Applications of tuned mass dampers to improve performance of large space mirrors

In order for future imaging spacecraft to meet higher resolution imaging capability, it will be necessary to build large space telescopes with primary mirror diameters that range from 10 m to 20 m and do so with nanometer surface accuracy. Due to launch vehicle mass and volume constraints, these mirrors have to be deployable and lightweight, such as segmented mirrors using active optics to correct mirror surfaces with closed loop control. As a part of this work, system identification tests revealed that dynamic disturbances inherent in a laboratory environment are significant enough to degrade the optical performance of the telescope. Research was performed at the Naval Postgraduate School to identify the vibration modes most affecting the optical performance and evaluate different techniques to increase damping of those modes. Based on this work, tuned mass dampers (TMDs) were selected because of their simplicity in implementation and effectiveness in targeting specific modes. The selected damping mechanism was an eddy current damper where the damping and frequency of the damper could be easily changed. System identification of segments was performed to derive TMD specifications. Several configurations of the damper were evaluated, including the number and placement of TMDs, damping constant, and targeted structural modes. The final configuration consisted of two dampers located at the edge of each segment and resulted in 80% reduction in vibrations. The WFE for the system without dampers was 1.5 waves, with one TMD the WFE was 0.9 waves, and with two TMDs the WFE was 0.25 waves. This paper provides details of some of the work done in this area and includes theoretical predictions for optimum damping which were experimentally verified on a large aperture segmented system.     

Formation of nucleobases from formamide in the presence of iron oxides: implication in chemical evolution and origin of life

Simple compounds like HCN, which have one C and one N, are proposed as the probable precursors for biomonomers. Formamide, a hydrolysis product of HCN, is known as the precursor of various biologically important compounds, for example, nucleobases (purines and pyrimidines). In this paper, we report our results on the synthesis of nucleobases, adenine, cytosine, purine, 9-(hydroxyacetyl) purine, and 4(3H)-pyrimidinone from formamide, using iron oxide (hematite) and oxide hydroxides (goethite and akaganeite) as a catalyst. Goethite and hematite produced purine in higher yield. The products formed were characterized by high-performance liquid chromatography and electrospray ionization mass spectrometry techniques. Results of our study reveal that iron oxides might have worked as efficient prebiotic catalysts.     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.