This paper studies a new decode-and-forward relaying scheme for a cooperative wireless network composed of one source, K relays, and one destination and with binary frequency-shift keying modulation. A single threshold is employed to select retransmitting relays as follows: a relay retransmits to the destination if its decision variable is larger than the threshold; otherwise, it remains silent...

—To improve the problems of noise amplification and error propagation of the fixed cooperation schemes in cooperative communications, a relay selection strategy is proposed based on the hybrid decode-amplify-forward protocol. It can adaptively determine cooperation schemes according to the Channel Status Information (CSI) of the source-relay. Then it selects the best relay from the candidate r...

In this paper we present an initialization method of hidden unit weights for cascade-correlation type constructive neural networks. The forward selection initialization (FSI) method uses a large pool of randomly initialized hidden units and then selects the best one of them by some predetermined criterion which in our simulations was the objective function used in the hidden unit training. The ...

In this study, the authors propose a new feature selection scheme, the incremental forward feature selection, which is inspired by incremental reduced support vector machines. In their method, a new feature is added into the current selected feature subset if it will bring in the most extra information. This information is measured by using the distance between the new feature vector and the co...

The LHC will soon provide proton-proton collisions at the unprecedented center of mass energy, √ s =14 TeV. This not only allows us to probe new regions of high-pT physics, but also low-x and forward physics. A selection of potential measurements are described to outline the prospects for low-x and forward physics in the ATLAS, CMS, TOTEM, and LHCf experiments. 1 Prospects of Forward Energy Flo...

Development of medical domain application has been one of the most active research areas. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. Model selection for a neural network includes various factors such as selection of the optimal number of hidden nodes, selection of the relevant input variables and selection of optimal c...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.04.077 * Corresponding author. Tel.: +852 2616 8245; fax: E-mail addresses: [email protected] (G. Cui), mlw [email protected] (G. Zhang). Selecting good variables to build forecasting models is a major challenge for direct marketing given the increasing amount and variety of data. This study adopts the Bayesian variable sel...

This paper tackles the problem of model complexity in the context of additive models. Several methods have been proposed to estimate smoothing parameters, as well as to perform variable selection. Nevertheless, these procedures are inefficient or computationally expensive in high dimension. Also, the lasso technique has been adapted to additive models, however its experimental performance has n...