7,213 research outputs found
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. 161ā173, 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. 37ā67, 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. 575ā585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167ā1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9ā14, 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. 208ā209, 2000. [48] M. KĀØoppen, C.H. Nowack and G. RĀØosel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195ā202, 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. 251ā267, 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. 175ā178, 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. 67ā73, 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. 169ā172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749ā750, 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. 167ā185, 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. 69ā87, 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. 674ā693, 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. 837ā842, 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. 367ā381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975
Numerical Simulation of the Lux Vertical Axis Wind Turbine
Wind energy can be characterized as a cheap, clean, and renewable energy source that is absolutely
sustainable. With increasing demand for wind energy, it is productive to investigate the structural and operational
factors that undermine the proficiency and the characteristic performance of the wind turbine. Of
paramount importance to efficient wind energy generation is the aerodynamics of the wind turbine blades.
The aerodynamic factors, such as drag, airfoil pro files, and wake interactions that often reduce the performance of the wind turbines, can be investigated through computational mathematics using computational fluid dynamics (CFD). CFD offers basic techniques and tools for simulating physical processes and proffers
important insights into the
ow data, which are demanding and costly to measure experimentally.
In this thesis, we develop a simulation model in an open-source software package called OpenFOAM to investigate the performance characteristics of the Lux Vertical Axis Wind Turbine (VAWT). The Lux VAWT has a simpler design than its horizontal counterparts; however, its performance is affected by the unsteady
aerodynamic due to a complex flow field. The turbulent flow field is governed by the incompressible Navier-
Stokes equations. Simulations are carried out with an unsteady incompressible and dynamic
flow solver, PimpleDyMFoam, on an unstructured mesh surface of the Lux VAWT geometry. The computational domain
includes both the stationary and rotating mesh domains to accommodate the rotating motion of the
turbine blades and the free-stream zone. The arbitrary mesh interface is applied as a boundary condition for the patches between the two domains to enable computation across disconnected but adjacent mesh domains.
Meshing was done using two separate meshing tools, snappyHexMesh and ANSYS Mesher. The
snappyHexMesh tool offered the most
flexible and effective control over the mesh generation and quality. In
order to derive the maximal power output from the Lux VAWT simulations, the Unsteady Reynolds Averaged Navier--Stokes (URANS) equations are solved with different time-stepping methods; the objective is to reduce
the computational costs. While attempting to reduce the numerical diffusion from the non-transient terms
of URANS, a stabilized trapezoidal rule with a second-order backward differentiation formula (TR--BDF2) time-stepping method was implemented in OpenFOAM.
As a result, the transient aerodynamic forces of the blades, the torque, and power output are evaluated.
The findings demonstrate that most of the transient aerodynamic force is generated along the axis of rotation of the rotor during one complete revolution. Similarly, the computations indicate that the BDF2 method results in the least computational cost and predicts a turbine power that is somewhat comparable to the
experimental results. The difference between the simulation results and the experimental data is attributed partly to the pressure fluctuations on the turbine blades due to the mesh topology
Development and characterization of methodology and technology for the alignment of fMRI time series
This dissertation has developed, implemented and tested a novel computer based system (AUTOALIGN) that incorporates an algorithm for the alignment of functional Magnetic Resonance Image (fMRI) time series. The algorithm assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lie on the directions correspondent to two of the eigenvectors of inertia of the volume, at the intersections with the head boundary. The eigenvectors are found weighting the inertia components with the voxel\u27s intensity values assumed as mass. The three reference points are found in the same position, relative to the origin of the head coordinate system, in both test and reference brain images. Intensity correction is performed at sub-voxel accuracy by tri-linear interpolation. A test fMR brain volume in which controlled simulations of rigid-body transformations have been introduced has preliminarily assessed system performance. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations have been retrieved automatically and the values of the motion parameters compared to those obtained by the Statistical Parametric Mapping (SPM99), and the Automatic Image Registration (AIR 3.08). Results indicated that AUTOALIGN offers subvoxel accuracy in correcting both misalignment and intensity among time points in fMR images time series, and also that its performance is comparable to that of SPM99 and AIR3.08
Effects of excitation light polarization on fluorescence emission in two-photon light-sheet microscopy
Light-sheet microscopy (LSM) is a powerful imaging technique that uses a
planar illumination oriented orthogonally to the detection axis. Two-photon
(2P) LSM is a variant of LSM that exploits the 2P absorption effect for sample
excitation. The light polarization state plays a significant, and often
overlooked, role in 2P absorption processes. The scope of this work is to test
whether using different polarization states for excitation light can affect the
detected signal levels in 2P LSM imaging of typical biological samples with a
spatially unordered dye population. Supported by a theoretical model, we
compared the fluorescence signals obtained using different polarization states
with various fluorophores (fluorescein, EGFP and GCaMP6s) and different samples
(liquid solution and fixed or living zebrafish larvae). In all conditions, in
agreement with our theoretical expectations, linear polarization oriented
parallel to the detection plane provided the largest signal levels, while
perpendicularly-oriented polarization gave low fluorescence signal with the
biological samples, but a large signal for the fluorescein solution. Finally,
circular polarization generally provided lower signal levels. These results
highlight the importance of controlling the light polarization state in 2P LSM
of biological samples. Furthermore, this characterization represents a useful
guide to choose the best light polarization state when maximization of signal
levels is needed, e.g. in high-speed 2P LSM.Comment: 16 pages, 4 figures. Version of the manuscript accepted for
publication on Biomedical Optics Expres
Aerospace Medicine and Biology: A continuing bibliography (supplement 160)
This bibliography lists 166 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1976
VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts
The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), CovilhĆ£, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)
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