Robust Savitzky-Golay Filters

Local polynomial approximation of data is an approach towards signal denoising. Savitzky-Golay (SG) filters are finite-impulse-response kernels, which convolve with the data to result in polynomial approximation for a chosen set of filter parameters. In the case of noise following Gaussian statistics, minimization of mean-squared error (MSE) between noisy signal and its polynomial approximation is optimum in the maximum-likelihood (ML) sense but the MSE criterion is not optimal for non-Gaussian noise conditions. In this paper, we robustify the SG filter for applications involving noise following a heavy-tailed distribution. The optimal filtering criterion is achieved by l(1) norm minimization of error through iteratively reweighted least-squares (IRLS) technique. It is interesting to note that at any stage of the iteration, we solve a weighted SG filter by minimizing l(2) norm but the process converges to l(1) minimized output. The results show consistent improvement over the standard SG filter performance.

Ordered Pd2Ge Intermetallic Nanoparticles as Highly Efficient and Robust Catalyst for Ethanol Oxidation

Pd2Ge nanoparticles were synthesized by superhydride reduction of K2PdCl4 and GeCl4. The syntheses were performed using a solvothermal method in the absence of surfactants, and the size of the nanoparticles was controlled by varying the reaction time. The powder X-ray diffraction (PXRD) and transmission electron microscopy data suggest that Pd2Ge nanoparticles were formed as an ordered intermetallic phase. In the crystal structure, Pd and Ge atoms occupy two different crystallographic positions with a vacancy in one of the Ge sites, which was proved by PXRD and energy-dispersive X-ray analysis. The catalyst is highly efficient for the electrochemical oxidation of ethanol and is stable up to the 250th cycle in alkaline medium. The electrochemical active surface area and current density values obtained, 1.41 cm(2) and 4.1 mA cm(-2), respectively, are superior to those of the commercial Pd on carbon. The experimentally observed data were interpreted in terms of the combined effect of adsorption energies of CH3CO and OH radical, d-band center model, and work function of the corresponding catalyst surfaces.

Robust Glyoxalase activity of Hsp31, a ThiJ/DJ-1/PfpI Family Member Protein, Is Critical for Oxidative Stress Resistance in Saccharomyces cerevisiae

Methylglyoxal (MG) is a reactive metabolic intermediate generated during various cellular biochemical reactions, including glycolysis. The accumulation of MG indiscriminately modifies proteins, including important cellular antioxidant machinery, leading to severe oxidative stress, which is implicated in multiple neurodegenerative disorders, aging, and cardiac disorders. Although cells possess efficient glyoxalase systems for detoxification, their functions are largely dependent on the glutathione cofactor, the availability of which is self-limiting under oxidative stress. Thus, higher organisms require alternate modes of reducing the MG-mediated toxicity and maintaining redox balance. In this report, we demonstrate that Hsp31 protein, a member of the ThiJ/DJ-1/PfpI family in Saccharomyces cerevisiae, plays an indispensable role in regulating redox homeostasis. Our results show that Hsp31 possesses robust glutathione-independent methylglyoxalase activity and suppresses MG-mediated toxicity and ROS levels as compared with another paralog, Hsp34. On the other hand, glyoxalase-defective mutants of Hsp31 were found highly compromised in regulating the ROS levels. Additionally, Hsp31 maintains cellular glutathione and NADPH levels, thus conferring protection against oxidative stress, and Hsp31 relocalizes to mitochondria to provide cytoprotection to the organelle under oxidative stress conditions. Importantly, human DJ-1, which is implicated in the familial form of Parkinson disease, complements the function of Hsp31 by suppressing methylglyoxal and oxidative stress, thus signifying the importance of these proteins in the maintenance of ROS homeostasis across phylogeny.

The time finite element as a robust general scheme for solving nonlinear dynamic equations including chaotic systems

Schemes that can be proven to be unconditionally stable in the linear context can yield unstable solutions when used to solve nonlinear dynamical problems. Hence, the formulation of numerical strategies for nonlinear dynamical problems can be particularly challenging. In this work, we show that time finite element methods because of their inherent energy momentum conserving property (in the case of linear and nonlinear elastodynamics), provide a robust time-stepping method for nonlinear dynamic equations (including chaotic systems). We also show that most of the existing schemes that are known to be robust for parabolic or hyperbolic problems can be derived within the time finite element framework; thus, the time finite element provides a unification of time-stepping schemes used in diverse disciplines. We demonstrate the robust performance of the time finite element method on several challenging examples from the literature where the solution behavior is known to be chaotic. (C) 2015 Elsevier Inc. All rights reserved.

The time finite element as a robust general scheme for solving nonlinear dynamic equations including chaotic systems

Schemes that can be proven to be unconditionally stable in the linear context can yield unstable solutions when used to solve nonlinear dynamical problems. Hence, the formulation of numerical strategies for nonlinear dynamical problems can be particularly challenging. In this work, we show that time finite element methods because of their inherent energy momentum conserving property (in the case of linear and nonlinear elastodynamics), provide a robust time-stepping method for nonlinear dynamic equations (including chaotic systems). We also show that most of the existing schemes that are known to be robust for parabolic or hyperbolic problems can be derived within the time finite element framework; thus, the time finite element provides a unification of time-stepping schemes used in diverse disciplines. We demonstrate the robust performance of the time finite element method on several challenging examples from the literature where the solution behavior is known to be chaotic. (C) 2015 Elsevier Inc. All rights reserved.

l(1)-K-SVD: A robust dictionary learning algorithm with simultaneous update

We develop a new dictionary learning algorithm called the l(1)-K-svp, by minimizing the l(1) distortion on the data term. The proposed formulation corresponds to maximum a posteriori estimation assuming a Laplacian prior on the coefficient matrix and additive noise, and is, in general, robust to non-Gaussian noise. The l(1) distortion is minimized by employing the iteratively reweighted least-squares algorithm. The dictionary atoms and the corresponding sparse coefficients are simultaneously estimated in the dictionary update step. Experimental results show that l(1)-K-SVD results in noise-robustness, faster convergence, and higher atom recovery rate than the method of optimal directions, K-SVD, and the robust dictionary learning algorithm (RDL), in Gaussian as well as non-Gaussian noise. For a fixed value of sparsity, number of dictionary atoms, and data dimension, l(1)-K-SVD outperforms K-SVD and RDL on small training sets. We also consider the generalized l(p), 0 < p < 1, data metric to tackle heavy-tailed/impulsive noise. In an image denoising application, l(1)-K-SVD was found to result in higher peak signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise. The structural similarity index increases by 0.1 for low input PSNR, which is significant and demonstrates the efficacy of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

A mode-shape classification technique for robust speech rate estimation and syllable nuclei detection

Acoustic feature based speech (syllable) rate estimation and syllable nuclei detection are important problems in automatic speech recognition (ASR), computer assisted language learning (CALL) and fluency analysis. A typical solution for both the problems consists of two stages. The first stage involves computing a short-time feature contour such that most of the peaks of the contour correspond to the syllabic nuclei. In the second stage, the peaks corresponding to the syllable nuclei are detected. In this work, instead of the peak detection, we perform a mode-shape classification, which is formulated as a supervised binary classification problem - mode-shapes representing the syllabic nuclei as one class and remaining as the other. We use the temporal correlation and selected sub-band correlation (TCSSBC) feature contour and the mode-shapes in the TCSSBC feature contour are converted into a set of feature vectors using an interpolation technique. A support vector machine classifier is used for the classification. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora in a five-fold cross validation setup. The average correlation coefficients for the syllable rate estimation turn out to be 0.6761, 0.6928 and 0.3604 for three corpora respectively, which outperform those obtained by the best of the existing peak detection techniques. Similarly, the average F-scores (syllable level) for the syllable nuclei detection are 0.8917, 0.8200 and 0.7637 for three corpora respectively. (C) 2016 Elsevier B.V. All rights reserved.