Typically utilized by larger corporations, social media marketing and strategy is lacking in small and medium-sized nonprofit organizations. Although these organizations are beginning to incorporate this form of online communication, there is still a need to understand the best practices and proper tools to enhance an organization's presence on the web. Cases on Strategic Social Media Utilization in the Nonprofit Sector brings together cases and chapters in order to examine both the practical and theoretical components of creating an online social community for nonprofit organizations. The technologies discussed in this publication provide organizations with the necessary cost-effective tools for fundraising, marketing, and civic engagement. This publication is an essential reference source for practitioners, academicians, researchers, and advanced-level students interested in learning how to effectively use social media technologies in the nonprofit sector.

This thesis introduces a new integrated algorithm for the detection of lane-level irregular driving. To date, there has been very little improvement in the ability to detect lane level irregular driving styles, mainly due to a lack of high performance positioning techniques and suitable driving pattern recognition algorithms. The algorithm combines data from the Global Positioning System (GPS), Inertial Measurement Unit (IMU) and lane information using advanced filtering methods. The vehicle state within a lane is estimated using a Particle Filter (PF) and an Extended Kalman Filter (EKF). The state information is then used within a novel Fuzzy Inference System (FIS) based algorithm to detect different types of irregular driving. Simulation and field trial results are used to demonstrate the accuracy and reliability of the proposed irregular driving detection method.