Full characterization of the quantum spiral bandwidth of entangled biphotons

Spontaneous parametric down-conversion has been shown to be a reliable source of entangled photons. Among the wide range of properties shown to be entangled, it is the orbital angular momentum that is the focus of our study. We investigate, in particular, the bi-photon state generated using a Gaussian pump beam. We derive an expression for the simultaneous correlations in the orbital angular momentum, l, and radial momentum, p, of the down-converted Laguerre-Gaussian beams. Our result allows us, for example, to calculate the spiral bandwidth with no restriction on the geometry of the beams: l, p, and the beam widths are all free parameters. Moreover, we show that, with the usual paraxial and collinear approximations, a fully analytic expression for the correlations can be derived

Levels and Variations of Violation in Rape.

This chapter investigates the variations in crime scene behaviour revealed in a sample of victim statements in cases of stranger sexual assault. Building on previous findings by Canter and Heritage (1990), and Canter (1994), it was hypothesised that there existed a scale of differing levels of violation by the offender. This scale, based upon actions in the offence, ranged from personal violation, through to physical violation and finally, at the most extreme level, sexual violation. Offences could also be differentiated at the personal and physical levels in terms of hostile, controlling, stealing or involving thematic emphases to the criminal’s actions. To test these hypotheses, crime scene data from the first detected offences of 112 British rapists were analysed using a multi-dimensional scaling procedure to explore the relationships amongst crime scene actions. The results provided empirical support for the four action themes as different expressions of various intensities of violation. The implications that these findings have for the investigation of stranger sexual assault and treatment of victims are discussed

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

Evaluating Pillar 2 Employment Impacts: Case Study Methodology and Results for East Wales

This case study evaluation aims to explore employment impacts of the reformed East Wales RDP in East Wales, a UK region which is highly spatially differentiated. It concentrates on analysis of documentary evidence and representative in-depth interviews which support an evaluative interpretation of mechanisms of rural change. Issues explored relate to problems of the rural economy requiring policy intervention, and CAP rural development reform impacts on rural employment of farm households and workers in other sectors. Major concerns relate to youth out-migration, inadequate childcare provision, age structure, lack of affordable housing, pockets of deprivation, deteriorating service provision, labour supply, spatial diversity, and predominance of small businesses. Dual market failures appear to occur in employment and housing, requiring action to improve productivity, and spatial planning policies sensitive to rural requirements. The reformed RDP has had minor impacts on economic development, on the development of competitive premium agricultural products, professionalisation of the agricultural service sector, farm business adaptation, agri-environment support, and development of the food supply chain. However, the evidence indicates that Axis 2 should be strengthened to mitigate adverse impacts of decoupling. Also, future RDP spending should concentrate on Axes 3 and 4, its budget should be allocated on evidence-based criteria, and compulsory modulation should be increased. It should include provision of childcare services and other elements favouring female participation, and LEADER groups should be strengthened within a framework Rural Action Plans.Wales, rural development, Community/Rural/Urban Development,

Classification of ordered texture images using regression modelling and granulometric features

Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images

Virtual communities and professional learning across a distributed remote membership

Headteachers, or Principals, of schools work in isolation from each other yet share common practice and domain of leadership and management. They exhibit the characteristics of a community of practice yet are remote from other members of their community. Similar communities of practice can be identified for other types of school leaders, subject co-ordinators for example, and for professionals in other disciplines – consultant registrars in health, optometrists working in dispensing opticians, museum curators, and so on. This paper explores ways of using virtual communities to develop professional learning in these communities of practice. We discuss our work in the context of education and formal and informal learning communities of school leaders and explore how the lessons learnt have general application. We present a model for professional learning through online collaboration and communication, and look, in particular, at the concept of time and its effects in the virtual community

Morphological granulometry for classification of evolving and ordered texture images.

In this work we investigate the use of morphological granulometric moments as texture descriptors to predict time or class of texture images which evolve over time or follow an intrinsic ordering of textures. A cubic polynomial regression was used to model each of several granulometric moments as a function of time or class. These models are then combined and used to predict time or class. The methodology was developed on synthetic images of evolving textures and then successfully applied to classify a sequence of corrosion images to a point on an evolution time scale. Classification performance of the new regression approach is compared to that of linear discriminant analysis, neural networks and support vector machines. We also apply our method to images of black tea leaves, which are ordered according to granule size, and very high classification accuracy was attained compared to existing published results for these images. It was also found that granulometric moments provide much improved classification compared to grey level co-occurrence features for shape-based texture images