Bacteria foraging optimization in antenna engineering: An application to array fault finding

Finding fault elements in linear antenna arrays using bacteria foraging optimization (BFO) is presented. One of the better options of array diagnosis is to perform it by measuring the radiated field, because in this case, removal of the array from its working site is not required and thereby not interrupting its normal operation. This task of fault finding from far‐field data is designed as an optimization problem where the difference between the far‐field power pattern obtained for a given configuration of failed element(s) and the measured one is minimized w. r. t. the excitations of the array elements. This set of excitations on comparison with the excitations of the original array gives the idea of the fault position and their type, such as either complete fault or partial fault. BFO being relatively new to microwave community when compared with other soft‐computing techniques, its performance was observed w. r. t. time of computation and convergence of the iterative process. Possibility of finding the faults from random sample points and use of minimum number of sample points for array fault finding are the novelties of the present work. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Bipartite coherent-state quantum key distribution with strong reference pulse

We propose an optical scheme for quantum key distribution in which bits are encoded in relative phases of four bipartite weak coherent states ${|\alpha, \alpha\rangle, |-\alpha, -\alpha\rangle, |-\alpha, \alpha\rangle}$ and ${|\alpha, -\alpha \rangle}$ , with respect to a strong reference pulse. We discuss security of the scheme against eavesdropping strategies like, photon number splitting, photon beam splitting and intercept-resend attacks. It is found that present scheme is more sensitive against these eavesdropping strategies than the two-dimensional non-orthogonal state based protocol and BB84 protocol. Our scheme is very simple, requires only passive optical elements like beam splitters, phase shifters and photon detectors, hence is at the reach of presently available technology.     

Security Driven Scheduling Model for Computational Grid Using NSGA-II

Number of software applications demands various levels of security at the time of scheduling in Computational Grid. Grid may offer these securities but may result in the performance degradation due to overhead in offering the desired security. Scheduling performance in a Grid is affected by the heterogeneities of security and computational power of resources. Customized Genetic Algorithms have been effectively used for solving complex optimization problems (NP Hard) and various heuristics have been suggested for solving Multi-objective optimization problems. In this paper a security driven, elitist non-dominated sorting genetic algorithm, Optimal Security with Optimal Overhead Scheduling (OSO₂S), based on NSGA-II, is proposed. The model considers dual objectives of minimizing the security overhead and maximizing the total security achieved. Simulation results exhibit that the proposed algorithm delivers improved makespan and lesser security overhead in comparison to other such algorithms viz. MinMin, MaxMin, SPMinMin, SPMaxMin and SDSG.     

Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.     

Design of observer based nonlinear model predictive controller for a continuous stirred tank reactor

Model predictive control (MPC) schemes are now widely used in process industries for the control of key unit operations. Linear model predictive control (LMPC) schemes which make use of linear dynamic model for prediction, limit their applicability to a narrow range of operation (or) to systems which exhibit mildly nonlinear dynamics.

In this paper, a nonlinear observer based model predictive controller (NMPC) for nonlinear system has been proposed. An approach to design NMPC based on fuzzy Kalman filter (FKF) and augmented state fuzzy Kalman filter (ASFKF) has been presented. The efficacy of the proposed NMPC schemes have been demonstrated by conducting simulation studies on the continuous stirred tank reactor (CSTR). The analysis of the extensive dynamic simulation studies revealed that, the NMPC schemes formulated produces satisfactory performance for both servo and regulatory problems. Simulation results also include an inferential control case, where the reactor concentration is not measured but estimated from temperature measurement and used in the NMPC based on FKF and ASFKF formulations.     

Configuring and enhancing measurement systems for damage identification

Engineers often decide to measure structures upon signs of damage to determine its extent and its location. Measurement locations, sensor types and numbers of sensors are selected based on judgment and experience. Rational and systematic methods for evaluating structural performance can help make better decisions. This paper proposes strategies for supporting two measurement tasks related to structural health monitoring - (1) installing an initial measurement system and (2) enhancing measurement systems for subsequent measurements once data interpretation has occurred. The strategies are based on previous research into system identification using multiple models. A global optimization approach is used to design the initial measurement system. Then a greedy strategy is used to select measurement locations with maximum entropy among candidate model predictions. Two bridges are used to illustrate the proposed methodology. First, a railway truss bridge in Zangenberg, Germany, is examined. For illustration purposes, the model space is reduced by assuming only a few types of possible damage in the truss bridge. The approach is then applied to the Schwandbach bridge in Switzerland, where a broad set of damage scenarios is evaluated. For the truss bridge, the approach correctly identifies the damage that represents the behaviour of the structure. For the Schwandbach bridge, the approach is able to significantly reduce the number of candidate models. Values of candidate model parameters are also useful for planning inspection and eventual repair.     

CNFET Based Ultra-Low-Power Schmitt Trigger SRAM for Internet of Things (IoT) Applications

Wireless Personal Communications - This paper presents Carbon Nanotube FET (CNFET) based ultra-low-power Schmitt trigger SRAM designs which can operate at voltage levels as low as 200 mV, with high...     

Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network

Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher’s attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.

     

A Workflow Scheduling Method for Cloud Computing Platform

Wireless Personal Communications - Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs...     

The synthesis of pixelated metamaterial cross polarizer using binary wind driven optimization algorithm

Microsystem Technologies - In this paper synthesis of two wideband Metamaterial Cross Polarizer (MCPs) is proposed. The synthesis of proposed MCPs is done by using Binary Wind Driven Optimization...