Performance analysis of energy detection over hyper-Rayleigh fading channels

This study investigates the performance of energy detection (ED)-based spectrum sensing over two-wave with diffused power (TWDP) fading channels, which have been found to provide accurate characterisation for a variety of fading conditions. A closed-form expression for the average detection probability of ED-based spectrum sensing over TWDP fading channels is derived. This expression is then used to describe the behaviour of ED-based spectrum sensing for a variety of channels that include Rayleigh, Rician and hyper-Rayleigh fading models. Such fading scenarios present a reliable behavioural model of machine-to-machine wireless nodes operating in confined structures such as in-vehicular environments

Reaction–diffusion chemistry implementation of associative memory neural network

Unconventional computing paradigms are typically very difficult to program. By implementing efficient parallel control architectures such as artificial neural networks, we show that it is possible to program unconventional paradigms with relative ease. The work presented implements correlation matrix memories (a form of artificial neural network based on associative memory) in reaction–diffusion chemistry, and shows that implementations of such artificial neural networks can be trained and act in a similar way to conventional implementations

Longitudinal stage profiles forecasting in rivers for flash floods

Copyright © 2010 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology Vol. 388 (2010), DOI: 10.1016/j.jhydrol.2010.05.028A flash flood routing model with artificial neural networks predictions was developed for stage profiles forecasting. The artificial neural network models were used to predict the 1-3 h lead time river stages at gauge stations along a river. The predictions were taken as interior boundaries in the flash flood routing model for the forecast of longitudinal stage profiles, including the un-gauged sites of a whole river. The flash flood routing model was based on the dynamic wave equations with discretization processes of the four-point finite difference method. Five typhoon events were applied to calibrate the rainfall-stage model and the other three events were simulated to verify the model's capability. The results revealed that the flash flood river routing model conjunction with artificial neural networks can provide accurate river stages for flood forecasting.National Science Council of Taiwa

INTCare: a knowledge discovery based intelligent decision support system for intensive care medicine

This paper introduces the INTCare system, an intelligent information system based on a completely automated Knowledge Discovery process and on the Agents paradigm. The system was designed to work in Hospital Intensive Care Units, supporting the physicians’ decisions by means of prognostic Data Mining models. In particular, these techniques were used to predict organ failure and mortality assessment. The main intention is to change the current reactive behaviour to a pro-active one, enhancing the quality of service. Current applications and experimentations, the functional and structural aspects, and technological options are presented