A fast algorithm for speech polarity detection using long-term linear prediction

Speech polarity detection is a crucial first step in many speech processing techniques. In this paper, an algorithm is proposed that improvises the existing technique using the skewness of the voice source (VS) signal. Here, the integrated linear prediction residual (ILPR) is used as the VS estimate, which is obtained using linear prediction on long-term frames of the low-pass filtered speech signal. This excludes the unvoiced regions from analysis and also reduces the computation. Further, a modified skewness measure is proposed for decision, which also considers the magnitude of the skewness of the ILPR along with its sign. With the detection error rate (DER) as the performance metric, the algorithm is tested on 8 large databases and its performance (DER=0.20%) is found to be comparable to that of the best technique (DER=0.06%) on both clean and noisy speech. Further, the proposed method is found to be ten times faster than the best technique.

A fast and accurate performance analysis of beaconless IEEE 802.15.4 multi-hop networks

We develop an approximate analytical technique for evaluating the performance of multi-hop networks based on beaconless IEEE 802.15.4 ( the ``ZigBee'' PHY and MAC), a popular standard for wireless sensor networks. The network comprises sensor nodes, which generate measurement packets, relay nodes which only forward packets, and a data sink (base station). We consider a detailed stochastic process at each node, and analyse this process taking into account the interaction with neighbouring nodes via certain time averaged unknown variables (e.g., channel sensing rates, collision probabilities, etc.). By coupling the analyses at various nodes, we obtain fixed point equations that can be solved numerically to obtain the unknown variables, thereby yielding approximations of time average performance measures, such as packet discard probabilities and average queueing delays. The model incorporates packet generation at the sensor nodes and queues at the sensor nodes and relay nodes. We demonstrate the accuracy of our model by an extensive comparison with simulations. As an additional assessment of the accuracy of the model, we utilize it in an algorithm for sensor network design with quality-of-service (QoS) objectives, and show that designs obtained using our model actually satisfy the QoS constraints (as validated by simulating the networks), and the predictions are accurate to well within 10% as compared to the simulation results in a regime where the packet discard probability is low. (C) 2015 Elsevier B.V. All rights reserved.

Learning-Based Fast Iterative Convergence of 3-D MoM via Eigen-AGMRES Method

Three-dimensional (3-D) full-wave electromagnetic simulation using method of moments (MoM) under the framework of fast solver algorithms like fast multipole method (FMM) is often bottlenecked by the speed of convergence of the Krylov-subspace-based iterative process. This is primarily because the electric field integral equation (EFIE) matrix, even with cutting-edge preconditioning techniques, often exhibits bad spectral properties arising from frequency or geometry-based ill-conditioning, which render iterative solvers slow to converge or stagnate occasionally. In this communication, a novel technique to expedite the convergence of MoMmatrix solution at a specific frequency is proposed, by extracting and applying Eigen-vectors from a previously solved neighboring frequency in an augmented generalized minimum residual (AGMRES) iterative framework. This technique can be applied in unison with any preconditioner. Numerical results demonstrate up to 40% speed-up in convergence using the proposed Eigen-AGMRES method.

Fast and slow climate responses to CO2 and solar forcing: A linear multivariate regression model characterizing transient climate change

Climate change in response to a change in external forcing can be understood in terms of fast response to the imposed forcing and slow feedback associated with surface temperature change. Previous studies have investigated the characteristics of fast response and slow feedback for different forcing agents. Here we examine to what extent that fast response and slow feedback derived from time-mean results of climate model simulations can be used to infer total climate change. To achieve this goal, we develop a multivariate regression model of climate change, in which the change in a climate variable is represented by a linear combination of its sensitivity to CO2 forcing, solar forcing, and change in global mean surface temperature. We derive the parameters of the regression model using time-mean results from a set of HadCM3L climate model step-forcing simulations, and then use the regression model to emulate HadCM3L-simulated transient climate change. Our results show that the regression model emulates well HadCM3L-simulated temporal evolution and spatial distribution of climate change, including surface temperature, precipitation, runoff, soil moisture, cloudiness, and radiative fluxes under transient CO2 and/or solar forcing scenarios. Our findings suggest that temporal and spatial patterns of total change for the climate variables considered here can be represented well by the sum of fast response and slow feedback. Furthermore, by using a simple 1-D heat-diffusion climate model, we show that the temporal and spatial characteristics of climate change under transient forcing scenarios can be emulated well using information from step-forcing simulations alone.

Anodization of Aluminium using a fast two-step process

Ultra-fast two-step anodization method is developed for obtaining ordered nano-pores on aluminium (Al) foil. First anodization was carried out for 10 min, followed by 3 min of second anodization at high voltage (150 V) compared to previous reports of anodization times of 12 h (40-60 V). The pore dimensions on anodized alumina are 180 nm for pore diameter and 130 nm for inter-pore distance. It was evident that by increasing the anodization voltage to 150 V, the diameter of the pores formed was above 150 nm. The electrolyte and its temperature affect the shape and size of the pore formation. At lower anodization temperature, controlled pore formation was observed. The anodized samples were characterized using the field emission scanning electron microscope (FE-SEM) to determine the pore diameter and inter-pore distance. Using UV-Visible spectroscopy, the reflectance spectra of anodized samples were measured. The alumina (Al2O3) peaks were identified by x-ray diffraction (XRD) technique. The x-ray photo electron spectroscopy (XPS) analysis confirmed the Al 2p peak at 73.1 eV along with the oxygen O 1s at 530.9 eV and carbon traces C 1s at 283.6 eV.

Sandwiched assembly of ZnO nanowires between graphene layers for a self-powered and fast responsive ultraviolet photodetector

A heterostructure of graphene and zinc oxide (ZnO) nanowires (NWs) is fabricated by sandwiching an array of ZnO NWs between two graphene layers for an ultraviolet (UV) photodetector. This unique structure allows NWs to be in direct contact with the graphene layers, minimizing the effect of the substrate or metal electrodes. In this device, graphene layers act as highly conducting electrodes with a high mobility of the generated charge carriers. An excellent sensitivity is demonstrated towards UV illumination, with a reversible photoresponse even for a short period of UV illumination. Response and recovery times of a few milliseconds demonstrated a much faster photoresponse than most of the conventional ZnO nanostructure-based photodetectors. It is shown that the generation of a built-in electric field between the interface of graphene and ZnO NWs effectively contributes to the separation of photogenerated electron-hole pairs for photocurrent generation without applying any external bias. Upon application of external bias voltage, the electric field further increases the drift velocity of photogenerated electrons by reducing the charge recombination rates, and results in an enhancement of the photocurrent. Therefore, the graphene-based heterostructure (G/ZnO NW/G) opens avenues to constructing a novel heterostructure with a combination of two functionally dissimilar materials.

Sandwiched assembly of ZnO nanowires between graphene layers for a self-powered and fast responsive ultraviolet photodetector

A heterostructure of graphene and zinc oxide (ZnO) nanowires (NWs) is fabricated by sandwiching an array of ZnO NWs between two graphene layers for an ultraviolet (UV) photodetector. This unique structure allows NWs to be in direct contact with the graphene layers, minimizing the effect of the substrate or metal electrodes. In this device, graphene layers act as highly conducting electrodes with a high mobility of the generated charge carriers. An excellent sensitivity is demonstrated towards UV illumination, with a reversible photoresponse even for a short period of UV illumination. Response and recovery times of a few milliseconds demonstrated a much faster photoresponse than most of the conventional ZnO nanostructure-based photodetectors. It is shown that the generation of a built-in electric field between the interface of graphene and ZnO NWs effectively contributes to the separation of photogenerated electron-hole pairs for photocurrent generation without applying any external bias. Upon application of external bias voltage, the electric field further increases the drift velocity of photogenerated electrons by reducing the charge recombination rates, and results in an enhancement of the photocurrent. Therefore, the graphene-based heterostructure (G/ZnO NW/G) opens avenues to constructing a novel heterostructure with a combination of two functionally dissimilar materials.

On Fast Bilateral Filtering Using Fourier Kernels

It was demonstrated in earlier work that, by approximating its range kernel using shiftable functions, the nonlinear bilateral filter can be computed using a series of fast convolutions. Previous approaches based on shiftable approximation have, however, been restricted to Gaussian range kernels. In this work, we propose a novel approximation that can be applied to any range kernel, provided it has a pointwise-convergent Fourier series. More specifically, we propose to approximate the Gaussian range kernel of the bilateral filter using a Fourier basis, where the coefficients of the basis are obtained by solving a series of least-squares problems. The coefficients can be efficiently computed using a recursive form of the QR decomposition. By controlling the cardinality of the Fourier basis, we can obtain a good tradeoff between the run-time and the filtering accuracy. In particular, we are able to guarantee subpixel accuracy for the overall filtering, which is not provided by the most existing methods for fast bilateral filtering. We present simulation results to demonstrate the speed and accuracy of the proposed algorithm.

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.

FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL DECOMPOSITION

The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires O(sigma(2)) operations per pixel, where sigma is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary sigma (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.