Enforcing RFID data visibility restrictions using XACML security policies

Radio Frequency Identification (RFID) technology allows automatic data capture from tagged objects moving in a supply chain. This data can be very useful if it is used to answer traceability queries, however it is distributed across many different repositories, owned by different companies. © 2012 IEEE.

Mobilization distance for upheaval buckling of shallowly buried pipelines

Buried pipelines may be subject to upheaval buckling because of thermally induced compressive stresses. As the buckling load of a strut decreases with increasing out of straightness, not only the maximum available resistance from the soil cover, but also the movement of the pipeline required to mobilize this are important factors in design. This paper will describe the results of 15 full-scale laboratory tests that have been carried out on pipeline uplift in both sandy and rocky backfills. The cover to diameter ratio ranged from 0.1 to 6. The results show that mobilization distance exhibits a linear relationship with H=D ratio and that the postpeak uplift force-displacement response can be accurately modeled using existing models. A tentative design approach is suggested; the maximum available uplift resistance may be reliably predicted from the postpeak response, and the mobilization distance may be predicted using the relationships described in this paper. © 2012 American Society of Civil Engineers.

Low-rank optimization for distance matrix completion

This paper addresses the problem of low-rank distance matrix completion. This problem amounts to recover the missing entries of a distance matrix when the dimension of the data embedding space is possibly unknown but small compared to the number of considered data points. The focus is on high-dimensional problems. We recast the considered problem into an optimization problem over the set of low-rank positive semidefinite matrices and propose two efficient algorithms for low-rank distance matrix completion. In addition, we propose a strategy to determine the dimension of the embedding space. The resulting algorithms scale to high-dimensional problems and monotonically converge to a global solution of the problem. Finally, numerical experiments illustrate the good performance of the proposed algorithms on benchmarks. © 2011 IEEE.

From subspace learning to distance learning: A geometrical optimization approach

In this paper, we adopt a differential-geometry viewpoint to tackle the problem of learning a distance online. As this problem can be cast into the estimation of a fixed-rank positive semidefinite (PSD) matrix, we develop algorithms that exploits the rich geometry structure of the set of fixed-rank PSD matrices. We propose a method which separately updates the subspace of the matrix and its projection onto that subspace. A proper weighting of the two iterations enables to continuously interpolate between the problem of learning a subspace and learning a distance when the subspace is fixed. © 2009 IEEE.

High-level scene structure using visibility and occlusion

We demonstrate a new method for extracting high-level scene information from the type of data available from simultaneous localisation and mapping systems. We model the scene with a collection of primitives (such as bounded planes), and make explicit use of both visible and occluded points in order to refine the model. Since our formulation allows for different kinds of primitives and an arbitrary number of each, we use Bayesian model evidence to compare very different models on an even footing. Additionally, by making use of Bayesian techniques we can also avoid explicitly finding the optimal assignment of map landmarks to primitives. The results show that explicit reasoning about occlusion improves model accuracy and yields models which are suitable for aiding data association. © 2011. The copyright of this document resides with its authors.