An efficient marching algorithm for waterhammer analysis by the method of characteristics

A new fast and efficient marching algorithm is introduced to solve the basic quasilinear, hyperbolic partial differential equations describing unsteady, flow in conduits by the method of characteristics. The details of the marching method are presented with an illustration of the waterhammer problem in a simple piping system both for friction and frictionless cases. It is shown that for the same accuracy the new marching method requires fewer computational steps, less computer memory and time.

An optimal parallel algorithm for sparse matrix bandwidth reduction on a linear array

A simple and efficient algorithm for the bandwidth reduction of sparse symmetric matrices is proposed. It involves column-row permutations and is well-suited to map onto the linear array topology of the SIMD architectures. The efficiency of the algorithm is compared with the other existing algorithms. The interconnectivity and the memory requirement of the linear array are discussed and the complexity of its layout area is derived. The parallel version of the algorithm mapped onto the linear array is then introduced and is explained with the help of an example. The optimality of the parallel algorithm is proved by deriving the time complexities of the algorithm on a single processor and the linear array.

Focal plane analysis of the Knox-Thompson algorithm in speckle interferometry

For the specific case of binary stars, this paper presents signal-to-noise ratio (SNR) calculations for the detection of the parity (the side of the brighter component) of the binary using the double correlation method. This double correlation method is a focal plane version of the well-known Knox-Thompson method used in speckle interferometry. It is shown that SNR for parity detection using double correlation depends linearly on binary separation. This new result was entirely missed by previous analytical calculations dealing with a point source. It is concluded that, for magnitudes relevant to the present day speckle interferometry and for binary separations close to the diffraction limit, speckle masking has better SNR for parity detection.

A near-optimal initial seed value selection in K-means means algorithm using a genetic algorithm

The K-means algorithm for clustering is very much dependent on the initial seed values. We use a genetic algorithm to find a near-optimal partitioning of the given data set by selecting proper initial seed values in the K-means algorithm. Results obtained are very encouraging and in most of the cases, on data sets having well separated clusters, the proposed scheme reached a global minimum.

A Parallel Progressive Refinement Image Rendering Algorithm on a Scalable Multithreaded VLSI Processor Array

In this paper we develop a multithreaded VLSI processor linear array architecture to render complex environments based on the radiosity approach. The processing elements are identical and multithreaded. They work in Single Program Multiple Data (SPMD) mode. A new algorithm to do the radiosity computations based on the progressive refinement approach[2] is proposed. Simulation results indicate that the architecture is latency tolerant and scalable. It is shown that a linear array of 128 uni-threaded processing elements sustains a throughput close to 0.4 million patches/sec.

Processing maps for hot working of Cu-Ni-Zn alloys. Part I - Alpha-nickel silver

The constitutive behaviour of agr — nickel silver in the temperature range 700–950 °C and strain rate range 0.001–100 s–1 was characterized with the help of a processing map generated on the basis of the principles of the ldquodynamic materials modelrdquo of Prasadet al Using the flow stress data, processing maps showing the variation of the efficiency of power dissipation (given by 2m/(m+1) wherem is the strain-rate sensitivity) with temperature and strain rate were obtained, agr-nickel silver exhibits a single domain at temperatures greater than 750 °C and at strain rates lower than 1s–1, with a maximum efficiency of 38% occurring at about 950 °C and at a strain rate of 0.1 s–1. In the domain the material undergoes dynamic recrystallization (DRX). On the basis of a model, it is shown that the DRX is controlled by the rate of interface formation (nucleation) which depends on the diffusion-controlled process of thermal recovery by climb. At high strain rates (10 and 100s–1) the material undergoes microstructural instabilities, the manifestations of which are in the form of adiabatic shear bands and strain markings.

Processing maps for hot working of Cu-Ni-Zn alloys Part II Alpha-Beta nickel silver

The constitutive behaviour of agr-beta nickel silver in the temperature range 600�850 °C and strainrate range 0.001�100s�1 was characterized with the help of a processing map generated on the principles of the dynamic materials model. On the basis of the flow-stress data, processing maps showing the variation of the efficiency of power dissipation (given by [2m/(m+1)], wherem is the strain-rate sensitivity) with temperature and strain rate were obtained, agr-beta nickel silver exhibits a single domain at temperatures greater than 700 °C and at strain rates lower than 1 s�1 with a maximum efficiency of power dissipation of about 42% occurring at about 850 °C and at 0.1 s�1. In the domain, the agr phase undergoes dynamic recrystallization and controls the deformation of the alloy, while the beta phase deforms superplastically. Optimum conditions for the processing of agr-beta nickel silver are 850 °C and 0.1 s�1. The material undergoes unstable flow at strain rates of 10 and 100 s�1 and in the temperature range 600�750 °C, manifestated in the form of adiabatic shear bands.

Concise row-pruning algorithm to invert a matrix

Presented here, in a vector formulation, is an O(mn2) direct concise algorithm that prunes/identifies the linearly dependent (ld) rows of an arbitrary m X n matrix A and computes its reflexive type minimum norm inverse A(mr)-, which will be the true inverse A-1 if A is nonsingular and the Moore-Penrose inverse A+ if A is full row-rank. The algorithm, without any additional computation, produces the projection operator P = (I - A(mr)- A) that provides a means to compute any of the solutions of the consistent linear equation Ax = b since the general solution may be expressed as x = A(mr)+b + Pz, where z is an arbitrary vector. The rank r of A will also be produced in the process. Some of the salient features of this algorithm are that (i) the algorithm is concise, (ii) the minimum norm least squares solution for consistent/inconsistent equations is readily computable when A is full row-rank (else, a minimum norm solution for consistent equations is obtainable), (iii) the algorithm identifies ld rows, if any, and reduces concerned computation and improves accuracy of the result, (iv) error-bounds for the inverse as well as the solution x for Ax = b are readily computable, (v) error-free computation of the inverse, solution vector, rank, and projection operator and its inherent parallel implementation are straightforward, (vi) it is suitable for vector (pipeline) machines, and (vii) the inverse produced by the algorithm can be used to solve under-/overdetermined linear systems.

An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes

We develop in this article the first actor-critic reinforcement learning algorithm with function approximation for a problem of control under multiple inequality constraints. We consider the infinite horizon discounted cost framework in which both the objective and the constraint functions are suitable expected policy-dependent discounted sums of certain sample path functions. We apply the Lagrange multiplier method to handle the inequality constraints. Our algorithm makes use of multi-timescale stochastic approximation and incorporates a temporal difference (TD) critic and an actor that makes a gradient search in the space of policy parameters using efficient simultaneous perturbation stochastic approximation (SPSA) gradient estimates. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal policy. (C) 2010 Elsevier B.V. All rights reserved.

Study Of Acoustic Source Localization Algorithm For Planar Arrays

The source localization algorithms in the earlier works, mostly used non-planar arrays. If we consider scenarios like human-computer communication, or human-television communication where the microphones need to be placed on the computer monitor or television front panel, i.e we need to use the planar arrays. The algorithm proposed in 1], is a Linear Closed Form source localization algorithm (LCF algorithm) which is based on Time Difference of Arrivals (TDOAs) that are obtained from the data collected using the microphones. It assumes non-planar arrays. The LCF algorithm is applied to planar arrays in the current work. The relationship between the error in the source location estimate and the perturbation in the TDOAs is derived using first order perturbation analysis and validated using simulations. If the TDOAs are erroneous, both the coefficient matrix and the data matrix used for obtaining source location will be perturbed. So, the Total least squares solution for source localization is proposed in the current work. The sensitivity analysis of the source localization algorithm for planar arrays and non-planar arrays is done by introducing perturbation in the TDOAs and the microphone locations. It is shown that the error in the source location estimate is less when we use planar array instead of the particular non-planar array considered for same perturbation in the TDOAs or microphone location. The location of the reference microphone is proved to be important for getting an accurate source location estimate if we are using the LCF algorithm.

Spectroscopic Studies on Silver Selenide Thin Films

Silver selenide thin films of thickness between 80 nm and 160 nm were prepared by thermal evaporation technique at a high vacuum better than 2x10(-5)mbar on well cleaned glass substrates at a deposition rate of 0.2 nm/sec. Silver selenide thin films were polycrystalline with orthorhombic structure. Ellipsometric spectra of silver selenide thin films have been recorded in the wavelength range between 300 nm and 700 nm. Optical constants like refractive index, extinction coefficient, absorption coefficient, and optical band gap of silver selenide thin film have been calculated from the recorded spectra. The refractive index of silver selenide has been found to vary between 1.9 and 3.2 and the extinction coefficient varies from 0.5 to 1.6 with respect to their corresponding thickness of the films. Transmittance spectra of these films have been recorded in the wavelength range between 300 nm and 900 nm and its spectral data are analysed. The photoluminescence studies have been carried out on silver selenide thin films and the strong emission peak is found around 1.7 eV. The calculated optical band of thermally evaporated silver selenide thin films is found to be around 1.7 eV from their Ellipsometric, UV-Visible and Photoluminescence spectroscopic studies.

Multi-Agent Rendezvous Algorithm with Rectilinear Decision Domain

The aim of this paper is to develop a computationally efficient decentralized rendezvous algorithm for a group of autonomous agents. The algorithm generalizes the notion of sensor domain and decision domain of agents to enable implementation of simple computational algorithms. Specifically, the algorithm proposed in this paper uses a rectilinear decision domain (RDD) as against the circular decision domain assumed in earlier work. Because of this, the computational complexity of the algorithm reduces considerably and, when compared to the standard Ando's algorithm available in the literature, the RDD algorithm shows very significant improvement in convergence time performance. Analytical results to prove convergence and supporting simulation results are presented in the paper.

Layered Tabu Search Algorithm for Large-MIMO Detection and a Lower Bound on ML Performance

In this paper, we are concerned with low-complexity detection in large multiple-input multiple-output (MIMO) systems with tens of transmit/receive antennas. Our new contributions in this paper are two-fold. First, we propose a low-complexity algorithm for large-MIMO detection based on a layered low-complexity local neighborhood search. Second, we obtain a lower bound on the maximum-likelihood (ML) bit error performance using the local neighborhood search. The advantages of the proposed ML lower bound are i) it is easily obtained for MIMO systems with large number of antennas because of the inherent low complexity of the search algorithm, ii) it is tight at moderate-to-high SNRs, and iii) it can be tightened at low SNRs by increasing the number of symbols in the neighborhood definition. Interestingly, the proposed detection algorithm based on the layered local search achieves bit error performances which are quite close to this lower bound for large number of antennas and higher-order QAM. For e. g., in a 32 x 32 V-BLAST MIMO system, the proposed detection algorithm performs close to within 1.7 dB of the proposed ML lower bound at 10(-3) BER for 16-QAM (128 bps/Hz), and close to within 4.5 dB of the bound for 64-QAM (192 bps/Hz).

Performance of a pipelined ring algorithm for Fast Fourier Transform on transputer arrays

In this paper a pipelined ring algorithm is presented for efficient computation of one and two dimensional Fast Fourier Transform (FFT) on a message passing multiprocessor. The algorithm has been implemented on a transputer based system and experiments reveal that the algorithm is very efficient. A model for analysing the performance of the algorithm is developed from its computation-communication characteristics. Expressions for execution time, speedup and efficiency are obtained and these expressions are validated with experimental results obtained on a four transputer system. The analytical model is then used to estimate the performance of the algorithm for different number of processors, and for different sizes of the input data.

Kinetic evolution studies of silver nanoparticles in a bio-based green synthesis process

Silver nanoparticles are being extensively studied due to their widespread applications and unique properties. In the present study, the growth kinetics of silver nanoparticles as synthesized on reduction of silver nitrate solution by aqueous extract of Azadirachta indica leaves was investigated. The formation of silver nanoparticles was preliminarily monitored by measuring the absorption maxima at different time intervals after adding the reducing agent to the silver salt solution (0.5, 1, 1.5, 2, 2.5, 3, 3.5 and 4 h). At different time points characterization studies were conducted using X-ray diffraction studies, FT-IR techniques, zeta potential studies and transmission electron microscopy. The total available silver in the reaction medium was determined at different durations using ICP-OES. The changes in reduction potential in the medium were also monitored using potentiometric analysis. The results confirm a definite change in the medium pertaining to formation of the stable nanoparticles after 2 h, and a significant increase in the agglomeration tendency after 4 h of interaction. The growth kinetic data of the nanoparticles till 3.5 h was found to fit the LSW model confirming diffusion limited growth. (C) 2011 Elsevier B.V. All rights reserved.