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dc.contributor.authorFoate, Joshua
dc.contributor.authorValdez, Luis
dc.contributor.authorAlamaniotis, Miltos
dc.date.accessioned2022-08-03T17:38:31Z
dc.date.available2022-08-03T17:38:31Z
dc.date.issued7/28/2022
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1075
dc.description.abstractCollected data using a radiation detector to identify anomalies in the presence of naturally occurring radioactive material. Samples of the anomaly signals were put into a Hopfield neural network to train the network to identify whether data from the detector was an anomaly or background radiation. Converting our data into a 3SAT (3- Satisfiability) problem and use Grover's algorithm to find the solutions for Hopfield Artificial Neural Network memoryen_US
dc.language.isoen_USen_US
dc.subjectundergraduate student works
dc.titleIdentifying Gamma Radiation Anomaly Signals Using Quantum Computation Methodsen_US
dc.typePosteren_US
dc.description.departmentElectrical and Computer Engineeringen_US


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