Linear and nonlinear optical studies on 3,6-bis (2 pyridyl) pyridazine

3,6-Bis (2 pyridyl) pyridazine has been synthesized and characterized by NMR, XRD and elemental analyses. The vibrational studies were carried out by using FTIR and Raman spectroscopy and the modes of vibrations were analysed and compared with the theoretically calculated values. The nonlinear optical property of the title compound was examined by Kurtz-Perry method and Hyper Raleigh scattering with the fundamental wavelength of 1064nm. This compound possesses less SHG efficiency but large first hyperpolarizability. (C) 2013 Elsevier GmbH. All rights reserved.

Effects of initial height on the steady-state persistence probability of linear growth models

The effects of the initial height on the temporal persistence probability of steady-state height fluctuations in up-down symmetric linear models of surface growth are investigated. We study the (1 + 1)-dimensional Family model and the (1 + 1)-and (2 + 1)-dimensional larger curvature (LC) model. Both the Family and LC models have up-down symmetry, so the positive and negative persistence probabilities in the steady state, averaged over all values of the initial height h(0), are equal to each other. However, these two probabilities are not equal if one considers a fixed nonzero value of h(0). Plots of the positive persistence probability for negative initial height versus time exhibit power-law behavior if the magnitude of the initial height is larger than the interface width at saturation. By symmetry, the negative persistence probability for positive initial height also exhibits the same behavior. The persistence exponent that describes this power-law decay decreases as the magnitude of the initial height is increased. The dependence of the persistence probability on the initial height, the system size, and the discrete sampling time is found to exhibit scaling behavior.

Assimilation of endmember variability in spectral for urban land cover extraction mixture analysis

Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember variability in the linear mixture model by incorporating the variance for each class, the signals of which varies from pixel to pixel due to change in urban land cover (LC) structures. VECLS is first tested with a computer simulated three class endmember considering four bands having small, medium and large variability with three different spatial resolutions. The technique is next validated with real datasets of IKONOS, Landsat ETM+ and MODIS. The results show that correlation between actual and estimated proportion is higher by an average of 0.25 for the artificial datasets compared to a situation where variability is not considered. With IKONOS, Landsat ETM+ and MODIS data, the average correlation increased by 0.15 for 2 and 3 classes and by 0.19 for 4 classes, when compared to single endmember per class. (C) 2013 COSPAR. Published by Elsevier Ltd. All rights reserved.

Analysis and design of split-capacitor resistive-inductive passive damping for LCL filters in grid-connected inverters

Grid-connected inverters require a third-order LCL filter to meet standards such as the IEEE Std. 519-1992 while being compact and cost-effective. LCL filter introduces resonance, which needs to be damped through active or passive methods. Passive damping schemes have less control complexity and are more reliable. This study explores the split-capacitor resistive-inductive (SC-RL) passive damping scheme. The SC-RL damped LCL filter is modelled using state space approach. Using this model, the power loss and damping are analysed. Based on the analysis, the SC-RL scheme is shown to have lower losses than other simpler passive damping methods. This makes the SC-RL scheme suitable for high power applications. A method for component selection that minimises the power loss in the damping resistors while keeping the system well damped is proposed. The design selection takes into account the influence of switching frequency, resonance frequency and the choice of inductance and capacitance values of the filter on the damping component selection. The use of normalised parameters makes it suitable for a wide range of design applications. Analytical results show the losses and quality factor to be in the range of 0.05-0.1% and 2.0-2.5, respectively, which are validated experimentally.

Iterated gain-based stochastic filters for dynamic system identification

We propose a novel form of nonlinear stochastic filtering based on an iterative evaluation of a Kalman-like gain matrix computed within a Monte Carlo scheme as suggested by the form of the parent equation of nonlinear filtering (Kushner-Stratonovich equation) and retains the simplicity of implementation of an ensemble Kalman filter (EnKF). The numerical results, presently obtained via EnKF-like simulations with or without a reduced-rank unscented transformation, clearly indicate remarkably superior filter convergence and accuracy vis-a-vis most available filtering schemes and eminent applicability of the methods to higher dimensional dynamic system identification problems of engineering interest. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Sliding mode control based terminal impact angle constrained guidance laws using dual sliding surfaces

In this paper, sliding mode control theory based guidance laws to intercept non-maneuvering targets at a desired impact angle are presented. The desired impact angle, defined in terms of a desired line-of-sight (LOS) angle, is achieved by selecting the missile's lateral acceleration (latax) to enforce sliding mode on a sliding surface based on this LOS angle. As will be shown, this guidance law does not ensure interception for all states of the missile and the target during the engagement. Hence, to satisfy the requirement of interception at the desired impact angle, a second sliding surface is designed and a switching logic, based on the conditions necessary for interception, is presented that allows the latax to switch between enforcing sliding mode on one of these surfaces so that the target can be intercepted at the desired impact angle. The guidance laws are designed using non-linear engagement dynamics.

Non-singular terminal sliding mode guidance and control with terminal angle constraints for non-maneuvering targets

In this paper guidance laws to intercept stationary and constant velocity targets at a desired impact angle, based on sliding mode control theory, are proposed. The desired impact angle, which is defined in terms of a desired line-of-sight (LOS) angle, is achieved in finite time by selecting the missile's lateral acceleration (latax) to enforce non-singular terminal sliding mode on a switching surface designed using this desired LOS angle and based on non-linear engagement dynamics. Numerical simulation results are presented to validate the proposed guidance laws for different initial engagement geometries and impact angles.

Nonlinear and linear autopilot performance comparison of tactical flight vehicle

n this paper, three-axis autopilot of a tactical flight vehicle has been designed for surface to air application. Both nonlinear and linear design synthesis and analysis have been carried out pertaining to present flight vehicle. Lateral autopilot performance has been compared by tracking lateral acceleration components along yaw and pitch plane at higher angles of attack in presence of side force and aerodynamic nonlinearity. The nonlinear lateral autopilot design is based on dynamic inversion and time scale separation principle. The linear lateral autopilot design is based on three-loop topology. Roll autopilot robustness performance has been enhanced against unmodeled roll disturbances by backstepping technique. Complete performance comparison results of both nonlinear and linear controller based on six degrees of freedom simulation along with stability and robustness studies with respect to plant parameter variation have been discussed in the paper.

Large-MIMO Receiver based on Linear Regression of MMSE Residual

Multiple input multiple output (MIMO) systems with large number of antennas have been gaining wide attention as they enable very high throughputs. A major impediment is the complexity at the receiver needed to detect the transmitted data. To this end we propose a new receiver, called LRR (Linear Regression of MMSE Residual), which improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel), to find the linear regression parameters. The proposed receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs quite well: at a bit error rate (BER) of 10(-3), the SNR gain over MMSE receiver is about 7 dB for a 16 x 16 system; for a 64 x 64 system the gain is about 8.5 dB. For large coherence time, the complexity order of the LRR receiver is the same as that of the MMSE receiver, and in simulations we find that it needs about 4 times as many floating point operations. We also show that further gain of about 4 dB is obtained by local search around the estimate given by the LRR receiver.

Large linear magnetoresistance in a GaAs/AlGaAs heterostructure

We report non-saturating linear magnetoresistance (MR) in a two-dimensional electron system (2DES) at a GaAs/AlGaAs heterointerface in the strongly insulating regime. We achieve this by driving the gate voltage below the pinch-off point of the device and operating it in the non-equilibrium regime with high source-drain bias. Remarkably, the magnitude of MR is as large as 500% per Tesla with respect to resistance at zero magnetic field, thus dwarfing most non-magnetic materials which exhibit this linearity. Its primary advantage over most other materials is that both linearity and the enormous magnitude are retained over a broad temperature range (0.3 K to 10 K), thus making it an attractive candidate for cryogenic sensor applications.

Copula-Based Modeling of TMI Brightness Temperature With Rainfall Type

Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

REDUCING COMPUTATIONAL EFFORT IN SOLVING GEOMECHANICS PROBLEMS WITH UPPER BOUND FINITE ELEMENTS LIMIT ANALYSIS AND LINEAR OPTIMIZATION

This paper presents a simple technique for reducing the computational effort while solving any geotechnical stability problem by using the upper bound finite element limit analysis and linear optimization. In the proposed method, the problem domain is discretized into a number of different regions in which a particular order (number of sides) of the polygon is chosen to linearize the Mohr-Coulomb yield criterion. A greater order of the polygon needs to be selected only in that region wherein the rate of the plastic strains becomes higher. The computational effort required to solve the problem with this implementation reduces considerably. By using the proposed method, the bearing capacity has been computed for smooth and rough strip footings and the results are found to be quite satisfactory.

Estimation of time variant reliability of randomly parametered non-linear vibrating systems

The problem of time variant reliability analysis of randomly parametered and randomly driven nonlinear vibrating systems is considered. The study combines two Monte Carlo variance reduction strategies into a single framework to tackle the problem. The first of these strategies is based on the application of the Girsanov transformation to account for the randomness in dynamic excitations, and the second approach is fashioned after the subset simulation method to deal with randomness in system parameters. Illustrative examples include study of single/multi degree of freedom linear/non-linear inelastic randomly parametered building frame models driven by stationary/non-stationary, white/filtered white noise support acceleration. The estimated reliability measures are demonstrated to compare well with results from direct Monte Carlo simulations. (C) 2014 Elsevier Ltd. All rights reserved.