Event-triggered Sampling and Reconstruction of Sparse Real-valued Trigonometric Polynomials

We propose data acquisition from continuous-time signals belonging to the class of real-valued trigonometric polynomials using an event-triggered sampling paradigm. The sampling schemes proposed are: level crossing (LC), close to extrema LC, and extrema sampling. Analysis of robustness of these schemes to jitter, and bandpass additive gaussian noise is presented. In general these sampling schemes will result in non-uniformly spaced sample instants. We address the issue of signal reconstruction from the acquired data-set by imposing structure of sparsity on the signal model to circumvent the problem of gap and density constraints. The recovery performance is contrasted amongst the various schemes and with random sampling scheme. In the proposed approach, both sampling and reconstruction are non-linear operations, and in contrast to random sampling methodologies proposed in compressive sensing these techniques may be implemented in practice with low-power circuitry.

Exceptional resistance to grain growth in nanocrystalline CoCrFeNi high entropy alloy at high homologous temperatures

Nanocrystalline CoCrFeNi high entropy alloy, synthesized by mechanical alloying followed by spark plasma sintering, demonstrated extremely sluggish grain growth even at very high homologous temperature of 0.68 T-m (900 degrees C) for annealing duration of 600 h. Mechanically alloyed powder had carbon and oxygen as impurities, which in turn led to the formation of two-phase mixture of FCC and Cr-rich carbide with fine distribution of Cr-rich oxide during spark plasma sintering. Sluggish grain growth is attributed to the Zener pinning effect from the fine dispersion of oxide, mutual retardation of grain boundaries in the presence of two phases, and sluggish diffusivity because of cooperative diffusion of multi-principle elements. (C) 2015 Elsevier B.V. All rights reserved.

SPEECH ENHANCEMENT USING DISCRIMINATIVE RANDOM FIELDS

Speech enhancement in stationary noise is addressed using the ideal channel selection framework. In order to estimate the binary mask, we propose to classify each time-frequency (T-F) bin of the noisy signal as speech or noise using Discriminative Random Fields (DRF). The DRF function contains two terms - an enhancement function and a smoothing term. On each T-F bin, we propose to use an enhancement function based on likelihood ratio test for speech presence, while Ising model is used as smoothing function for spectro-temporal continuity in the estimated binary mask. The effect of the smoothing function over successive iterations is found to reduce musical noise as opposed to using only enhancement function. The binary mask is inferred from the noisy signal using Iterated Conditional Modes (ICM) algorithm. Sentences from NOIZEUS corpus are evaluated from 0 dB to 15 dB Signal to Noise Ratio (SNR) in 4 kinds of additive noise settings: additive white Gaussian noise, car noise, street noise and pink noise. The reconstructed speech using the proposed technique is evaluated in terms of average segmental SNR, Perceptual Evaluation of Speech Quality (PESQ) and Mean opinion Score (MOS).

Transition metal-free Minisci reaction promoted by NCS, and TBHP: acylation of heteroarenes

A method for acylation for heteroarenes under metal-free conditions has been described using NCS as an additive and TBHP as an oxidant. This method has been successfully employed in acylation of a variety of aldehyde with heteroarenes. The application of the method has been illustrated in synthesizing isoquinoline derived natural products. This strategy provides an efficient, mild and inexpensive method for acylation of heteroarenes. (C) 2015 Elsevier Ltd. All rights reserved.

Pd-Catalyzed Hydroborylation of Alkynes: A Ligand Controlled Regioselectivity Switch for the Synthesis of alpha- or beta-Vinylboronates

A ligand controlled selective hydroborylation of alkynes to alpha- or beta-vinylboronates has been developed using a Pd catalyst. The high alpha-selectivity displayed by this reaction can be switched to furnish beta-vinylboronates by altering the ligand from a trialkylphosphine to N-heterocyclic carbene. A variety of terminal alkynes are shown to furnish the corresponding alpha- or beta-vinylboronates in good to excellent selectivity and yield. The mechanistic studies suggest that the solvent is the proton source and bromobenzene functions as an important additive in driving this reaction forward.

Pd-Catalyzed Hydroborylation of Alkynes: A Ligand Controlled Regioselectivity Switch for the Synthesis of alpha- or beta-Vinylboronates

A ligand controlled selective hydroborylation of alkynes to alpha- or beta-vinylboronates has been developed using a Pd catalyst. The high alpha-selectivity displayed by this reaction can be switched to furnish beta-vinylboronates by altering the ligand from a trialkylphosphine to N-heterocyclic carbene. A variety of terminal alkynes are shown to furnish the corresponding alpha- or beta-vinylboronates in good to excellent selectivity and yield. The mechanistic studies suggest that the solvent is the proton source and bromobenzene functions as an important additive in driving this reaction forward.

Characterization of silver-kaolinite (AgK): an adsorbent for long-lived I-129 species

Bentonite is a preferred buffer and backfill material for deep geological disposal of high-level nuclear waste (HLW). Bentonite does not retain anions by virtue of its negatively charged basal surface. Imparting anion retention ability to bentonite is important to enable the expansive clay to retain long-lived I-129 (iodine-129; half-life = 16 million years) species that may escape from the HLW geological repository. Silver-kaolinite (AgK) material is prepared as an additive to improve the iodide retention capacity of bentonite. The AgK is prepared by heating kaolinite-silver nitrate mix at 400 degrees C to study the kaolinite influence on the transition metal ion when reacting at its dehydroxylation temperature. Thermo gravimetric-Evolved Gas Detection analysis, X-ray diffraction analysis, X-ray photo electron spectroscopy and electron probe micro analysis indicated that silver occurs as AgO/Ag2O surface coating on thermally reacting kaolinite with silver nitrate at 400 degrees C.

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.

Cumulative Impulse Strength for Epoch Extraction

Algorithms for extracting epochs or glottal closure instants (GCIs) from voiced speech typically fall into two categories: i) ones which operate on linear prediction residual (LPR) and ii) those which operate directly on the speech signal. While the former class of algorithms (such as YAGA and DPI) tend to be more accurate, the latter ones (such as ZFR and SEDREAMS) tend to be more noise-robust. In this letter, a temporal measure termed the cumulative impulse strength is proposed for locating the impulses in a quasi-periodic impulse-sequence embedded in noise. Subsequently, it is applied for detecting the GCIs from the inverted integrated LPR using a recursive algorithm. Experiments on two large corpora of speech with simultaneous electroglottographic recordings demonstrate that the proposed method is more robust to additive noise than the state-of-the-art algorithms, despite operating on the LPR.

An Ensemble Kushner-Stratonovich-Poisson Filter for Recursive Estimation in Nonlinear Dynamical Systems

We propose a Monte Carlo filter for recursive estimation of diffusive processes that modulate the instantaneous rates of Poisson measurements. A key aspect is the additive update, through a gain-like correction term, empirically approximated from the innovation integral in the time-discretized Kushner-Stratonovich equation. The additive filter-update scheme eliminates the problem of particle collapse encountered in many conventional particle filters. Through a few numerical demonstrations, the versatility of the proposed filter is brought forth.

Object attributes combine additively in visual search

We perceive objects as containing a variety of attributes: local features, relations between features, internal details, and global properties. But we know little about how they combine. Here, we report a remarkably simple additive rule that governs how these diverse object attributes combine in vision. The perceived dissimilarity between two objects was accurately explained as a sum of (a) spatially tuned local contour-matching processes modulated by part decomposition; (b) differences in internal details, such as texture; (c) differences in emergent attributes, such as symmetry; and (d) differences in global properties, such as orientation or overall configuration of parts. Our results elucidate an enduring question in object vision by showing that the whole object is not a sum of its parts but a sum of its many attributes.

IMAGE DENOISING USING OPTIMALLY WEIGHTED BILATERAL FILTERS: A SURE AND FAST APPROACH

The bilateral filter is known to be quite effective in denoising images corrupted with small dosages of additive Gaussian noise. The denoising performance of the filter, however, is known to degrade quickly with the increase in noise level. Several adaptations of the filter have been proposed in the literature to address this shortcoming, but often at a substantial computational overhead. In this paper, we report a simple pre-processing step that can substantially improve the denoising performance of the bilateral filter, at almost no additional cost. The modified filter is designed to be robust at large noise levels, and often tends to perform poorly below a certain noise threshold. To get the best of the original and the modified filter, we propose to combine them in a weighted fashion, where the weights are chosen to minimize (a surrogate of) the oracle mean-squared-error (MSE). The optimally-weighted filter is thus guaranteed to perform better than either of the component filters in terms of the MSE, at all noise levels. We also provide a fast algorithm for the weighted filtering. Visual and quantitative denoising results on standard test images are reported which demonstrate that the improvement over the original filter is significant both visually and in terms of PSNR. Moreover, the denoising performance of the optimally-weighted bilateral filter is competitive with the computation-intensive non-local means filter.

IMAGE DENOISING IN MULTIPLICATIVE NOISE

We address the problem of denoising images corrupted by multiplicative noise. The noise is assumed to follow a Gamma distribution. Compared with additive noise distortion, the effect of multiplicative noise on the visual quality of images is quite severe. We consider the mean-square error (MSE) cost function and derive an expression for an unbiased estimate of the MSE. The resulting multiplicative noise unbiased risk estimator is referred to as MURE. The denoising operation is performed in the wavelet domain by considering the image-domain MURE. The parameters of the denoising function (typically, a shrinkage of wavelet coefficients) are optimized for by minimizing MURE. We show that MURE is accurate and close to the oracle MSE. This makes MURE-based image denoising reliable and on par with oracle-MSE-based estimates. Analogous to the other popular risk estimation approaches developed for additive, Poisson, and chi-squared noise degradations, the proposed approach does not assume any prior on the underlying noise-free image. We report denoising results for various noise levels and show that the quality of denoising obtained is on par with the oracle result and better than that obtained using some state-of-the-art denoisers.

Molecular dynamics simulation of the role of dislocations in microcrack healing

The molecular dynamics method is used to simulate microcrack healing during heating or/and under compressive stress. A centre microcrack in Cu crystal would be sealed under compressive stress or by heating. The role of compressive stress and heating in crack healing was additive. During microcrack healing, dislocation generation and motion occurred. When there were pre-existing dislocations around the microcrack, the critical temperature or compressive stress necessary for microcrack healing would decrease, and, the higher the number of dislocations, the lower the critical temperature or compressive stress. The critical temperature necessary for microcrack healing depended upon the orientation of the crack plane. For example, the critical temperature for the crack along the (001) plane was the lowest, i.e. 770K.

Essential and Non-Essential Work of Fracture of PI/SiO2 Hybrid Thin Films

Essential work of fracture (EWF) analysis is used to study the effect of the silica doping level on fracture toughness of polyimide/silica (PI/SiO2) hybrid films. By using double-edge-notched-tension (DENT) specimens with different ligament lengths, it seems that the introduction of silica additive can improve the specific essential work of fracture (w (e) ) of PI thin films, but the specific non-essential work of fracture (beta w (p) ) will decease significantly as the silica doping level increasing from 1 to 5 wt.%, and even lower than that of neat PI. The failure process of the fracture is investigated with online scanning electron microscope (SEM) observation and the parameters of non-essential work of fracture, beta and w (p) , are calculated based on finite element (FE) method.