpful since they reduce the number of death and injuries by getting to the scenes of accidents and dealing with the case immediately. As saving one's life is the prime goal in these centers, any findings and techniques which will improve the services and ease achieving the goal will be highly welcomed. The first and the major factor in giving this kind of service is the time. The place where these centers are located can play an important role to reduce the time so as to offer the service right away and on time. Hence, finding the best places in a big city or cities to set up these centers to be able to give the services urgently will have a crucial importance. The method offered in this paper is to give the service qualitatively while we reduce the number of ambulances. Artificial bee colony (ABC) algorithm is an extended algorithm based on the bees' vigilant behavior that they search for food. This paper uses The ABC algorithm to solve the problem of positioning. The findings and the correlative coefficient of the study show that this Algorithm can be helpful especially in larger cities where it is difficult to locate a proper position to offer the service in an expected time.

The original idea of semantic kernels is to use semantic features instead of terms appeared in the text document. In this article, the documents are transformed into a new k-dimensional feature space by applying Singular Value Decomposition on the Term-Document matrix and extracting k eigenvectors with higher energy. The suggested semantic kernel causes severe reduction of dimensions which leads to two main conclusions. First, the computational complexity of the classifier is severely reduced. Second, the trained classifier has less sensitivity on the input terms; therefore, it can classify documents effectively. Experiments on Persian documents indicate the absolute superiority of the suggested semantic kernel in comparison to well-known vector space (Bag-of-Words) kernel, especially under the circumstances in which external semantic resources are not available and the amount of available training data is not sufficient.