3 resultados para Sociología de Niklas Luhmann

em Indian Institute of Science - Bangalore - Índia


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Background: Butterflies of the subtribe Mycalesina (Nymphalidae: Satyrinae) are important model organisms in ecology and evolution. This group has radiated spectacularly in the Old World tropics and presents an exciting opportunity to better understand processes of invertebrate rapid radiations. However, the generic-level taxonomy of the subtribe has been in a constant state of flux, and relationships among genera are unknown. There are six currently recognized genera in the group. Mycalesis, Lohora and Nirvanopsis are found in the Oriental region, the first of which is the most speciose genus among mycalesines, and extends into the Australasian region. Hallelesis and Bicyclus are found in mainland Africa, while Heteropsis is primarily Madagascan, with a few species in Africa. We infer the phylogeny of the group with data from three genes (total of 3139 bp) and use these data to reconstruct events in the biogeographic history of the group.,Results: The results indicate that the group Mycalesina radiated rapidly around the Oligocene-Miocene boundary. Basal relationships are unresolved, but we recover six well-supported clades. Some species of Mycalesis are nested within a primarily Madagascan clade of Heteropsis,while Nirvanopsis is nested within Lohora. The phylogeny suggests that the group had its origin either in Asia or Africa, and diversified through dispersals between the two regions, during the late Oligocene and early Miocene. The current dataset tentatively suggests that the Madagascan fauna comprises two independent radiations. The Australasian radiation shares a common ancestor derived from Asia. We discuss factors that are likely to have played a key role in the diversification of the group. Conclusions: We propose a significantly revised classification scheme for Mycalesina. We conclude that the group originated and radiated from an ancestor that was found either in Asia or Africa, with dispersals between the two regions and to Australasia. Our phylogeny paves the way for further comparative studies on this group that will help us understand the processes underlying diversification in rapid radiations of invertebrates.

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Following rising demands in positioning with GPS, low-cost receivers are becoming widely available; but their energy demands are still too high. For energy efficient GPS sensing in delay-tolerant applications, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose Sparse-GPS(1): a new computing framework for GPS acquisition via sparse approximation. Within the framework, GPS signals can be efficiently compressed by random ensembles. The sparse acquisition information, pertaining to the visible satellites that are embedded within these limited measurements, can subsequently be recovered by our proposed representation dictionary. By extensive empirical evaluations, we demonstrate the acquisition quality and energy gains of Sparse-GPS. We show that it is twice as energy efficient than offloading uncompressed data, and has 5-10 times lower energy costs than standalone GPS; with a median positioning accuracy of 40 m.

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We present a localization system that targets rapid deployment of stationary wireless sensor networks (WSN). The system uses a particle filter to fuse measurements from multiple localization modalities, such as RF ranging, neighbor information or maps, to obtain position estimations with higher accuracy than that of the individual modalities. The system isolates different modalities into separate components which can be included or excluded independently to tailor the system to a specific scenario. We show that position estimations can be improved with our system by combining multiple modalities. We evaluate the performance of the system in both an indoor and outdoor environment using combinations of five different modalities. Using two anchor nodes as reference points and combining all five modalities, we obtain RMS (Root Mean Square) estimation errors of approximately 2.5m in both cases, while using the components individually results in errors within the range of 3.5 and 9 m.