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.

Between photocatalysis and photosynthesis: Synchrotron spectroscopy methods on molecules and materials for solar hydrogen generation

Energy research is to a large extent materials research, encompassing the physics and chemistry of materials, including their synthesis, processing toward components and design toward architectures, allowing for their functionality as energy devices, extending toward their operation parameters and environment, including also their degradation, limited life, ultimate failure and potential recycling. In all these stages, X-ray and electron spectroscopy are helpful methods for analysis, characterization and diagnostics for the engineer and for the researcher working in basic science.This paper gives a short overview of experiments with X-ray and electron spectroscopy for solar energy and water splitting materials and addresses also the issue of solar fuel, a relatively new topic in energy research. The featured systems are iron oxide and tungsten oxide as photoanodes, and hydrogenases as molecular systems. We present surface and subsurface studies with ambient pressure XPS and hard X-ray XPS, resonant photoemission, light induced effects in resonant photoemission experiments and a photo-electrochemical in situ/operando NEXAFS experiment in a liquid cell, and nuclear resonant vibrational spectroscopy (NRVS). (C) 2012 Elsevier B.V. All rights reserved.

Optical microscopy methods for understanding learning and memory

Structural dynamics of dendritic spines is one of the key correlative measures of synaptic plasticity for encoding short-term and long-term memory. Optical studies of structural changes in brain tissue using confocal microscopy face difficulties of scattering. This results in low signal-to-noise ratio and thus limiting the imaging depth to few tens of microns. Multiphoton microscopy (MpM) overcomes this limitation by using low-energy photons to cause localized excitation and achieve high resolution in all three dimensions. Multiple low-energy photons with longer wavelengths minimize scattering and allow access to deeper brain regions at several hundred microns. In this article, we provide a basic understanding of the physical phenomena that give MpM an edge over conventional microscopy. Further, we highlight a few of the key studies in the field of learning and memory which would not have been possible without the advent of MpM.

Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction

The sparse recovery methods utilize the l(p)-normbased regularization in the estimation problem with 0 <= p <= 1. These methods have a better utility when the number of independent measurements are limited in nature, which is a typical case for diffuse optical tomographic image reconstruction problem. These sparse recovery methods, along with an approximation to utilize the l(0)-norm, have been deployed for the reconstruction of diffuse optical images. Their performancewas compared systematically using both numerical and gelatin phantom cases to show that these methods hold promise in improving the reconstructed image quality.

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.

BILATERAL EDGE DETECTORS

We propose to employ bilateral filters to solve the problem of edge detection. The proposed methodology presents an efficient and noise robust method for detecting edges. Classical bilateral filters smooth images without distorting edges. In this paper, we modify the bilateral filter to perform edge detection, which is the opposite of bilateral smoothing. The Gaussian domain kernel of the bilateral filter is replaced with an edge detection mask, and Gaussian range kernel is replaced with an inverted Gaussian kernel. The modified range kernel serves to emphasize dissimilar regions. The resulting approach effectively adapts the detection mask according as the pixel intensity differences. The results of the proposed algorithm are compared with those of standard edge detection masks. Comparisons of the bilateral edge detector with Canny edge detection algorithm, both after non-maximal suppression, are also provided. The results of our technique are observed to be better and noise-robust than those offered by methods employing masks alone, and are also comparable to the results from Canny edge detector, outperforming it in certain cases.

A Kushner-Stratonovich Monte Carlo filter applied to nonlinear dynamical system identification

A Monte Carlo filter, based on the idea of averaging over characteristics and fashioned after a particle-based time-discretized approximation to the Kushner-Stratonovich (KS) nonlinear filtering equation, is proposed. A key aspect of the new filter is the gain-like additive update, designed to approximate the innovation integral in the KS equation and implemented through an annealing-type iterative procedure, which is aimed at rendering the innovation (observation prediction mismatch) for a given time-step to a zero-mean Brownian increment corresponding to the measurement noise. This may be contrasted with the weight-based multiplicative updates in most particle filters that are known to precipitate the numerical problem of weight collapse within a finite-ensemble setting. A study to estimate the a-priori error bounds in the proposed scheme is undertaken. The numerical evidence, presently gathered from the assessed performance of the proposed and a few other competing filters on a class of nonlinear dynamic system identification and target tracking problems, is suggestive of the remarkably improved convergence and accuracy of the new filter. (C) 2013 Elsevier B.V. All rights reserved.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Electronic structure of Nd1-xYxMnO3 from Mn K edge absorption spectroscopy and DFT methods

The electronic structure of Nd1-xYxMnO3 (x-0-0.5) is studied using x-ray absorption near-edge structure (XANES) spectroscopy at the Mn K-edge along with the DFT-based LSDA+U and real space cluster calculations. The main edge of the spectra does not show any variation with doping. The pre-edge shows two distinct features which appear well-separated with doping. The intensity of the pre-edge decreases with doping. The theoretical XANES were calculated using real space multiple scattering methods which reproduces the entire experimental spectra at the main edge as well as the pre-edge. Density functional theory calculations are used to obtain the Mn 4p, Mn 3d and O 2p density of states. For x=0, the site-projected density of states at 1.7 eV above Fermi energy shows a singular peak of unoccupied e(g) (spin-up) states which is hybridized Mn 4p and O 2p states. For x=0.5, this feature develops at a higher energy and is highly delocalized and overlaps with the 3d spin-down states which changes the pre-edge intensity. The Mn 4p DOS for both compositions, show considerable difference between the individual p(x), p(y) and p(z)), states. For x=0.5, there is a considerable change in the 4p orbital polarization suggesting changes in the Jahn-Teller effect with doping. (C) 2013 Elsevier Ltd. All rights reserved.

Quantifying Vocal Mimicry in the Greater Racket-Tailed Drongo: A Comparison of Automated Methods and Human Assessment

Objective identification and description of mimicked calls is a primary component of any study on avian vocal mimicry but few studies have adopted a quantitative approach. We used spectral feature representations commonly used in human speech analysis in combination with various distance metrics to distinguish between mimicked and non-mimicked calls of the greater racket-tailed drongo, Dicrurus paradiseus and cross-validated the results with human assessment of spectral similarity. We found that the automated method and human subjects performed similarly in terms of the overall number of correct matches of mimicked calls to putative model calls. However, the two methods also misclassified different subsets of calls and we achieved a maximum accuracy of ninety five per cent only when we combined the results of both the methods. This study is the first to use Mel-frequency Cepstral Coefficients and Relative Spectral Amplitude - filtered Linear Predictive Coding coefficients to quantify vocal mimicry. Our findings also suggest that in spite of several advances in automated methods of song analysis, corresponding cross-validation by humans remains essential.

Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

A Communication-Theoretic Framework for 2-DMR Channel Modeling: Performance Evaluation of Coding and Signal Processing Methods

We develop a communication theoretic framework for modeling 2-D magnetic recording channels. Using the model, we define the signal-to-noise ratio (SNR) for the channel considering several physical parameters, such as the channel bit density, code rate, bit aspect ratio, and noise parameters. We analyze the problem of optimizing the bit aspect ratio for maximizing SNR. The read channel architecture comprises a novel 2-D joint self-iterating equalizer and detection system with noise prediction capability. We evaluate the system performance based on our channel model through simulations. The coded performance with the 2-D equalizer detector indicates similar to 5.5 dB of SNR gain over uncoded data.

Error analysis of discontinuous Galerkin methods for the Stokes problem under minimal regularity

In this article, we analyse several discontinuous Galerkin (DG) methods for the Stokes problem under minimal regularity on the solution. We assume that the velocity u belongs to H-0(1)(Omega)](d) and the pressure p is an element of L-0(2)(Omega). First, we analyse standard DG methods assuming that the right-hand side f belongs to H-1(Omega) boolean AND L-1(Omega)](d). A DG method that is well defined for f belonging to H-1(Omega)](d) is then investigated. The methods under study include stabilized DG methods using equal-order spaces and inf-sup stable ones where the pressure space is one polynomial degree less than the velocity space.

CONVERGENCE ANALYSIS OF THE LOWEST ORDER WEAKLY PENALIZED ADAPTIVE DISCONTINUOUS GALERKIN METHODS

In this article, we prove convergence of the weakly penalized adaptive discontinuous Galerkin methods. Unlike other works, we derive the contraction property for various discontinuous Galerkin methods only assuming the stabilizing parameters are large enough to stabilize the method. A central idea in the analysis is to construct an auxiliary solution from the discontinuous Galerkin solution by a simple post processing. Based on the auxiliary solution, we define the adaptive algorithm which guides to the convergence of adaptive discontinuous Galerkin methods.

Incipient straining in severe plastic deformation methods

Knowledge of the plasticity associated with the incipient stage of chip formation is useful toward developing an understanding of the deformation field underlying severe plastic deformation processes. The transition from a transient state of straining to a steady state was investigated in plane strain machining of a model material system-copper. Characterization of the evolution to a steady-state deformation field was made by image correlation, hardness mapping, load analysis, and microstructure characterization. Empirical relationships relating the deformation heterogeneity and the process parameters were found and explained by the corresponding effects on shear plane geometry. The results are potentially useful to facilitate a framework for process design of large strain deformation configurations, wherein transient deformation fields prevail. These implications are considered in the present study to quantify the efficiency of processing methods for bulk ultrafine-grained metals by large strain extrusion machining and equal channel angular pressing.