configurations and k-space sampling patterns. Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. For this case the feasibility of the proposed methods was verified both in vitro and in vivo. Scan time was reduced to one-half using a two

Abstract — Identification of regions in space that a robotic manipulator can reach in a given amount of time is important for many applications, such as safety monitoring of industrial manipulators and trajectory and task planning. However, due to the high-dimensional configuration space of many

support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning tech-nique is employed for high-resolution clutter profile estimation. The proposed method does

whether this assumption is justified. The observation density (15) can be computed via a discrete approximation, the simplest being: where s m = m/M. This is simply the product of one-dimensional densities (14) with σ = √ r M, evaluated independently along M curve normals as in Applying the CONDENSATION

Public reporting burden for this collection of Information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send

with knowledge of the support set, for almost all feasible parameter values. Consequently, in the unbiased case, our bound is identical to the MSE of the oracle estimator. Combined with the fact that the CRB is achieved at high signal-to-noise ratios by the maximum likelihood technique, our result provides a new

, and forecast irradiances and proxies, including in high time resolution, for ground-and space-based operations; these fee-based applications are available to operational users on any platform through an IDL VM GUI application (S2K PG: daily values), server access (S2K OP: daily plus high time resolution

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Abstract—We address the problem of Bayesian estimation where the statistical relation between the signal and measure-ments is only partially known. We propose modeling partial Bayesian knowledge by using an auxiliary random vector called instrument. The statistical relations between the instrument

Descent and spreading of high salinity water generated by salt rejection during sea ice formation in an Antarctic coastal polynya is studied using a hydrostatic, primitive equation three-dimensional ocean model called the Proudman Oceanographic Laboratory Coastal Ocean Modeling System (POLCOMS