Relaxation: Application to the matrix reconstruction problem

This report describes some preliminary experiments on the use of the relaxation technique for the reconstruction of the elements of a matrix given their various directional sums (or projections).

Structure and properties of zinc alloy based metal matrix composites

Zinc-aluminium cast alloys (ZA alloys) exhibit good castability and mechanical properties but these alloys lack creep resistance and high temperature stability. One solution to improve these properties is to reinforce with ceramic particles or fibres, to result in MMCs. MMCs can be produced using casting technique involving infiltration. A systematic investigation was taken and this paper discusses the salient findings of the study on the ZA-27 alloy based MMCs produced through squeeze casting. (Reinforcing fibers: SAFFIL (chopped alumina) or mullite.)

Carbon Nanotube based Composite Fibers for Strain Sensing, Signal Processing and Computing

Carbon nanotubes dispersed in polymer matrix have been aligned in the form of fibers and interconnects and cured electrically and by UV light. Conductivity and effective semiconductor tunneling against reverse to forward bias field have been designed to have differentiable current-voltage response of each of the fiber/channel. The current-voltage response is a function of the strain applied to the fibers along axial direction. Biaxial and shear strains are correlated by differentiating signals from the aligned fibers/channels. Using a small doping of magnetic nanoparticles in these composite fibers, magneto-resistance properties are realized which are strong enough to use the resulting magnetostriction as a state variable for signal processing and computing. Various basic analog signal processing tasks such as addition, convolution and filtering etc. can be performed. These preliminary study shows promising application of the concept in combined analog-digital computation in carbon nanotube based fibers. Various dynamic effects such as relaxation, electric field dependent nonlinearities and hysteresis on the output signals are studied using experimental data and analytical model.

On the microstructure-tensile property correlations in bulk metallic glass matrix composites with crystalline dendrites

Bulk metallic glass (BMG) matrix composites with crystalline dendrites as reinforcements exhibit a wide variance in their microstructures (and thus mechanical properties), which in turn can be attributed to the processing route employed, which affects the size and distribution of the dendrites. A critical investigation on the microstructure and tensile properties of Zr/Ti-based BMG composites of the same composition, but produced by different routes, was conducted so as to identify ``structure-property'' connections in these materials. This was accomplished by employing four different processing methods-arc melting, suction casting, semi-solid forging and induction melting on a water-cooled copper boat-on composites with two different dendrite volume fractions, V-d. The change in processing parameters only affects microstructural length scales such as the interdendritic spacing, lambda, and dendrite size, delta, whereas compositions of the matrix and dendrite are unaffected. Broadly, the composite's properties are insensitive to the microstructural length scales when V-d is high (similar to 75%), whereas they become process dependent for relatively lower V-d (similar to 55%). Larger delta in arc-melted and forged specimens result in higher ductility (7-9%) and lower hardening rates, whereas smaller dendrites increase the hardening rate. A bimodal distribution of dendrites offers excellent ductility at a marginal cost of yield strength. Finer lambda result in marked improvements in both ductility and yield strength, due to the confinement of shear band nucleation sites in smaller volumes of the glassy phase. Forging in the semi-solid state imparts such a microstructure. (c) 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Fast acoustic likelihood computation using low-rank matrix approximation

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for LVCSR systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication.In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on a 1138 word vocabulary RM1 task using Sphinx 3.7 system show that, for a typical case the matrix multiplication approach leads to overall speedup of 46%. Both the low-rank approximation methods increase the speedup to around 60%, with the former method increasing the word error rate (WER) from 3.2% to 6.6%, while the latter increases the WER from 3.2% to 3.5%.

Hybrid online non-negative matrix factorization for clustering of documents

When document corpus is very large, we often need to reduce the number of features. But it is not possible to apply conventional Non-negative Matrix Factorization(NMF) on billion by million matrix as the matrix may not fit in memory. Here we present novel Online NMF algorithm. Using Online NMF, we reduced original high-dimensional space to low-dimensional space. Then we cluster all the documents in reduced dimension using k-means algorithm. We experimentally show that by processing small subsets of documents we will be able to achieve good performance. The method proposed outperforms existing algorithms.

Performance improvement of short-length regular low-density parity-check codes with low-complexity post-processing

It is well known that extremely long low-density parity-check (LDPC) codes perform exceptionally well for error correction applications, short-length codes are preferable in practical applications. However, short-length LDPC codes suffer from performance degradation owing to graph-based impairments such as short cycles, trapping sets and stopping sets and so on in the bipartite graph of the LDPC matrix. In particular, performance degradation at moderate to high E-b/N-0 is caused by the oscillations in bit node a posteriori probabilities induced by short cycles and trapping sets in bipartite graphs. In this study, a computationally efficient algorithm is proposed to improve the performance of short-length LDPC codes at moderate to high E-b/N-0. This algorithm makes use of the information generated by the belief propagation (BP) algorithm in previous iterations before a decoding failure occurs. Using this information, a reliability-based estimation is performed on each bit node to supplement the BP algorithm. The proposed algorithm gives an appreciable coding gain as compared with BP decoding for LDPC codes of a code rate equal to or less than 1/2 rate coding. The coding gains are modest to significant in the case of optimised (for bipartite graph conditioning) regular LDPC codes, whereas the coding gains are huge in the case of unoptimised codes. Hence, this algorithm is useful for relaxing some stringent constraints on the graphical structure of the LDPC code and for developing hardware-friendly designs.

OPTIMALITY OF THE PRODUCT-MATRIX CONSTRUCTION FOR SECURE MSR REGENERATING CODES

In this paper, we consider the security of exact-repair regenerating codes operating at the minimum-storage-regenerating (MSR) point. The security requirement (introduced in Shah et. al.) is that no information about the stored data file must be leaked in the presence of an eavesdropper who has access to the contents of l(1) nodes as well as all the repair traffic entering a second disjoint set of l(2) nodes. We derive an upper bound on the size of a data file that can be securely stored that holds whenever l(2) <= d - k +1. This upper bound proves the optimality of the product-matrix-based construction of secure MSR regenerating codes by Shah et. al.

Novel architecture for anomalous strengthening of a particulate filled polymer matrix composite

We propose an architecture for dramatically enhancing the stress bearing and energy absorption capacities of a polymer based composite. Different weight fractions of iron oxide nano-particles (NPs) are mixed in a poly(dimethylesiloxane) (PDMS) matrix either uniformly or into several vertically aligned cylindrical pillars. These composites are compressed up to a strain of 60% at a strain rate of 0.01 s(-1) following which they are fully unloaded at the same rate. Load bearing and energy absorption capacities of the composite with uniform distribution of NPs increase by similar to 50% upon addition of 5 wt% of NPs; however, these properties monotonically decrease with further addition of NPs so much so that the load bearing capacity of the composite becomes 1/6th of PDMS upon addition of 20 wt% of NPs. On the contrary, stress at a strain of 60% and energy absorption capacity of the composites with pillar configuration monotonically increase with the weight fraction of NPs in the pillars wherein the load bearing capacity becomes 1.5 times of PDMS when the pillars consisted of 20 wt% of NPs. In situ mechanical testing of composites with pillars reveals outward bending of the pillars wherein the pillars and the PDMS in between two pillars, located along a radius, are significantly compressed. Reasoning based on effects of compressive hydrostatic stress and shape of fillers is developed to explain the observed anomalous strengthening of the composite with pillar architecture.

A novel in-situ polymer derived nano ceramic MMC by friction stir processing

Fiction stir processing (FSP) is a solid state technique used for material processing. Tool wear and the agglomeration of ceramic particles have been serious issues in FSP of metal matrix composites. In the present study, FSP has been employed to disperse the nanoscale particles of a polymer-derived silicon carbonitride (SiCN) ceramic phase into copper by an in-situ process. SiCN cross linked polymer particles were incorporated using multi-pass ESP into pure copper to form bulk particulate metal matrix composites. The polymer was then converted into ceramic through an in-situ pyrolysis process and dispersed by ESP. Multi-pass processing was carried out to remove porosity from the samples and also for the uniform dispersion of polymer derived ceramic particles. Microstructural observations were carried out using Field Emission Scanning Electron Microscopy (FE-SEM) and Transmission Electron Microscopy (TEM) of the composite. The results indicate a uniform distribution of similar to 100 nm size particles of the ceramic phase in the copper matrix after ESP. The nanocomposite exhibits a five fold increase in microhardness (260HV(100)) which is attributed to the nano scale dispersion of ceramic particles. A mechanism has been proposed for the fracturing of PDC particles during multi pass FSP. (C) 2015 Elsevier Ltd. All rights reserved

Laser synthesizing NiAl intermetallic and TiC reinforced NiAl intermetallic matrix composite

The NiAl intermetallic layers and NiAl matrix composite layers with TiC particulate reinforcement were successfully synthesized by laser cladding with coaxial powder feeding of Ni/Al clad powder and Ni/Al + TiC powder mixture, respectively. With optimized processing parameters and powder mixture compositions, the synthesized layers were free of cracks and metallurgical bond with the substrate. The microstructure of the laser-synthesized layers was composed of 6-NiAl phase and a few gamma phases for NiAl intermetallic; unmelted TiC, dispersive fine precipitated TiC particles and refined beta-NiAl phase matrix for TiC reinforced NiAl intermetallic composite. The average microhardness was 355 HV0.1 and 538 HV0.1, respectively. Laser synthesizing and direct metal depositing offer promising approaches for producing NiAl intermetallic and TiC-reinforced NiAl metal matrix composite coatings and for fabricating NiAl intermetallic bulk structure. (C) 2004 Laser Institute of America.