21,137 research outputs found

    Location-free Spectrum Cartography

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    Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on Signal Processin

    Integrating influenza antigenic dynamics with molecular evolution.

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    Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution. DOI: http://dx.doi.org/10.7554/eLife.01914.001

    Planetary cartography in the next decade: Digital cartography and emerging opportunities

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    Planetary maps being produced today will represent views of the solar system for many decades to come. The primary objective of the planetary cartography program is to produce the most complete and accurate maps from hundreds of thousands of planetary images in support of scientific studies and future missions. Here, the utilization of digital techniques and digital bases in response to recent advances in computer technology are emphasized

    Group-Lasso on Splines for Spectrum Cartography

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    The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finitely-parameterized (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that ``better'' explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Simulated tests corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced.Comment: Submitted to IEEE Transactions on Signal Processin

    A minimum cross entropy model to generate disaggregated agricultural data at the local level

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    This work presents an entropy approach to disaggregate agricultural data at a local level. It comprises two different steps. In a first one, an information prior at disaggregated level based on experts’ opinions, available cartography of land use and biophysical data is created. In a second step, it’s used a minimum cross entropy process in order to manage these information inputs and to guaranty a solution compatible with all the different restrictions. The model was applied to the region of Algarve in the year of 1999, in order to disaggregate the data at the pixel and county level. Results show that the model was able to provide some satisfactory results since the estimated values obtained for the different areas revealed a good approximation to the true values. These results were then analyzed and provided a new insight about the policies’ consequences in the territory.Minimum cross entropy, data disaggregation, local level, Algarve, agricultural policies, Agricultural and Food Policy, C11, C81, Q15, R14,

    Measuring Earthquakes from Optical Satellite Images

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    Système pour l'Observation de la Terre images are used to map ground displacements induced by earthquakes. Deformations (offsets) induced by stereoscopic effect and roll, pitch, and yaw of satellite and detector artifacts are estimated and compensated. Images are then resampled in a cartographic projection with a low-bias interpolator. A subpixel correlator in the Fourier domain provides two-dimensional offset maps with independent measurements approximately every 160 m. Biases on offsets are compensated from calibration. High-frequency noise (0.125 m^-1 ) is ~0.01 pixels. Low-frequency noise (lower than 0.001 m^-1 ) exceeds 0.2 pixels and is partially compensated from modeling. Applied to the Landers earthquake, measurements show the fault with an accuracy of a few tens of meters and yields displacement on the fault with an accuracy of better than 20 cm. Comparison with a model derived from geodetic data shows that offsets bring new insights into the faulting process
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