Parametric FIR Design of Propagation Loss Filters in Digital Waveguide String Models

One of the attractive features of sound synthesis by physical modeling is the potential to build acoustic-sounding digital instruments that offer more flexibility and different options in its design and control than their real-life counterparts. In order to develop such virtual-acoustic instruments, the models they are based on need to be fully parametric, i.e., all coefficients employed in the model are functions of physical parameters that are controlled either online or at the (offline) design stage. In this letter we show how propagation losses can be parametrically incorporated in digital waveguide string models with the use of zero-phase FIR filters. Starting from the simplest possible design in the form of a three-tap FIR filter, a higher-order FIR strategy is presented and discussed within the perspective of string sound synthesis with digital waveguide models.

Parametric and non-parametric estimates of willingness to pay for forest recreation in Northern Ireland: A discrete choice contingent valuation study with follow-ups

In this study we show that forest areas contribute significantly to the estimated benefits from om outdoor recreation in Northern Ireland. Secondly we provide empirical evidence of the gains in the statistical efficiency of both benefit and parameter estimates obtained by analysing follow-up responses with Double Bounded interval data analysis. As these gains are considerable, it is clearly worth considering this method in CVM survey design even when moderately large sample sizes are used. Finally we demonstrate that estimates of means and medians of WTP distributions for access to forest recreation show plausible magnitude, are consistent with previous UK studies, and converge across parametric and non-parametic methods of estimation.

Parametric instabilities and localization of nonlinearly coupled electromagnetic modes in astrophysical dusty plasmas

The nonlinear coupling between the Alfven-Rao (AR) and dust-Alfven (DA) modes in a uniform magnetized dusty plasma is considered. For this purpose, multi- fluid equations (composed of the continuity and momentum equations), the laws of Faraday and Ampere and the quasi-neutrality condition are adopted to derive a set of equations, which show how the fields of the modes are nonlinearly coupled. The equations are then used to investigate decay and modulational instabilities in magnetized dusty plasmas. Stationary nonlinear solutions of the coupled AR and DA equations are presented. The relevance of the investigation to nonlinear phenomena (instabilities and localized structures) in interstellar molecular clouds is also discussed.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.