119 resultados para bandpass filters


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The aim of this paper is to explore the use of both an Eulerian and system-centered method of storm track diagnosis applied to a wide range of meteorological fields at multiple levels to provide a range of perspectives on the Northern Hemisphere winter transient motions and to give new insight into the storm track organization and behavior. The data used are primarily from the European Centre for Medium-Range Weather Forecasts reanalyses project extended with operational analyses to the period 1979-2000. This is supplemented by data from the National Centers for Environmental Prediction and Goddard Earth Observing System 1 reanalyses. The range of fields explored include the usual mean sea level pressure and the lower- and upper-tropospheric height, meridional wind, vorticity, and temperature, as well as the potential vorticity (PV) on a 330-K isentropic surface (PV330) and potential temperature on a PV = 2 PVU surface (theta(PV2)). As well as reporting the primary analysis based on feature tracking, the standard Eulerian 2-6-day bandpass filtered variance analysis is also reported and contrasted with the tracking diagnostics. To enable the feature points to be identified as extrema for all the chosen fields, a planetary wave background structure is removed at each data time. The bandpass filtered variance derived from the different fields yield a rich picture of the nature and comparative magnitudes of the North Pacific and Atlantic storm tracks, and of the Siberian and Mediterranean candidates for storm tracks. The feature tracking allows the cyclonic and anticyclonic activities to be considered seperately. The analysis indicates that anticyclonic features are generally much weaker with less coherence than the cyclonic systems. Cyclones and features associated with them are shown to have much greater coherence and give tracking diagnostics that create a vivid storm track picture that includes the aspects highlighted by the variances as well as highlighting aspects that are not readily available from Eulerian studies. In particular, the upper-tropospheric features as shown by negative theta(PV2), for example, occur in a band spiraling around the hemisphere from the subtropical North Atlantic eastward to the high latitudes of the same ocean basin. Lower-troposphere storm tracks occupy more limited longitudinal sectors, with many of the individual storms possibly triggered from the upper-tropospheric disturbances in the spiral band of activity.

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A driver controls a car by turning the steering wheel or by pressing on the accelerator or the brake. These actions are modelled by Gaussian processes, leading to a stochastic model for the motion of the car. The stochastic model is the basis of a new filter for tracking and predicting the motion of the car, using measurements obtained by fitting a rigid 3D model to a monocular sequence of video images. Experiments show that the filter easily outperforms traditional filters.

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The experimental variogram computed in the usual way by the method of moments and the Haar wavelet transform are similar in that they filter data and yield informative summaries that may be interpreted. The variogram filters out constant values; wavelets can filter variation at several spatial scales and thereby provide a richer repertoire for analysis and demand no assumptions other than that of finite variance. This paper compares the two functions, identifying that part of the Haar wavelet transform that gives it its advantages. It goes on to show that the generalized variogram of order k=1, 2, and 3 filters linear, quadratic, and cubic polynomials from the data, respectively, which correspond with more complex wavelets in Daubechies's family. The additional filter coefficients of the latter can reveal features of the data that are not evident in its usual form. Three examples in which data recorded at regular intervals on transects are analyzed illustrate the extended form of the variogram. The apparent periodicity of gilgais in Australia seems to be accentuated as filter coefficients are added, but otherwise the analysis provides no new insight. Analysis of hyerpsectral data with a strong linear trend showed that the wavelet-based variograms filtered it out. Adding filter coefficients in the analysis of the topsoil across the Jurassic scarplands of England changed the upper bound of the variogram; it then resembled the within-class variogram computed by the method of moments. To elucidate these results, we simulated several series of data to represent a random process with values fluctuating about a mean, data with long-range linear trend, data with local trend, and data with stepped transitions. The results suggest that the wavelet variogram can filter out the effects of long-range trend, but not local trend, and of transitions from one class to another, as across boundaries.

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Acid mine drainage (AMD) is a widespread environmental problem associated with both working and abandoned mining operations. As part of an overall strategy to determine a long-term treatment option for AMD, a pilot passive treatment plant was constructed in 1994 at Wheal Jane Mine in Cornwall, UK. The plant consists of three separate systems, each containing aerobic reed beds, anaerobic cell and rock filters, and represents the largest European experimental facility of its kind. The systems only differ by the type of pretreatment utilised to increase the pH of the influent minewater (pH <4): lime dosed (LD), anoxic limestone drain (ALD) and lime free (LF), which receives no form of pretreatment. Historical data (1994-1997) indicate median Fe reduction between 55% and 92%, sulphate removal in the range of 3-38% and removal of target metals (cadmium, copper and zinc) below detection limits, depending on pretreatment and flow rates through the system. A new model to simulate the processes and dynamics of the wetlands systems is described, as well as the application of the model to experimental data collected at the pilot plant. The model is process based, and utilises reaction kinetic approaches based on experimental microbial techniques rather than an equilibrium approach to metal precipitation. The model is dynamic and utilises numerical integration routines to solve a set of differential equations that describe the behaviour of 20 variables over the 17 pilot plant cells on a daily basis. The model outputs at each cell boundary are evaluated and compared with the measured data, and the model is demonstrated to provide a good representation of the complex behaviour of the wetland system for a wide range of variables. (C) 2004 Elsevier B.V/ All rights reserved.

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Acid mine drainage (AMD) is a widespread environmental problem associated with both working and abandoned mining operations. As part of an overall strategy to determine a long-term treatment option for AMD, a pilot passive treatment plant was constructed in 1994 at Wheat Jane Mine in Cornwall, UK. The plant consists of three separate systems; each containing aerobic reed beds, anaerobic cell and rock filters, and represents the largest European experimental facility of its kind. The systems only differ by the type of pre-treatment utilised to increase the pH of the influent minewater (pH<4): lime-dosed (LD), anoxic limestone drain (ALD) and lime free (LF), which receives no form of pre-treatment. The Wheal Jane pilot plant offered a unique facility and a major research project was established to evaluate the pilot plant and study in detail the biological mechanisms and the geochemical and physical processes that control passive treatment systems. The project has led to data, knowledge, models and design criteria for the future design, planning and sustainable management of passive treatment systems. A multidisciplinary team of scientists and managers from the U.K. universities, the Environment Agency and the Mining Industry has been put together to obtain the maximum advantage from the excellent facilities facility at Wheal Jane. (C) 2004 Elseaier B.V All rights reserved.

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Changes in ocean circulation associated with internal climate variability have a major influence on upper ocean temperatures, particularly in regions such as the North Atlantic, which are relatively well-observed and therefore over-represented in the observational record. As a result, global estimates of upper ocean heat content can give misleading estimates of the roles of natural and anthropogenic factors in causing oceanic warming. We present a method to quantify ocean warming that filters out the natural internal variability from both observations and climate simulations and better isolates externally forced air-sea heat flux changes. We obtain a much clearer picture of the drivers of oceanic temperature changes, being able to detect the effects of both anthropogenic and volcanic influences simultaneously in the observed record. Our results show that climate models are capable of capturing in remarkable detail the externally forced component of ocean temperature evolution over the last five decades.

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The application of particle filters in geophysical systems is reviewed. Some background on Bayesian filtering is provided, and the existing methods are discussed. The emphasis is on the methodology, and not so much on the applications themselves. It is shown that direct application of the basic particle filter (i.e., importance sampling using the prior as the importance density) does not work in high-dimensional systems, but several variants are shown to have potential. Approximations to the full problem that try to keep some aspects of the particle filter beyond the Gaussian approximation are also presented and discussed.

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To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size. Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.

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P>1. Ants show complex interactions with plants, both facultative and mutualistic, ranging from grazers through seed predators and dispersers to herders of some herbivores and guards against others. But ants are rarely pollinators, and their visits to flowers may be detrimental to plant fitness. 2. Plants therefore have various strategies to control ant distributions, and restrict them to foliage rather than flowers. These 'filters' may involve physical barriers on or around flowers, or 'decoys and bribes' sited on the foliage (usually extrafloral nectaries - EFNs). Alternatively, volatile organic compounds (VOCs) are used as signals to control ant behaviour, attracting ants to leaves and/or deterring them from functional flowers. Some of the past evidence that flowers repel ants by VOCs has been equivocal and we describe the shortcomings of some experimental approaches, which involve behavioural tests in artificial conditions. 3. We review our previous study of myrmecophytic acacias, which used in situ experiments to show that volatiles derived from pollen can specifically and transiently deter ants during dehiscence, the effects being stronger in ant-guarded species and more effective on resident ants, both in African and Neotropical species. In these plants, repellence involves at least some volatiles that are known components of ant alarm pheromones, but are not repellent to beneficial bee visitors. 4. We also present new evidence of ant repellence by VOCs in temperate flowers, which is usually pollen-based and active on common European ants. We use these data to indicate that across a wide range of plants there is an apparent trade-off in ant-controlling filter strategies between the use of defensive floral volatiles and the alternatives of decoying EFNs or physical barriers.

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Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

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Modal filtering is based on the capability of single-mode waveguides to transmit only one complex amplitude function to eliminate virtually any perturbation of the interfering wavefronts, thus making very high rejection ratios possible in a nulling interferometer. In the present paper we focus on the progress of Integrated Optics in the thermal infrared [6-20 mu m] range, one of the two candidate technologies for the fabrication of Modal Filters, together with fiber optics. In conclusion of the European Space Agency's (ESA) "Integrated Optics for Darwin" activity, etched layers of clialcogenide material deposited on chalcogenide glass substrates was selected among four candidates as the technology with the best potential to simultaneously meet the filtering efficiency, absolute and spectral transmission, and beam coupling requirements. ESA's new "Integrated Optics" activity started at mid-2007 with the purpose of improving the technology until compliant prototypes can be manufactured and validated, expectedly by the end of 2009. The present paper aims at introducing the project and the components requirements and functions. The selected materials and preliminary designs, as well as the experimental validation logic and test benches are presented. More details are provided on the progress of the main technology: vacuum deposition in the co-evaporation mode and subsequent etching of chalcogenide layers. In addition., preliminary investigations of an alternative technology based on burying a chalcogenide optical fiber core into a chalcogenide substrate are presented. Specific developments of anti-reflective solutions designed for the mitigation of Fresnel losses at the input and output surface of the components are also introduced.

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The health risks associated with the inhalation or ingestion of cadmium are well documented([1,2]). During the past 18 years, EU legislation has steadily been introduced to restrict its use, leaving a requirement for the development of replacement materials. This paper looks at possible alternatives to various cadmium II-VI dielectric compounds used in the deposition of optical thin-films for various opto-electronic devices. Application areas of particular interest are for infrared multilayer interference filter fabrication and solar cell industries, where cadmium-based coatings currently find widespread use. The results of single and multilayer designs comprising CdTe, CdS, CdSe and PbTe deposited onto group IV and II-VI materials as interference filters for the mid-IR region are presented. Thin films of SnN, SnO2, SnS and SnSe are fabricated by plasma assisted CVD, reactive RF sputtering and thermal evaporation. Examination of these films using FTIR spectroscopy, SEM, EDX analysis and optical characterisation methods provide details of material dispersion, absorption, composition, refractive index, energy band gap and layer thicknesses. The optimisation of deposition parameters in order to synthesise coatings with similar optical and semiconductor properties as those containing cadmium has been investigated. Results of environmental, durability and stability trials are also presented.

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In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

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Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony.

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Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.