981 resultados para Multirate signal model


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The present dataset contain source data for Figure 5a from Schilling et al., 2009. Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal-regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. The authors combined quantitative data from erythropoietin-induced pathway activation in primary erythroid progenitor (colony-forming unit erythroid stage, CFU-E) cells with mathematical modelling, in order to predict and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU-E cells. The authors found evidences that double-phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Phosphorylation levels of JAK2 at 7 min after stimulation for Epo concentrations ranging from 0.1 to 1000 U/ml were simulated.

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Here we present a 1200 yr long benthic foraminiferal Mg/Ca based temperature and oxygen isotope record from a ~900 m deep sediment core off northwest Africa to show that atmosphere-ocean interactions in the eastern subpolar gyre are transferred at central water depth into the eastern boundary of the subtropical gyre. Further we link the variability of the NAO (over the past 165 yrs) and solar irradiance (Late Holocene) and their control on subpolar mode water formation to the multidecadal variability observed at mid-depth in the eastern subtropical gyre. Our results show that eastern North Atlantic central waters cooled by up to ~0.8± 0.7 °C and densities decreased by Sigma theta=0.3±0.2 during positive NAO years and during minima in solar irradiance during the Late Holocene. The presented records demonstrate the sensitivity of central water formation to enhanced atmospheric forcing and ice/freshwater fluxes into the eastern subpolar gyre and the importance of central water circulation for cross-gyre climate signal propagation during the Late Holocene.

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Hide Intense debate persists about the climatic mechanisms governing hydrologic changes in tropical and subtropical southeast Africa since the Last Glacial Maximum, about 20,000 years ago. In particular, the relative importance of atmospheric and oceanic processes is not firmly established. Southward shifts of the intertropical convergence zone (ITCZ) driven by high-latitude climate changes have been suggested as a primary forcing, whereas other studies infer a predominant influence of Indian Ocean sea surface temperatures on regional rainfall changes. To address this question, a continuous record representing an integrated signal of regional climate variability is required, but has until now been missing. Here we show that remote atmospheric forcing by cold events in the northern high latitudes appears to have been the main driver of hydro-climatology in southeast Africa during rapid climate changes over the past 17,000 years. Our results are based on a reconstruction of precipitation and river discharge changes, as recorded in a marine sediment core off the mouth of the Zambezi River, near the southern boundary of the modern seasonal ITCZ migration. Indian Ocean sea surface temperatures did not exert a primary control over southeast African hydrologic variability. Instead, phases of high precipitation and terrestrial discharge occurred when the ITCZ was forced southwards during Northern Hemisphere cold events, such as Heinrich stadial 1 (around 16,000 years ago) and the Younger Dryas (around 12,000 years ago), or when local summer insolation was high in the late Holocene, i.e., during the last 4,000 years.

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The present study is the first study on the stable oxygen isotope composition of the photosynthetic calcareous-walled dinoflagellate species Thoracosphaera heimii off NW Africa during the last 45,000 yr. T. heimii based temperature estimates of sediment core GeoB 8507-3 were compared with those obtained from the stable oxygen isotopes of the planktic foraminifera Globigerina bulloides and Globigerinoides ruber (pink), and the Mg/Ca ratio of G. ruber (pink). We show that the isotopic composition of T. heimii and the temperature estimates based on the equation for inorganically precipitated calcite provide comparable results to those obtained from G. ruber (pink) isotopes and Mg/Ca ratios with exception of the Early Holocene and the Younger Dryas. The recently proposed palaeotemperature equation of Zonneveld et al. (2007), however, provides unrealistic temperature reconstructions that are about 16 °C lower than those based on planktic foraminifera. Thus, this equation needs to be revised. The difference between T. heimii and G. bulloides isotopic and temperature reconstructions can be ascribed to differences in the ecology of both species, especially with regard to their depth habitat and/or seasonal production in the research area. All temperature proxies suggest comparable conditions during the last glacial and Holocene. Small differences between the reconstructed temperature values of T. heimii and the other proxies can be explained by differences in seasonal production of the individual species. The relatively low temperatures recorded by T. heimii at about 15,000 to 8,000 yr BP are interpreted to reflect an increase in duration and/or intensity of the upwelling in the vicinity of the core site in comparison to the last glacial, with an abrupt and strong decrease of upwelling intensity and/or duration during the Early Holocene and the Younger Dryas.

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Received signal strength-based localization systems usually rely on a calibration process that aims at characterizing the propagation channel. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. This paper proposes a dynamic calibration method to initially calibrate and subsequently update the parameters of the propagation channel model using a Least Mean Squares approach. The method assumes that each anchor node in the localization infrastructure is characterized by its own propagation channel model. In practice, a set of sniffers is used to collect RSS samples, which will be used to automatically calibrate each channel model by iteratively minimizing the positioning error. The proposed method is validated through numerical simulation, showing that the positioning error of the mobile nodes is effectively reduced. Furthermore, the method has a very low computational cost; therefore it can be used in real-time operation for wireless resource-constrained nodes.

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This paper presents the architecture and the methods used to dynamically simulate the sea backscatter of an airborne radar operating in a medium repetition frequency mode (MPRF). It offers a method of generating a sea backscatter signal which fulfills the intensity statistics of real clutter in time domain, spatial correlation and local Doppler spectrum of real data. Three antenna channels (sum, guard and difference) and their cross-correlation properties are simulated. The objective of this clutter generator is to serve as the signal source for the simulation of complex airborne pulsed radar signal processors

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We consider the problem of developing efficient sampling schemes for multiband sparse signals. Previous results on multicoset sampling implementations that lead to universal sampling patterns (which guarantee perfect reconstruction), are based on a set of appropriate interleaved analog to digital converters, all of them operating at the same sampling frequency. In this paper we propose an alternative multirate synchronous implementation of multicoset codes, that is, all the analog to digital converters in the sampling scheme operate at different sampling frequencies, without need of introducing any delay. The interleaving is achieved through the usage of different rates, whose sum is significantly lower than the Nyquist rate of the multiband signal. To obtain universal patterns the sampling matrix is formulated and analyzed. Appropriate choices of the parameters, that is the block length and the sampling rates, are also proposed.

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Based on a previously reported logic cell structure (see SPIE, vol. 2038, p. 67-77, 1993), the two types of cells present at the inner and ganglion cell layers of the vertebrate retina and their intracellular response, as well as their connections with each other, have been simulated. These cells are amacrines and ganglion cells. The main scheme of the authors' configuration is shown in a figure. These two types of cells, as well as some of their possible interconnections, have been implemented with the authors' previously reported optical-processing element. As it has been shown, the authors' logic structure is able to process two optical input binary signals, being the output two logical functions. Moreover, if a delayed feedback from one of the two possible outputs to one or both of the inputs is introduced, a very different behaviour is obtained. Depending on the value of the time delay, an oscillatory output can be obtained from a constant optical input signal. Period and length pulses are dependent on delay values, both external and internal, as well as on other control signals. Moreover, a chaotic behaviour can be obtained too under certain conditions

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Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

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Computing the modal parameters of structural systems often requires processing data from multiple non-simultaneously recorded setups of sensors. These setups share some sensors in common, the so-called reference sensors, which are fixed for all measurements, while the other sensors change their position from one setup to the next. One possibility is to process the setups separately resulting in different modal parameter estimates for each setup. Then, the reference sensors are used to merge or glue the different parts of the mode shapes to obtain global mode shapes, while the natural frequencies and damping ratios are usually averaged. In this paper we present a new state space model that processes all setups at once. The result is that the global mode shapes are obtained automatically, and only a value for the natural frequency and damping ratio of each mode is estimated. We also investigate the estimation of this model using maximum likelihood and the Expectation Maximization algorithm, and apply this technique to simulated and measured data corresponding to different structures.

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Purpose: A fully three-dimensional (3D) massively parallelizable list-mode ordered-subsets expectation-maximization (LM-OSEM) reconstruction algorithm has been developed for high-resolution PET cameras. System response probabilities are calculated online from a set of parameters derived from Monte Carlo simulations. The shape of a system response for a given line of response (LOR) has been shown to be asymmetrical around the LOR. This work has been focused on the development of efficient region-search techniques to sample the system response probabilities, which are suitable for asymmetric kernel models, including elliptical Gaussian models that allow for high accuracy and high parallelization efficiency. The novel region-search scheme using variable kernel models is applied in the proposed PET reconstruction algorithm. Methods: A novel region-search technique has been used to sample the probability density function in correspondence with a small dynamic subset of the field of view that constitutes the region of response (ROR). The ROR is identified around the LOR by searching for any voxel within a dynamically calculated contour. The contour condition is currently defined as a fixed threshold over the posterior probability, and arbitrary kernel models can be applied using a numerical approach. The processing of the LORs is distributed in batches among the available computing devices, then, individual LORs are processed within different processing units. In this way, both multicore and multiple many-core processing units can be efficiently exploited. Tests have been conducted with probability models that take into account the noncolinearity, positron range, and crystal penetration effects, that produced tubes of response with varying elliptical sections whose axes were a function of the crystal's thickness and angle of incidence of the given LOR. The algorithm treats the probability model as a 3D scalar field defined within a reference system aligned with the ideal LOR. Results: This new technique provides superior image quality in terms of signal-to-noise ratio as compared with the histogram-mode method based on precomputed system matrices available for a commercial small animal scanner. Reconstruction times can be kept low with the use of multicore, many-core architectures, including multiple graphic processing units. Conclusions: A highly parallelizable LM reconstruction method has been proposed based on Monte Carlo simulations and new parallelization techniques aimed at improving the reconstruction speed and the image signal-to-noise of a given OSEM algorithm. The method has been validated using simulated and real phantoms. A special advantage of the new method is the possibility of defining dynamically the cut-off threshold over the calculated probabilities thus allowing for a direct control on the trade-off between speed and quality during the reconstruction.

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With the growing body of research on traumatic brain injury and spinal cord injury, computational neuroscience has recently focused its modeling efforts on neuronal functional deficits following mechanical loading. However, in most of these efforts, cell damage is generally only characterized by purely mechanistic criteria, function of quantities such as stress, strain or their corresponding rates. The modeling of functional deficits in neurites as a consequence of macroscopic mechanical insults has been rarely explored. In particular, a quantitative mechanically based model of electrophysiological impairment in neuronal cells has only very recently been proposed (Jerusalem et al., 2013). In this paper, we present the implementation details of Neurite: the finite difference parallel program used in this reference. Following the application of a macroscopic strain at a given strain rate produced by a mechanical insult, Neurite is able to simulate the resulting neuronal electrical signal propagation, and thus the corresponding functional deficits. The simulation of the coupled mechanical and electrophysiological behaviors requires computational expensive calculations that increase in complexity as the network of the simulated cells grows. The solvers implemented in Neurite-explicit and implicit-were therefore parallelized using graphics processing units in order to reduce the burden of the simulation costs of large scale scenarios. Cable Theory and Hodgkin-Huxley models were implemented to account for the electrophysiological passive and active regions of a neurite, respectively, whereas a coupled mechanical model accounting for the neurite mechanical behavior within its surrounding medium was adopted as a link between lectrophysiology and mechanics (Jerusalem et al., 2013). This paper provides the details of the parallel implementation of Neurite, along with three different application examples: a long myelinated axon, a segmented dendritic tree, and a damaged axon. The capabilities of the program to deal with large scale scenarios, segmented neuronal structures, and functional deficits under mechanical loading are specifically highlighted.