A risk-estimation-based formulation for speech enhancement and its relation to Wiener filtering

The goal of speech enhancement algorithms is to provide an estimate of clean speech starting from noisy observations. The often-employed cost function is the mean square error (MSE). However, the MSE can never be computed in practice. Therefore, it becomes necessary to find practical alternatives to the MSE. In image denoising problems, the cost function (also referred to as risk) is often replaced by an unbiased estimator. Motivated by this approach, we reformulate the problem of speech enhancement from the perspective of risk minimization. Some recent contributions in risk estimation have employed Stein's unbiased risk estimator (SURE) together with a parametric denoising function, which is a linear expansion of threshold/bases (LET). We show that the first-order case of SURE-LET results in a Wiener-filter type solution if the denoising function is made frequency-dependent. We also provide enhancement results obtained with both techniques and characterize the improvement by means of local as well as global SNR calculations.

A generalized Stein's estimation approach to speech enhancement based on perceptual criteria

We address the problem of speech enhancement using a risk- estimation approach. In particular, we propose the use the Stein’s unbiased risk estimator (SURE) for solving the problem. The need for a suitable finite-sample risk estimator arises because the actual risks invariably depend on the unknown ground truth. We consider the popular mean-squared error (MSE) criterion first, and then compare it against the perceptually-motivated Itakura-Saito (IS) distortion, by deriving unbiased estimators of the corresponding risks. We use a generalized SURE (GSURE) development, recently proposed by Eldar for MSE. We consider dependent observation models from the exponential family with an additive noise model,and derive an unbiased estimator for the risk corresponding to the IS distortion, which is non-quadratic. This serves to address the speech enhancement problem in a more general setting. Experimental results illustrate that the IS metric is efficient in suppressing musical noise, which affects the MSE-enhanced speech. However, in terms of global signal-to-noise ratio (SNR), the minimum MSE solution gives better results.

Estimation of design basis earthquake using region-specific M-max, for the NPP site at Kalpakkam, Tamil Nadu, India

The objective of the paper is to estimate Safe Shutdown Earthquake (SSE) and Operating/Design Basis Earthquake (OBE/DBE) for the Nuclear Power Plant (NPP) site located at Kalpakkam, Tamil Nadu, India. The NPP is located at 12.558 degrees N, 80.175 degrees E and a 500 km circular area around NPP site is considered as `seismic study area' based on past regional earthquake damage distribution. The geology, seismicity and seismotectonics of the study area are studied and the seismotectonic map is prepared showing the seismic sources and the past earthquakes. Earthquake data gathered from many literatures are homogenized and declustered to form a complete earthquake catalogue for the seismic study area. The conventional maximum magnitude of each source is estimated considering the maximum observed magnitude (M-max(obs)) and/or the addition of 0.3 to 0.5 to M-max(obs). In this study maximum earthquake magnitude has been estimated by establishing a region's rupture character based on source length and associated M-max(obs). A final source-specific M-max is selected from the three M-max values by following the logical criteria. To estimate hazard at the NPP site, ten Ground-Motion Prediction Equations (GMPEs) valid for the study area are considered. These GMPEs are ranked based on Log-Likelihood (LLH) values. Top five GMPEs are considered to estimate the peak ground acceleration (PGA) for the site. Maximum PGA is obtained from three faults and named as vulnerable sources to decide the magnitudes of OBE and SSE. The average and normalized site specific response spectrum is prepared considering three vulnerable sources and further used to establish site-specific design spectrum at NPP site.

Resonance raman detection and estimation in the aqueous phase using water dispersible cyclodextrin: reduced-graphene oxide sheets

Resonance Raman spectroscopy is a powerful analytical tool for detecting and identifying analytes, but the associated strong fluorescence background severely limits the use of the technique. Here, we show that by attaching beta-cyclodextrin (beta-CD) cavities to reduced graphene-oxide (rGO) sheets we obtain a water dispersible material (beta-CD: rGO) that combines the hydrophobicity associated with rGO with that of the cyclodextrin cavities and provides a versatile platform for resonance Raman detection. Planar aromatic and dye molecules that adsorb on the rGO domains and nonplanar molecules included within the tethered beta-CD cavities have their fluorescence effectively quenched. We show that it is possible using the water dispersible beta-CD: rGO sheets to record the resonance Raman spectra of adsorbed and included organic chromophores directly in aqueous media without having to extract or deposit on a substrate. This is significant, as it allows us to identify and estimate organic analytes present in water by resonance Raman spectroscopy.

State estimation in power systems using linear model infinity norm-based trust region approach

State estimation is one of the most important functions in an energy control centre. An computationally efficient state estimator which is free from numerical instability/ill-conditioning is essential for security assessment of electric power grid. Whereas approaches to successfully overcome the numerical ill-conditioning issues have been proposed, an efficient algorithm for addressing the convergence issues in the presence of topological errors is yet to be evolved. Trust region (TR) methods have been successfully employed to overcome the divergence problem to certain extent. In this study, case studies are presented where the conventional algorithms including the existing TR methods would fail to converge. A linearised model-based TR method for successfully overcoming the convergence issues is proposed. On the computational front, unlike the existing TR methods for state estimation which employ quadratic models, the proposed linear model-based estimator is computationally efficient because the model minimiser can be computed in a single step. The model minimiser at each step is computed by minimising the linearised model in the presence of TR and measurement mismatch constraints. The infinity norm is used to define the geometry of the TR. Measurement mismatch constraints are employed to improve the accuracy. The proposed algorithm is compared with the quadratic model-based TR algorithm with case studies on the IEEE 30-bus system, 205-bus and 514-bus equivalent systems of part of Indian grid.

Efficient gradient-free simplex method for estimation of optical properties in image-guided diffuse optical tomography

Typical image-guided diffuse optical tomographic image reconstruction procedures involve reduction of the number of optical parameters to be reconstructed equal to the number of distinct regions identified in the structural information provided by the traditional imaging modality. This makes the image reconstruction problem less ill-posed compared to traditional underdetermined cases. Still, the methods that are deployed in this case are same as those used for traditional diffuse optical image reconstruction, which involves a regularization term as well as computation of the Jacobian. A gradient-free Nelder-Mead simplex method is proposed here to perform the image reconstruction procedure and is shown to provide solutions that closely match ones obtained using established methods, even in highly noisy data. The proposed method also has the distinct advantage of being more efficient owing to being regularization free, involving only repeated forward calculations. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

Novel procedure for the estimation of swelling pressures of compacted bentonites based on diffuse double layer theory

Bentonite clays are proven to be attractive as buffer and backfill material in high-level nuclear waste repositories around the world. A quick estimation of swelling pressures of the compacted bentonites for different clay-water-electrolyte interactions is essential in the design of buffer and backfill materials. The theoretical studies on the swelling behavior of bentonites are based on diffuse double layer (DDL) theory. To establish theoretical relationship between void ratio and swelling pressure (e versus P), evaluation of elliptic integral and inverse analysis are unavoidable. In this paper, a novel procedure is presented to establish theoretical relationship of e versus P based on the Gouy-Chapman method. The proposed procedure establishes a unique relationship between electric potentials of interacting and non-interacting diffuse clay-water-electrolyte systems. A procedure is, thus, proposed to deduce the relation between swelling pressures and void ratio from the established relation between electric potentials. This approach is simple and alleviates the need for elliptic integral evaluation and also the inverse analysis. Further, application of the proposed approach to estimate swelling pressures of four compacted bentonites, for example, MX 80, Febex, Montigel and Kunigel V1, at different dry densities, shows that the method is very simple and predicts solutions with very good accuracy. Moreover, the proposed procedure provides continuous distributions of e versus P and thus it is computationally efficient when compared with the existing techniques.

Block Sparse Estimator for Grid Matching in Single Snapshot DoA Estimation

In this work, the grid mismatch problem for a single snapshot direction of arrival estimation problem is studied. We derive a Bayesian Cramer-Rao bound for the grid mismatch problem with the errors in variables model and propose a block sparse estimator for grid matching and sparse recovery.

Estimation of Probabilistic Bounds on Phase CPI and Relevance in WCET Analysis

Estimating program worst case execution time(WCET) accurately and efficiently is a challenging task. Several programs exhibit phase behavior wherein cycles per instruction (CPI) varies in phases during execution. Recent work has suggested the use of phases in such programs to estimate WCET with minimal instrumentation. However the suggested model uses a function of mean CPI that has no probabilistic guarantees. We propose to use Chebyshev's inequality that can be applied to any arbitrary distribution of CPI samples, to probabilistically bound CPI of a phase. Applying Chebyshev's inequality to phases that exhibit high CPI variation leads to pessimistic upper bounds. We propose a mechanism that refines such phases into sub-phases based on program counter(PC) signatures collected using profiling and also allows the user to control variance of CPI within a sub-phase. We describe a WCET analyzer built on these lines and evaluate it with standard WCET and embedded benchmark suites on two different architectures for three chosen probabilities, p={0.9, 0.95 and 0.99}. For p= 0.99, refinement based on PC signatures alone, reduces average pessimism of WCET estimate by 36%(77%) on Arch1 (Arch2). Compared to Chronos, an open source static WCET analyzer, the average improvement in estimates obtained by refinement is 5%(125%) on Arch1 (Arch2). On limiting variance of CPI within a sub-phase to {50%, 10%, 5% and 1%} of its original value, average accuracy of WCET estimate improves further to {9%, 11%, 12% and 13%} respectively, on Arch1. On Arch2, average accuracy of WCET improves to 159% when CPI variance is limited to 50% of its original value and improvement is marginal beyond that point.

Estimation of Series Capacitance for a Three-Phase Transformer Winding From Its Measured Frequency Response

Recently, authors published a method to indirectly measure series capacitance (C-s) of a single, isolated, uniformly wound transformer winding, from its measured frequency response. The next step was to implement it on an actual three-phase transformer. This task is not as straightforward as it might appear at first glance, since the measured frequency response on a three-phase transformer is influenced by nontested windings and their terminal connections, core, tank, etc. To extract the correct value of C-s from this composite frequency response, the formulation has to be reworked to first identify all significant influences and then include their effects. Initially, the modified method and experimental results on a three-phase transformer (4 MVA, 33 kV/433 V) are presented along with results on the winding considered in isolation (for cross validation). Later, the method is directly implemented on another three-phase unit (3.5 MVA, 13.8 kV/765 V) to show repeatability.

Maximum likelihood algorithms for joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing system

Maximum likelihood (ML) algorithms, for the joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system, are investigated in this work. A system model that takes into account the effects of carrier frequency offset, sampling frequency offset, symbol timing error and channel impulse response is formulated. Cramer-Rao lower bounds for the estimation of continuous parameters are derived, which show the coupling effect among different impairments and the significance of the joint estimation. The authors propose an ML algorithm for the estimation of synchronisation impairments and channel together, using the grid search method. To reduce the complexity of the joint grid search in the ML algorithm, a modified ML (MML) algorithm with multiple one-dimensional searches is also proposed. Further, a stage-wise ML (SML) algorithm using existing algorithms, which estimate less number of parameters, is also proposed. Performance of the estimation algorithms is studied through numerical simulations and it is found that the proposed ML and MML algorithms exhibit better performance than SML algorithm.

Dynamic state estimation for identifying earthquake support motions in instrumented structures

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

Estimation of fracture properties for high strength and ultra high strength concrete beams and size effect

This article presents the details of estimation of fracture parameters for high strength concrete (HSC, HSC1) and ultra high strength concrete (UHSC). Brief details about characterization of ingredients of HSC, HSC1 and UHSC have been provided. Experiments have been carried out on beams made up of HSC, HSC1 and UHSC considering various sizes and notch depths. Fracture characteristics such as size independent fracture energy (G(f)), size of fracture process zone (C-f), fracture toughness (K-IC) and crack tip opening displacement (CTODc) have been estimated based on the experimental observations. From the studies, it is observed that (i) UHSC has high fracture energy and ductility inspite of having a very low value of C-f; (ii) relatively much more homogeneous than other concretes, because of absence of coarse aggregates and well-graded smaller size particles; (iii) the critical SIF (K-IC) values are increasing with increase of beam depth and decreasing with increase of notch depth. Generally, it can be noted that there is significant increase in fracture toughness and CTODc. They are about 7 times in HSC1 and about 10 times in UHSC compared to those in HSC; (iv) for notch-to-depth ratio 0.1, Bazant's size effect model slightly overestimates the maximum failure loads compared to experimental observations and Karihaloo's model slightly underestimates the maximum failure loads. For the notch-to-depth ratio ranging from 0.2 to 0.4 for the case of UHSC, it can be observed that, both the size effect models predict more or less similar maximum failure loads compared to corresponding experimental values.

Joint estimation of synchronization impairments in MIMO-OFDM system

A joint Maximum Likelihood (ML) estimation algorithm for the synchronization impairments such as Carrier Frequency Offset (CFO), Sampling Frequency Offset (SFO) and Symbol Timing Error (STE) in single user MIMO-OFDM system is investigated in this work. A received signal model that takes into account the nonlinear effects of CFO, SFO, STE and Channel Impulse Response (CIR) is formulated. Based on the signal model, a joint ML estimation algorithm is proposed. Cramer-Rao Lower Bound (CRLB) for the continuous parameters CFO and SFO is derived for the cases of with and without channel response effects and is used to compare the effect of coupling between different estimated parameters. The performance of the estimation method is studied through numerical simulations.