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Articles 1 - 3 of 3
Full-Text Articles in Computer Engineering
Robust Course-Boundary Extraction Algorithms For Autonomous Vehicles, Chris Roman, Charles Reinholtz
Robust Course-Boundary Extraction Algorithms For Autonomous Vehicles, Chris Roman, Charles Reinholtz
Christopher N. Roman
Practical autonomous robotic vehicles require dependable methods for accurately identifying course or roadway boundaries. The authors have developed a method to reliably extract the boundary line using simple dynamic thresholding, noise filtering, and blob removal. This article describes their efforts to apply this procedure in developing an autonomous vehicle.
Autonomous Underwater Vehicles As Tools For Deep-Submergence Archaeology, Christopher N. Roman, Ian Roderick Mather
Autonomous Underwater Vehicles As Tools For Deep-Submergence Archaeology, Christopher N. Roman, Ian Roderick Mather
Christopher N. Roman
Marine archaeology beyond the capabilities of scuba divers is a technologically enabled field. The tool suite includes ship-based systems such as towed side-scan sonars and remotely operated vehicles, and more recently free-swimming autonomous underwater vehicles (AUVs). Each of these platforms has various imaging and mapping capabilities appropriate for specific scales and tasks. Broadly speaking, AUVs are becoming effective tools for locating, identifying, and surveying archaeological sites. This paper discusses the role of AUVs in this suite of tools, outlines some specific design criteria necessary to maximize their utility in the field, and presents directions for future developments. Results are presented …
Querie: Collaborative Database Exploration, Magdalini Eirinaki, S. Abraham, N. Polyzotis, N. Shaikh
Querie: Collaborative Database Exploration, Magdalini Eirinaki, S. Abraham, N. Polyzotis, N. Shaikh
Faculty Publications
Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where …