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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / This paper describes a flexible telemetry data link developed by National Nuclear Safety Administration’s Kansas City Plant (NNSA-KCP) and the University of Kansas (KU) in support of NNSA’s Remote Sensing Laboratory (NNSA-RSL) located at the Nevada Test Site. This data link is based on a beam steerable phased array antenna (PAA). The paper describes the PAA and the Airborne Measurement System (AMS) application requiring signal source tracking. It highlights flight test data collected during recent flight testing on the Nevada Test Site for the AMS.

No description available.

Data association problem for multitarget tracking is determination of the relationship between targets and the incoming measurements from sensors of the target tracking system. Performance of a multitarget tracking system is strongly related to the chosen method for data association and target tracking algorithm. Incorrect data association effects state estimation of targets. In this thesis, we propose a new multi-scan data association algorithm for multitarget tracking systems. This algorithm was implemented by using MATLAB programming tool. Performances of the new algorithm and JPDA method for multiple targets tracking are compared. During simulations linear models are used and the uncertainties in the sensor and motion models are modeled by Gaussian density. Simulation results are presented. Results show that the new algorithm&#039 / s performance is better than that of JPDA method. Moreover, a survey over target tracking literature is presented including basics of multitarget tracking systems and existing data association methods.

A central task in computer vision is detecting object classes such as cars and horses in complex scenes. Training an object class detector typically requires a large set of images labeled with tight bounding boxes around every object instance. Obtaining such data requires human annotation, which is very expensive and time consuming. Alternatively, researchers have tried to train models in a weakly supervised setting (i.e., given only image-level labels), which is much cheaper but leads to weaker detectors. In this thesis, we propose new and efficient human annotation schemes for training object class detectors that bypass the need for drawing bounding boxes and reduce the annotation cost while still obtaining high quality object detectors. First, we propose to train object class detectors from eye tracking data. Instead of drawing tight bounding boxes, the annotators only need to look at the image and find the target object. We track the eye movements of annotators while they perform this visual search task and we propose a technique for deriving object bounding boxes from these eye fixations. To validate our idea, we augment an existing object detection dataset with eye tracking data. Second, we propose a scheme for training object class detectors, which only requires annotators to verify bounding-boxes produced automatically by the learning algorithm. Our scheme introduces human verification as a new step into a standard weakly supervised framework which typically iterates between re-training object detectors and re-localizing objects in the training images. We use the verification signal to improve both re-training and re-localization. Third, we propose another scheme where annotators are asked to click on the center of an imaginary bounding box, which tightly encloses the object. We then incorporate these clicks into a weakly supervised object localization technique, to jointly localize object bounding boxes over all training images. Both our center-clicking and human verification schemes deliver detectors performing almost as well as those trained in a fully supervised setting. Finally, we propose extreme clicking. We ask the annotator to click on four physical points on the object: the top, bottom, left- and right-most points. This task is more natural than the traditional way of drawing boxes and these points are easy to find. Our experiments show that annotating objects with extreme clicking is 5 X faster than the traditional way of drawing boxes and it leads to boxes of the same quality as the original ground-truth drawn the traditional way. Moreover, we use the resulting extreme points to obtain more accurate segmentations than those derived from bounding boxes.

abstract: Growing popularity of alternatively certifying teachers has created challenges for teacher preparation programs. Many non-traditional routes into classroom include no full-time mentor teacher. Absence of a mentor teacher in the classroom leaves teachers with a deficit. This study follows ten teachers on the intern certificate enrolled in both an alternative certification teacher preparation program and the Teach for America organization as they pursue a master's degree in education and state teaching certification from a large southwestern university. The five randomly chosen for the treatment group and the control group contained 1 male and 4 female teachers, some of whom teach at public schools and others at charter schools. All were secondary education language arts teachers ranging in age from 22- 29. The treatment used in this study is a job-embedded, professional development, software tool designed to help teachers track their classroom practices called MyiLOGS. The purpose of this action research project was to study the effect using MyiLOGS had on six of the nine areas evaluated by a modified version of the Teacher Advancement Program evaluation rubric, alignment with Opportunity To Learn constructs, and the tool's influence on the efficacy of these first year teachers. The data generated from this study indicate that the MyiLOGS tool did have a positive effect on the teachers' TAP evaluation performances. Also, the MyiLOGS tool had a large impact on the teachers' instruction as measured by the constructs of Opportunity to Learn and their teaching self-efficacy. Implications suggested the tool was an asset to these teachers because they tracked their data, became more reflective, and self-sufficient. / Dissertation/Thesis / Ed.D. Educational Leadership and Policy Studies 2013

The mobility of people, freight and information is fundamental to economic and social activities such as commuting, manufacturing, distributing consumer goods and supplying energy. There are two major problems that arise as a result of mobility. The first is economic cost and the second is environmental impact which is of increasing concern in sustainable development due to emission levels, particularly as a result of car use. This study focuses on constructing a network of induced emissions (NOIEs) by using three models and checking the robustness of NOIEs under varying parameters and models. The three models are Stead’s model, the NAEI model, and Oguchi’s model. This study uses the Swedish city of Borlänge as the case study. Calculating CO2 emissions by constructing the NOIEs using Stead’s model appears to give an underestimation when compared to results from a NOIEs which applies Oguchi’s model. Results when applying the NAEI model in constructing a NOIEs also give an underestimation compared to a NOIEs applying Oguchi’s model. Applying the NAEI model is, however, more accurate than applying Stead’s model in constructing a NOIEs. The outcomes of this study show that constructing a NOIEs is robust using Oguchi’s model. This model is preferable since it takes into account more important variables such as driving behavior and the length of the road segments which have a significant impact when estimating CO2 emissions.

M. S.

M. S.

Species’ ranges arise from the interplay between environmental preferences, biotic and abiotic environmental conditions, and accessibility. Understanding of – and predictive models on – species distributions often build from the assumption that these factors apply homogenously within each species, but there is growing evidence for individual variation. Here, I use movement data to investigate individual-level decisions and compromises regarding the different costs and benefits influencing individuals’ geographic locations, and the species-level spatial patterns that emerge from these. I first developed a new method that uses tracking data to quantify individual specialisation in geographic space (site fidelity) or in environmental space (environmental specialisation). Applying it to two species of albatrosses, I found evidence of site fidelity but weak environmental specialisation. My results have implications for how limited research efforts are best-targeted: if animals are generalists, effort are best spent by understanding in depth individual patterns, i.e., better to track fewer individuals for long periods of time; whereas if animals tend to be specialists, efforts should be dedicated to tracking as many individuals as possible, even if for shorter periods. I then investigated individual migratory strategies and their drivers in nine North American bird species, using ringing/recovery data. I found latitudinal redistribution of individuals within the breeding and non-breeding ranges that generally did not follow textbook patterns (‘chain migration’ or ‘leapfrog migration’). Migratory individuals tend to trade off the benefits of migration (better tracking of climatic niche; better access to resources) and its costs (increasing with migratory distance). I found that birds are more likely to remain as residents in areas with warmer winter temperatures, higher summer resource surpluses and higher human population densities (presumably because of a buffering effect of urban areas). Overall, my results highlight the importance of considering individual variation to understanding the ecological processes underpinning species’ spatial patterns.