1,617 research outputs found
Interaction effects in assembly of magnetic nanoparticles
A specific absorption rate of a dilute assembly of various random clusters of
iron oxide nanoparticles in alternating magnetic field has been calculated
using Landau- Lifshitz stochastic equation. This approach simultaneously takes
into account both the presence of thermal fluctuations of the nanoparticle
magnetic moments, and magneto-dipole interaction between the nanoparticles of
the clusters. It is shown that for usual 3D clusters the intensity of magneto-
dipole interaction is determined mainly by the cluster packing density eta =
Np*V/Vcl, where Np is the average number of the particles in the cluster, V is
the nanoparticle volume, and Vcl is the cluster volume. The area of the low
frequency hysteresis loop and the assembly specific absorption rate have been
found to be considerably reduced when the packing density of the clusters
increases in the range of 0.005 < eta < 0.4. The dependence of the specific
absorption rate on the mean nanoparticle diameter is retained with increase of
eta, but becomes less pronounced. For fractal clusters of nanoparticles, which
arise in biological media, in addition to considerable reduction of the
absorption rate, the absorption maximum is shifted to smaller particle
diameters. It is found also that the specific absorption rate of fractal
clusters increases appreciably with increase of the thickness of nonmagnetic
shells at the nanoparticle surfaces.Comment: The paper is accepted for Nanoscale Res. Let
Coupled modelling of land surface microwave interactions using ENVISAT ASAR data
In the last decades microwave remote sensing has proven its capability to provide
valuable information about the land surface. New sensor generations as e.g.
ENVISAT ASAR are capable to provide frequent imagery with an high information
content. To make use of these multiple imaging capabilities, sophisticated
parameter inversion and assimilation strategies have to be applied. A profound
understanding of the microwave interactions at the land surface is therefore
essential.
The objective of the presented work is the analysis and quantitative description of
the backscattering processes of vegetated areas by means of microwave
backscattering models. The effect of changing imaging geometries is investigated
and models for the description of bare soil and vegetation backscattering are
developed. Spatially distributed model parameterisation is realized by synergistic
coupling of the microwave scattering models with a physically based land surface
process model. This enables the simulation of realistic SAR images, based on bioand
geophysical parameters.
The adequate preprocessing of the datasets is crucial for quantitative image
analysis. A stringent preprocessing and sophisticated terrain geocoding and
correction procedure is therefore suggested. It corrects the geometric and
radiometric distortions of the image products and is taken as the basis for further
analysis steps.
A problem in recently available microwave backscattering models is the inadequate
parameterisation of the surface roughness. It is shown, that the use of classical
roughness descriptors, as the rms height and autocorrelation length, will lead to
ambiguous model parameterisations. A new two parameter bare soil backscattering
model is therefore recommended to overcome this drawback. It is derived from
theoretical electromagnetic model simulations. The new bare soil surface scattering
model allows for the accurate description of the bare soil backscattering coefficients.
A new surface roughness parameter is introduced in this context, capable to
describe the surface roughness components, affecting the backscattering
coefficient. It is shown, that this parameter can be directly related to the intrinsic
fractal properties of the surface.
Spatially distributed information about the surface roughness is needed to derive
land surface parameters from SAR imagery. An algorithm for the derivation of the
new surface roughness parameter is therefore suggested. It is shown, that it can be
derived directly from multitemporal SAR imagery.
Starting from that point, the bare soil backscattering model is used to assess the
vegetation influence on the signal. By comparison of the residuals between
measured backscattering coefficients and those predicted by the bare soil
backscattering model, the vegetation influence on the signal can be quantified.
Significant difference between cereals (wheat and triticale) and maize is observed in
this context.
It is shown, that the vegetation influence on the signal can be directly derived from
alternating polarisation data for cereal fields. It is dependant on plant biophysical
variables as vegetation biomass and water content.
The backscattering behaviour of a maize stand is significantly different from that of
other cereals, due to its completely different density and shape of the plants. A
dihedral corner reflection between the soil and the stalk is identified as the major
source of backscattering from the vegetation. A semiempirical maize backscattering
model is suggested to quantify the influences of the canopy over the vegetation
period.
Thus, the different scattering contributions of the soil and vegetation components
are successfully separated. The combination of the bare soil and vegetation
backscattering models allows for the accurate prediction of the backscattering
coefficient for a wide range of surface conditions and variable incidence angles.
To enable the spatially distributed simulation of the SAR backscattering coefficient,
an interface to a process oriented land surface model is established, which provides
the necessary input variables for the backscattering model. Using this synergistic,
coupled modelling approach, a realistic simulation of SAR images becomes possible
based on land surface model output variables. It is shown, that this coupled
modelling approach leads to promising and accurate estimates of the backscattering
coefficients. The remaining residuals between simulated and measured backscatter
values are analysed to identify the sources of uncertainty in the model. A detailed
field based analysis of the simulation results revealed that imprecise soil moisture
predictions by the land surface model are a major source of uncertainty, which can
be related to imprecise soil texture distribution and soil hydrological properties.
The sensitivity of the backscattering coefficient to the soil moisture content of the
upper soil layer can be used to generate soil moisture maps from SAR imagery. An
algorithm for the inversion of soil moisture from the upper soil layer is suggested
and validated. It makes use of initial soil moisture values, provided by the land
surface process model. Soil moisture values are inverted by means of the coupled
land surface backscattering model. The retrieved soil moisture results have an RMSE
of 3.5 Vol %, which is comparable to the measurement accuracy of the reference
field data.
The developed models allow for the accurate prediction of the SAR backscattering
coefficient. The various soil and vegetation scattering contributions can be
separated. The direct interface to a physically based land surface process model
allows for the spatially distributed modelling of the backscattering coefficient and
the direct assimilation of remote sensing data into a land surface process model.
The developed models allow for the derivation of static and dynamic landsurface
parameters, as e.g. surface roughness, soil texture, soil moisture and biomass from
remote sensing data and their assimilation in process models. They are therefore
reliable tools, which can be used for sophisticated practice oriented problem
solutions in manifold manner in the earth and environmental sciences
Current challenges for preseismic electromagnetic emissions: shedding light from micro-scale plastic flow, granular packings, phase transitions and self-affinity notion of fracture process
Are there credible electromagnetic (EM) EQ precursors? This a question
debated in the scientific community and there may be legitimate reasons for the
critical views. The negative view concerning the existence of EM precursors is
enhanced by features that accompany their observation which are considered as
paradox ones, namely, these signals: (i) are not observed at the time of EQs
occurrence and during the aftershock period, (ii) are not accompanied by large
precursory strain changes, (iii) are not accompanied by simultaneous geodetic
or seismological precursors and (v) their traceability is considered
problematic. In this work, the detected candidate EM precursors are studied
through a shift in thinking towards the basic science findings relative to
granular packings, micron-scale plastic flow, interface depinning, fracture
size effects, concepts drawn from phase transitions, self-affine notion of
fracture and faulting process, universal features of fracture surfaces, recent
high quality laboratory studies, theoretical models and numerical simulations.
Strict criteria are established for the definition of an emerged EM anomaly as
a preseismic one, while, precursory EM features, which have been considered as
paradoxes, are explained. A three-stage model for EQ generation by means of
preseismic fracture-induced EM emissions is proposed. The claim that the
observed EM precursors may permit a real-time and step-by-step monitoring of
the EQ generation is tested
Use of High Resolution Satellite Images for the Calibration of Hydro-geological Models in Semi-Arid Regions: A Case Study
In this paper we present the preliminary results of a
project devoted to use hydrologic and remote sensing models and
data for water resource management in semi-arid regions. The
project is developed in the Sahel region of Burkina Faso, where a
set of high resolution synthetic aperture radar (SAR) images was
acquired. The rationale of the project along with the preliminary
results obtained by the processing of high resolution Cosmo-
SkyMed data are presented and discussed
Measurement of the electromagnetic field backscattered by a fractal surface for the verification of electromagnetic scattering models
Fractal geometry is widely accepted as an efficient theory for the characterization of natural surfaces; the opportunity of describing irregularity of natural surfaces in terms of few fractal parameters makes its use in direct and inverse electromagnetic (EM) scattering theories highly desirable. In this paper, we present an innovative procedure for manufacturing fractal surfaces and for measuring their scattering properties.
A cardboard–aluminum fractal surface was built as a representation of a Weiestrass–Mandelbrot fractal process; the EM field scattered from it was measured in an anechoic chamber. A monostatic radarlike configuration was employed. Measurement results were compared to Kirchhoff approximation and small perturbation method closed-form results that were analytically obtained by employing the fractional Brownian motion to model the surface shape. Matching and discrepancies between theories andmeasurements are then discussed. Finally, fractal and classical surface models are compared as far as their use in the EM scattering is concerned.Postprint (published version
Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 2: TIMS Workshop
This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; the summaries for this workshop appear in Volume 1; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; the summaries for this workshop appear in Volume 2; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5; the summaries for this workshop appear in Volume 3
Modeling of evolving textures using granulometries
This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975
Geometric texture indicators for safety on AC pavements with 1mm 3D laser texture data
AbstractSurface texture and friction are two primary characteristics for pavement safety evaluation. Understanding their relationship is critical to reduce potential traffic crashes especially at wet conditions. Texture data obtained from existing systems are restricted on either a small portion on pavement surface or one line-of-sight profile, and the currently used texture indicators, such as Mean Profile Depth (MPD), and Mean Texture Depth (MTD) only reveal partial aspects of texture property. With the emerging 3D laser imaging technology, acquiring full-lane 3D pavement surface data at sub-millimeter resolution and at highway speeds has been made possible via the newly developed PaveVision3D Ultra data collection system. In this study using 1mm 3D data collected from PaveVision3D Ultra, four types of texture indicators (amplitude, spacing, hybrid, and functional parameters) are calculated to represent various texture properties for pavement friction estimation. The relationships among those texture indicators and pavement friction are examined. MPD and Skewness – two height texture parameters, Texture Aspect Ratio (TAR) – a spatial parameter, and Surface Bearing Index (SBI) – a functional parameter are found to be the four most contributing parameters for pavement friction prediction. Finally a multivariate regression model is developed based on residual plot analysis methods to estimate pavement friction with the R-squared value of 0.95. This study would be beneficial in the continuous measurement and evaluation of pavement safety for project- and network-level pavement surveys
- …