NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION

In this work, we address the recovery of block sparse vectors with intra-block correlation, i.e., the recovery of vectors in which the correlated nonzero entries are constrained to lie in a few clusters, from noisy underdetermined linear measurements. Among Bayesian sparse recovery techniques, the cluster Sparse Bayesian Learning (SBL) is an efficient tool for block-sparse vector recovery, with intra-block correlation. However, this technique uses a heuristic method to estimate the intra-block correlation. In this paper, we propose the Nested SBL (NSBL) algorithm, which we derive using a novel Bayesian formulation that facilitates the use of the monotonically convergent nested Expectation Maximization (EM) and a Kalman filtering based learning framework. Unlike the cluster-SBL algorithm, this formulation leads to closed-form EMupdates for estimating the correlation coefficient. We demonstrate the efficacy of the proposed NSBL algorithm using Monte Carlo simulations.

Linear programming approach for state estimation incorporating phasor measurements

This paper presents methodologies for incorporating phasor measurements into conventional state estimator. The angle measurements obtained from Phasor Measurement Units are handled as angle difference measurements rather than incorporating the angle measurements directly. Handling in such a manner overcomes the problems arising due to the choice of reference bus. Current measurements obtained from Phasor Measurement Units are treated as equivalent pseudo-voltage measurements at the neighboring buses. Two solution approaches namely normal equations approach and linear programming approach are presented to show how the Phasor Measurement Unit measurements can be handled. Comparative evaluation of both the approaches is also presented. Test results on IEEE 14 bus system are presented to validate both the approaches.

A conditionally linearized Monte Carlo filter in non-linear structural dynamics

State and parameter estimations of non-linear dynamical systems, based on incomplete and noisy measurements, are considered using Monte Carlo simulations. Given the measurements. the proposed method obtains the marginalized posterior distribution of an appropriately chosen (ideally small) subset of the state vector using a particle filter. Samples (particles) of the marginalized states are then used to construct a family of conditionally linearized system of equations and thus obtain the posterior distribution of the states using a bank of Kalman filters. Discrete process equations for the marginalized states are derived through truncated Ito-Taylor expansions. Increased analyticity and reduced dispersion of weights computed over a smaller sample space of marginalized states are the key features of the filter that help achieve smaller sample variance of the estimates. Numerical illustrations are provided for state/parameter estimations of a Duffing oscillator and a 3-DOF non-linear oscillator. Performance of the filter in parameter estimation is also assessed using measurements obtained through experiments on simple models in the laboratory. Despite an added computational cost, the results verify that the proposed filter generally produces estimates with lower sample variance over the standard sequential importance sampling (SIS) filter.

A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data

The problem of identification of stiffness, mass and damping properties of linear structural systems, based on multiple sets of measurement data originating from static and dynamic tests is considered. A strategy, within the framework of Kalman filter based dynamic state estimation, is proposed to tackle this problem. The static tests consists of measurement of response of the structure to slowly moving loads, and to static loads whose magnitude are varied incrementally; the dynamic tests involve measurement of a few elements of the frequency response function (FRF) matrix. These measurements are taken to be contaminated by additive Gaussian noise. An artificial independent variable τ, that simultaneously parameterizes the point of application of the moving load, the magnitude of the incrementally varied static load and the driving frequency in the FRFs, is introduced. The state vector is taken to consist of system parameters to be identified. The fact that these parameters are independent of the variable τ is taken to constitute the set of ‘process’ equations. The measurement equations are derived based on the mechanics of the problem and, quantities, such as displacements and/or strains, are taken to be measured. A recursive algorithm that employs a linearization strategy based on Neumann’s expansion of structural static and dynamic stiffness matrices, and, which provides posterior estimates of the mean and covariance of the unknown system parameters, is developed. The satisfactory performance of the proposed approach is illustrated by considering the problem of the identification of the dynamic properties of an inhomogeneous beam and the axial rigidities of members of a truss structure.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time,recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through a pseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets of measurements involving various load cases, we expedite the speed of thePD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small.

Self-regularized pseudo time-marching schemes for structural system identification with static measurements

We explore the application of pseudo time marching schemes, involving either deterministic integration or stochastic filtering, to solve the inverse problem of parameter identification of large dimensional structural systems from partial and noisy measurements of strictly static response. Solutions of such non-linear inverse problems could provide useful local stiffness variations and do not have to confront modeling uncertainties in damping, an important, yet inadequately understood, aspect in dynamic system identification problems. The usual method of least-square solution is through a regularized Gauss-Newton method (GNM) whose results are known to be sensitively dependent on the regularization parameter and data noise intensity. Finite time, recursive integration of the pseudo-dynamical GNM (PD-GNM) update equation addresses the major numerical difficulty associated with the near-zero singular values of the linearized operator and gives results that are not sensitive to the time step of integration. Therefore, we also propose a pseudo-dynamic stochastic filtering approach for the same problem using a parsimonious representation of states and specifically solve the linearized filtering equations through apseudo-dynamic ensemble Kalman filter (PD-EnKF). For multiple sets ofmeasurements involving various load cases, we expedite the speed of the PD-EnKF by proposing an inner iteration within every time step. Results using the pseudo-dynamic strategy obtained through PD-EnKF and recursive integration are compared with those from the conventional GNM, which prove that the PD-EnKF is the best performer showing little sensitivity to process noise covariance and yielding reconstructions with less artifacts even when the ensemble size is small. Copyright (C) 2009 John Wiley & Sons, Ltd.

Direct Synthesis of Terminally ``Clickable'' Linear and Hyperbranched Polyesters

1,3-Dipolar cycloaddition of an organic azide and an acetylenic unit,often referred to as the ``click reaction'', has become an important ligation tool both in the context of materials chemistry and biology. Thus, development of simple approaches to directly generate polymers that bear either an azide or an alkyne unit has gained considerable importance. We describe here a straightforward approach to directly prepare linear and hyperbranched polyesters that carry terminal propargyl groups. To achieve the former, we designed an AB-type monomer that carries a hydroxyl group and a propargyl ester, which upon self-condensation under standard transesterification conditions yielded a polyester that carries a single propargyl group at one of its chain-ends. Similarly, an AB(2) type monomer that carries one hydroxyl group and two propargyl ester groups, when polymerized under the same conditions yielded a hyperbranched polymer with numerous clickable'' propargyl groups at its molecular periphery. These propargyl groups can be readily clicked with different organic azides, such as benzyl azide, omega-azido heptaethyleneglycol monomethylether or 9-azidomethyl anthracene. When an anthracene chromophore is clicked, the molecular weight of the linear polyester could be readily estimated using both UV-visible and fluorescence spectroscopic measurements. Furthermore, the reactive propargyl end group could also provide an opportunity to prepare block copolymers in the case of linear polyesters and to generate nanodimensional scaffolds to anchor variety of functional units, in the case of the hyperbranched polymer. (C) 2010 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 48: 3200-3208, 2010.

A formate bridged Ni(II) sheet and its 3D analogue in presence of a linear organic linker: Synthesis, crystal structure and magnetic properties

Reaction of formamide with Ni(NO3)(2)center dot 6H(2)O under hydrothermal condition in a mixture of MeOH/H2O forms a two-dimensional formate bridged sheet Ni(HCOO)(2)(MeOH)(2) (1). X-ray structure analysis reveals the conversion of formamide to formate which acts as a bridging ligand in complex 1 where the axial sites of Ni(II) are occupied by methanol used as a solvent. An analogous reaction in presence of 4,4'-bipyridyl (4,4'-bipy) yielded a three-dimensional structure Ni(HCOO)(2)(4,4'-bpy) (2). DC magnetic measurements as a function of temperature and field established the presence of spontaneous magnetization with T-c (Curie temperature) = 17 and 20.8 K in 1 and 2, respectively, which can be attributed due to spin-canting. DFT calculations were performed to corroborate the magnetic results of 1 and 2. (C) 2010 Elsevier Ltd. All rights reserved.

Data rectification and detection of trend shifts in jet engine path measurements using median filters and fuzzy logic

Filtering methods are explored for removing noise from data while preserving sharp edges that many indicate a trend shift in gas turbine measurements. Linear filters are found to be have problems with removing noise while preserving features in the signal. The nonlinear hybrid median filter is found to accurately reproduce the root signal from noisy data. Simulated faulty data and fault-free gas path measurement data are passed through median filters and health residuals for the data set are created. The health residual is a scalar norm of the gas path measurement deltas and is used to partition the faulty engine from the healthy engine using fuzzy sets. The fuzzy detection system is developed and tested with noisy data and with filtered data. It is found from tests with simulated fault-free and faulty data that fuzzy trend shift detection based on filtered data is very accurate with no false alarms and negligible missed alarms.

Structure of the Pyridine-Chloranil Complex in Solution: A Surprise from Depolarized Hyper-Rayleigh Scattering Measurements

In this article, we report the structure of a 1:1 charge transfer complex between pyridine (PYR) and chloranil (CHL) in solution (CHCl(3)) from the measurement of hyperpolarizability (beta(HRS)) and linear and circular depolarization ratios, D and D', respectively, by the hyper-Rayleigh scattering technique and state-of-the-art quantum chemical calculations. Using linearly (electric field vector along X) and circularly polarized incident light, respectively, we have measured two macroscopic depolarization ratios D = I(X,X)(2 omega)/I(X,Z)(2 omega) and D' = I(X,C)(2 omega)/I(Z,C)(2 omega) in the laboratory fixed XYZ frame by detecting the second harmonic (SH) scattered light in a polarization resolved fashion. The stabilization energy and the optical gap calculated through the MP2/cc-pVDZ method using Gaussian09 were not significantly different to distinguish between the cofacial and T-shape structures. Only when the experimentally obtained beta(HRS) and the depolarization ratios, D and D', were matched with the theoretically computed values from single and double configuration interaction (SDCI) calculations performed using the ZINDO-SCRF technique, we concluded that the room temperature equilibrium structure of the complex is cofacial. This is in sharp contrast to an earlier theoretical prediction of the T-shape structure of the complex.

Decentralized linear quadratic power system stabilizers for multi-machine power systems

Linear quadratic stabilizers are well-known for their superior control capabilities when compared to the conventional lead-lag power system stabilizers. However, they have not seen much of practical importance as the state variables are generally not measurable; especially the generator rotor angle measurement is not available in most of the power plants. Full state feedback controllers require feedback of other machine states in a multi-machine power system and necessitate block diagonal structure constraints for decentralized implementation. This paper investigates the design of Linear Quadratic Power System Stabilizers using a recently proposed modified Heffron-Phillip's model. This model is derived by taking the secondary bus voltage of the step-up transformer as reference instead of the infinite bus. The state variables of this model can be obtained by local measurements. This model allows a coordinated linear quadratic control design in multi machine systems. The performance of the proposed controller has been evaluated on two widely used multi-machine power systems, 4 generator 10 bus and 10 generator 39 bus systems. It has been observed that the performance of the proposed controller is superior to that of the conventional Power System Stabilizers (PSS) over a wide range of operating and system conditions.

Fiber Bragg Grating Sensor Package for Submicron Level Displacement Measurements

In this article, the design and development of a Fiber Bragg Grating (FBG) based displacement sensor package for submicron level displacement measurements are presented. A linear shift of 12.12 nm in Bragg wavelength of the FBG sensor is obtained for a displacement of 6 mm with a calibration factor of 0.495 mu m/pm. Field trials have also been conducted by comparing the FBG displacement sensor package against a conventional dial gauge, on a five block masonry prism specimen loaded using three-point bending technique. The responses from both the sensors are in good agreement, up to the failure of the masonry prism. Furthermore, from the real-time displacement data recorded using FBG, it is possible to detect the time at which early creaks generated inside the body of the specimen which then prorogate to the surface to develop visible surface cracks; the respective load from the load cell can be obtained from the inflection (stress release point) in the displacement curve. Thus the developed FBG displacement sensor package can be used to detect failures in structures much earlier and to provide an adequate time to exercise necessary action, thereby avoiding the possible disaster.

Within-Die Gate Delay Variability Measurement using Re-configurable Ring Oscillator

We report a circuit technique to measure the on-chip delay of an individual logic gate (both inverting and non-inverting) in its unmodified form using digitally reconfigurable ring oscillator (RO). Solving a system of linear equations with different configuration setting of the RO gives delay of an individual gate. Experimental results from a test chip in 65nm process node show the feasibility of measuring the delay of an individual inverter to within 1pS accuracy. Delay measurements of different nominally identical inverters in close physical proximity show variations of up to 26% indicating the large impact of local or within-die variations.

Constrained Linear Spectral Unmixing Technique for Regional Land Cover Mapping Using MODIS Data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up-to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat-TM/ETM+, IRS-ICID LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (similar to 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 in), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end-members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications. Index Terms-Remote sensing, digital

A new load distribution strategy for linear network with communication delays

In this paper, we propose a new load distribution strategy called `send-and-receive' for scheduling divisible loads, in a linear network of processors with communication delay. This strategy is designed to optimally utilize the network resources and thereby minimizes the processing time of entire processing load. A closed-form expression for optimal size of load fractions and processing time are derived when the processing load originates at processor located in boundary and interior of the network. A condition on processor and link speed is also derived to ensure that the processors are continuously engaged in load distributions. This paper also presents a parallel implementation of `digital watermarking problem' on a personal computer-based Pentium Linear Network (PLN) topology. Experiments are carried out to study the performance of the proposed strategy and results are compared with other strategies found in literature.