Journal of Advances in Computer Research
Volume:8 Issue: 1, Winter 2017

In this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to appropriate resources. The proposed method has less Makespan and price. In addition to implementing a grid computing system, the proposed method which is using three standard test functions in evolutionary multi-objective optimization is evaluated. In this paper, the number of elements in the assessment of the Pareto optimizes set, uniformity and error. The results show that this Search method has more optimization in particle number density and high accuracy with less error than the MOPSO and can be replaced as an effective solution for solving multi-objective optimization.

Ad hoc mobile networks have dynamic topology with no central management. Because of the high mobility of nodes, the network topology may change constantly, so creating a routing with high reliability is one of the major challenges of these networks .In the proposed framework first, by finding directions to the destination and calculating the value of the rout the combination of this value with the average total probability of nodes for each route is considered to be a final value and by choosing a route among all routes leading to destination, routing operation is performed randomly and desirability or undesirability of the rout will be examined based on learning automata technique to select the optimal route for the next times. The proposed method attempts to encompass all parameters to control congestion on the network accurately and efficiently. To evaluate the proposed method, the results were compared with previous related works and compared with other methods which indicated that the proposed method has a better performance.

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high-dimensional feature-space via an unsupervised learning including an attribute discrimination component. The unsupervised clustering component assigns degree of typicality to each data pattern in order to identify and reduce the effect of noisy or outlaid data patterns. Then, the suggested technique obtains the best combination parameters for each background. The experimentations on artificial datasets and standard SONAR dataset demonstrate that our classifier ensemble does better than individual classifiers in the ensemble.

A high accurate and low-voltage analog CMOS current divider which operates with a single power supply voltage is designed in 0.18µm CMOS standard technology. The proposed divider uses a differential amplifier and transistor in triode region in order to perform the division. The proposed divider is modeled with neural network while TLBO algorithm is used to optimize it. The proposed optimization method shows a close characteristic to the ideal current-input voltage-output divider behavior over wide input range. By using the achieved results of the TLBO algorithm simulation results using HSPICE shows the maximum linearity error less than 0.5% .The total power consumption is below 0.14 mW with a single 1.5 V power supply. The proposed divider was laid out in standard 0.18µm CMOS technology and shows high linearity .The output voltage offset is less than 3 mV under all situations. The proposed scheme has potential to be employed in modern high-performance low-voltage analog signal processing systems.

The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of learning problems. As a solution, this paper proposes a new methodology of using WOC theory for evaluating and selecting basic result partitions in semi-supervised clustering problems. This paper introduces new technique for reducing the data dimensions based on supervision information, a new semi-supervised clustering algorithm based on k-means for generating basic results, a new strategy for evaluating and selecting basic results based on feedback mechanism, a new metric for evaluating diversity of basic results. The results demonstrate the efficiency of proposed method's aggregate decision-making compared to other algorithms.

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In this paper, we propose a new pupil localization method based on the sparse representation and sparse recovery (SR). The main advantage of our segmentation algorithm based on sparse representation in respect to other approaches is capability of searching the whole image for iris region very fast. Also we have proposed a new method for enhancing the extracted iris template when the pupil boundary is noncircular, and also a new method for creating occlusion mask based on the histogram thresholding. We have compared the SR classifier and the Hamming distance (HD) with the same size dictionary and shown that using the principal component analysis (PCA) with the SR classifier makes it very faster, whereas preserves the accuracy. The achieved results are evaluated with others in terms of the recognition accuracy and the segmentation time consuming where the CASIA V4 Lamp database used.

This paper proposes an efficient network architecture to improve energy consumption in Wireless Sensor Networks (WSN). The proposed architecture uses a mobile data collector to a partitioned network. The mobile data collector moves to center of each logical partition after each decision period. The mobile data collector must declare its new location by packet broadcasting to all sensor nodes. However, packet broadcasting leads to increase in the network congestion as well as the network energy consumption. In this regard, this paper proposes an efficient routing algorithm to control number of packets broadcasting. The proposed algorithm declares the mobile data collector new location to sensor nodes within a special area. Special area has been considered around of the mobile data collector. Therefore, the proposed routing algorithm does not permit packets reaching to this special area. Indeed, the proposed algorithm directs data toward the mobile data collector by boundary sensor nodes. In fact, the proposed algorithm considers sensor nodes within special area have more traffic and energy consumption than other sensor nodes. Simulation results that have been implemented in ns-2 show the proposed algorithm increases the network lifetime as well as the sensor nodes energy consumption balancing.

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weighted average are not able to produce safe outputs when obtaining a correct output is impossible and also both of them are not able to perform appropriately in small error limit. In the present paper, delivering a voter for safety system, Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed. The above mentioned model is trained through Hybrid learning algorithm that is effective and using basic Fuzzy inference system, subtractive clustering and fuzzy C-means method. Results show that delivered voter produced more safety outputs especially for small error amplitude.

Due to the high density and the low consumption power in the digital integrated circuits, mostly technology of CMOS is used. During the past times, the Metal oxide silicon field effect transistors (MOSFET) had been used for the design and implementation of the digital integrated circuits because they are compact and also they have the less consumption power and delay to the other transistors. But after discovering the carbon nano-tubes by Ijima et al., several studies have been done on these structures in the other sciences. Single cover nano-tubes due to the electrical traits such as low consumption power, high speed, the compact area with the smallest dimensions in the form of nano by the unique configuration, multiple threshold recognition, least threshold of noise, etc. better than the other nano-tubes. Over the past times, bi-valued logic was used but these days, multi-valued logic (due to the features such as high speed in the transfer of information, decrease of the number of gate, the decrease of operation, etc) is being used. Among the multi-valued logics, triple one because of less evaluated cost of installation and the simple method for implementation of the electronic circuits, is considered more than the other. In this article, by the use of triple-valued field of Galois, the multiplier circuits based on Metal oxide silicon field effect transistors (MOSFET) as well, the transistors of field effect of semi-carbon nano-tubes were designed and implemented.