An Artificial neural network-based signal classifier for automated identification of detection signals from a dielectrophoretic cytometer
| dc.contributor.author | Bhide, Ashlesha | |
| dc.contributor.examiningcommittee | McNeill, Dean (Electrical and Computer Engineering) Paliwal, Jitendra (Biosystems) | en_US |
| dc.contributor.supervisor | Thomson, Douglas (Electrical and Computer Engineering) | en_US |
| dc.date.accessioned | 2014-02-26T16:34:20Z | |
| dc.date.available | 2014-02-26T16:34:20Z | |
| dc.date.issued | 2014-02-26 | |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | An automated signal classifier and a semi-automated signal identifier are designed for collecting the dielectrophoretic signatures of cells flowing through a dielectrophoretic cytometer. In past work, the DEP cytometer signals were manually sorted by going through all recorded signals, which is impractical when analyzing 1000’s of cells per day. In the semi-automated method of collection, signals are automatically identified as events and displayed on the user interface to be accepted or rejected by the user. This approach reduced signal collection time by more than half and produced statistics nearly identical to the manual method. The automated signal classifier based on pattern recognition categorizes detection signals as ‘Accept’ or ‘Reject’. Analyzing large volumes of detection signals is possible in much reduced times and may be approaching real time capability. | en_US |
| dc.description.note | May 2014 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/23318 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Artificial Neural Network | en_US |
| dc.subject | DEP | en_US |
| dc.title | An Artificial neural network-based signal classifier for automated identification of detection signals from a dielectrophoretic cytometer | en_US |
| dc.type | master thesis | en_US |