Journal of Computer and Robotics
Volume:12 Issue: 2, Summer and Autumn 2019

In recent years, the use of MRI images has been very much considered due to their high clarity and high quality in the diagnosis and determination of brain tumor and its features. In this study, to improve the performance of tumor detection, we investigated comparative approach of the different classifiers to select the most appropriate classifier for identifying and extracting abnormal tissue and selected the best one by comparing their detection accuracies rate. In this research, GLCM and GLRM methods are used to extracting discriminating features. Thus results in they reduce the computational complexity. fuzzy entropy measurement method is used to determine the optimal properties and finally, we compared the four FFNN, MLP, BPNN, ANFIS neural networks to perform the decision making and classification process. The purpose of these four neural networks are to develop tools for discriminating the malignant tumors from benign ones assisting deciding in clinical diagnosis. Based on the results, we achieved high results among all classifiers. The proposed methodology results in accurate and speedy detection of tumor in brain along with identification of precise location of the tumor. In our opinion, the use of these classifiers can be very useful in the diagnosis of brain tumors in MRI images. Our other goal is to prove the suitability of the ANN method as a valuable method for statistical methods. The novelty of the paper lies in the implementation of the proposed method for discriminating the malignant tumors from benign which results in accurate and speedy detection of tumor in brain along with identification of precise location of the tumor. The efficiency of the method is proved through plenty of simulations and comparisons.

In This Paper, A Fault-Tolerant Control (FTC) Method Is Presented For A DC Microgrid With Constant Power Loads (Cpls) That Is Prone To Sensor Faults. This FTC Method's Main Idea Is Based On Hiding The Sensor Faults From The Controller Point Of View Using A Suitable Virtual Sensor. After Presenting The System's Nonlinear Model, The Model Is Then Converted To A Takagi-Sugeno (TS) Fuzzy Representation. The Nominal Controller Is Designed For The Fuzzy Model In The Form Of State Feedback, And The States Are Estimated Using A Suitable Observer. In The Event Of A Sensor Fault Detection, The Effects Of The Fault In The Control Loop Are Compensated By A Virtual Sensor. The Controller's Gains, The Virtual Sensor, And The Observer Are Designed Using Related Linear Matrix Inequalities (Lmis) And Applying Some Appropriate LMI Regions To Achieve Appropriate Performance. The Proposed Method Is An Active Fault-Tolerant Control (AFTC) Strategy In Which The Virtual Sensor Hides The Sensor Faults From The Controller And The Observer. In This Method, From The Controller's Point Of View, The Faulty System Plus The Virtual Sensor Acts As A Healthy System, And The Nominal Controller Continues To Its Work Without The Need To Be Reconfigured. The Efficiency Of The Proposed

In this paper, a generalized Benjamin-Bona-Mahony equation ( BBM) is solved by using the Adomian's decomposition method (ADM) , modified Adomian's decomposition method (MADM), variational iteration method (VIM), modified variational iteration method (MVIM) and homotopy analysis method (HAM). The approximate solution of this equation is calculated in the form of series which its components are computed by applying a recursive relation. The existence and uniqueness of the solution and the convergence of the proposed methods are proved. A numerical example is studied to demonstrate the accuracy of the presented methods.The MVIM has been shown to solve effectively, easily and accurately a large class of nonlinear problems with the approximations which convergent are rapidly to exact solutions.

Four-bar mechanisms are one of the most common and effective components in the industry. As an example of their applications, they are designed to generate the desired output motion. In this paper, the nonlinear problem of optimal defect-free synthesis of four-bar mechanisms is analyzed by using a constrained version of the newly developed adaptive particularly tunable fuzzy particle swarm optimization (APT-FPSO) algorithm. The analyzed case study is designing a four-bar mechanism to generate a path that included three loops and 90 precision points. The results obtained support the superior performance of APT-FPSO compared to the standard PSO in solving the path generation problem.

Nano-scale technology has brought more susceptibility to soft errors for the generation of complicated and state of the art devices. Soft errors are the impacts of radiation of the particles like a neutron, alpha, and ions on the surface of the circuits. To tackle the system malfunctions and provide a reliable device, studying the transient fault effects on the logic circuits can be a more significant issue. This paper presents a new approach based on Recurrent Neural Networks (RNNs) to estimate ICs' Soft Errors Rate (SER). As RNN can be deployed for signal processing and time series, we applied it to investigate transient fault effects while propagating through the combinational and sequential parts of a test chip and compute its SER by simulating and analyzing the circuit outputs. In this paper, the results of utilizing the proposed RNN model to estimate the SER of the ISCAS-85 benchmark circuits have been provided.

Grid computing environments include heterogeneous resources shared by a large number of computers to handle the data and process intensive applications. The required resources must be accessible for Grid applications on demand, which makes the resource discovery a critical service in Grid environments. In recent years, diverse techniques are provided to index and discover the Grid resources. The response time and message load during the search process highly affect the efficiency of resource discovery. This paper proposes a new technique to forward the queries based on the resource types which are accessible through each branch in hierarchical Grid resource discovery approaches. The proposed technique is simulated in GridSim and the experimental results indicated that it is able to reduce the response time and message load during the search process especially when the Grid environment contains a large number of nodes.