GLOBAL REGISTRATION OF MULTIPLE POINT CLOUDS USING SEMIDEFINITE PROGRAMMING

Consider N points in R-d and M local coordinate systems that are related through unknown rigid transforms. For each point, we are given (possibly noisy) measurements of its local coordinates in some of the coordinate systems. Alternatively, for each coordinate system, we observe the coordinates of a subset of the points. The problem of estimating the global coordinates of the N points (up to a rigid transform) from such measurements comes up in distributed approaches to molecular conformation and sensor network localization, and also in computer vision and graphics. The least-squares formulation of this problem, although nonconvex, has a well-known closed-form solution when M = 2 (based on the singular value decomposition (SVD)). However, no closed-form solution is known for M >= 3. In this paper, we demonstrate how the least-squares formulation can be relaxed into a convex program, namely, a semidefinite program (SDP). By setting up connections between the uniqueness of this SDP and results from rigidity theory, we prove conditions for exact and stable recovery for the SDP relaxation. In particular, we prove that the SDP relaxation can guarantee recovery under more adversarial conditions compared to earlier proposed spectral relaxations, and we derive error bounds for the registration error incurred by the SDP relaxation. We also present results of numerical experiments on simulated data to confirm the theoretical findings. We empirically demonstrate that (a) unlike the spectral relaxation, the relaxation gap is mostly zero for the SDP (i.e., we are able to solve the original nonconvex least-squares problem) up to a certain noise threshold, and (b) the SDP performs significantly better than spectral and manifold-optimization methods, particularly at large noise levels.

Tailoring local density of optical states to control emission intensity and anisotropy of quantum dots in hybrid photonic-plasmonic templates

We report results of controlled tuning of the local density of states (LDOS) in versatile, flexible, and hierarchical self assembled plasmonic templates. Using 5 nm diameter gold (Au) spherical nanoantenna within a polymer template randomly dispersed with quantum dots, we show how the photoluminescence intensity and lifetime anisotropy of these dots can be significantly enhanced through LDOS tuning. Finite difference time domain simulations corroborate the experimental observations and extend the regime of enhancement to a wider range of geometric and spectral parameters bringing out the versatility of these functional plasmonic templates. It is also demonstrated how the templates act as plasmonic resonators for effectively engineer giant enhancement of the scattering efficiency of these nano antenna embedded in the templates. Our work provides an alternative method to achieve spontaneous emission intensity and anisotropy enhancement with true nanoscale plasmon resonators. (C) 2015 AIP Publishing LLC.

Stability of Filaments in Star-Forming Clouds and the Formation of Prestellar Cores in Them

The exact process(es) that generate(s) dense filaments which then form prestellar cores within them is unclear. Here we study the formation of a dense filament using a relatively simple set-up of a pressure-confined, uniform-density cylinder. We examine if its propensity to form a dense filament and further, to the formation of prestellar cores along this filament, bears on the gravitational state of the initial volume of gas. We report a radial collapse leading to the formation of a dense filamentary cloud is likely when the initial volume of gas is at least critically stable (characterised by the approximate equality between the mass line-density for this volume and its maximum value). Though self-gravitating, this volume of gas, however, is not seen to be in free-fall. This post-collapse filament then fragments along its length due to the growth of a Jeans-like instability to form prestellar cores. We suggest dense filaments in typical star-forming clouds classified as gravitationally super-critical under the assumption of: (i) isothermality when in fact, they are not, and (ii) extended radial profiles as against pressure-truncated, that significantly over-estimates their mass line-density, are unlikely to experience gravitational free-fall. The radial density and temperature profile derived for this post-collapse filament is consistent with that deduced for typical filamentary clouds mapped in recent surveys of nearby star-forming regions.

Efficient charge transfer and field-induced tunneling transport in hybrid composite device of organic semiconductor and cadmium telluride quantum dots

Temperature and photo-dependent current-voltage characteristics are investigated in thin film devices of a hybrid-composite comprising of organic semiconductor poly(3,4-ethylenedioxythiophene): polystyrenesulfonate (PEDOT: PSS) and cadmium telluride quantum dots (CdTe QDs). A detailed study of the charge injection mechanism in ITO/PEDOT: PSS-CdTe QDs/Al device exhibits a transition from direct tunneling to Fowler-Nordheim tunneling with increasing electric field due to formation of high barrier at the QD interface. In addition, the hybrid-composite exhibits a huge photoluminescence quenching compared to aboriginal CdTe QDs and high increment in photoconductivity (similar to 400%), which is attributed to the charge transfer phenomena. The effective barrier height (Phi(B) approximate to 0.68 eV) is estimated from the transition voltage and the possible origin of its variation with temperature and photo-illumination is discussed. (C) 2015 AIP Publishing LLC.

Morphology control synthesis of Au-Cu2S metal-semiconductor hybrid nanostructures by modulating reaction constituents

A facile methodology for synthesizing Au-Cu2S hybrid nanoparticles is presented. Au-Cu2S nanoparticles have application in visible light driven photocatalytic degradation of dyes. Detailed microstructural and compositional characterization illustrated that the hybrid nanoparticles are composed of cube shaped Au-Cu solid solution and hemispherical shaped Cu2S phases. Investigation of nanoparticles extracted at different stages of the synthesis process revealed that the mechanism of formation of hybrid nanoparticles involved initial formation of isolated cube shaped pure Au nanoparticles and Cu-thiolate complex. In the subsequent stages, the Au nanoparticles get adsorbed onto the Cu-thiolate complex which is followed by the decomposition of the Cu-thiolate complex to form Au-Cu2S hybrid nanoparticles. This study also illustrates that an optimum concentration of dodecanethiol is required both for achieving size and morphological uniformity of the participating phases and for their attachment to form a hybrid nanoparticle.

Studies on 12 V substrate-integrated lead-carbon hybrid ultracapacitors

A cost-effective 12 V substrate-integrated lead-carbon hybrid ultracapacitor is developed and performance tested. These hybrid ultracapacitors employ flexible-graphite sheets as negative plate current-collectors that are coated amperometrically with a thin layer of conducting polymer, namely poly-aniline to provide good adhesivity to activated-carbon layer. The positive plate of the hybrid ultracapacitors comprise conventional lead-sheet that is converted electrochemically into a substrate-integrated lead-dioxide electrode. 12 V substrate-integrated lead-carbon hybrid ultracapacitors both in absorbent-glass-mat and polymeric silica-gel electrolyte configurations are fabricated and characterized. It is possible to realize 12 V configurations with capacitance values of similar to 200 F and similar to 300 F, energy densities of similar to 1.9 Wh kg(-1) and similar to 2.5 Wh kg(-1) and power densities of similar to 2 kW kg(-1) and similar to 0.8 kW kg(-1), respectively, having faradaic-efficiency values of similar to 90 % with cycle-life in excess of 100,000 cycles. The effective cost of the mentioned hybrid ultracapacitors is estimated to be about similar to 4 US$/Wh as compared to similar to 20 US$/Wh for commercially available ultracapacitors.

ANRC Hybrid Test Bed Implementation and an End-to-End Performance Characterization of Dynamic Spectrum Access

An abundance of spectrum access and sensing algorithms are available in the dynamic spectrum access (DSA) and cognitive radio (CR) literature. Often, however, the functionality and performance of such algorithms are validated against theoretical calculations using only simulations. Both the theoretical calculations and simulations come with their attendant sets of assumptions. For instance, designers of dynamic spectrum access algorithms often take spectrum sensing and rendezvous mechanisms between transmitter-receiver pairs for granted. Test bed designers, on the other hand, either customize so much of their design that it becomes difficult to replicate using commercial off the shelf (COTS) components or restrict themselves to simulation, emulation /hardware-in-Ioop (HIL), or pure hardware but not all three. Implementation studies on test beds sophisticated enough to combine the three aforementioned aspects, but at the same time can also be put together using COTS hardware and software packages are rare. In this paper we describe i) the implementation of a hybrid test bed using a previously proposed hardware agnostic system architecture ii) the implementation of DSA on this test bed, and iii) the realistic hardware and software-constrained performance of DSA. Snapshot energy detector (ED) and Cumulative Summation (CUSUM), a sequential change detection algorithm, are available for spectrum sensing and a two-way handshake mechanism in a dedicated control channel facilitates transmitter-receiver rendezvous.

Solid-state optical absorption from optimally tuned time-dependent range-separated hybrid density functional theory

We present a framework for obtaining reliable solid-state charge and optical excitations and spectra from optimally tuned range-separated hybrid density functional theory. The approach, which is fully couched within the formal framework of generalized Kohn-Sham theory, allows for the accurate prediction of exciton binding energies. We demonstrate our approach through first principles calculations of one- and two-particle excitations in pentacene, a molecular semiconducting crystal, where our work is in excellent agreement with experiments and prior computations. We further show that with one adjustable parameter, set to produce the known band gap, this method accurately predicts band structures and optical spectra of silicon and lithium fluoride, prototypical covalent and ionic solids. Our findings indicate that for a broad range of extended bulk systems, this method may provide a computationally inexpensive alternative to many-body perturbation theory, opening the door to studies of materials of increasing size and complexity.

PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds

Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous dataflows due to the variations in the input data rates and the need for sustained throughput. The elastic resource provisioning of cloud infrastructure is valuable to meet the changing resource needs of such continuous applications. However, multi-tenant cloud resources introduce yet another dimension of performance variability that impacts the application's throughput. In this paper we propose PLAStiCC, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based lookahead approach. It not only addresses variations in the input data rates but also the underlying cloud infrastructure. In addition, we also propose several simpler static scheduling heuristics that operate in the absence of accurate performance prediction model. These static and adaptive heuristics are evaluated through extensive simulations using performance traces obtained from Amazon AWS IaaS public cloud. Our results show an improvement of up to 20% in the overall profit as compared to the reactive adaptation algorithm.

TiO2-RGO hybrid nanomaterials for enhanced water splitting reaction

The production of H-2 via photocatalytic water splitting reaction has attracted a great attention as a clean and renewable energy for next generation. Despite tremendous efforts, the present challenge for materials scientist is to develop highly active photo catalysts for splitting of water at low cost. This article reports the synthesis of TiO2-reduced graphene oxide hybrid nanomaterials through ionothermal method using functionalized ionic liquid for the enhanced hydrogen generation via water splitting reaction. The structural and morphological properties of the samples were investigated by XFtD, Raman spectroscopy, TG-DTA, UV-vis spectroscopy and TEM. A substantial increase of H-2 evolution was observed for TiO2-reduced graphene oxide hybrid nanomaterials. This is due to the high migration efficiency of photo-induced electrons and the inhibition of charge carrier recombination due to the electronic interaction between TiO2 and reduced graphene oxide. i.e, reduced graphene oxide acts as an electron-acceptor which effectively hinders the electron hole pair recombination of TiO2. Copyright (C) 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

A hybrid method for stochastic response analysis of a vibrating structure

Response analysis of a linear structure with uncertainties in both structural parameters and external excitation is considered here. When such an analysis is carried out using the spectral stochastic finite element method (SSFEM), often the computational cost tends to be prohibitive due to the rapid growth of the number of spectral bases with the number of random variables and the order of expansion. For instance, if the excitation contains a random frequency, or if it is a general random process, then a good approximation of these excitations using polynomial chaos expansion (PCE) involves a large number of terms, which leads to very high cost. To address this issue of high computational cost, a hybrid method is proposed in this work. In this method, first the random eigenvalue problem is solved using the weak formulation of SSFEM, which involves solving a system of deterministic nonlinear algebraic equations to estimate the PCE coefficients of the random eigenvalues and eigenvectors. Then the response is estimated using a Monte Carlo (MC) simulation, where the modal bases are sampled from the PCE of the random eigenvectors estimated in the previous step, followed by a numerical time integration. It is observed through numerical studies that this proposed method successfully reduces the computational burden compared with either a pure SSFEM of a pure MC simulation and more accurate than a perturbation method. The computational gain improves as the problem size in terms of degrees of freedom grows. It also improves as the timespan of interest reduces.

Synthesis of Optically Active 2-Amino-1,3,4-oxadiazoles and their Hybrid Peptides

Synthesis of 2-amino-1,3,4-oxadiazole derivatives of N-Cbz(benzyloxycarbonyl)/Boc-protected amino/peptide acids under sonication is described. The conditions involved in the present protocol are simple, mild, and racemization free. The utility of 2-amino group in the substituted oxadiazoles for the incorporation of peptide and ureido bonds to obtain hybrid peptidomimetics is also delineated. The 2-amino-1,3,4-oxadiazole 3b was obtained as a single crystal, and its molecular structure has been confirmed through X-ray crystallographic study.

Corrosion behaviour of creep-resistant AE42 magnesium alloy-based hybrid composites developed for powertrain applications

The corrosion behaviour of AE42 magnesium alloy and its composites reinforced with Saffil short fibres and SiC particles in various combinations was investigated. The corrosion rate of the unreinforced alloy was the lowest. The composite reinforced with Saffil short fibre alone exhibited slightly lower corrosion rate than the hybrid composites containing both Saffil short fibres and SiC particles. However, there was no specific trend observed in the corrosion rate of the hybrid composites with respect to the SiC particle content. The degradation of corrosion resistance of the composites was mainly attributed to the irregular and loose surface films.

Molecular structure of the discotic liquid crystalline phase of hexa-peri-hexabenzocoronene/oligothiophene hybrid and their charge transport properties

Using atomistic molecular dynamics simulation, we study the discotic columnar liquid crystalline (LC) phases formed by a new organic compound having hexa-peri-Hexabenzocoronene (HBC) core with six pendant oligothiophene units recently synthesized by Nan Hu et al. Adv. Mater. 26, 2066 (2014)]. This HBC core based LC phase was shown to have electric field responsive behavior and has important applications in organic electronics. Our simulation results confirm the hexagonal arrangement of columnar LC phase with a lattice spacing consistent with that obtained from small angle X-ray diffraction data. We have also calculated various positional and orientational correlation functions to characterize the ordering of the molecules in the columnar arrangement. The molecules in a column are arranged with an average twist of 25 degrees having an average inter-molecular separation of similar to 5 angstrom. Interestingly, we find an overall tilt angle of 43 degrees between the columnar axis and HBC core. We also simulate the charge transport through this columnar phase and report the numerical value of charge carrier mobility for this liquid crystal phase. The charge carrier mobility is strongly influenced by the twist angle and average spacing of the molecules in the column. (C) 2015 AIP Publishing LLC.

Hybrid Working Set Algorithm for SVM Learning With a Kernel Coprocessor on FPGA

Support vector machines (SVM) are a popular class of supervised models in machine learning. The associated compute intensive learning algorithm limits their use in real-time applications. This paper presents a fully scalable architecture of a coprocessor, which can compute multiple rows of the kernel matrix in parallel. Further, we propose an extended variant of the popular decomposition technique, sequential minimal optimization, which we call hybrid working set (HWS) algorithm, to effectively utilize the benefits of cached kernel columns and the parallel computational power of the coprocessor. The coprocessor is implemented on Xilinx Virtex 7 field-programmable gate array-based VC707 board and achieves a speedup of upto 25x for kernel computation over single threaded computation on Intel Core i5. An application speedup of upto 15x over software implementation of LIBSVM and speedup of upto 23x over SVMLight is achieved using the HWS algorithm in unison with the coprocessor. The reduction in the number of iterations and sensitivity of the optimization time to variation in cache size using the HWS algorithm are also shown.