Analysis procedure for calculation of electron energy distribution functions from incoherent Thomson scattering spectra

Incoherent Thomson scattering (ITS) provides a nonintrusive diagnostic for the determination of one-dimensional (1D) electron velocity distribution in plasmas. When the ITS spectrum is Gaussian its interpretation as a three-dimensional (3D) Maxwellian velocity distribution is straightforward. For more complex ITS line shapes derivation of the corresponding 3D velocity distribution and electron energy probability distribution function is more difficult. This article reviews current techniques and proposes an approach to making the transformation between a 1D velocity distribution and the corresponding 3D energy distribution. Previous approaches have either transformed the ITS spectra directly from a 1D distribution to a 3D or fitted two Gaussians assuming a Maxwellian or bi-Maxwellian distribution. Here, the measured ITS spectrum transformed into a 1D velocity distribution and the probability of finding a particle with speed within 0 and given value v is calculated. The differentiation of this probability function is shown to be the normalized electron velocity distribution function. (C) 2003 American Institute of Physics.

Deciding between theories of how reasoning develops is hard

Although Sloutsky agrees with our interpretation of our data, he argues that the totality of the evidence supports his claim that children make inductive generalisations on the basis of similarity. Here we take issue with his characterisation of the alternative hypotheses in his informal analysis of the data, and suggest that a thorough Bayesian analysis, although practically very difficult, is likely to result in a more finely balanced outcome than he suggests. (c) 2008 Elsevier B.V. All rights reserved.

Deciding between accounts of the selection task: A reply to Oaksford (2002)

In this paper we report on our attempts to fit the optimal data selection (ODS) model (Oaksford Chater, 1994; Oaksford, Chater, & Larkin, 2000) to the selection task data reported in Feeney and Handley (2000) and Handley, Feeney, and Harper (2002). Although Oaksford (2002b) reports good fits to the data described in Feeney and Handley (2000), the model does not adequately capture the data described in Handley et al. (2002). Furthermore, across all six of the experiments modelled here, the ODS model does not predict participants' behaviour at the level of selection rates for individual cards. Finally, when people's probability estimates are used in the modelling exercise, the model adequately captures only I out of 18 conditions described in Handley et al. We discuss the implications of these results for models of the selection task and claim that they support deductive, rather than probabilistic, accounts of the task.

Hierarchical synthesis of complex DSP functions on FPGAs

SoC systems are now being increasingly constructed using a hierarchy of subsystems or silicon Intellectual Property (IP) cores. The key challenge is to use these cores in a highly efficient manner which can be difficult as the internal core structure may not be known. A design methodology based on synthesizing hierarchical circuit descriptions is presented. The paper employs the MARS synthesis scheduling algorithm within the existing IRIS synthesis flow and details how it can be enhanced to allow for design exploration of IP cores. It is shown that by accessing parameterised expressions for the datapath latencies in the cores, highly efficient FPGA solutions can be achieved. Hardware sharing at both the hierarchical and flattened levels is explored for a normalized lattice filter and results are presented.