Maide BucoloWinner of the Award at Como Workshop on Biosignal Interpretation
In this issue, we are very happy to introduce to the readers of the newsletter Maide Bucolo, a young scientist who was awarded 1st prize for her scientific achievements presented at the Workshop. Maide got the Award for the best poster for a paper entitled 'A Nonlinear Circuit Architecture for Magnetoencephalographic Signals Analysis'. Maide studied at the School of Engineering, University of Catania, and got her degree in Computer Engineering in July 1997. She continued with her Ph.D. and took a course in Electronic and Control Engineering at the University of Catania, Dipartimento Elettrico Elettronico e Sistemistico. During the Ph.D. course she has studied at the University of California San Diego, Institute of Nonlinear Science, conducting research in 'Information Processing in Neural Networks' as a visiting researcher of the 'Agreement of Cooperation between the University of Catania, Italy and the University of California-San Diego'. She was also awarded a grant to attend the 4th International Summer School/Conference 'Let's Face Chaos through Nonlinear Dynamics', at The Centre for Applied Mathematics and Theoretical Physics (CAMPT) University of Maribor, Slovenia and the European School on Intelligent Data Analysis (IDA 2001), Palermo, Italy. In January 2001 she completed her Ph.D. thesis entitled 'Arrays of Fuzzy Logic Based Dynamical Systems: the Role of Spatial Diversity'. She is currently Contract Researcher at University of Catania working on:
Her research project entitled "Innovative Analysis Technique in Neuro-Engineering" has been selected to receive funding for the next two years by the Research Commission of the University of Catania. She is a lecturer at the faculty of Engineering of the University of Catania teaching the course "System Theory". Her research activities extend to more than 30 scientific contributions in international journals and conferences and are mainly to be found in the areas listed below: Analysis and Regularization of Complex DynamicsThese complex systems pervade our daily life as nature. They are characterized by the connection, often local, of a large quantity of simple systems (with few state variables) each nonlinear [3]. During the last few years, investigation on the identification and control of complex nonlinear dynamics in distributed systems has gained particular attention because of the perspective applications in different fields of science. The study that has been carried out focuses on the following tasks:
Biomedical Application of the Cellular Nonlinear NetworksThe potentiality of the Cellular Nonlinear Networks (CNN's), an analog nonliner dynamic processor array, has been developed performing examples in fields ranging from the image processing to the time series analysis. The CNNs have been developed to overcome the massive interconnection problem of the parallel distributed processing; thus their key features are asynchronous parallel processing, continuous time dynamics and local interactions among network elements. The CNN, as Medical Imaging Systems, is applied for the real-time DNA microarray analysis and for on-line image filtering during the laparoscopic surgical operations [5-7]. Human DNA microarrays have an advantage of allowing an analysis of multiple samples to be performed simultaneously, thereby generating a large amount of gene expression data ready to be analysed. The CNN is a powerful system to process the DNA chip, to enhance the whole procedure, making it fully parallel. Moreover the real-time image processing due to the intrinsic properties of the CNN architecture gives the possibility to optimise on-line camera images during different surgical operations; laparoscopic operations being just one. As time series technique, CNN is used to implement nonlinear blind sources separation on Magnetoencephalographic (MEG) signals. The intrinsic nonlinear and parallel architecture of the CNN has allowed the limit of the Independent Components Analysis (ICA) on MEG signals to be overcome, solving the limits of the blind sources separation [9]. Time Series Analysis and ModelingThe activity developed concerns the characterization and modeling of data collected in different fields. In physiology the neural activity has been studied through Magnetoencephalographic (MEG) signals performing the blind source separation, using both linear and nonlinear technique [8-9], and spatio-temporal analysis [11]. In economics, much attention has been focussed on the development of reliable dynamic linear and nonlinear models to realize a Decision Supporting System for the capacity demand requirements in semiconductor company [12]. In chemical plants the potentiality of the time series modeling has been used to execute the control quality [4]. Micro-circulation based InstrumentationThe development of two real-time non-invasive optical micro-circulation-based instruments; one for the estimation of the blood flow velocity and the other for oxygen delivery has proved invaluable. As far as the micro-circulation equipment is concerned, we have to consider that the blood accomplishes its main functionalities at microcirculation level, through the oxygen delivery and the collection of bio-products of cellular metabolism. The development of a blood substitute requires the understanding of the blood behaviour and therefore the development of an analytical framework, to assess the consequences of theoretical prediction on altering some physical properties [10]. Publications
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