This paper present an optimal Fractional Order PID (FOPID) controller based on Particle Swarm Optimization (PSO) for controlling the trajectory tracking of Wheeled Mobile Robot(WMR).The issue of trajectory tracking with given a desired reference velocity is minimized to get the distance and deviation angle equal to zero, to realize the objective of trajectory tracking a two FOPID controllers are used for velocity control and azimuth control to implement the trajectory tracking control. A path planning and path tracking methodologies are used to give different desired tracking trajectories. PSO algorithm is using to find the optimal parameters of FOPID controllers. The kinematic and dynamic models of wheeled mobile robot for desired trajectory tracking with PSO algorithm are simulated in Simulink-Matlab. Simulation results show that the optimal FOPID controllers are more effective and has better dynamic performance than the conventional methods.

In multihop networks, such as the Internet and the Mobile Ad-hoc Networks, routing is one of the most importantissues that has an important effect on the network’s performance. This work explores the possibility of using the shortest path routingin wireless sensor network . An ideal routing algorithm should combat to find an perfect path for data that transmitted within anexact time. First an overview of shortest path algorithm is given. Then a congestion estimation algorithm based on multilayerperceptron neural networks (MLP-NNs) with sigmoid activation function, (Radial Basis Neural Network Congestion Controller(RBNNCC) )as a controller at the memory space of the base station node. The trained network model was used to estimate trafficcongestion along the selected route. A comparison study between the network with and without controller in terms of: trafficreceived to the base station, execution time, data lost, and memory utilization . The result clearly shows the effectiveness of RadialBasis Neural Network Congestion Controller (RBNNCC) in traffic congestion prediction and control.

<p>This research puts forth an optimization- based analog beamforming scheme for millimeter-wave (mmWave) massive MIMO systems. Main aim is to optimize the combination of analog precoder / combiner matrices for the purpose of getting near-optimal performance. Codebook-based analog beamforming with transmit precoding and receive combining serves the purpose of compensating the severe attenuation of mmWave signals. The existing and traditional beamforming schemes involve a complex search for the best pair of analog precoder / combiner matrices from predefined codebooks. In this research, we have solved this problem by using Particle Swarm Optimization (PSO) to find the best combination of precoder / combiner matrices among all possible pairs with the objective of achieving near-optimal performance with regard to maximum achievable rate. Experiments prove the robustness of the proposed approach in comparison to the benchmarks considered. <strong></strong></p><p class="IndexTerms"> </p>

Face recognition is the process of finding the face of one or more people in an image or even in a video. There are variety techniques for face recognition used in the researches. In this paper various algorithms for face recognition on mobile phones or other electronic device are applied. firstly the face detection should be implemented in any face recognition system. To get the face detection many algorithms like color segmentation, template matching etc are applicated. Then the second phase of the proposed algorithm is implemented by using neural network Gabor with fuzzy system. The algorithm has been represented using MATLAB and then implemented it on the device. While implementing the proposed algorithm, a tradeoff between accuracy and computational complexity of the algorithm are made, because the face recognition system is implemented on a device with limited hardware capabilities