Novel method to transform ZnO nanorods into interlaced tattered nanosheets by changing the target inclination angle

ZnO nanoparticles were synthesized by liquid-phase pulse laser ablation of a Zn foil target immersed in deionized water. Nanosecond Q-switched Nd:YAG laser pulses of 532 nm were applied to the Zn foil target at a perpendicular and inclined (θ = 45°) angles. X-ray diffraction analysis revealed that both cases feature a ZnO nanostructure with a hexagonal wurtzite structure and that the particle size increases with the inclined target angle. Field emission scanning electron microscopy results of a colloidal drop cast on a glass substrate showed the ZnO has a nanorod structure in the case of a perpendicular target angle and an interlaced tattered nanosheet structure in the case of an inclined target angle. Photoluminescence spectra showed emission peaks in the UV, violet, blue, and green spectral regions, which correspond to excitonic and various defects resulting in an enhancement of emissions at inclined target angle.     

Geometrical approach of planar hyper-redundant manipulators: Inverse kinematics,path planning and workspace

A new method for inverse kinematics for hyper-redundant manipulators is proposed in this paper to plan the path of the end-effector. The basic idea is that for a given smooth path consisting of points close enough to each other; computing the inverse kinematics for these points is carried out geometrically using the proposed method. In this method, the angles between the adjacent links are set to be the same, which makes lining up of two or more joint axes impossible; therefore, avoiding singularities. The manipulability index has been used to show how far the manipulator from the singularity configuration is. The determination of the workspace of the manipulator using the proposed method has been presented in this paper. The simulation results have been carried out on a planar and a three dimensional manipulators. The effectiveness of the proposed method is clearly demonstrated by comparing its result with results calculated by the well-known method of measuring manipulability which is used for singularity avoidance for the last two decades.     

Performance analysis of opportunistic broadcast for delay-tolerant wireless sensor networks

This paper investigates a class of mobile wireless sensor networks that are unconnected most of the times; we refer to them as delay-tolerant wireless sensor networks (DTWSN). These networks inherit their characteristics from both delay tolerant networks (DTN) and traditional wireless sensor networks. After introducing DTWSNs, three main problems in the design space of these networks are discussed: routing, data gathering, and neighbor discovery. A general protocol is proposed for DTWSNs based on opportunistic broadcasting in delay-tolerant networks with radio device on-off periods. Three performance measures are defined in the study: the energy for sending queries to ask for data from possible neighbors (querying energy), data transfer energy, and absorption time (delay). A simple yet accurate approximation for the data-transfer energy is proposed. An analytic model is provided to evaluate the querying energy per contact (epc). Simulation results for the data propagation delay show that the querying energy per contact measure obtained from the analytic model is proportional to the product of the querying energy and the delay. A practical rule of thumb for an optimal query interval in terms of delay and energy is derived from different parts of the study.     

The performance of multiwavelets based OFDM system under different channel conditions

In wireless communication reception, the reliability of orthogonal frequency division multiplexing (OFDM) is limited because of the time-varying nature of the channel. This causes inter-carrier interference (ICI) and increases inaccuracies in channel tracking. This can effectively be avoided at the cost of power loss and bandwidth expansion by inserting a cyclic prefix guard interval before each block of parallel data symbols. However, this guard interval decreases the spectral efficiency of the OFDM system as the corresponding amount. Recently, it was found that based on Haar-orthonormal wavelets, discrete wavelet-based OFDM (DWT-OFDM) is capable of reducing the inter symbol interference (ISI) and ICI, which are caused by the loss in orthogonality between the carriers. DWT-OFDM can also support much higher spectrum efficiency than discrete Fourier-based OFDM (DFT-OFDM). In this paper the DFT-OFDM is replaced by Multiwavelets OFDM (DMWT-OFDM) in order to further reduce the level of interference and increase spectral efficiency. It is found that proposed Multiwavelet design achieves much lower bit error rates, increases signal to noise power ratio (SNR), and can be used as an alternative to the conventional OFDM. The proposed OFDM system was modeled tested, and its performance was found under different channel conditions.     

Design and optimization of LNA topologies with image rejection filters

An important problem in designing RFIC in CMOS technology is the parasitic elements of passive and active devices that complicate design calculations. This article presents three LNA topologies including cascode, folded cascade, and differential cascode and then introduces image rejection filters for low‐side and high‐side injection. Then, a new method for design and optimization of the circuits based on a Pareto‐based multiobjective genetic algorithm is proposed. A set of optimum device values and dimensions that best match design specifications are obtained. The optimization method is layout aware, parasitic aware, and simulation based. Circuit simulations are carried out based on TSMC 0.18 μm CMOS technology by using Hspice. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Estimation of magnitude and time duration of temporary overvoltages using ANN in transmission lines during power system restoration

In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. Artificial neural network (ANN) is employed for performing a nonlinear input–output mapping in this work, in order to estimate the temporary overvoltages (TOVs) due to transmission lines energization. In the proposed methodology, Levenberg–Marquardt second order method is used to train the multilayer perceptron. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters, trained ANN has therefore satisfactory generalization capability. Both single and three-phase line energizations are analyzed. The simulated results for 39-bus New England test system, indicate that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

From dynamic influence nets to dynamic Bayesian networks: A transformation algorithm

This paper presents an algorithm to transform a dynamic influence net (DIN) into a dynamic Bayesian network (DBN). The transformation aims to bring the best of both probabilistic reasoning paradigms. The advantages of DINs lie in their ability to represent causal and time‐varying information in a compact and easy‐to‐understand manner. They facilitate a system modeler in connecting a set of desired effects and a set of actionable events through a series of dynamically changing cause and effect relationships. The resultant probabilistic model is then used to analyze different courses of action in terms of their effectiveness to achieve the desired effect(s). The major drawback of DINs is their inability to incorporate evidence that arrive during the execution of a course of action (COA). Several belief‐updating algorithms, on the other hand, have been developed for DBNs that enable a system modeler to insert evidence in dynamic probabilistic models. Dynamic Bayesian networks, however, suffer from the intractability of knowledge acquisition. The presented transformation algorithm combines the advantages of both DINs and DBNs. It enables a system analyst to capture a complex situation using a DIN and pick the best (or close‐to‐best) COA that maximizes the likelihood of achieving the desired effect. During the execution, if evidence becomes available, the DIN is converted into an equivalent DBN and beliefs of other nodes in the network are updated. If required, the selected COA can be revised on the basis of the recently received evidence. The presented methodology is applicable in domains requiring strategic level decision making in highly complex situations, such as war games, real‐time strategy video games, and business simulation games. © 2009 Wiley Periodicals, Inc.     

On partial-information optimal singular control problem for mean-field stochastic differential equations driven by Teugels martingales measures

This paper is concerned with partial-information mixed optimal stochastic continuous–singular control problem for mean-field stochastic differential equation driven by Teugels martingales and an independent Brownian motion, where the Teugels martingales are a family of pairwise strongly orthonormal martingales associated with Lévy processes. The control variable has two components; the first being absolutely continuous, and the second singular. Partial-information necessary and sufficient conditions of optimal continuous–singular control for these mean-field models are investigated. As an illustration, this paper studies a partial-information linear quadratic control problem of mean-field type involving continuous–singular control.