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Title: Detection performance for sampled-data signals in a grammar based pattern recognition system
Author: Lin, Duncan T. T.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1996
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The detection performance of a new syntactic pattern recognition system based on an augmented programmed grammar (APG) is investigated. APGs are a generalisation of programmed grammars where each production rule has a label, a core and two associated lists of labels stating which rule is to apply next. The core of an APG, provided by core-functions, allows functions additional to the regular phrase structure productions to be implemented. The ability to manipulate grammar variables with true functions adds intelligence and flexibility to the parser formulation. This investigation uses an improved APG recogniser developed from a prior design to achieve an enhanced noise tolerance capability. It is able to correctly recognise one-dimensional waveforms with a wide range of sizes or scale factors using a single grammatical representation. Parser recognition performance is obtained by applying Monte Carlo tests at various values of signal-to-noise ratio and different operation parameter settings. The acquired detection statistics reveal both the recognition response for different constitutions of input signal and the influence on performance due to the various operation parameters. An idea for modifying the transmitted waveform design to suppress the formation of false waveforms is subsequently developed. The detection statistics are endorsed by a theoretical analysis. Finally, the provision of a waveform-deviation tolerance capability is shown to improve the recognition of quadratic and linear waveform segments.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available