International Melodies

https://digitalcommons.acu.edu/crs_books/1602/thumbnail.jp

Scene-based nonuniformity correction with video sequences and registration

We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance (or true scene value). These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and the corresponding observed values through a given detector, a curve-fitting procedure is used to estimate the individual detector response parameters. These can then be used to correct for detector nonuniformity. The strength of the algorithm lies in its simplicity and low computational complexity. Experimental results, to illustrate the performance of the algorithm, include the use of visible-range imagery with simulated nonuniformity and infrared imagery with real nonuniformity

Lunar Roving Vehicle Navigation System Performance Review

The design and operation of the lunar roving vehicle (LRV) navigation system are briefly described. The basis for the premission LRV navigation error analysis is explained and an example included. The real time mission support operations philosophy is presented. The LRV navigation system operation and accuracy during the lunar missions are evaluated

A Valid Analysis of a Small Subsample: The Case of Non-Citizen Registration and Voting

The development of large sample surveys creates new opportunities for analysis of subpopulations that would hitherto have been impossible to examine systematically. But it also raises key challenges. Low level measurement error can potentially lead to substantial biases in estimates drawn from small subsamples. This study details strategies researchers may take to make inferences in the context of this subsample-response-error problem. In the non-citizen voting case, which recently has received substantial attention, we show that attention to any of these strategies -- group-specific response error estimates, correlated higher-frequency events, test-retest validity, or analysis of associated hypotheses – produces significant evidence that non-citizens participated in recent US elections. This reaffirms the validity of the core claim made by Richman, Chattha, and Earnest (2014): a small percentage of non-citizens vote in US elections

Learning from Small Subsamples without Cherry Picking: The Case of Non-Citizen Registration and Voting

The development of large sample surveys creates new opportunities for analysis of subpopulations that would hitherto have been impossible to examine systematically. But it also raises key challenges. Low level measurement error can potentially lead to substantial biases in estimates drawn from small subsamples. This study details strategies researchers may take to make inferences in the context of this subsample-response-error problem. In the non-citizen voting case, which recently has received substantial attention, we show that attention to any of these strategies -- group-specific response error estimates, correlated higher-frequency events, or test-retest validity – produces significant evidence that non-citizens participated in recent US elections. Additional hypotheses that follow from the measurement error assumption are also not supported. We identify future steps to improve the reliability of estimates through in-survey test-retest in order to facilitate accurate sub-population identification for analyses