Excited electronic state decomposition mechanisms of clusters of dimethylnitramine and aluminum

In this report, electronically non-adiabatic decomposition pathways of clusters of dimethylnitramine and aluminum (DMNA-Al and DMNA-Al-2) are discussed in comparison to isolated dimethylnitramine (DMNA). Electronically excited state processes of DMNA-Al and DMNA-Al-2 are explored using the complete active space self-consistent field (CASSCF) and the restricted active space self-consistent field (RASSCF) theories, respectively. Similar to the nitro-nitrite isomerization reaction pathway of DMNA, DMNA-Al-n clusters also exhibit isomerization pathway. However, it involves several other steps, such as, first Al-O bond dissociation, then N-N bond dissociation followed by isomerization and finally NO elimination. Furthermore, DMNA-Al-n clusters exhibit overall exothermic decomposition reaction pathway and isolated DMNA shows overall endothermic reaction channel.

Efficient QR Decomposition Using Low Complexity Column-wise Givens Rotation (CGR)

QR decomposition (QRD) is a widely used Numerical Linear Algebra (NLA) kernel with applications ranging from SONAR beamforming to wireless MIMO receivers. In this paper, we propose a novel Givens Rotation (GR) based QRD (GR QRD) where we reduce the computational complexity of GR and exploit higher degree of parallelism. This low complexity Column-wise GR (CGR) can annihilate multiple elements of a column of a matrix simultaneously. The algorithm is first realized on a Two-Dimensional (2 D) systolic array and then implemented on REDEFINE which is a Coarse Grained run-time Reconfigurable Architecture (CGRA). We benchmark the proposed implementation against state-of-the-art implementations to report better throughput, convergence and scalability.

An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints

We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating l(p) - l(2) projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an l(p)-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.

Iridium-decorated multiwall carbon nanotubes and its catalytic activity with Shell 405 in hydrazine decomposition

Iridium-functionalized multiwalled carbon nanotubes (Ir-MWNT) are the future catalyst support material for hydrazine fuel decomposition. The present work demonstrates decoration of iridium particle on iron-encapsulated multiwalled carbon nanotubes (MWNT) by wet impregnation method in the absence of any stabilizer. Electron microscopy studies reveal the coated iridium particle size in the range of 5-10 nm. Elemental analysis by energy dispersive X-ray diffraction confirms 21 wt% of Ir coated over MWNT. X-ray photoelectron spectroscopy (XPS) shows 4f(5/2) and 4f(7/2) lines of iridium and confirms the metallic nature. The catalytic activity of Ir-MWNT/Shell 405 combination is performed in 1 N hydrazine micro-thrusters. The thruster performance shows increase in chamber pressure and decrease in chamber temperature when compared to Shell 405 alone. This enhanced performance is due to high thermal conducting nature of MWNTs and the presence of Ir active sites over MWNTs.

Prediction of electronically nonadiabatic decomposition mechanisms of isolated gas phase nitrogen-rich energetic salt: Guanidium-triazolate

Electronically nonadiabatic decomposition pathways of guanidium triazolate are explored theoretically. Nonadiabatically coupled potential energy surfaces are explored at the complete active space self-consistent field (CASSCF) level of theory. For better estimation of energies complete active space second order perturbation theories (CASPT2 and CASMP2) are also employed. Density functional theory (DFT) with B3LYP functional and MP2 level of theory are used to explore subsequent ground state decomposition pathways. In comparison with all possible stable decomposition products (such as, N-2, NH3, HNC, HCN, NH2CN and CH3NC), only NH3 (with NH2CN) and N-2 are predicted to be energetically most accessible initial decomposition products. Furthermore, different conical intersections between the S-1 and S-0 surfaces, which are computed at the CASSCF(14,10)/6-31G(d) level of theory, are found to play an essential role in the excited state deactivation process of guanidium triazolate. This is the first report on the electronically nonadiabatic decomposition mechanisms of isolated guanidium triazolate salt. (C) 2015 Elsevier B.V. All rights reserved.

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.