106,139 research outputs found

### Pipelined genetic propagation

© 2015 IEEE.Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially useful for solving complex non-linear and non-convex problems. However, the required execution time often limits their application to small-scale or latency-insensitive problems, so techniques to increase the computational efficiency of GAs are needed. FPGA-based acceleration has significant potential for speeding up genetic algorithms, but existing FPGA GAs are limited by the generational approaches inherited from software GAs. Many parts of the generational approach do not map well to hardware, such as the large shared population memory and intrinsic loop-carried dependency. To address this problem, this paper proposes a new hardware-oriented approach to GAs, called Pipelined Genetic Propagation (PGP), which is intrinsically distributed and pipelined. PGP represents a GA solver as a graph of loosely coupled genetic operators, which allows the solution to be scaled to the available resources, and also to dynamically change topology at run-time to explore different solution strategies. Experiments show that pipelined genetic propagation is effective in solving seven different applications. Our PGP design is 5 times faster than a recent FPGA-based GA system, and 90 times faster than a CPU-based GA system

### Reliability Ratio Based Weighted Bit-Flipping Decoding for LDPC Codes

In this contribution, a novel reliability-ratio based weighted bit-flipping(RRWBF) algorithm is proposed for decoding Low Density Parity Check (LDPC) codes. The RRWBF algorithm proposed is benchmarked against the conventional weighted bit-flipping (WBF) algorithm [1] and the improved weighted bit-flipping (IWBF) algorithm [2]. More than 1 and 2 dB coding gain was achieved at an BER of 10-5 while invoking the RRWBF algorithm in comparison to the two benchmarking schemes, when communicating over an AWGN and an uncorrelated Rayleigh channel, respectively. Furthermore, the decoding complexity of the proposed RRWBF algorithm is maintained at the same level as that of the conventional WBF algorithm

### Photoproduction of $K^{*+}\Lambda$ and $K^+\Sigma(1385)$ in the reaction \gamma \lowercase{p} \to K^+ \Lambda \pi^0 at Jefferson Lab

The search for missing nucleon resonances using coupled channel analysis has
mostly been concentrated on $N\pi$ and $KY$ channels, while the contributions
of $K^*Y$ and $KY^*$ channels have not been investigated thoroughly mostly due
to the lack of data. With an integrated luminosity of about 75 $pb^{-1}$, the
photoproduction data using a proton target recently collected by the CLAS
Collaboration at Jefferson Lab with a photon energy range of 1.5-3.8 GeV
provided large statistics for the study of light hyperon photoproduction
through exclusive reactions. The reaction $\gamma p \to K^+ \Lambda \pi^0$ has
been investigated. Preliminary results of the $K^{*+}\Lambda$ and
$K^+\Sigma(1385)$ cross sections are not negligible compared with the $KY$
channels. The $\Lambda \pi^0$ invariant mass spectrum is dominated by the
$\Sigma(1385)$ signal and no significant structure was found around the
$\Sigma(1480)$ region.Comment: 4 pages, 3 figures, to be publised on the NSTAR05 proceeding

### Asymptotic inference in some heteroscedastic regression models with long memory design and errors

This paper discusses asymptotic distributions of various estimators of the
underlying parameters in some regression models with long memory (LM) Gaussian
design and nonparametric heteroscedastic LM moving average errors. In the
simple linear regression model, the first-order asymptotic distribution of the
least square estimator of the slope parameter is observed to be degenerate.
However, in the second order, this estimator is $n^{1/2}$-consistent and
asymptotically normal for $h+H<3/2$; nonnormal otherwise, where $h$ and $H$ are
LM parameters of design and error processes, respectively. The
finite-dimensional asymptotic distributions of a class of kernel type
estimators of the conditional variance function $\sigma^2(x)$ in a more general
heteroscedastic regression model are found to be normal whenever $H<(1+h)/2$,
and non-normal otherwise. In addition, in this general model,
$\log(n)$-consistency of the local Whittle estimator of $H$ based on pseudo
residuals and consistency of a cross validation type estimator of $\sigma^2(x)$
are established. All of these findings are then used to propose a lack-of-fit
test of a parametric regression model, with an application to some currency
exchange rate data which exhibit LM.Comment: Published in at http://dx.doi.org/10.1214/009053607000000686 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org

### Blow up solutions to a viscoelastic fluid system and a coupled Navier-Stokes/Phase-Field system in R^2

We find explicit solutions to both the Oldroyd-B model with infinite
Weissenberg number and the coupled Navier-Stokes/Phase-Field system. The
solutions blow up in finite time.Comment: 5 page

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