In this work, the performance of a peer-to-peer (P2P) network has been discussed. As being one of the most promising technology in today’s world, P2P systems exploit computation, bandwidth and other resources at the edge of the Internet, with the lower cost of building and maintenance. In the past, a lot of research has been done to improve the reliability of P2P systems focusing on paradigms like- routing strategies, reliable file exchange, resource location and placement, security. However, not much effort has been focused on studying their overall reliability incorporating essential failure causes altogether. In this research work, the authors have designed a mathematical model for a P2P network using Markov Process to holistically determine the availability, reliability and MTTF (Mean time to failure) and examined the most vulnerable components in the system whose failure rates can be controlled to achieve a more reliable network. In such networks, performance is affected to some degree due to failure or disturbance in any component.

Many popular online services provide "local" or "yellow-pages" search, but none of them allow users to customize the search over user-specified data. This paper describes a novel system for providing custom local search over user-provided spatial datasets logically combined with detailed street-level vector data. We present the algorithms and architecture underlying our prototype system, which handles complex queries and elegantly solves the problem of parsing terms from multiple datasets in the presence of ambiguity and misspellings. We show that the system provides custom local search with precision and recall figures that often exceed that of a commercial local search service. Further, we show that our system scales gracefully with the number of individual custom data sets being served simultaneously.

Address geocoding, the process of finding the map location for a structured postal address, is a relatively well-studied problem. In this paper we consider the more general problem of crosslingual location search, where the queries are not limited to postal addresses, and the language and script used in the search query is different from the one in which the underlying data is stored. To the best of our knowledge, our system is the first crosslingual location search system that is able to geocode complex addresses. We use a statistical machine transliteration system to convert location names from the script of the query to that of the stored data. However, we show that it is not sufficient to simply feed the resulting transliterations into a monolingual geocoding system, as the ambiguity inherent in the conversion drastically expands the location search space and significantly lowers the quality of results. The strength of our approach lies in its integrated, end-to-end nature: we use abstraction and fuzzy search (in the text domain) to achieve maximum coverage despite transliteration ambiguities, while applying spatial constraints (in the geographic domain) to focus only on viable interpretations of the query. Our experiments with structured and unstructured queries in a set of diverse languages and scripts (Arabic, English, Hindi and Japanese) searching for locations in different regions of the world, show full crosslingual location search accuracy at levels comparable to that of commercial monolingual systems. We achieve these levels of performance using techniques that may be applied to crosslingual searches in any language/script, and over arbitrary spatial data.

We present a crosslingual location search service that works across multiple countries and deals effectively with ambiguous and ill-formed queries. The system returns a ranked list of spatial regions (points, lines and polygons) that best match users' text queries, which can range from postal addresses to unstructured queries that list a few col-located map features. The system's robustness comes from a novel approach that exploits spatial coherence to identify viable interpretations of input text. Unlike existing state of the art geocoding systems, our system requires no region-specific rules, training or customization, and thus may be built to cover any region for which detailed map data is available, making it possible, for the first time, to rapidly build location search services for new regions, and more generally, approximate text search over arbitrary spatial repositories. Our system has been shown to outperform commercial geocoding systems, especially when spelling or format variations are introduced. We demonstrate our sys-tem's capabilities by showing results for a variety of text queries over a large dataset from several countries with widely differing address formats.

Robust, global, address geocoding is challenging because there is no single address format that applies to all geographies, and in any case, users may not restrict themselves to well-formed addresses. Particularly in online mapping systems, users frequently enter queries with missing or conflicting information, misspellings, address transpositions, and other such variations.

: We represent arbitrary smooth curved 3D shapes by a discrete set of HOT curves where a surface admits High Order Tangents. These curves determine the structure of the image contours and its catastrophic changes, and there is a natural correspondence between some of them and monocular contour features such as inflections and bitangents. We present a method for automatically constructing the HOT curves from continuous sequences of video images and describe an approach to object recognition using viewpoint-dependent monocular image features as indices into a database of models and as a basis for pose estimation. We have implemented both the methods and present results obtained from real images. 1 Introduction While implemented recognition systems based on parametric shape representations such as algebraic surfaces or superquadrics have demonstrated their usefulness, the ultimate utility of a representation is limited by its scope. This suggests looking for a more general representation ...

Addresses the problem of estimating the structure and motion of a smooth curved object from its silhouettes observed over time by a trinocular stereo rig under perspective projection. We first construct a model for the local structure along the silhouette for each frame in the temporal sequence. Successive local models are then integrated into a global surface description by estimating the motion between successive time instants. The algorithm tracks certain surface features (parabolic points) and image features (silhouette inflections and frontier points) which are used to bootstrap the motion estimation process. The entire silhouette along with the reconstructed local structure are then used to refine the initial motion estimate. We have implemented the proposed approach and report results on real images.>

Arbitrary smooth curved 3D shapes are represented by a discrete set of high-order tangent (HOT) curves, where a surface admits HOTs. These curves determine the structure of the image contours and its catastrophic changes, and there is a natural correspondence between some of them and monocular contour features such as inflections and bitangents. We present a method for automatically constructing the HOT curves from continuous sequences of video images and describe an approach to object recognition using viewpoint-dependent monocular image features as indices into a database of models and as a basis for pose estimation. We have implemented both of the methods, and present results obtained from real images.>