A Communication-Theoretic Framework for 2-DMR Channel Modeling: Performance Evaluation of Coding and Signal Processing Methods

We develop a communication theoretic framework for modeling 2-D magnetic recording channels. Using the model, we define the signal-to-noise ratio (SNR) for the channel considering several physical parameters, such as the channel bit density, code rate, bit aspect ratio, and noise parameters. We analyze the problem of optimizing the bit aspect ratio for maximizing SNR. The read channel architecture comprises a novel 2-D joint self-iterating equalizer and detection system with noise prediction capability. We evaluate the system performance based on our channel model through simulations. The coded performance with the 2-D equalizer detector indicates similar to 5.5 dB of SNR gain over uncoded data.

Structural characterization of electrodeposited boron

Structural characterization of electrodeposited boron was carried out by using transmission electron microscopy and Raman spectroscopy. Electron diffraction and phase contrast imaging were carried out by using transmission electron microscopy. Phase identification was done based on the analysis of electron diffraction patterns and the power spectrum calculated from the lattice images from thin regions of the sample. Raman spectroscopic examination was carried out to study the nature of bonding and the allotropic form of boron obtained after electrodeposition. The results obtained from transmission electron microscopy showed the presence of nanocrystallites embedded in an amorphous mass of boron. Raman microscopic studies showed that amorphous boron could be converted to its crystalline form at high temperatures.

A performance-based framework for assessing liquefaction potential based on CPT data

This article describes a new performance-based approach for evaluating the return period of seismic soil liquefaction based on standard penetration test (SPT) and cone penetration test (CPT) data. The conventional liquefaction evaluation methods consider a single acceleration level and magnitude and these approaches fail to take into account the uncertainty in earthquake loading. The seismic hazard analysis based on the probabilistic method clearly shows that a particular acceleration value is being contributed by different magnitudes with varying probability. In the new method presented in this article, the entire range of ground shaking and the entire range of earthquake magnitude are considered and the liquefaction return period is evaluated based on the SPT and CPT data. This article explains the performance-based methodology for the liquefaction analysis – starting from probabilistic seismic hazard analysis (PSHA) for the evaluation of seismic hazard and the performance-based method to evaluate the liquefaction return period. A case study has been done for Bangalore, India, based on SPT data and converted CPT values. The comparison of results obtained from both the methods have been presented. In an area of 220 km2 in Bangalore city, the site class was assessed based on large number of borehole data and 58 Multi-channel analysis of surface wave survey. Using the site class and peak acceleration at rock depth from PSHA, the peak ground acceleration at the ground surface was estimated using probabilistic approach. The liquefaction analysis was done based on 450 borehole data obtained in the study area. The results of CPT match well with the results obtained from similar analysis with SPT data.

In-situ Stabilization of Tin Nanoparticles in Porous Carbon Matrix derived from Metal Organic Framework: High Capacity and High Rate Capability Anodes for Lithium-ion Batteries

It is a formidable challenge to arrange tin nanoparticles in a porous matrix for the achievement of high specific capacity and current rate capability anode for lithium-ion batteries. This article discusses a simple and novel synthesis of arranging tin nanoparticles with carbon in a porous configuration for application as anode in lithium-ion batteries. Direct carbonization of synthesized three-dimensional Sn-based MOF: K2Sn2(1,4-bdc)(3)](H2O) (1) (bdc = benzenedicarboxylate) resulted in stabilization of tin nanoparticles in a porous carbon matrix (abbreviated as Sn@C). Sn@C exhibited remarkably high electrochemical lithium stability (tested over 100 charge and discharge cycles) and high specific capacities over a wide range of operating currents (0.2-5 Ag-1). The novel synthesis strategy to obtain Sn@C from a single precursor as discussed herein provides an optimal combination of particle size and dispersion for buffering severe volume changes due to Li-Sn alloying reaction and provides fast pathways for lithium and electron transport.

Enhancing hydrogen storage capacity of pyridine-based metal organic framework

Using first principles calculations, we show that the storage capacity as well as desorption temperature of MOFs can be significantly enhanced by decorating pyridine (a common linker in MOFs) by metal atoms. The storage capacity of metal-pyridine complexes are found to be dependent on the type of decorating metal atom. Among the 3d transition metal atoms, Sc turns out to be the most efficient storing unto four H-2 molecules. Most importantly, Sc does not suffer dimerisation on the surface of pyridine, keeping the storage capacity of every metal atom intact. Based on these findings, we propose a metal-decorated pyridine-based MOFs, which has potential to meet the required H-2 storage capacity for vehicular usage. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Pressure-Induced Bond Rearrangement and Reversible Phase Transformation in a Metal-Organic Framework

Pressure-induced phase transformations (PIPTs) occur in a wide range of materials. In general, the bonding characteristics, before and after the PIPT, remain invariant in most materials, and the bond rearrangement is usually irreversible due to the strain induced under pressure. A reversible PIPT associated with a substantial bond rearrangement has been found in a metal-organic framework material, namely tmenH(2)]Er(HCOO)(4)](2) (tmenH(2)(2+) = N,N,N',N'-tetramethylethylenediammonium). The transition is first-order and is accompanied by a unit cell volume change of about 10%. High-pressure single-crystal X-ray diffraction studies reveal the complex bond rearrangement through the transition. The reversible nature of the transition is confirmed by means of independent nanoindentation measurements on single crystals.

Effect of boron addition and processing of Ti-6Al-4V on corrosion behaviour and biocompatibility

Ti-6Al-4V is widely used to prepare biomedical implant for orthopaedic and dental applications, but it is an expensive choice relative to other implant materials such as stainless steels and Co-Cr alloys, in large part due to the high manufacturing cost. Adding boron to refine the as cast microstructure of Ti-6Al-4V can eliminate the need for extensive hot working and thereby reduce processing costs. The effect of 0.1 wt-% boron addition and the choice of processing route (forging or extrusion) was studied in the context of potential biomedical applications. Corrosion tests in simulated body fluid indicated that the presence of boron increased the corrosion rate of Ti-6Al-4V and that the increase was higher for forged alloys than for extruded alloys. Boron addition and processing route were found to have a minimal effect on the viability of osteoblasts on the alloy surfaces. It is concluded that the addition of boron could offer advantages during the processing of Ti-6Al-4V for biomedical applications.

Boron doped defective graphene as a potential anode material for Li-ion batteries

Graphene with large surface area and robust structure has been proposed as a high storage capacity anode material for Li ion batteries. While the inertness of pristine graphene leads to better Li kinetics, poor adsorption leads to Li clustering, significantly affecting the performance of the battery. Here, we show the role of defects and doping in achieving enhanced adsorption without compromising on the high diffusivity of Li. Using first principles density functional theory (DFT) calculations, we carry out a comprehensive study of diffusion kinetics of Li over the plane of the defective structures and calculate the change in the number of Li atoms in the vicinity of defects, with respect to pristine graphene. Our results show that the Li-C interaction, storage capacity and the energy barriers depend sensitively on the type of defects. The un-doped and boron doped mono-vacancy, doped di-vacancy up to two boron, one nitrogen doped di-vacancy, and Stone-Wales defects show low energy barriers that are comparable to pristine graphene. Furthermore, boron doping at mono-vacancy enhances the adsorption of Li. In particular, the two boron doped mono-vacancy graphene shows both a low energy barrier of 0.31 eV and better adsorption, and hence can be considered as a potential candidate for anode material.

Defect induced plasticity and failure mechanism of boron nitride nanotubes under tension

The effects of Stone-Wales (SW) and vacancy defects on the failure behavior of boron nitride nanotubes (BNNTs) under tension are investigated using molecular dynamics simulations. The Tersoff-Brenner potential is used to model the atomic interaction and the temperature is maintained close to 300 K. The effect of a SW defect is studied by determining the failure strength and failure mechanism of nanotubes with different radii. In the case of a vacancy defect, the effect of an N-vacancy and a B-vacancy is studied separately. Nanotubes with different chiralities but similar diameter is considered first to evaluate the chirality dependence. The variation of failure strength with the radius is then studied by considering nanotubes of different diameters but same chirality. It is observed that the armchair BNNTs are extremely sensitive to defects, whereas the zigzag configurations are the least sensitive. In the case of pristine BNNTs, both armchair and zigzag nanotubes undergo brittle failure, whereas in the case of defective BNNTs, only the zigzag ones undergo brittle failure. An interesting defect induced plastic behavior is observed in defective armchair BNNTs. For this nanotube, the presence of a defect triggers mechanical relaxation by bond breaking along the closest zigzag helical path, with the defect as the nucleus. This mechanism results in a plastic failure. (C) 2014 AIP Publishing LLC.

Low-density three-dimensional foam using self-reinforced hybrid two-dimensional atomic layers

Low-density nanostructured foams are often limited in applications due to their low mechanical and thermal stabilities. Here we report an approach of building the structural units of three-dimensional (3D) foams using hybrid two-dimensional (2D) atomic layers made of stacked graphene oxide layers reinforced with conformal hexagonal boron nitride (h-BN) platelets. The ultra-low density (1/400 times density of graphite) 3D porous structures are scalably synthesized using solution processing method. A layered 3D foam structure forms due to presence of h-BN and significant improvements in the mechanical properties are observed for the hybrid foam structures, over a range of temperatures, compared with pristine graphene oxide or reduced graphene oxide foams. It is found that domains of h-BN layers on the graphene oxide framework help to reinforce the 2D structural units, providing the observed improvement in mechanical integrity of the 3D foam structure.

A framework for post-silicon realization of arbitrary instruction extensions on reconfigurable data-paths

In this paper we present a framework for realizing arbitrary instruction set extensions (IE) that are identified post-silicon. The proposed framework has two components viz., an IE synthesis methodology and the architecture of a reconfigurable data-path for realization of the such IEs. The IE synthesis methodology ensures maximal utilization of resources on the reconfigurable data-path. In this context we present the techniques used to realize IEs for applications that demand high throughput or those that must process data streams. The reconfigurable hardware called HyperCell comprises a reconfigurable execution fabric. The fabric is a collection of interconnected compute units. A typical use case of HyperCell is where it acts as a co-processor with a host and accelerates execution of IEs that are defined post-silicon. We demonstrate the effectiveness of our approach by evaluating the performance of some well-known integer kernels that are realized as IEs on HyperCell. Our methodology for realizing IEs through HyperCells permits overlapping of potentially all memory transactions with computations. We show significant improvement in performance for streaming applications over general purpose processor based solutions, by fully pipelining the data-path. (C) 2014 Elsevier B.V. All rights reserved.

Computational framework to integrate the effect of antigen recognition on disease epidemiology outcome: multi-scale approach

Human Leukocyte Antigen (HLA) plays an important role, in presenting foreign pathogens to our immune system, there by eliciting early immune responses. HLA genes are highly polymorphic, giving rise to diverse antigen presentation capability. An important factor contributing to enormous variations in individual responses to diseases is differences in their HLA profiles. The heterogeneity in allele specific disease responses decides the overall disease epidemiological outcome. Here we propose an agent based computational framework, capable of incorporating allele specific information, to analyze disease epidemiology. This framework assumes a SIR model to estimate average disease transmission and recovery rate. Using epitope prediction tool, it performs sequence based epitope detection for a given the pathogenic genome and derives an allele specific disease susceptibility index depending on the epitope detection efficiency. The allele specific disease transmission rate, that follows, is then fed to the agent based epidemiology model, to analyze the disease outcome. The methodology presented here has a potential use in understanding how a disease spreads and effective measures to control the disease.

Synthesis, properties and applications of graphene doped with boron, nitrogen and other elements

Chemical doping of graphene becomes necessary to create a band gap which is useful for various applications. Furthermore, chemical doping of elements like boron and nitrogen in graphene gives rise to useful properties. Since chemically doped graphene is both of academic and technical importance, we have prepared this article on the present status of various aspects of this important class of materials. In doing so, we have covered the recent literature on this subject citing all the major references. Some of the aspects that we have covered are the synthesis of chemically doped graphene followed by properties and applications. The applications discussed relate to gas adsorption, lithium batteries, supercapacitors, oxygen reduction reaction, field emission and photochemical water splitting. Characterization of chemically doped graphene also included. We believe that the article will be useful to all those interested in graphene and related materials and provides the present status of the subject. (C) 2014 Elsevier Ltd. All rights reserved.

PolyGLoT: A Polyhedral Loop Transformation Framework for a Graphical Dataflow Language

Polyhedral techniques for program transformation are now used in several proprietary and open source compilers. However, most of the research on polyhedral compilation has focused on imperative languages such as C, where the computation is specified in terms of statements with zero or more nested loops and other control structures around them. Graphical dataflow languages, where there is no notion of statements or a schedule specifying their relative execution order, have so far not been studied using a powerful transformation or optimization approach. The execution semantics and referential transparency of dataflow languages impose a different set of challenges. In this paper, we attempt to bridge this gap by presenting techniques that can be used to extract polyhedral representation from dataflow programs and to synthesize them from their equivalent polyhedral representation. We then describe PolyGLoT, a framework for automatic transformation of dataflow programs which we built using our techniques and other popular research tools such as Clan and Pluto. For the purpose of experimental evaluation, we used our tools to compile LabVIEW, one of the most widely used dataflow programming languages. Results show that dataflow programs transformed using our framework are able to outperform those compiled otherwise by up to a factor of seventeen, with a mean speed-up of 2.30x while running on an 8-core Intel system.

An iterative framework for sparse signal reconstruction algorithms

It has been shown that iterative re-weighted strategies will often improve the performance of many sparse reconstruction algorithms. However, these strategies are algorithm dependent and cannot be easily extended for an arbitrary sparse reconstruction algorithm. In this paper, we propose a general iterative framework and a novel algorithm which iteratively enhance the performance of any given arbitrary sparse reconstruction algorithm. We theoretically analyze the proposed method using restricted isometry property and derive sufficient conditions for convergence and performance improvement. We also evaluate the performance of the proposed method using numerical experiments with both synthetic and real-world data. (C) 2014 Elsevier B.V. All rights reserved.