Flower Pollination Algorithm for Adaptive Beamforming of Phased Array Antennas

This paper introduces the flower pollination algorithm (FPA) as an optimization technique suitable for adaptive beamforming of phased array antennas. The FPA is a new nature-inspired evolutionary computation algorithm that is based on pollinating behaviour of flowering plants. Unlike the other nature-inspired algorithms, the FPA has fewer tuning parameters to fit into different optimization problems. The FPA is used to compute the complex beamforming weights of the phased array antenna. In order to exhibit the robustness of the new technique, the FPA has been applied to a uniform linear array antenna with different array sizes. The results reveal that the FPA leads to the optimum Wiener weights in each array size with less number of iterations compared with two other evolutionary optimization algorithms namely, particle swarm optimization and cuckoo search.

Target Tracking in WSN using Time Delay Neural Network

Energy efficient tracking is a challenging application of resource constrained wireless sensor network. Prediction based schemes play a vital role in energy saving by reducing an avoidable communication. Efficient tracking can be achieved only if state transition matrix used in filter closely resembles the target movement. Kalman filter has been widely used as prediction algorithm but fails in case of maneuvering target because of state transition matrix mismatch. To make tracking algorithm model free, Time delay neural network based prediction algorithm is proposed in this paper. Performance of Time Delay neural network (TDNN) is compared with Kalman filter and Interacting multiple model filter in terms of mean square error. Results shows that TDNN outperforms both the filters.

The Effects of Using Some Different Types of Learning Automata on Presentation of Adaptive Neighbor-Based and Energy Efficient Topology Control Protocols in Wireless Sensor Networks

The high number of nodes and dynamic and periodic topological changes, as well as constraints in the physical size of nodes, energy resources, and power of processing are some characteristics of sensor networks that make them different from other networks. One method to overcome these constraints is topology control with the aim of reducing energy consumption and increasing the network’s capacity, which has the most influence on the network’s efficiency, especially in terms of energy consumption and lifetime. In consideration of learning Automata’s abilities, such as low computational load and adaptability to changes via low environmental feedbacks, in this paper, neighbor-based topology control protocols based on learning Automata have been proposed somehow that all nodes are equipped with Automata. The nodes try to adapt their selected actions with required conditions for creating a connected and energy efficient network by selecting the best radio range for themselves. This approach finally forms a proper topology, and in this way it lowers the network’s energy consumption in its lifetime. The exclusive characteristic of these methods is the high number of transmission ranges that each node can select as transmission radius. In the first proposed protocol, a P-model environment is used for learning phase, but in the second proposed protocol, a Q-model environment is applied. Simulation results show favorite functionality of proposed protocols in comparison with some other similar protocols from topology control point of view, as well as high improvement of achieved results for the Q-model environment.

THD and Compensation Time Analysis of Three-Phase Shunt Active Power Filter Using Adaptive Spider Net Search Algorithm (ASNS) for Aircraft System

A novel Adaptive Spider Net Search Algorithm (ASNS) has been presented, which has been used for the optimization of conventional control scheme used in shunt active power filter for aircraft system. The superiority of this algorithm over existing Genetic Algorithm results has been presented by analyzing the THD and compensation time of both the algorithms. The simulation results using MATLAB model ratify that algorithm has optimized the control technique, which unmistakably prove the usefulness of the proposed algorithm in aircraft supply system.

Big Data Classification Using Distributed Optimized Hoeffding Trees

Large usage of social media, online shopping or transactions gives birth to voluminous data. Visual representation and analysis of this large amount of data is one of the major research topics today. As this data is changing over the period of time, we need an approach which will take care of velocity of data as well as volume and variety. In this paper, author has proposed a distributed method which will handle three dimensions of data and gives good results as compared to other method. Traditional algorithms are based on global optima which are basically memory resident programs. Our approach which is based on optimized hoeffding bound uses local optima method and distributed map-reduce architecture. It does not require copying whole data set onto a memory. As the model build is frequently updated on multiple nodes concurrently, it is more suitable for time varying data. Hoeffding bound is basically suitable for real time data stream. We have proposed very efficient distributed map-reduce architecture to implement hoeffding tree efficiently. We have used deep learning at leaf level to optimize the hoeffding tree. Drift detection is taken care by the architecture itself no separate provision is required for this. In this paper, with experimental results it is proved that our method takes less learning time with more accuracy. Also distributed algorithm for hoeffding tree implementation is proposed.

Real Time Call Monitoring System Using Spark Streaming and Network Intrusion Detection Using Distributed WekaSpark

With the increase of calls in industries it is very difficult to identify the calls made in a huge organization. The study and developing analytics out of the call history generated in terms of real time or the information stored helps in the improvement of the quality of calls in terms of network failure analysis, analysing call usage pattern from minimal to maximum to increase server efficiency, analyse user level pattern. The capability to process, analyse and evaluate real time data in a system is a challenging task, the test of building up an adaptable, shortcoming tolerant and flexible observing framework that Can deal with information continuously and at a huge scale is nontrivial. We exhibit a novel framework for real time processing and batch processing by using spark streaming and spark, also an ensemble model is used with distributed weka-spark for intrusion detection.