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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2108/945
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| 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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Files in This Item:
| File |
Description |
Size | Format |
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| Thesis.pdf | | 23067Kb | Adobe PDF | View/Open |
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