This article presents the dynamic path planning for a mobile robot to track a randomly moving goal with avoidance of multiple randomly moving obstacles. The main feature of the developed scheme is its capability of dealing with the situation that the paths of both the goal and the obstacles are unknown a priori to the mobile robot. A new mathematical approach that is based on the concepts of 3-D geometry is proposed to generate the path of the mobile robot. The mobile robot decides its path in real time to avoid the randomly moving obstacles and to track the randomly moving goal. The developed scheme results in faster decision-making for successful goal tracking. 3-D simulations using MATLAB validate the developed scheme.

This paper has proposed new materials based conventional arrayed waveguide grating (AWG) devices such as pure silica glass (SiO2), Lithium niobate (LiNbO3) , and gallium aluminum arsenide (Ga(1-x)Al(x)As) materials for multiplexing and demultiplexing applications in interval of 1.45 μm to 1.65 μm wavelength band, which including the short, conventional, long, and ultra long wavelength band. Moreover we have taken into account a comparison between these new materials within operating design parameters of conventional AWG devices such as diffraction order, length difference of adjacent waveguides, focal path length, free spectral range or region, maximum number of input/output wavelength channels, and maximum number of arrayed waveguides. As well as we have employed these materials based AWG to include Multi band applications under the effect of ambient temperature variations.

This paper is concerned with the observer designing problem for a class of uncertain delayed nonlinear systems using reinforcement learning. Reinforcement learning is used via two Wavelet Neural networks (WNN), critic WNN and action WNN, which are combined to form an adaptive WNN controller. The “strategic” utility function is approximated by the critic WNN and is minimized by the action WNN. Adaptation laws are developed for the online tuning of wavelets parameters. By Lyapunov approach, the uniformly ultimate boundedness of the closed-loop tracking error is verified. Finally, a simulation example is shown to verify the effectiveness and performance of the proposed method.

Meditation is commonly perceived as an alternative medicine method of psychological diseases management tool that assist in alleviating depression and anxiety disorders. The purpose of this study is to evaluate the accuracy of different classifiers on the heart rate signals in a specific psychological state. Two types of heart rate time series (before, and during meditation) of 25 healthy women are collected in the meditation clinic in Mashhad. Nonlinear features such as Lyapunov Exponents and Entropy were extracted. To evaluate performance of the classifiers, the classification accuracies and mean square error (MSE) of the classifiers were examined. Different classifiers were tested and the studies confirmed that for the heart rate signals, Quadratic classifier trained on Lyapunov Exponents and Entropy results in higher classification accuracy. The classification accuracy of the Quadratic classifier is 92.31%. However, the accuracies of Fisher and k-Nearest Neighbor (k-NN) classifiers are encouraging. The classification results demonstrate that the dynamical measures are useful parameters which contain comprehensive information about signals and the Quadratic classifier using nonlinear features can be useful in analyzing the heart rate signals in a specific psychological state.

One of the main categories in Data Clustering is density based clustering. Density based clustering techniques like DBSCAN are attractive because they can find arbitrary shaped clusters along with noisy outlier. The main weakness of the traditional density based algorithms like DBSCAN is clustering the different density level data sets. DBSCAN calculations done according to given parameters applied to all points in a data set, while densities of the data set clusters may be totally different. The proposed algorithm overcomes this weakness of the traditional density based algorithms. The algorithm starts with partitioning the data within a cluster to units based on a user parameter and compute the density for each unit separately. Consequently, the algorithm compares the results and merges neighboring units with closer approximate density values to become a new cluster. The experimental results of the simulation show that the proposed algorithm gives good results in finding clusters for different density cluster data set.

The novel Imperialist Competitive Algorithm (ICA) that was recently introduced has a good performance in some optimization problems. The ICA inspired by socio-political process of imperialistic competition of human being in the real world. In this paper, a new Imperialist Competitive Algorithm with Adaptive Radius of Colonies movement (ICAR) is proposed. In the proposed algorithm, for an effective search, the Absorption Policy changed dynamically to adapt the radius of colonies movement towards imperialist’s position. The ICA is easily stuck into a local optimum when solves high-dimensional multi-modal numerical optimization problems. To overcome this shortcoming, we use probabilistic model that utilize the information of colonies positions to balance the exploration and exploitation abilities of the Imperialist Competitive Algorithm. Using this mechanism, ICA exploration capability will enhance. Some famous unconstraint benchmark functions used to test the ICAR performance. Simulation results show this strategy can improve the performance of the ICA algorithm significantly.

We describe a multiset of agents based modeling and simulation paradigm for synthetic biology. The multiset of agents –based programming paradigm, can be interpreted as the outcome arising out of deterministic, nondeterministic or stochastic interaction among elements in a multiset object space, that includes the environment. These interactions are like chemical reactions and the evolution of the multiset can emulate the system biological functions. Since the reaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of elements, so that the elements evolve toward equilibrium or emergent state. Practical realization of this paradigm for system biological simulation is achieved through the concept of transactional style programming with agents, as well as soft computing (neural- network) principles. Also we briefly describe currently available tools for agent-based-modeling, simulation and animation.

This paper presents a novel approach for designing a decentralized controller for load frequency control of interconnected power areas. The proposed fuzzy logic load frequency controller (FLFC) has been designed to improve the dynamic performance of the frequency and tie line power under a sudden load change in the power areas. The effect of generation rate constraint (GRC) for both areas has been considered in the controller design. The proposed FLFC consists of two internal fuzzy logic controllers namely, the PD-like fuzzy logic controller and the PI-like fuzzy logic controller. The FLFC has been co-coordinated with the conventional integral controller. Time-domain simulations using MATALB/SIMULINK program has been performed to demonstrate the effectiveness of the proposed FLFC. The simulation results show that the proposed FLFC can provide good damping and reduce the overshoot even in the presence of the GRC.