This study was conducted to gain insight into the challenges and issues facing a relatively new (2nd year on the job) principal of a rural high school in relation to effectively evaluating teachers using a state mandated program referred to as Mississippi Statewide Teacher Appraisal Rubric (MSTAR). Okolona High School (OHS), a secondary school, in a problem-laden school district located in Okolona, Mississippi served as the site for the study. The MSTAR acronym refers to the state’s new evaluation program for teachers developed by the Mississippi Department of Education (MDE).

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / The Defense Evaluation Support Activity (DESA) is an independent Office of the Secretary of Defense (OSD) activity that provides tailored evaluation support to government organizations. DESA provides quick-response support capabilities and performs activities ranging from studies to large-scale field activities that include deployment, instrumentation, site setup, event execution, analysis and report writing. The National Guard Bureau requested DESA's assistance in the development and field testing of the Light Armored Vehicle (LAV) Sensor Suite (LSS). LSS was integrated by DESA to provide a multi-sensor suite that detects and identifies ground targets on foot or in vehicles with minimal operator workload. The LSS was designed primarily for deployment in high density drug trafficking areas along the northern and southern borders using primarily commercial-off-the-shelf and government-off-the-shelf equipment. Field testing of the system prototype in summer of 1995 indicates that the LSS will provide a significant new data collection and transfer capability to the National Guard in control of illegal drug transfer across the U.S. borders.

Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007. A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) database, target orientation was determined to be the best feature for ATR. A rotationally variant algorithm taking advantage of the combination of target orientation and pixel location for classification is proposed in this thesis. Extensive classification results yielding an overall accuracy of 76.78% are presented to demonstrate algorithm functionality.