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.

Three-dimensional plotted hydroxyapatite scaffolds with predefined architecture: comparison of stabilization by alginate cross-linking versus sintering

Scaffolds for bone tissue engineering are essentially characterized by porous three-dimensional structures with interconnected pores to facilitate the exchange of nutrients and removal of waste products from cells, thereby promoting cell proliferation in such engineered scaffolds. Although hydroxyapatite is widely being considered for bone tissue engineering applications due to its occurrence in the natural extracellular matrix of this tissue, limited reports are available on additive manufacturing of hydroxyapatite-based materials. In this perspective, hydroxyapatite-based three-dimensional porous scaffolds with two different binders (maltodextrin and sodium alginate) were fabricated using the extrusion method of three-dimensional plotting and the results were compared in reference to the structural properties of scaffolds processed via chemical stabilization and sintering routes, respectively. With the optimal processing conditions regarding to pH and viscosity of binder-loaded hydroxyapatite pastes, scaffolds with parallelepiped porous architecture having up to 74% porosity were fabricated. Interestingly, sintering of the as-plotted hydroxyapatite-sodium alginate (cross-linked with CaCl2 solution) scaffolds led to the formation of chlorapatite (Ca9.54P5.98O23.8Cl1.60(OH)(2.74)). Both the sintered scaffolds displayed progressive deformation and delayed fracture under compressive loading, with hydroxyapatite-alginate scaffolds exhibiting a higher compressive strength (9.5 +/- 0.5MPa) than hydroxyapatite-maltodextrin scaffolds (7.0 +/- 0.6MPa). The difference in properties is explained in terms of the phase assemblage and microstructure.

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.