Probing ultrafast carrier dynamics, nonlinear absorption and refraction in core-shell silicon nanowires

We investigate the relaxation dynamics of photogenerated carriers in silicon nanowires consisting of a crystalline core and a surrounding amorphous shell, using femtosecond time-resolved differential reflectivity and transmission spectroscopy at 3.15 eV and 1.57 eV photon energies. The complex behaviour of the differential transmission and reflectivity transients is the mixed contributions from the crystalline core and the amorphous silicon on the nanowire surface and the substrate where competing effects of state-filling and photoinduced absorption govern the carrier dynamics. Faster relaxation rates are observed on increasing the photogenerated carrier density. Independent experimental results on crystalline silicon-on-sapphire (SOS) help us in separating the contributions from the carrier dynamics in crystalline core and the amorphous regions in the nanowire samples. Further, single-beam z-scan nonlinear transmission experiments at 1.57 eV in both open- and close-aperture configurations yield two-photon absorption coefficient beta (similar to 3 cm/GW) and nonlinear refraction coefficient gamma (-2.5 x 10 (-aEuro parts per thousand 4) cm(2)/GW).

Optimization of thermoacoustic primemover using response surface methodology

Thermoacoustic engines are energy conversion devices that convert thermal energy from a high-temperature heat source into useful work in the form of acoustic power while diverting waste heat into a cold sink; it can be used as a drive for cryocoolers and refrigerators. Though the devices are simple to fabricate, it is very challenging to design an optimized thermoacoustic primemover with better performance. The study presented here aims to optimize the thermoacoustic primemover using response surface methodology. The influence of stack position and its length, resonator length, plate thickness, and plate spacing on pressure amplitude and frequency in a thermoacoustic primemover is investigated in this study. For the desired frequency of 207 Hz, the optimized value of the above parameters suggested by the response surface methodology has been conducted experimentally, and simulations are also performed using DeltaEC. The experimental and simulation results showed similar output performance.

Large nonlinear refraction and two photon absorption in ferroelectric Bi2VO5.5 thin films

Ferroelectric c-oriented Bi2VO5.5 (BVO) thin films (thickness approximate to 300 nm) were fabricated by pulsed laser deposition on corning glass substrates. Nonlinear refractive index (n(2)) and two photon absorption coefficient (beta) were measured by Z-scan technique at 532 nm wavelength delivering pulses with 10 ns duration. Relatively large values of n(2) = 2.05 +/- 0.2 x 10(-10) cm(2)/W and beta = 9.36 +/- 0.3 cm/MW were obtained for BVO thin films. Origin of the large optical nonlinearities in BVO thin films was discussed based on bond-orbital theory of transition metal oxides. (c) 2012 Elsevier B.V. All rights reserved.

MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem. (c) 2012 Elsevier Ltd. All rights reserved.

Urea-based constructs readily amplify and attenuate nonlinear optical activity in response to H-bonding and anion recognition

Urea-based molecular constructs are shown for the first time to be nonlinear optically (NLO) active in solution. We demonstrate self-assembly triggered large amplification and specific anion recognition driven attenuation of the NLO activity. This orthogonal modulation along with an excellent nonlinearity-transparency trade-off makes them attractive NLO probes for studies related to weak self-assembly and anion transportation by second harmonic microscopy.

q-Gaussian based Smoothed Functional Algorithms for Stochastic Optimization

The q-Gaussian distribution results from maximizing certain generalizations of Shannon entropy under some constraints. The importance of q-Gaussian distributions stems from the fact that they exhibit power-law behavior, and also generalize Gaussian distributions. In this paper, we propose a Smoothed Functional (SF) scheme for gradient estimation using q-Gaussian distribution, and also propose an algorithm for optimization based on the above scheme. Convergence results of the algorithm are presented. Performance of the proposed algorithm is shown by simulation results on a queuing model.

Lithium D-Isoascorbate Monohydrate, a New Nonlinear Optical Material

Single crystals of lithium D-isoascorbate monohydrate (LDAM), (C6H7O6Li center dot H2O), are grown by a solution growth method. The crystal structure of LDAM is solved using single crystal X-ray diffraction. The space group is orthorhombic P2(1)2(1)2(1) with four formula units per unit cell and lattice parameters a = 7.7836(3) angstrom, b = 8.7456(3) angstrom, and c = 11.0368(4) angstrom. Solubility of the material in water is determined thermogravimetrically and found to have a positive temperature coefficient of solubility. Large optical quality single crystals are subsequently grown from aqueous solution by a slow cooling method. The crystal has a bulky prismatic habit and among the prominent faces the c face appears as the only principal morphological face. The crystal exhibits a (010) cleavage. Dielectric spectroscopy reveals a nearly Debye type Cole-Cole behavior with anisotropy in relaxation. Optical transmission range is found to be from 300 to 1400 nm. The principal refractive indices of this biaxial crystal, measured using Brewster's angle method, at wavelengths 405, 543, and 632.8 nm, show high dispersion. The crystal is negative biaxial with 2V(z) = 107.8 degrees (405 nm) and belongs to the Hobden class 3. Theoretically generated type 1 and type 2 second order phase matching curves match very well with the experimental results. The second-order nonlinear coefficient d(14) was determined to be 7 x 10(-13) m/V. For the optimum phase matching direction (type 2), the second-order effective nonlinear coefficient and the walk off angle are determined to be 0.84 times d(14) and 3.5 degrees respectively. The crystal possesses high multiple surface damage thresholds of 18 GW/cm(2) and 8 GW/cm(2) at laser wavelengths 1064 and 532 nm, respectively.

A UNIFIED APPROACH FOR OPTIMIZATION OF SNAKUSCULES AND OVUSCULES

Automated image segmentation techniques are useful tools in biological image analysis and are an essential step in tracking applications. Typically, snakes or active contours are used for segmentation and they evolve under the influence of certain internal and external forces. Recently, a new class of shape-specific active contours have been introduced, which are known as Snakuscules and Ovuscules. These contours are based on a pair of concentric circles and ellipses as the shape templates, and the optimization is carried out by maximizing a contrast function between the outer and inner templates. In this paper, we present a unified approach to the formulation and optimization of Snakuscules and Ovuscules by considering a specific form of affine transformations acting on a pair of concentric circles. We show how the parameters of the affine transformation may be optimized for, to generate either Snakuscules or Ovuscules. Our approach allows for a unified formulation and relies only on generic regularization terms and not shape-specific regularization functions. We show how the calculations of the partial derivatives may be made efficient thanks to the Green's theorem. Results on synthesized as well as real data are presented.

SURE-FAST BILATERAL FILTERS

Edge-preserving smoothing is widely used in image processing and bilateral filtering is one way to achieve it. Bilateral filter is a nonlinear combination of domain and range filters. Implementing the classical bilateral filter is computationally intensive, owing to the nonlinearity of the range filter. In the standard form, the domain and range filters are Gaussian functions and the performance depends on the choice of the filter parameters. Recently, a constant time implementation of the bilateral filter has been proposed based on raisedcosine approximation to the Gaussian to facilitate fast implementation of the bilateral filter. We address the problem of determining the optimal parameters for raised-cosine-based constant time implementation of the bilateral filter. To determine the optimal parameters, we propose the use of Stein's unbiased risk estimator (SURE). The fast bilateral filter accelerates the search for optimal parameters by faster optimization of the SURE cost. Experimental results show that the SURE-optimal raised-cosine-based bilateral filter has nearly the same performance as the SURE-optimal standard Gaussian bilateral filter and the Oracle mean squared error (MSE)-based optimal bilateral filter.

Studies on lithium L-ascorbate dihydrate: An interesting chiral nonlinear optical crystal

Lithium L-Ascorbate dihydrate (LLA) is a new metal organic nonlinear optical crystal belonging to the saccharide family. Single crystals of LLA were grown from aqueous solution. Solubility of the crystal has a positive temperature coefficient facilitating growth by slow cooling. Rietveld refinement was used to confirm the phase formation. The crystal has prismatic habit with (010), (001) and (10-1) prominent faces. Thermal analysis shows that the crystal is stable up to 102 degrees C. Transmission spectrum of the crystal extends from 302 nm to 1600 nm. Dielectric spectroscopic analysis revealed Cole Cole behaviour and prominent piezoelectric resonance peaks were observed in the range of 100-200 kHz. Second harmonic generation (SHG) conversion efficiency of up to 2.56 times that of a phase matched KDP crystal was achieved when the (010) plate of LLA single crystal was rotated about the +ve c axis, by 9.4 degrees in the clockwise direction. We also observed SHG conical sections which were attributed to noncollinear phase matching. The observation of the third conical section suggests very high birefringence and large nonlinear coefficients. A detailed study of surface laser damage showed that the crystal has high multiple damage thresholds of 9.7 GW cm(-2) and 42 GW cm(-2) at 1064 nm and 532 nm radiation respectively. (C) 2012 Elsevier B.V. All rights reserved.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

Stochastic optimization for adaptive labor staffing in service systems

Service systems are labor intensive. Further, the workload tends to vary greatly with time. Adapting the staffing levels to the workloads in such systems is nontrivial due to a large number of parameters and operational variations, but crucial for business objectives such as minimal labor inventory. One of the central challenges is to optimize the staffing while maintaining system steady-state and compliance to aggregate SLA constraints. We formulate this problem as a parametrized constrained Markov process and propose a novel stochastic optimization algorithm for solving it. Our algorithm is a multi-timescale stochastic approximation scheme that incorporates a SPSA based algorithm for ‘primal descent' and couples it with a ‘dual ascent' scheme for the Lagrange multipliers. We validate this optimization scheme on five real-life service systems and compare it with a state-of-the-art optimization tool-kit OptQuest. Being two orders of magnitude faster than OptQuest, our scheme is particularly suitable for adaptive labor staffing. Also, we observe that it guarantees convergence and finds better solutions than OptQuest in many cases.

Loop transformations: convexity, pruning, and optimization

High-level loop transformations are a key instrument in mapping computational kernels to effectively exploit the resources in modern processor architectures. Nevertheless, selecting required compositions of loop transformations to achieve this remains a significantly challenging task; current compilers may be off by orders of magnitude in performance compared to hand-optimized programs. To address this fundamental challenge, we first present a convex characterization of all distinct, semantics-preserving, multidimensional affine transformations. We then bring together algebraic, algorithmic, and performance analysis results to design a tractable optimization algorithm over this highly expressive space. Our framework has been implemented and validated experimentally on a representative set of benchmarks running on state-of-the-art multi-core platforms.

Adaptive power optimization of on-chip SNUCA cache on tiled chip multicore architecture using remap policy

Advances in technology have increased the number of cores and size of caches present on chip multicore platforms(CMPs). As a result, leakage power consumption of on-chip caches has already become a major power consuming component of the memory subsystem. We propose to reduce leakage power consumption in static nonuniform cache architecture(SNUCA) on a tiled CMP by dynamically varying the number of cache slices used and switching off unused cache slices. A cache slice in a tile includes all cache banks present in that tile. Switched-off cache slices are remapped considering the communication costs to reduce cache usage with minimal impact on execution time. This saves leakage power consumption in switched-off L2 cache slices. On an average, there map policy achieves 41% and 49% higher EDP savings compared to static and dynamic NUCA (DNUCA) cache policies on a scalable tiled CMP, respectively.

Precoder optimization in cognitive radio with interference constraints

We consider precoding strategies at the secondary base station (SBS) in a cognitive radio network with interference constraints at the primary users (PUs). Precoding strategies at the SBS which satisfy interference constraints at the PUs in cognitive radio networks have not been adequately addressed in the literature so far. In this paper, we consider two scenarios: i) when the primary base station (PBS) data is not available at SBS, and ii) when the PBS data is made available at the SBS. We derive the optimum MMSE and Tomlinson-Harashima precoding (THP) matrix Alters at the SBS which satisfy the interference constraints at the PUs for the former case. For the latter case, we propose a precoding scheme at the SBS which performs pre-cancellation of the PBS data, followed by THP on the pre-cancelled data. The optimum precoding matrix filters are computed through an iterative search. To illustrate the robustness of the proposed approach against imperfect CSI at the SBS, we then derive robust precoding filters under imperfect CSI for the latter case. Simulation results show that the proposed optimum precoders achieve good bit error performance at the secondary users while meeting the interference constraints at the PUs.