Title :
Unsupervised pattern recognition for digital waveform classification from radiation detectors
Author :
Miao, Jianwei ; Clements, Mark A.
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
We have addressed the problem of analyzing the digital pulse waveforms of radiation detector outputs. With the availability of extremely high-speed A/D conversion with good resolution, it is now possible to look more deeply at the waveform shapes than is currently done. In our studies, a new technique of unsupervised pattern recognition has been applied which has demonstrated accurate classification (98.33% in probability) of digital pulse waveforms. To the best of our knowledge, application of such a technique is novel. The preliminary results of this system, which show clearly improved measurement conditions, are therefore very promising
Keywords :
digital signals; particle detectors; pattern classification; signal detection; waveform analysis; A/D conversion; classification accuracy; digital pulse waveforms; digital pulse waveforms analysis; digital waveform classification; measurement conditions; probability; radiation detectors; resolution; unsupervised pattern recognition; waveform shapes; Contracts; Histograms; Instruments; Particle measurements; Pattern recognition; Performance analysis; Pulse shaping methods; Radiation detectors; Sampling methods; Shape;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-2431-5
DOI :
10.1109/ICASSP.1995.480131
