BOWL:augmenting the semantic web with beliefs

As the Semantic Web is an open, complex and constantly evolving medium, it is the norm, but not exception that information at different sites is incomplete or inconsistent. This poses challenges for the engineering and development of agent systems on the Semantic Web, since autonomous software agents need to understand, process and aggregate this information. Ontology language OWL provides core language constructs to semantically markup resources on the Semantic Web, on which software agents interact and cooperate to accomplish complex tasks. However, as OWL was designed on top of (a subset of) classic predicate logic, it lacks the ability to reason about inconsistent or incomplete information. Belief-augmented Frames (BAF) is a frame-based logic system that associates with each frame a supporting and a refuting belief value. In this paper, we propose a new ontology language Belief-augmented OWL (BOWL) by integrating OWL DL and BAF to incorporate the notion of confidence. BOWL is paraconsistent, hence it can perform useful reasoning services in the presence of inconsistencies and incompleteness. We define the abstract syntax and semantics of BOWL by extending those of OWL. We have proposed reasoning algorithms for various reasoning tasks in the BOWL framework and we have implemented the algorithms using the constraint logic programming framework. One example in the sensor fusion domain is presented to demonstrate the application of BOWL.

A generative model for online depth fusion

We present a probabilistic, online, depth map fusion framework, whose generative model for the sensor measurement process accurately incorporates both long-range visibility constraints and a spatially varying, probabilistic outlier model. In addition, we propose an inference algorithm that updates the state variables of this model in linear time each frame. Our detailed evaluation compares our approach against several others, demonstrating and explaining the improvements that this model offers, as well as highlighting a problem with all current methods: systemic bias. © 2012 Springer-Verlag.

High-visibility photonic crystal fiber interferometer as multifunctional sensor

A photonic crystal fiber (PCF) interferometer that exhibits record fringe contrast (~40 dB) is demonstrated along with its sensing applications. The device operates in reflection mode and consists of a centimeter-long segment of properly selected PCF fusion spliced to single mode optical fibers. Two identical collapsed zones in the PCF combined with its modal properties allow high-visibility interference patterns. The interferometer is suitable for refractometric and liquid level sensing. The measuring refractive index range goes from 1.33 to 1.43 and the maximum resolution is ~1.6 × 10-5. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Long period grating in multicore optical fiber:an ultra-sensitive vector bending sensor for low curvatures

Long period grating was UV inscribed into a multicore fiber consisting of 120 single mode cores. The multicore fiber that hosts the grating was fusion spliced into a single mode fiber at both ends. The splice creates a taper transition between the two types of fiber that produces a nonadiabatic mode evolution; this results in the illumination of all the modes in the multicore fiber. The spectral characteristics of this fiber device as a function of curvature were investigated. The device yielded a significant spectral sensitivity as high as 1.23 nm/m-1 and 3.57 dB/m-1 to the ultra-low curvature values from 0 to 1 m-1. This fiber device can also distinguish the orientation of curvature experienced by the fiber as the long period grating attenuation bands producing either a blue or red wavelength shift. The finite element method (FEM) model was used to investigate the modal behavior in multicore fiber and to predict the phase-matching curves of the long period grating inscribed into multicore fiber. © 2014 Optical Society of America.