Abstract-Automatic self-localization is a critical need for the effective use of ad-hoc sensor networks in military or civilian applications. In general, self-localization involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. distance measurements between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of inter-sensor communication. We demonstrate that the information used for sensor localization is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then present and demonstrate the utility of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multi-modal uncertainty. Using simulations of small-to moderately-sized sensor networks, we show that NBP may be made robust to outlier measurement errors by a simple model augmentation, and that judicious message construction can result in better estimates. Furthermore, we provide an analysis of NBP's communications requirements, showing that typically only a few messages per sensor are required, and that even low bit-rate approximations of these messages can have little or no performance impact.

Fisher (1998) proposed a spiritual well-being model, comprising the domains of personal, communal, environmental and transcendental well-being, and a single global spiritual well-being dimension. This paper reports on four studies aimed at testing Fisher's theoretical model, and establishing the validity and reliability of a new self-rating questionnaire (Spiritual Well-Being Questionnaire; SWBQ), developed to reflect this model. All four studies supported Fisher's model. The SWBQ showed good reliability (Cronbach's alpha, composite reliability and variance extracted), and validity (construct, concurrent, discriminant, predictive and factorial independence from personality). The SWBQ has the advantage over other existing spiritual well-being measures in that it is based on a broader and more empirically based conceptualization of spiritual well-being, and has well established psychometric properties. Spiritual Well-Being 3 Domains of Spiritual Well-Being and Development and Validation of the Spiritual Well-Being Questionnaire

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A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation. D 2005 Elsevier Inc. All rights reserved.Keywords: Registration; Segmentation; Subcortical segmentation; Bayesian modeling; Expectation -Maximization IntroductionTo better understand brain diseases, many neuroscience studies focus on the anatomical differences between control and diseased subjects. In order to find these differences, scientists often analyze medical images for brain structures which seem to be influenced by the disease. The analysis is frequently based on segmentations of the structures of interest that are mostly performed by human experts. However, this manual process is not only very expensive, but in addition, it increases risks related to inter-and intra-observer reliability (Kikinis et al., 1992). Neuroscientists are keenly interested in automatic methods, which often rely on prior information, to perform this task (Collins et al., 1999;Leventon et al., 2000;Marroquin et al., 2003;Fischl et al., 2004;Pohl et al., 2004a;Ashburner and Friston, 2005). With notable exceptions, these methods first register the prior information, i.e., an atlas, to the medical image and then segment the medical image into anatomical structures based on that aligned information. The goal of this work is to unify this process into a single Bayesian framework in order to overcome biases caused by commitment to the initial registration.When automatic segmentation methods are guided by prior information, they frequently are used to segment anatomical structures defined by weakly visible boundaries in medical images. For example, the intensity properties of the thalamus in T1-weighted magnetic resonance (MR) images are very similar to those of the neighboring white matter (Fig. 1). Algorithms cannot rely on the MR images alone in order to distinguish these two structures. However, the ventricles, the dark structures above the thalamus, are more easily identified. In order for the ventricles to guide the detection of the boundary between the thalamus and the white matter, automatic segmentation algorithms use spatial priors (Mazziotta et al., 1995;Thompson et al., 1996). These spatial priors capture the relationship between structures such as the fact that the ventricles are above the thalamus.As mentioned previously, most atlas-based algorithms perform registration and segmentation sequentially (Cocosco et al., 2003;Van Leemput et al., 1999;Fischl et al., 200...

Abstract

We address the problem of face recognition from a large set of images obtained over time -a task arising in many surveillance and authentication applications. A set or a sequence of images provides information about the variability in the appearance of the face which can be used for more robust recognition. We discuss different approaches to the use of this information, and show that when cast as a statistical hypothesis testing problem, the classification task leads naturally to an information-theoretic algorithm that classifies sets of images using the relative entropy (Kullback-Leibler divergence) between the estimated density of the input set and that of stored collections of images for each class. We demonstrate the performance of the proposed algorithm on two medium-sized data sets of approximately frontal face images, and describe an application of the method as part of a view-independent recognition system.

At our core, or coeur, we humans are spiritual beings. Spirituality can be viewed in a variety of ways from a traditional understanding of spirituality as an expression of religiosity, in search of the sacred, through to a humanistic view of spirituality devoid of religion. Health is also multi-faceted, with increasing evidence reporting the relationship of spirituality with physical, mental, emotional, social and vocational well-being. This paper presents spiritual health as a, if not THE, fundamental dimension of people"s overall health and well-being, permeating and integrating all the other dimensions of health. Spiritual health is a dynamic state of being, reflected in the quality of relationships that people have in up to four domains of spiritual well-being: Personal domain where a person intra-relates with self; Communal domain, with in-depth inter-personal relationships; Environmental domain, connecting with nature; Transcendental domain, relating to some-thing or some-One beyond the human level. The Four Domains Model of Spiritual Health and Well-Being embraces all extant world-views from the ardently religious to the atheistic rationalist.

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