Forty years among the Indians ; a descriptive history of the long and busy life of Jeremiah Hubbard.

http://www.archive.org/details/fortyyearsamongi00hubbrich

What To Trust

sermon text; MS Word document

Love and the Spirit

sermon text; MS Word document

History of Baptist Indian missions : embracing remarks on the former and present condition of the aboriginal tribes; their settlement within the Indian Territory, and their future prospects / by Isaac McCoy.

http://www.archive.org/details/baptistindianmiss00mccorich

Extracting Location from Contact Traces

Localization is essential feature for many mobile wireless applications. Data collected from applications such as environmental monitoring, package tracking or position tracking has no meaning without knowing the location of this data. Other applications have location information as a building block for example, geographic routing protocols, data dissemination protocols and location-based services such as sensing coverage. Many of the techniques have the trade-off among many features such as deployment of special hardware, level of accuracy and computation power. In this paper, we present an algorithm that extracts location constraints from the connectivity information. Our solution, which does not require any special hardware and a small number of landmark nodes, uses two types of location constraints. The spatial constraints derive the estimated locations observing which nodes are within communication range of each other. The temporal constraints refine the areas, computed by the spatial constraints, using properties of time and space extracted from a contact trace. The intuition of the temporal constraints is to limit the possible locations that a node can be using its previous and future locations. To quantify this intuitive improvement in refine the nodes estimated areas adding temporal information, we performed simulations using synthetic and real contact traces. The results show this improvement and also the difficulties of using real traces.

Combinations of Deformable Shape Prototypes

We propose to investigate a model-based technique for encoding non-rigid object classes in terms of object prototypes. Objects from the same class can be parameterized by identifying shape and appearance invariants of the class to devise low-level representations. The approach presented here creates a flexible model for an object class from a set of prototypes. This model is then used to estimate the parameters of low-level representation of novel objects as combinations of the prototype parameters. Variations in the object shape are modeled as non-rigid deformations. Appearance variations are modeled as intensity variations. In the training phase, the system is presented with several example prototype images. These prototype images are registered to a reference image by a finite element-based technique called Active Blobs. The deformations of the finite element model to register a prototype image with the reference image provide the shape description or shape vector for the prototype. The shape vector for each prototype, is then used to warp the prototype image onto the reference image and obtain the corresponding texture vector. The prototype texture vectors, being warped onto the same reference image have a pixel by pixel correspondence with each other and hence are "shape normalized". Given sufficient number of prototypes that exhibit appropriate in-class variations, the shape and the texture vectors define a linear prototype subspace that spans the object class. Each prototype is a vector in this subspace. The matching phase involves the estimation of a set of combination parameters for synthesis of the novel object by combining the prototype shape and texture vectors. The strengths of this technique lie in the combined estimation of both shape and appearance parameters. This is in contrast with the previous approaches where shape and appearance parameters were estimated separately.