Riccardo Poli

Prof. Riccardo Poli

Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electonic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
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Academic Career Length

36Years (1991-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 August 2007 in Swarm Intelligence
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Journal Article
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RPRiccardo Poli
Riccardo Poli
JKJames Kennedy
James Kennedy
TBTim Blackwell
Tim Blackwell
1 January 2008 in Journal of Artificial Evolution and Applications
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Journal Article
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RPRiccardo Poli
Riccardo Poli
Particle swarm optimisation (PSO) has been enormously successful. Within little more than a decade hundreds of papers have reported successful applications of PSO. In fact, there are so many of them, that it is difficult for PSO practitioners and researchers to have a clear up‐to‐date vision of what has been done in the area of PSO applications. This brief paper attempts to fill this gap, by categorising a large number of publications dealing with PSO applications stored in the IEEE Xplore database at the time of writing.
13 March 2006
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Book Chapter
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JKJohn R. Koza
John R. Koza
RPRiccardo Poli
Riccardo Poli
28 July 2009 in IEEE Transactions on Evolutionary Computation
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Journal Article
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RPRiccardo Poli
Riccardo Poli
Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterization of the sampling distribution of the particle swarm optimizer (PSO). In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO sampling distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the sampling distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.
1 January 1995 in IEEE Transactions on Biomedical Engineering
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RPRiccardo Poli
Riccardo Poli
Stefano CagnoniStefano Cagnoni
Stefano Cagnoni
GVG. Valli
G. Valli
This paper describes an approach to the design of optimum QRS detectors. We report on detectors including a linear or nonlinear polynomial filter, which enhances and rectifies the QRS complex, and a simple, adaptive maxima detector. The parameters of the filter and the detector, and the samples to be processed are selected by a genetic algorithm which minimizes the detection errors made on a set of reference ECG signals. Three different architectures and the experimental results achieved on the MIT-BIH Arrhythmia Database are described.
12 February 2008 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
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Journal Article
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LCLuca Citi
Luca Citi
RPRiccardo Poli
Riccardo Poli
CCCaterina Cinel
Caterina Cinel
FSFrancisco Sepulveda
Francisco Sepulveda
In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG) signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually produced in response to the stimuli. Instead, the 2D motion of the pointer on the screen, using a novel BCI paradigm, is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by an evolutionary algorithm.