Journal of Advances in Computer Research
Volume:7 Issue: 2, Spring 2016

Grid computing is an emerging computing paradigm that will have significant impact on the next generation information infrastructure. Due to the largeness and complexity of grid system, its quality of service, performance and reliability are difficult to model, analyze and evaluate. This paper proposes a virtual tree-structured model of the grid service. This model simplifies the physical structure of a grid service because there are several links between RMS and resources. In this paper, the task scheduling by RMS and the task execution within grid resources considering data dependence and failure correlation are modeled using colored Petri nets (CPNs). We have also proposed a method for evaluation the grid service reliability based on the analysis of the model. In addition, an instance of the proposed model for a sample grid environment is constructed and analyzed using CPN Tools.

In this paper, an efficient fault-tolerant routing algorithm for Mobile Ad-hoc Networks (MANETs) is presented. The proposed algorithm increases the network fault-tolerance using natural redundancy of Ad-hoc networks. This algorithm is carried out in two stages; 1) the selection of backup nodes 2) the selection of backup route(s). In the first stage, the proposed algorithm chooses nodes with the same path as backup nodes. Prediction and diagnosis of nodes` paths is performed through backup tables. Since the selection of backup nodes is fulfilled, the proposed algorithm begins fault-tolerance routing. For this purpose, initially the proposed algorithm provides the main route between each pair of source & destination nodes based on DSR routing algorithm. Then, from a destination node towards a source node, the backup route(s) is established between the chosen backup nodes in the first stage. Experimental results taken from NS-2 simulator demonstrate that in comparison with previous methods the proposed increases; 1) 10% the package delivery ratio against the percentages of faulty nodes and, 2) 22% package delivery ratio against the pause time of various mobile nodes.

Digital image processing in recent decades has made considerable progress in theoretical and practical aspects. Nowadays, machine vision techniques have important application in the field of agriculture. One of these applications is detection of different varieties of rice from the bulk sample of rice image. These techniques also have high speed, accuracy and reliability. Texture feature selection is one of the important characteristics used in pattern recognition. The better feature selection of a feature set usually results in better performance in a classification problem. In This work we try to extract features by using co_occurrence matrix and select the best feature set for classification of rice varieties based on image of bulk samples using hybrid algorithm which is called "fuzzy_ imperialist competition” and then classify the best features using support vector machine(SVM). Results of the proposed method showed, the classification accuracy is improved to 96/79%. The feature set which is selected by the fuzzy-Ica provides the better classification performance compared to that obtained by Imperialist competition algorithm.

Nowadays, it is common to find optimal point of the dynamic problem; dynamic problems whose optimal point changes over time require algorithms which dynamically adapt the search space instability. In the most of them, the exploitation of some information from the past allows to quickly adapt after an environmental change (some optimal points change). This is the idea underlining the use of memory in the field, which involves key design issues concerning the memory content, the process of memory update, and the process of memory retrieval. With use of the Aging Best Solution and Keeping Diversity in Population, the speed convergence of algorithm can be increased. This article presents a genetic algorithm based on memory for dealing with dynamic optimization problems and focuses on explicit placement of memory schemes, and performs a comprehensive analysis on current design of Moving Peaks Benchmark (MPB) problem. The MPB problem is the most proper benchmark for simulation of dynamic environments. The experimental study show the efficiency of the proposed approach for solving dynamic optimization problems in comparison with other algorithms presented in the literature.

The idea of this paper is designing an automatic fault detection system based on fuzzy logic, therefore two signals of PMSM in fault condition are analyzed for inter turn fault detection: current and torque. In this fault type there is some distortion in these signals, but it is not good enough to detecting with fuzzy logic solely, so with combination of wavelet transform and FCM a new method for fault detection is introduced. In this method one detail signal of wavelet transform is chosen and then with FCM it is divided into 6 clusters, these clusters describe the situation of signal truly. Using FCM has two advantages: first in some clusters there had been fault therefore fault was detected, and second it is used for fuzzy logic system to deciding amount and intensity of fault of PMSM. By applying combination of wavelet transform and FCM, designing of fuzzy logic has been more effective, the MFs are directly come from output of FCM, therefore fuzzy logic system have more accurate answer. The output of fuzzy logic that is showed in surface view is based on tree situation that is defined in output MF, and describes whole conditions of PMSM and shows the amount of inter turn fault.

In this paper we have proposed an approach for emotion detection in implicit texts. We have introduced a combinational system based on three subsystems. Each one analyzes input data from a different aspect and produces an emotion label as output. The first subsystem is a machine learning method. The second one is a statistical approach based on vector space model (VSM) and the last one is a keyword-based subsystem with an information fusion component to aggregate the final output of main system. We analyzed the performance of our proposed system on ISEAR dataset with seven emotions: anger, joy, sad, shame, fear, disgust and guilt. The results show that our combinational system outperforms each subsystem with overall f-measure of 0.68 and at least up to 0.71 in terms of F1 in emotion level except for anger. The overall performance of the main system is 9.13% better than machine learning module, 16.6% better than VSM and 23% better than keyword-based.

Fuzzy logic has a high potential for managing the uncertainty sources associated with the medical expert systems. Application of fuzzy inference model has been widely concentrated for managing uncertainties in computer based practices of medicine. This paper has proposed two fuzzy expert systems for prognosis of the heart disease based on: 1) Mamdani inference model, and 2) Sugeno inference model. These methods initially received clinical parameters as input sand define their corresponding fuzzy sets. The performance of the FESs (Fuzzy Expert System) based on the Mamdani and Sugeno model, have been evaluated using real patients dataset through conducting two different studies. The dataset includes 380 real cases collected from the Parsian Hospital in Karaj. The accuracy of the proposed Mamdani FES is equal to79.47% and its accuracy using Sugeno model is equal to 88.43%. This FES is promising for prognosis of the heart disease and consequently early diagnosis of the disease and improving survival rates.

Full adder cell is often placed in the critical path of other circuits. Therefore it plays an important role in determining the entire performance of digital system. Moreover, portable electronic systems rely on battery and low-power design is another concern. In conclusion it is a vital task to design high-performance and low-power full adder cells. Since delay opposes against power consumption, we focus on Power-Delay Product (PDP) as a figure of merit. In this paper using carbon nanotube field-effect transistors (CNFETs) a novel low power and low PDP 1-bit full adder cell is proposed. The novel cell is based on capacitive threshold logic (CTL) and to strengthen its internal signals transmission gates (TGs) are applied. Using both CTL and TG techniques lead to achieving low power consumption full adder cell. Intensive simulations with 32nm technology node using Synopsys HSPICE with regard to different power supplies, temperatures, output loads, and operating frequencies are performed. All simulations confirm the superiority of the proposed cell compared to other state-of-the-art cells.

In this paper, we define the concepts of a complex fuzzy subset and a complex fuzzy finite state automaton. Then we extend the notion of a complex fuzzy finite state automaton and introduce the notion of a general complex fuzzy automaton. After that we define the concept of a max- min general complex fuzzy automaton and construct some equivalence relations and some congruence relations in a max-min general complex fuzzy automaton and obtain different types of monoids in a max-min general complex fuzzy automaton and define a homomorphism between them. Then we define the concepts of a general complex fuzzy transformation semi- group, a faithful general complex fuzzy transformation semigroup and a faithful general complex fuzzy transformation semigroup associated with a max-min general complex fuzzy automaton. Then we derive relationships between a max-min general complex fuzzy automaton and a general complex fuzzy transformation semigroup.

According to world health organization, breast cancer is one of the most deadly cancers occurred in women. Therefore accurate diagnosis and prediction is important to decrease the high death rate. The aim of this paper is twofold. First, improving breast cancer detection accuracy using Modified Fuzzy Logic (MFL) then improving the performance of MFL algorithm using GPU platform. The experimental results show that the accuracy of the breast cancer detection using MFL is higher than other techniques. In addition, by exploiting loop-level parallelism and pipeline parallel communication pattern in MFL algorithm, its performance is improved up to 19.17x for different image sizes.