Bayesian Estimation of Soil Parameters from Remote Sensing Data
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Power spectrum estimation and evaluation of associated errors in the presence of incomplete sky coverage; nonhomogeneous, correlated instrumental noise; and foreground emission are problems of central importance for the extraction of cosmological information from the cosmic microwave background (CMB).
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In this paper we report on our progress on addressing these issues. We have developed an approximate expression for the uniformity of phase coverage that can be used when scheduling to assess candidate sample times. We describe the results obtained using this estimator, and compare them with detailed simulations. We describe our progress and plans for integrating optimizing criteria for both periodic and non-periodic observations into a single observation sequence.
The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.
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Two satellites predicted to come within close proximity of one another, usually a high-value satellite and a piece of space debris moving the active satellite is a means of reducing collision risk but reduces satellite lifetime, perturbs satellite mission, and introduces its own risks. So important to get a good statement of the risk of collision in order to determine whether a maneuver is truly necessary. Two aspects of this Calculation of the Probability of Collision (Pc) based on the most recent set of position velocity and uncertainty data for both satellites. Examination of the changes in the Pc value as the event develops. Events should follow a canonical development (Pc vs time to closest approach (TCA)). Helpful to be able to guess where the present data point fits in the canonical development in order to guide operational response.
Satellites have become an integral part of modern life, supporting phone communication, television and radio broadcasting, internet access, and military activities. Indeed, it is difficult to imagine modern society without many of these technologies, especially in an age when the world is increasingly interconnected via long-distance communications. As of 2013, there were over one thousand operational satellites in orbit about Earth. About half of these active satellites are in Low-Earth Orbit (LEO, meaning an orbital period less than 225 minutes), which is where the International Space Station (ISS) conducts operations, along with other commercial missions such as earth observation and satellite telephone communications. An increasing amount of attention is being placed on protecting satellites in LEO, as the frequency of object launches and satellite fragmentation events has contributed to the proliferation of space debris, resulting in increased congestion.
In this document, we describe a simple autonomous star identification algorithm which is effective using a narrow field of view (2 degrees), making the use of a science camera for star identification feasible.
A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture.
A Beyasian method was adopted to combine the instantaneous measurements of the Tropical Rainfall Measureing Mission (TRMM)'s rada and radiometer ([4]).
We have elicited a reliable Raman spectral signature for glucose in rabbit aqueous humor across mammalian physiological ranges in a rabbit model stressed by recent myocardial infarction.
The proposed research addresses all three focus areas, but primarily (2): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model /component/ parameterization selection)
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