The Bayesian inference is leveraged for target tracking from radio signals collected from access points (APs). The density of the moving object as well as its distance from the transmitted wireless signal substantially affect the signal strength arriving at the receiving end. The target tracking objective is formulated as an inference problem, by which we show how the Bayesian framework can be exploited to infer the parameters of interest for a given physics model. The channel state information (CSI) is collected from wireless APs, on which the inference is carried out. We employ a non-linear forward physics model of propagation, where we differentially infer the location, the velocity, and the fractional area of the moving surfaces in 3D space, versus time. The optimization is conducted via a Levenberg–Marquardt algorithm with analytically derived Jacobian and prior. The proposed model easily scales for any given number of access points. Experiments are conducted on Cisco wireless 4800 Access Point series; operating at 5 GHz radio frequency, and the probabilistic results for position and effective surface area estimates are provided, as well as numerical results for the point spread function from the statistics of the surface location.

In this paper we describe an approach and system for managing enterprise semi-structured data that is high-throughput, nimble, and scalable. We present the NETMARK system, which provides for a "schemaless" way of managing semi-structured documents. We describe in particular detail the unique underlying data storage approach and efficient query processing mechanisms given this storage system. We present an extensive benchmark evaluation of the NETMARK system and also compare it with related XML management systems. At the heart of the approach is the philosophy of a focus on most common data management requirements in the enterprise, and not burdening users and application developers with unnecessary complexity and formal schemas.

This paper describes an approach to achieving data integration across multiple sources in an enterprise, in a manner that does not require heavy investment in database and middleware maintenance. This "lean" approach to integration leads to cost-effectiveness and scalability of data integration in the enterprise.

We are concerned with the problem of measuring the uncertainty in a broad class of belief networks, as encountered in evidential reasoning applications. In our discussion, we give an explicit account of the networks concerned, and call them the Dempster-Shafer (D-S) belief networks. We examine the essence and the requirement of such an uncertainty measure based on well-defined discrete event dynamical systems concepts. Furthermore, we extend the notion of entropy for the D-S belief networks in order to obtain an improved optimal dynamical observer. The significance and generality of the proposed dynamical observer of measuring uncertainty for the D-S belief networks lie in that it can serve as a performance estimator as well as a feedback for improving both the efficiency and the quality of the D-S belief network-based evidential inferencing. We demonstrate, with Monte Carlo simulation, the implementation and the effectiveness of the proposed dynamical observer in solving the problem of evidential inferencing with optimal evidence node selection.

When combining data from distinct sources, there is a need to share meta-data and other knowledge about various source domains. Due to semantic inconsistencies and heterogeneity of representations, problems arise in combining multiple domains when the domains are merged. The knowledge that is irrelevant to the task of interoperation will be included, making the result unnecessarily complex. This heterogeneity problem can be eliminated by mediating the conflicts and managing the intersections of the domains. For interoperation and intelligent access to heterogeneous information, the focus is on the intersection of the knowledge. An algebra over domain is proposed which uses articulation rules to support disciplined manipulation of domain knowledge resources. The objective of a domain algebra is to provide the capability for interrogating many domain knowledge resources. The algebra formally supports the tasks of selecting, combining, extending, specializing, and modifying components from a diverse set of domains. The paper presents a domain algebra and demonstrates the use of articulation rules to link declarative interfaces for Internet and enterprise applications. In particular, it discusses the articulation implementation as part of a production system capable of operating over the domain described by the interface description language (IDL) of objects registered in multiple CORBA servers.

Bayesian inference has been used successfully for many problems where the aim is to infer the parameters of a model of interest. In this paper we formulate the three dimensional reconstruction problem as the problem of inferring the parameters of a surface model from image data, and show how Bayesian methods can be used to estimate the parameters of this model given the image data. Thus we recover the three dimensional description of the scene. This approach also gives great flexibility. We can specify the geometrical properties of the model to suit our purpose, and can also use different models for how the surface reflects the light incident upon it. In common with other Bayesian inference problems, the estimation methodology requires that we can simulate the data that would have been recorded for any values of the model parameters. In this application this means that if we have image data we must be able to render the surface model. However it also means that we can infer the parameters of a model whose resolution can be chosen irrespective of the resolution of the images, and may be super-resolved. We present results of the inference of surface models from simulated aerial photographs for the case of super-resolution, where many surface elements project into a single pixel in the low-resolution images.

This paper is concerned with two issues: (1) how to enable distributed robots to dynamically acquire their goal-directed collective behaviors, and (2) how to apply the methodology of collective behavioral learning to solve the world modeling problems in mobile robot navigation. We have developed an evolutionary self-organization approach to collective task handling, and furthermore, demonstrated the implemented approach in tackling the specific problem of collectively constructing a global spatial representation, i.e., an artificial potential field map, in an unknown environment.

This article addresses the issue of quantitative information measurement within the Dempster-Shafer belief function formalism. Entropy computation in Dempster-Shafer depends on the way uncertainty measures are conceptualized. However, freed of most probability constraints, uncertainty measures in Dempster-Shafer theory can lead to further advances in optimization in information theory, which in turn may have a wide impact on decision and control. This article examines one form of current development regarding the entropy measure induced from the measure of dissonance. For a significant period, the measure of dissonance has been taken as a measure of entropy. We present in this article the entropy measure as a monotonically decreasing function, symmetrical to the measure of dissonance.

This paper describes an approach for constructing consistent observers for dynamic systems based on a formalism of nondeterministic event modeling (NEM). The first part of this paper establishes the definitions of NEM with a main focus on the notion of entropy that is extended for measuring the amount of information from nondeterministic events. The paper demonstrates the importance of the proposed entropy measure in contrast to the classical uncertainty measure when used in dynamic systems. The importance of the information measure stems from a potential failure of the classical uncertainty when observing nondeterministic events. Similar problems, which we describe as unobservable states, are often seen in dynamic systems where an uncertainty measure is used in conjunction with some arbitrary decision process. Based on well-defined dynamic systems concepts, the second part of the paper formulates the notion of consistent observer for nondeterministic event modeling. The consistent observer utilizes the monotonically decreasing entropy function of nondeterministic events. An example will be given in this paper which numerically illustrates the notions of NEM and consistent observer.