Unweighted least squares and maximum likelihood procedures were used and compared for the estimation of genetic variance for eight quantitative traits in maize (Zea mays L.). The genetic material was developed from unselected inbred lines isolated from a strain of Krug Yellow Dent maize. All possible single and 3‐way crosses were produced from 60 inbred lines, which traced back to 51 S0 plants. The mean squares from the diallel and triallel analyses were used in estimating the genetic components of variance.Fitting the error and a six‐parameter genetic model showed that: 1) it was not possible to obtain realistic estimates of the epistatic components, although significant effects were detected in the analyses of variance; 2) the estimates of additive geuetic variance were significant for all traits for both estimation procedures; 3) the nonadditive components accounted for only a small proportion of the total genetic variance; 4) three iterations of the maximum likelihood procedure were sufficient to stabilize the estimates; and 5) the maximum likelihood procedure generally reduced the errors of the estimates.For the two‐parameter genetic model the largest proportion of the total genetic variance was additive for all traits. The estimates of deviations due to dominance were larger than twice their standard errors for all traits in the two‐parameter model for the combined single and three‐way cross data. The frequency of significant interactions with environments was higher for the additive than for the dominance effects.
The Coastal Systems Station (CSS) at Panama City, FL is developing an airborne multispectral sensor system which flies on an unmanned aerial vehicle for detecting mines in a coastal environment. This system is called the Coastal Battlefield Reconnaissance and Analysis (COBRA) system and has successfully completed preliminary developmental testing (DT-0). For this program, the Environmental Research Institute of Michigan (ERIM) developed a fieldable ground station including integrated aircraft tracking, real-time sensor data analysis, and a post processor testbed for developing and evaluating mine and minefield detection algorithms. A fully adaptive multispectral Constant False Alarm Rate (CFAR) mine detection algorithm was implemented in the post-processor by ERIM, along with patterned and scatterable minefield detection algorithms developed by SS. The algorithms do not require prior knowledge of mine spectral signatures and thus are ideal for detecting a wide variety of mines with unknown or changing spectral signatures. COBRA DT-0 testing has been performed on actual minefields deployed at coastal and inland test sites. Preliminary results show that the COBRA system, coupled with these algorithms, meets the required minefield detection performance goals. This paper reviews the algorithm theory and implementation, overviews the ground station design, and presents minefield detection results from actual minefield imagery collected over realistic scenes during DT-0 testing.
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