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Dauphin A Programming Language for Statistical Signal Processing - from principles to practice.

Authors: Kyprianou, Ross;

Dauphin A Programming Language for Statistical Signal Processing - from principles to practice.

Abstract

© 2018 Ross Kyprianou ; This dissertation describes the design and implementation of a new programming language called Dauphin for the signal processing domain. Dauphin's focus is on the primitive concepts and algorithmic structures of signal processing. In this language, random variables and probability distributions are as fundamental and easy to use as the numeric types of other languages. The basic algorithms of signal processing --- estimation, detection, classification and so on --- become the standard function calls. Too much time is expended by researchers in re-writing these basic algorithms for each application. Dauphin allows you to code these algorithms directly, so they can be coded once and put into libraries for future use. Ultimately, Dauphin aims to extend the power of the researcher by allowing them to focus on the real problems and simplify the process of implementing their ideas. The first half of this dissertation describes Dauphin and the design issues of existing languages used for signal processing that motivated its development. It includes a general investigation into programming language design and the identification of specific design criteria that impact signal processing programming. These criteria directed the features in Dauphin that support writing signal processing algorithms. Of equal importance, the criteria also provide a means to compare, with some objectivity, the suitability of different languages for signal processing. Following the discussion on language design, Dauphin's features are described in detail, then details related to Dauphin's implementation are presented, including a description of Dauphin's semantics and type system. The second half of the dissertation presents practical applications of the Dauphin language, focussing on three broad areas associated with signal processing: classification, estimation and Monte Carlo methods. These non-trivial applications, combined with examples throughout the dissertation, demonstrate that Dauphin is simple and natural to ...

Country
Australia
Related Organizations
Keywords

estimation, programming language, high level programming language, functional language, 004, 620, statistical system modelling, statistical estimation, machine learning, signal detection, classification, statistical signal processing, signal processing, monte carlo methods, object tracking, recursive estimation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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