No borders : virtual communities in online learning

It can be argued that technological advances and increasing familiarity with technology in the general population has created a huge potential for expansion of online learning (OL) across the educational spectrum. The growth of OL at the university level over the last few years has brought with it an increasing need to understand the learning processes and social processes involved in the ‘cyber’ or ‘virtual’ lecture hall and seminar room by asking questions such as: What are ‘virtual universities’? How – or more critically whether – virtual learning environments are different from face-to-face (F2F) ones? In other words, there is a critical need to explore how students relate to each other and their lecturer(s) in a literal ‘school without walls’? This paper explores the development of a virtual community within a wholly online MA in Applied Linguistics program within the framework of online community development proposed by Haythornthwaite et al (2000).

YAWL : power through patterns

Workflow Management Systems (WfMSs) enable the development and maintenance of workflow specifications at design time and their execution and monitoring at runtime. The open source WfMS YAWL supports the YAWL language – a formally defined language based on Petri nets which offers comprehensive support for control-flow and resource patterns. In addition, the YAWL system provides extensive support for process flexibility, in particular for process configuration, exception handling, dynamic workflow and declarative workflow. Due to its formal foundation, sophisticated verification support can also be achieved. This paper presents the YAWL system and its main applications.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.