EESM-Based Link Adaptation in Point-to-Point and Multi-Cell OFDM Systems: Modeling and Analysis

In contemporary wideband orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE) and WiMAX, different subcarriers over which a codeword is transmitted may experience different signal-to-noise-ratios (SNRs). Thus, adaptive modulation and coding (AMC) in these systems is driven by a vector of subcarrier SNRs experienced by the codeword, and is more involved. Exponential effective SNR mapping (EESM) simplifies the problem by mapping this vector into a single equivalent fiat-fading SNR. Analysis of AMC using EESM is challenging owing to its non-linear nature and its dependence on the modulation and coding scheme. We first propose a novel statistical model for the EESM, which is based on the Beta distribution. It is motivated by the central limit approximation for random variables with a finite support. It is simpler and as accurate as the more involved ad hoc models proposed earlier. Using it, we develop novel expressions for the throughput of a point-to-point OFDM link with multi-antenna diversity that uses EESM for AMC. We then analyze a general, multi-cell OFDM deployment with co-channel interference for various frequency-domain schedulers. Extensive results based on LTE and WiMAX are presented to verify the model and analysis, and gain new insights.

Evolution of Nanocrystalline Ba2NaNb5O15 in 2BaO-0.5Na(2)O-2.5Nb(2)O(5)-4.5B(2)O(3) Glass System and Its Refractive Index and Band Gap Tunability

Monophasic Ba2NaNb5O15 was crystallized at nanometer scale (12-36 nm) in 2BaO-0.5Na(2)O-2.5Nb(2)O(5)- 4.5B(2)O(3) glass system. To begin with, optically transparent glasses, in this system, were fabricated via the conventional melt. quenching technique. The amorphous and glassy characteristics of the as-quenched samples were respectively confirmed by X-ray powder diffraction and differential thermal analyses. Nearly homogeneous distribution of Ba2NaNb5O15 (BNN) nanocrystals associated with tungsten bronze structure akin to their bulk parent structure was accomplished by subjecting the as-fabricated glasses to appropriate heat-treatment temperatures. Indeed transmission electron microscopy (TEM) carried out on these samples corroborated the presence of Ba2NaNb5O15 nanocrystals dispersed in a continuous glass matrix. The as-quenched glasses were similar to 75% transparent in the visible range of the electromagnetic spectrum. The optical band gap and refractive index were found to have crystallite size (at nanoscale) dependence. The optical band gap increased with the decrease in crystallite size. The refractive indices of the glass nanocrystal composites as determined by Brewster angle method were rationalized using different empirical models. The refractive index dispersion with wavelength of light was analyzed on the basis of the Sellmeier relations. At room temperature under UV excitation (355 nm) these glass nanocrystal composites displayed violet-blue emission which was ascribed to the defects states.

Magnetic field induced delocalization in multi-wall carbon nanotube-polystyrene composite at high fields

In well dispersed multi-wall carbon nanotube-polystyrene composite of 15 wt%, with room temperature conductivity of similar to 5 S/cm and resistivity ratio R-2K/R-200K] of similar to 1.4, the temperature dependence of conductivity follows a power-law behavior. The conductivity increases with magnetic field for a wide range of temperature (2-200 K), and power-law fits to conductivity data show that localization length (xi) increases with magnetic field, resulting in a large negative magnetoresistance (MR). At 50T, the negative MR at 8 K is similar to 13% and it shows a maximum at 90K (similar to 25%). This unusually large negative MR indicates that the field is delocalizing the charge carriers even at higher temperatures, apart from the smaller weak localization contribution at T < 20 K. This field-induced delocalization mechanism of MR can provide insight into the intra and inter tube transport. (C) 2013 Elsevier Ltd. All rights reserved.

Glacial mass balance changes in the Karakoram and Himalaya based on CMIP5 multi-model climate projections

The impact of future climate change on the glaciers in the Karakoram and Himalaya (KH) is investigated using CMIP5 multi-model temperature and precipitation projections, and a relationship between glacial accumulation-area ratio and mass balance developed for the region based on the last 30 to 40 years of observational data. We estimate that the current glacial mass balance (year 2000) for the entire KH region is -6.6 +/- 1 Gta(-1), which decreases about sixfold to -35 +/- 2 Gta(-1) by the 2080s under the high emission scenario of RCP8.5. However, under the low emission scenario of RCP2.6 the glacial mass loss only doubles to -12 +/- 2 Gta(-1) by the 2080s. We also find that 10.6 and 27 % of the glaciers could face `eventual disappearance' by the end of the century under RCP2.6 and RCP8.5 respectively, underscoring the threat to water resources under high emission scenarios.

Doping a Correlated Band Insulator: A New Route to Half-Metallic Behavior

We demonstrate in a simple model the surprising result that turning on an on-site Coulomb interaction U in a doped band insulator leads to the formation of a half-metallic state. In the undoped system, we show that increasing U leads to a first order transition at a finite value U-AF between a paramagnetic band insulator and an antiferomagnetic Mott insulator. Upon doping, the system exhibits half-metallic ferrimagnetism over a wide range of doping and interaction strengths on either side of U-AF. Our results, based on dynamical mean field theory, suggest a new route to half metallicity, and will hopefully motivate searches for new materials for spintronics.

Electronic band structure and Fermi surfaces of the quasi-two-dimensional monophosphate tungsten bronze, P4W12O44

The electronic structure of quasi-two-dimensional monophosphate tungsten bronze, P4W12O44, has been investigated by high-resolution angle-resolved photoemission spectroscopy and density functional theoretical calculations. Experimental electron-like bands around Gamma point and Fermi surfaces have similar shapes as predicted by calculations. Fermi surface mapping at different temperatures shows a depletion of density of states at low temperature in certain flat portions of the Fermi surfaces. These flat portions of the Fermi surfaces satisfy the partial nesting condition with incommensurate nesting vectors q(1) and q(2), which leads to the formation of charge density waves in this phosphate tungsten bronzes. The setting up of charge density wave in these bronzes can well explain the anomaly observed in its transport properties. Copyright (C) EPLA, 2014

Efficiency limitations in a low band-gap diketopyrrolopyrrole-based polymer solar cell

We report the performance and photophysics of a low band-gap diketopyrrolopyrrole-based copolymer used in bulk heterojunction devices in combination with PC71BM. We show that the short lifetime of photogenerated excitons in the polymer constitutes an obstacle towards device efficiency by limiting the diffusion range of the exciton to the donor-acceptor heterojunction. We employ ultrafast transient-probe and fluorescence spectroscopy techniques to examine the excited state loss channels inside the devices. We use the high boiling point solvent additive 1,8-diiodooctane (DIO) to study the photoexcited state losses in different blend morphologies. The solvent additive acts as a compatibiliser between the donor and the acceptor material and leads to smaller domain sizes, higher charge formation yields and increased device efficiency.

Cooperative Communication of UAV to Perform Multi-task Using Nature Inspired Techniques

In this paper, the approach for assigning cooperative communication of Uninhabited Aerial Vehicles (UAV) to perform multiple tasks on multiple targets is posed as a combinatorial optimization problem. The multiple task such as classification, attack and verification of target using UAV is employed using nature inspired techniques such as Artificial Immune System (AIS), Particle Swarm Optimization (PSO) and Virtual Bee Algorithm (VBA). The nature inspired techniques have an advantage over classical combinatorial optimization methods like prohibitive computational complexity to solve this NP-hard problem. Using the algorithms we find the best sequence in which to attack and destroy the targets while minimizing the total distance traveled or the maximum distance traveled by an UAV. The performance analysis of the UAV to classify, attack and verify the target is evaluated using AIS, PSO and VBA.

The relationship between classification of multi-domain proteins using an alignment-free approach and their functions: a case study with immunoglobulins

Establishing functional relationships between multi-domain protein sequences is a non-trivial task. Traditionally, delineating functional assignment and relationships of proteins requires domain assignments as a prerequisite. This process is sensitive to alignment quality and domain definitions. In multi-domain proteins due to multiple reasons, the quality of alignments is poor. We report the correspondence between the classification of proteins represented as full-length gene products and their functions. Our approach differs fundamentally from traditional methods in not performing the classification at the level of domains. Our method is based on an alignment free local matching scores (LMS) computation at the amino-acid sequence level followed by hierarchical clustering. As there are no gold standards for full-length protein sequence classification, we resorted to Gene Ontology and domain-architecture based similarity measures to assess our classification. The final clusters obtained using LMS show high functional and domain architectural similarities. Comparison of the current method with alignment based approaches at both domain and full-length protein showed superiority of the LMS scores. Using this method we have recreated objective relationships among different protein kinase sub-families and also classified immunoglobulin containing proteins where sub-family definitions do not exist currently. This method can be applied to any set of protein sequences and hence will be instrumental in analysis of large numbers of full-length protein sequences.

Fermionic superfluid from a bilayer band insulator in an optical lattice

We propose a model to realize a fermionic superfluid state in an optical lattice circumventing the cooling problem. Our proposal exploits the idea of tuning the interaction in a characteristically low-entropy state, a band insulator in an optical bilayer system, to obtain a superfluid. By performing a detailed analysis of the model including fluctuations and augmented by a variational quantum Monte Carlo calculation of the ground state, we show that the superfluid state obtained has a high transition temperature of the order of the hopping energy. Our system is designed to suppress other competing orders such as a charge density wave. We suggest a laboratory realization of this model via an orthogonally shaken optical lattice bilayer.

Automatic Data Allocation and Buffer Management for Multi-GPU Machines

Multi-GPU machines are being increasingly used in high-performance computing. Each GPU in such a machine has its own memory and does not share the address space either with the host CPU or other GPUs. Hence, applications utilizing multiple GPUs have to manually allocate and manage data on each GPU. Existing works that propose to automate data allocations for GPUs have limitations and inefficiencies in terms of allocation sizes, exploiting reuse, transfer costs, and scalability. We propose a scalable and fully automatic data allocation and buffer management scheme for affine loop nests on multi-GPU machines. We call it the Bounding-Box-based Memory Manager (BBMM). BBMM can perform at runtime, during standard set operations like union, intersection, and difference, finding subset and superset relations on hyperrectangular regions of array data (bounding boxes). It uses these operations along with some compiler assistance to identify, allocate, and manage data required by applications in terms of disjoint bounding boxes. This allows it to (1) allocate exactly or nearly as much data as is required by computations running on each GPU, (2) efficiently track buffer allocations and hence maximize data reuse across tiles and minimize data transfer overhead, and (3) and as a result, maximize utilization of the combined memory on multi-GPU machines. BBMM can work with any choice of parallelizing transformations, computation placement, and scheduling schemes, whether static or dynamic. Experiments run on a four-GPU machine with various scientific programs showed that BBMM reduces data allocations on each GPU by up to 75% compared to current allocation schemes, yields performance of at least 88% of manually written code, and allows excellent weak scaling.

Discrete time second order sliding mode observer for uncertain linear multi-output system

This paper presents a second order sliding mode observer (SOSMO) design for discrete time uncertain linear multi-output system. The design procedure is effective for both matched and unmatched bounded uncertainties and/or disturbances. A second order sliding function and corresponding sliding manifold for discrete time system are defined similar to the lines of continuous time counterpart. A boundary layer concept is employed to avoid switching across the defined sliding manifold and the sliding trajectory is confined to a boundary layer once it converges to it. The condition for existence of convergent quasi-sliding mode (QSM) is derived. The observer estimation errors satisfying given stability conditions converge to an ultimate finite bound (within the specified boundary layer) with thickness O(T-2) where T is the sampling period. A relation between sliding mode gain and boundary layer is established for the existence of second order discrete sliding motion. The design strategy is very simple to apply and is demonstrated for three examples with different class of disturbances (matched and unmatched) to show the effectiveness of the design. Simulation results to show the robustness with respect to the measurement noise are given for SOSMO and the performance is compared with pseudo-linear Kalman filter (PLKF). (C) 2013 Published by Elsevier Ltd. on behalf of The Franklin Institute

Selection and thermodynamic analysis of a turbocharger for a producer gas-fuelled multi-cylinder engine

The current work addresses the use of producer gas, a bio-derived gaseous alternative fuel, in engines designed for natural gas, derived from diesel engine frames. Impact of the use of producer gas on the general engine performance with specific focus on turbo-charging is addressed. The operation of a particular engine frame with diesel, natural gas and producer gas indicates that the peak load achieved is highest with diesel fuel (in compression ignition mode) followed by natural gas and producer gas (both in spark ignite mode). Detailed analysis of the engine power de-rating on fuelling with natural gas and producer gas indicates that the change in compression ratio (migration from compression to spark ignited mode), difference in mixture calorific value and turbocharger mismatch are the primary contributing factors. The largest de-rating occurs due to turbocharger mismatch. Turbocharger selection and optimization is identified as the strategy to recover the non-thermodynamic power loss, identified as the recovery potential (the loss due to mixture calorific value and turbocharger mismatch) on operating the engine with a fuel different from the base fuel. A turbocharged after-cooled six cylinder, 5.9 l, 90 kWe (diesel rating) engine (12.2 bar BMEP) is available commercially as a naturally aspirated natural gas engine delivering a peak load of 44.0 kWe (6.0 bar BMEP). The engine delivers a load of 27.3 kWe with producer gas under naturally aspirated mode. On charge boosting the engine with a turbocharger similar in configuration to the diesel engine turbocharger, the peak load delivered with producer gas is 36 kWe (4.8 bar BMEP) indicating a de-rating of about 60% over the baseline diesel mode. Estimation of knock limited peak load for producer gas-fuelled operation on the engine frame using a Wiebe function-based zero-dimensional code indicates a knock limited peak load of 76 kWe, indicating the potential to recover about 40 kWe. As a part of the recovery strategy, optimizing the ignition timing for maximum brake torque based on both spark sweep tests and established combustion descriptors and engine-turbocharger matching for producer gas-fuelled operation resulted in a knock limited peak load of 72.8 kWe (9.9 bar BMEP) at a compressor pressure ratio of 2.30. The de-rating of about 17.0 kWe compared to diesel rating is attributed to the reduction in compression ratio. With load recovery, the specific biomass consumption reduces from 1.2 kg/kWh to 1.0 kg/kWh, an improvement of over 16% while the engine thermal efficiency increases from 28% to 32%. The thermodynamic analysis of the compressor and the turbine indicates an isentropic efficiency of 74.5% and 73%, respectively.

Development of design charts for stress intensity factors at tips of multi-site cracks in unstiffened curved panels

Multi-site damage need to be addressed and evaluated in order to assess the integrity of aging aircraft structures. One of the problems recognized in the recent times is the effect of interaction between two or more cracks in the close neighborhood in such structures. The present paper deals with such a problem and presents numerical estimates of stress intensity factors at a crack tip in an un-stiffened curved panel with a secondary crack in the vicinity of a primary crack. The results are presented in the form of design charts. These results should be useful in evaluation in the damage tolerance evaluation of aircraft structures with multi-site damage. (C) 2014 Elsevier Ltd. All rights reserved.

Multi Label Classification of Discrete Data

The paper describes an algorithm for multi-label classification. Since a pattern can belong to more than one class, the task of classifying a test pattern is a challenging one. We propose a new algorithm to carry out multi-label classification which works for discrete data. We have implemented the algorithm and presented the results for different multi-label data sets. The results have been compared with the algorithm multi-label KNN or ML-KNN and found to give good results.