Name
Abstract | ||
|---|---|---|
Dauphin is a new statistical signal processing language designed for easier formulation of detection, classification and estimation algorithms. This paper demonstrates the ease of developing signal processing algorithms in Dauphin. We illustrate this by providing exemplar code for two classifiers: Bayesian and k-means, and for an estimator: the Kalman filter. In all cases, and especially the last named, the code provides a more conceptually defined approach to these problems than other languages such as Matlab. Some Dauphin features under development are also highlighted, for instance a infinite list construct called streams, which is designed to be used as a natural representation of random processes. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/DICTA.2015.7371250 | 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) |
Keywords | Field | DocType |
Dauphin,statistical signal processing language,detection algorithm,classification algorithm,estimation algorithm,Bayesian classifier,k-means classifier,Kalman filter,natural representation,random process | Signal processing,Multidimensional signal processing,MATLAB,Pattern recognition,Computer science,Stochastic process,Kalman filter,Artificial intelligence,Statistical signal processing,Estimator,Bayesian probability | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ross Kyprianou | 1 | 0 | 0.34 |
| Peter Schachte | 2 | 256 | 22.76 |
| B. Moran | 3 | 111 | 21.09 |