Weighted subspace methods and spatial smoothing: analysis and comparison

The effect of using a spatially smoothed forward-backward covariance matrix on the performance of weighted eigen-based state space methods/ESPRIT, and weighted MUSIC for direction-of-arrival (DOA) estimation is analyzed. Expressions for the mean-squared error in the estimates of the signal zeros and the DOA estimates, along with some general properties of the estimates and optimal weighting matrices, are derived. A key result is that optimally weighted MUSIC and weighted state-space methods/ESPRIT have identical asymptotic performance. Moreover, by properly choosing the number of subarrays, the performance of unweighted state space methods can be significantly improved. It is also shown that the mean-squared error in the DOA estimates is independent of the exact distribution of the source amplitudes. This results in a unified framework for dealing with DOA estimation using a uniformly spaced linear sensor array and the time series frequency estimation problems.

Performance of a mixed flow pump with varying tip clearance .1.

Measurements of the three-dimensional flow field entering and leaving a mixed flow pump of non-dimensional specific speed k = 1.89 [N-s = 100 r/min (metric)] are discussed as a function of flowrate. Flow reversal at inlet at reduced flows is seen to result in abnormally high total pressures in the casing region, but causes no noticeable discontinuities on the head-flow characteristics. Inlet prerotation is associated with the transport of angular momentum by the reversal eddy and begins with the initiation of flow reversal.

Stability of stochastic Leipholz column with stochastic loading

The Leipholz column which is having the Young modulus and mass per unit length as stochastic processes and also the distributed tangential follower load behaving stochastically is considered. The non self-adjoint differential equation and boundary conditions are considered to have random field coefficients. The standard perturbation method is employed. The non self-adjoint operators are used within the regularity domain. Full covariance structure of the free vibration eigenvalues and critical loads is derived in terms of second order properties of input random fields characterizing the system parameter fluctuations. The mean value of critical load is calculated using the averaged problem and the corresponding eigenvalue statistics are sought. Through the frequency equation a transformation is done to yield load parameter statistics. A numerical study incorporating commonly observed correlation models is reported which illustrates the full potentials of the derived expressions.

Some Algorithms for Eigensubspace Estimation

An important tool in signal processing is the use of eigenvalue and singular value decompositions for extracting information from time-series/sensor array data. These tools are used in the so-called subspace methods that underlie solutions to the harmonic retrieval problem in time series and the directions-of-arrival (DOA) estimation problem in array processing. The subspace methods require the knowledge of eigenvectors of the underlying covariance matrix to estimate the parameters of interest. Eigenstructure estimation in signal processing has two important classes: (i) estimating the eigenstructure of the given covariance matrix and (ii) updating the eigenstructure estimates given the current estimate and new data. In this paper, we survey some algorithms for both these classes useful for harmonic retrieval and DOA estimation problems. We begin by surveying key results in the literature and then describe, in some detail, energy function minimization approaches that underlie a class of feedback neural networks. Our approaches estimate some or all of the eigenvectors corresponding to the repeated minimum eigenvalue and also multiple orthogonal eigenvectors corresponding to the ordered eigenvalues of the covariance matrix. Our presentation includes some supporting analysis and simulation results. We may point out here that eigensubspace estimation is a vast area and all aspects of this cannot be fully covered in a single paper. (C) 1995 Academic Press, Inc.

Turbulent heat flux variation over the Monsoon-Trough region during MONTBLEX-90

Tower data collected during the Monsoon-Trough Boundary Layer Experiment (MONTBLEX-90) have been analysed to understand the observed structure of the surface layer over an arid region (Jodhpur) and a moist region (Kharagpur) during active and weak phases of the 1990 southwest monsoon. Turbulent heat and momentum fluxes are estimated by the eddy correlation method using sonic data. The turbulent momentum flux at both Jodhpur and Kharagpur was larger when the winds were stronger, reaching a maximum of the order of 0.5 N m(-2) on 5 and 6 August when a low pressure system was located over the region. The heat flux at Jodhpur is high during weak monsoon days, the maximum being 450 W m(-2), whereas during active days the flux never exceeds 200 W m(-2). At Kharagpur, the flux does not vary significantly between active and weak monsoon days, the maximum in either phase being 160 W m(-2) At Jodhpur, there is significant contrast in the near-surface air temperature, being higher during weak monsoon days as compared to active days. Cloud cover did not vary significantly in both the regions. The turbulent heat flux variation at both the sites appears to be correlated mainly with soil mixture, and less sensitive to cloud cover.

Direction of arrival estimation in the presence of distributed noise sources: Cumulant based approach

In the past few years there have been attempts to develop subspace methods for DoA (direction of arrival) estimation using a fourth?order cumulant which is known to de?emphasize Gaussian background noise. To gauge the relative performance of the cumulant MUSIC (MUltiple SIgnal Classification) (c?MUSIC) and the standard MUSIC, based on the covariance function, an extensive numerical study has been carried out, where a narrow?band signal source has been considered and Gaussian noise sources, which produce a spatially correlated background noise, have been distributed. These simulations indicate that, even though the cumulant approach is capable of de?emphasizing the Gaussian noise, both bias and variance of the DoA estimates are higher than those for MUSIC. To achieve comparable results the cumulant approach requires much larger data, three to ten times that for MUSIC, depending upon the number of sources and how close they are. This is attributed to the fact that in the estimation of the cumulant, an average of a product of four random variables is needed to make an evaluation. Therefore, compared to those in the evaluation of the covariance function, there are more cross terms which do not go to zero unless the data length is very large. It is felt that these cross terms contribute to the large bias and variance observed in c?MUSIC. However, the ability to de?emphasize Gaussian noise, white or colored, is of great significance since the standard MUSIC fails when there is colored background noise. Through simulation it is shown that c?MUSIC does yield good results, but only at the cost of more data.

Performance analysis of a modified spatial smoothing technique for direction estimation

The statistical performance analysis of ESPRIT, root-MUSIC, minimum-norm methods for direction estimation, due to finite data perturbations, using the modified spatially smoothed covariance matrix, is developed. Expressions for the mean-squared error in the direction estimates are derived based on a common framework. Based on the analysis, the use of the modified smoothed covariance matrix improves the performance of the methods when the sources are fully correlated. Also, the performance is better even when the number of subarrays is large unlike in the case of the conventionally smoothed covariance matrix. However, the performance for uncorrelated sources deteriorates due to an artificial correlation introduced by the modified smoothing. The theoretical expressions are validated using extensive simulations. (C) 1999 Elsevier Science B.V. All rights reserved.

Turbulent near-wake development behind a flat plate

An asymptotic analysis of the two-dimensional turbulent near-wake flow behind a Rat plate with sharp trailing edge has been formulated, The feature that the near-wake, which is dominated by the mixing of the oncoming turbulent boundary layers retains, to a large extent, the memory of the turbulent structure of the upstream boundary layer has been exploited to develop the analysis. This analysis leads to two regions of the near-wake flow (the inner near-wake and the outer near-wake) for which the governing equations are derived. The matching conditions among these regions lead to a logarithmic variation in the normal direction in the overlapping region surrounding the inner near-wake. These features are validated by the available experimental data. Similarity solutions for the velocity distribution (which satisfy the required matching conditions) in the inner near-wake and outer near-wake regions have been obtained by making the appropriate eddy-viscosity assumptions, Uniformly valid solutions for velocity distribution have been constructed for the near-wake. The solutions show good agreement with available experimental data. (C) Elsevier, Paris.

Turbulent free convection above a heated plate inclined at a small angle to the horizontal Journal

An investigation has been made of the structure of the motion above a heated plate inclined at a small angle (about 10°) to the horizontal. The turbulence is considered in terms of the similarities to and differences from the motion above an exactly horizontal surface. One effect of inclination is, of course, that there is also a mean motion. Accurate data on the mean temperature field and the intensity of the temperature fluctuations have been obtained with platinum resistance thermometers, the signals being processed electronically. More approximate information on the velocity field has been obtained with quartz fibre anemometers. These results have been supplemented qualitatively by simultaneous observations of the temperature and velocity fluctuations and also by smoke experiments. The principal features of the flow inferred from these observations are as follows. The heat transfer and the mean temperature field are not much altered by the inclination, though small, not very systematic, variations may result from the complexities of the velocity field. This supports the view that the mean temperature field is largely governed by the large-scale motions. The temperature fluctuations show a systematic variation with distance from the lower edge and resemble those above a horizontal plate when this distance is large. The largescale motions of the turbulence start close to the lower edge, but the smaller eddies do not attain full intensity until the air has moved some distance up the plate. The mean velocity receives a sizable contribution from a ‘through-flow’ between the side-walls. Superimposed on this are developments that show that the momentum transfer processes are complex and certainly not capable of representation by any simple theory such as an eddy viscosity. On the lower part of the plate there is surprisingly large acceleration, but further up the mixing action of the small eddies has a decelerating effect.

On cavity flow at high Reynolds numbers

The flow in a square cavity is studied by solving the full Navier–Stokes and energy equations numerically, employing finite-difference techniques. Solutions are obtained over a wide range of Reynolds numbers from 0 to 50000. The solutions show that only at very high Reynolds numbers (Re [gt-or-equal, slanted] 30000) does the flow in the cavity completely correspond to that assumed by Batchelor's model for separated flows. The flow and thermal fields at such high Reynolds numbers clearly exhibit a boundary-layer character. For the first time, it is demonstrated that the downstream secondary eddy grows and decays in a manner similar to the upstream one. The upstream and downstream secondary eddies remain completely viscous throughout the range of Reynolds numbers of their existence. It is suggested that the behaviour of the secondary eddies may be characteristic of internal separated flows.

Two Dimensional Principal Component Analysis for Online Tamil Character Recognition

This paper presents a new application of two dimensional Principal Component Analysis (2DPCA) to the problem of online character recognition in Tamil Script. A novel set of features employing polynomial fits and quartiles in combination with conventional features are derived for each sample point of the Tamil character obtained after smoothing and resampling. These are stacked to form a matrix, using which a covariance matrix is constructed. A subset of the eigenvectors of the covariance matrix is employed to get the features in the reduced sub space. Each character is modeled as a separate subspace and a modified form of the Mahalanobis distance is derived to classify a given test character. Results indicate that the recognition accuracy using the 2DPCA scheme shows an approximate 3% improvement over the conventional PCA technique.

Wetlands of Greater Bangalore, India: Automatic Delineation through Pattern Classifiers

Wetlands are the most productive and biologically diverse but very fragile ecosystems. They are vulnerable to even small changes in their biotic and abiotic factors. In recent years, there has been concern over the continuous degradation of wetlands due to unplanned developmental activities. This necessitates inventorying, mapping, and monitoring of wetlands to implement sustainable management approaches. The principal objective of this work is to evolve a strategy to identify and monitor wetlands using temporal remote sensing (RS) data. Pattern classifiers were used to extract wetlands automatically from NIR bands of MODIS, Landsat MSS and Landsat TM remote sensing data. MODIS provided data for 2002 to 2007, while for 1973 and 1992 IR Bands of Landsat MSS and TM (79m and 30m spatial resolution) data were used. Principal components of IR bands of MODIS (250 m) were fused with IRS LISS-3 NIR (23.5 m). To extract wetlands, statistical unsupervised learning of IR bands for the respective temporal data was performed using Bayesian approach based on prior probability, mean and covariance. Temporal analysis of wetlands indicates a sharp decline of 58% in Greater Bangalore attributing to intense urbanization processes, evident from a 466% increase in built-up area from 1973 to 2007.

Explicit filtering LES for turbulent non-premixed combustion

Two models for large eddy simulation of turbulent reacting flow in homogeneous turbulence were studied. The sub-grid stress arising out of non-linearities of the Navier-Stokes equations were modeled using an explicit filtering approach. A filtered mass density function (FMDF) approach was used for closure of the sub-grid scalar fluctuations. A posteriori calculations, when compared with the results from the direct numerical simulation, indicate that the explicit filtering is adequate in representing the effect of sub-grid stress on the filtered velocity field in the absence of reaction. Discrepancies arise when reactions occur, but the FMDF approach suffices to account for sub-grid scale fluctuations of the reacting scalars, accurately.

Clustering Based Large Margin Classification: A Scalable Approach using SOCP Formulation

This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture distributions, where each component of the mixture has a spherical covariance, the second order statistics of the components can be estimated efficiently using clustering algorithms like BIRCH. For each cluster, the second order moments are used to derive a second order cone constraint via a Chebyshev-Cantelli inequality. This constraint ensures that any data point in the cluster is classified correctly with a high probability. This leads to a large margin SOCP formulation whose size depends on the number of clusters rather than the number of training data points. Hence, the proposed formulation scales well for large datasets when compared to the state-of-the-art classifiers, Support Vector Machines (SVMs). Experiments on real world and synthetic datasets show that the proposed algorithm outperforms SVM solvers in terms of training time and achieves similar accuracies.

Single Cavity Trapped Vortex Combustor Simulations

The present work describes steady and unsteady computation of reacting flow in a Trapped Vortex Combustor. The primary motivation of this study is to develop this concept into a working combustor in modern gas turbines. The present work is an effort towards development of an experimental model test rig for further understanding dynamics of a single cavity trapped vortex combustor. The steady computations with and without combustion have been done for L/D of 0.8, 1 and 1.2; also unsteady non-reacting flow simulation has been done for L/D of 1. Fuel used for the present study is methane and Eddy-Dissipation model has been used for combustion-turbulence interactions. For L/D of 0.8, combustion efficiency is maximum and pattern factor is minimum. Also, primary vortex in the cavity is more stable and symmetric for L/D of 0.8. From unsteady non-reacting flow simulations, it is found that there is no vortex shedding from the cavity but there are oscillations in the span-wise direction of the combustor.