| dc.contributor.author | Annapureddy, Anupama Reddy | |
| dc.description.abstract | The objective of this paper is to develop and test a software system that uses
incomplete information from a collection of sensors to classify different objects present in
a particular area with a pre-specified probability. The objects in the study are referred to as
vehicles called the Bus and the Truck. Intruding vehicles move across a designated
geographical area. Sensors that have been placed in that area detect vehicles and calculate
probabilities that a vehicle is of a specific type conditioned on the type of vehicle that is
actually detected. The goal is to determine unconditional probabilities that a given
detection is of a particular type. The main idea is to find which vehicle is located at a
geographical point in a designated area using the Bayesian approach to calculate the
probabilities for this detection classification problem. Each sensor tries to detect the vehicle
based on its sensing radius, which is nothing but the distance between the sensor and the
vehicle. To test the methodology, I assumed that the probabilities vary depending on the
color of the vehicles. For example, if a vehicle is red in color, it is assumed to be easier for
the sensors to classify than if it is blue. The framework uses Bayesian inference to calculate
the probabilities and to distinguish two types of moving vehicles. Experiments are
conducted to find the number of sensors that successfully distinguish two types of moving
objects with a given probability threshold. In the future the Detection Classification Model
can be used to distinguish any number of objects with the mobile sensors and also some
obstacles included in a designated geographical area. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU policy 190.6.2 | en_US |
| dc.title | Bayesian Approach for Detection Classification | en_US |
| dc.type | Master's Paper | en_US |
| dc.date.accessioned | 2023-12-27T16:26:38Z | |
| dc.date.available | 2023-12-27T16:26:38Z | |
| dc.date.issued | 2011 | |
| dc.identifier.uri | https://hdl.handle.net/10365/33465 | |
| dc.subject.lcsh | Detectors. | en_US |
| dc.subject.lcsh | Signal processing. | en_US |
| dc.subject.lcsh | Sensor networks. | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | en_US |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Nygard, Kendall E. | |
