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Please use this identifier to cite or link to this item: http://hdl.handle.net/2108/945

Title: Design of hardware architectures for HMM–based signal processing systems with applications to advanced human-machine interfaces
Authors: Cardarilli, Gian Carlo
Malatesta, Alessandro
Keywords: digital hardware
digital architectures
signal processing
HMM
human–machine interface
speech recognition
brain–computer interface
pattern recognition
FPGA
space systems
Issue Date: 29-Jul-2009
Abstract: In this thesis a new approach is described for the development of human–computer interfaces. In particular the case of pattern recognition systems based on Hidden Markov Models have been taken into account. The research started from he development of techniques for the realization of natural language speech recognition systems. The Hidden Markov Model (HMM) was chosen as the main algorithmic tool to be used to build the system. After the early work the goal was extended to the development of an hardware architecture that provided a reconfigurable tool to be used in any pattern recognition task, and not only in speech recognition. The whole work is thus focused on the development of dedicated hardware architectures, but also some new results have been obtained on the classification of electroencephalographic signals through the use of HMMs. Firstly a system–level architecture has been developed to be used in HMM based pattern recognition systems. The architecture has been con...
Description: 19.ciclo
URI: http://hdl.handle.net/2108/945
Appears in Collections:Tesi di dottorato in ingegneria

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