Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

A Semantics-based Approach to Machine Perception

Henson, Cory Andrew
http://rave.ohiolink.edu/etdc/view?acc_num=wright1387645909

Abstract Details

2013, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
Machine perception can be formalized using semantic web technologies in order to derive abstractions from sensor data using background knowledge on the Web, and efficiently executed on resource-constrained devices. Advances in sensing technology hold the promise to revolutionize our ability to observe and understand the world around us. Yet the gap between observation and understanding is vast. As sensors are becoming more advanced and cost-effective, the result is an avalanche of data of high volume, velocity, and of varied type, leading to the problem of too much data and not enough knowledge (i.e., insights leading to actions). Current estimates predict over 50 billion sensors connected to the Web by 2020. While the challenge of data deluge is formidable, a resolution has profound implications. The ability to translate low-level data into high-level abstractions closer to human understanding and decision-making has the potential to disrupt data-driven interdisciplinary sciences, such as environmental science, healthcare, and bioinformatics, as well as enable other emerging technologies, such as the Internet of Things. The ability to make sense of sensory input is called perception; and while people are able to perceive their environment almost instantaneously, and seemingly without effort, machines continue to struggle with the task. Machine perception is a hard problem in computer science, with many fundamental issues that are yet to be adequately addressed, including: (a) annotation of sensor data, (b) interpretation of sensor data, and (c) efficient implementation and execution. This dissertation presents a semantics-based machine perception framework to address these issues. The tangible primary contributions created to support the thesis of this dissertation include the development of a Semantic Sensor Observation Service (SemSOS) for accessing and querying sensor data on the Web, an ontology of perception (Intellego) that provides a formal semantics of machine perception and reasoning framework for the interpretation of sensor data, and efficient algorithms for the machine perception inference tasks to enable interpretation of sensor data on resource-constrained devices, such as smart phones. Each of these contributions has been prototyped, evaluated, and applied towards solving real-world problems in multiple domains including weather and healthcare.
Amit Sheth, Ph.D. (Advisor)
Krishnaprasad Thirunarayan,, Ph.D. (Committee Member)
Payam Barnaghi, Ph.D. (Committee Member)
Satya Sahoo, Ph.D. (Committee Member)
John Gallagher, Ph.D. (Committee Member)
166 p.
Artificial Intelligence; Computer Science; Information Science
Semantic Web; Semantic Sensor Web; Semantic Sensor Networks; Semantic Perception; Machine Perception

Recommended Citations

Citations

  • Henson, C. A. (2013). A Semantics-based Approach to Machine Perception [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1387645909

    APA Style (7th edition)

  • Henson, Cory. A Semantics-based Approach to Machine Perception. 2013. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1387645909.

    MLA Style (8th edition)

  • Henson, Cory. "A Semantics-based Approach to Machine Perception." Doctoral dissertation, Wright State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1387645909

    Chicago Manual of Style (17th edition)

wright1387645909
1,405
© 2013, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.