A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

The medical community is producing and manipulating a tremendous volume of digital data for which computerized archiving, processing and analysis is needed. Grid infrastructures are promising for dealing with challenges arising in computerized medicine but the manipulation of medical data on such infrastructures faces both the problem of interconnecting medical information systems to Grid middlewares and of preserving patients’ privacy in a wide and distributed multi-user system. These constraints are often limiting the use of Grids for manipulating sensitive medical data. This paper describes our design of a medical data management system taking advantage of the advanced gLite data management services, developed in the context of the EGEE project, to fulfill the stringent needs of the medical community. It ensures medical data protection through strict data access control, anonymization and encryption. The multi-level access control provides the flexibility needed for implementing complex medical use-cases. Data anonymization prevents the exposure of most sensitive data to unauthorized users, and data encryption guarantees data protection even when it is stored at remote sites. Moreover, the developed prototype provides a Grid storage resource manager (SRM) interface to standard medical DICOM servers thereby enabling transparent access to medical data without interfering with medical practice.     

Trajectory reconstruction with uncertainty estimation using mosaic registration

This paper addresses the problem of estimating the 3D trajectory and associated uncertainty of an underwater autonomous vehicle from a set of images of the seabed taken by an onboard camera. The presented algorithms resort to the use of video mosaics and build upon previous work on image registration and visual pose estimation. The pose estimation is accomplished in two steps. Firstly, a video mosaic is created automatically, covering a region of interest of the seabed. Then, after associating a 3D referential for the mosaic, the estimation of the camera position from a new view of the scene becomes possible.

The main contribution of this paper lies on the assessment of the performance of the 3D pose algorithms. In order to do this, an image sequence with available ground-truth is used for precise error measuring. A first-order error propagation analysis is presented, relating the uncertainty in the location of the match points with the uncertainty in the pose parameters. The importance of predicting the estimate uncertainty is emphasized by the fact that it can be used for comparing algorithms and for the on-line monitoring of the vehicle trajectory reconstruction quality.

Several iterative and non-iterative pose estimation methods are discussed, differing both on the criteria being minimized and on the required information about the camera intrinsic parameters. This information ranges from the full knowledge of the parameters, to the case where they are estimated using self-calibration from an image sequence under pure rotation. The implemented pose algorithms are compared for the accuracy and estimate covariance.     

Locally perceiving hard global constraints in multi-agent scheduling

We propose how to model enterprise facilities (like factories, warehouses, etc.) in a multi-product production/distribution network, capacity management at those facilities, and scheduling agents which act as enterprise managers, taking decisions that affect the available capacity. A coordination mechanism through which scheduling agents can locally perceive hard global temporal constraints is also proposed.     

The removal and degradation of pharmaceutical compounds during membrane bioreactor treatment

Pharmaceutical compounds such as non-steroidal anti-inflammatory drugs (NSAIDs) and antibiotics have been detected in sewage treatment plant (STP) effluents, surface and ground water and even in drinking water all over the world, and therefore have developed as compounds of concern. Membrane bioreactor (MBR) treatment has gained significant popularity as an advanced wastewater treatment technology and might be effective for an advanced removal of these pollutants. This paper evaluates the treatment of wastewater containing three NSAIDs (acetaminophen, ketoprofen and naproxen) and three antibiotics (roxithromycin, sulfamethoxazole and trimethoprim) performed in two MBRs with sludge retention times (SRTs) of 15 (MBR-15) and 30 (MBR-30) days over a period of four weeks. It was observed that NSAIDs were removed with higher efficiencies than the antibiotics for both MBRs, and the MBR-30 presented higher removal efficiencies for all the compounds than obtained by MBR-15. Removal rates ranged from 55% (sulfamethoxazole) up to 100% (acetaminophen, ketoprofen). Besides mineralisation biological transformation products of ketoprofen and naproxen produced by wastewater biocoenosis were identified in both MBR permeates using liquid chromatography coupled with mass spectrometry (LC-MS). The results indicated the importance of investigating the environmental fate of pharmaceuticals and their transformation products reaching the environment.     

Holistic context models for visual recognition

A novel framework to context modeling based on the probability of co-occurrence of objects and scenes is proposed. The modeling is quite simple, and builds upon the availability of robust appearance classifiers. Images are represented by their posterior probabilities with respect to a set of contextual models, built upon the bag-of-features image representation, through two layers of probabilistic modeling. The first layer represents the image in a semantic space, where each dimension encodes an appearance-based posterior probability with respect to a concept. Due to the inherent ambiguity of classifying image patches, this representation suffers from a certain amount of contextual noise. The second layer enables robust inference in the presence of this noise by modeling the distribution of each concept in the semantic space. A thorough and systematic experimental evaluation of the proposed context modeling is presented. It is shown that it captures the contextual “gist” of natural images. Scene classification experiments show that contextual classifiers outperform their appearance-based counterparts, irrespective of the precise choice and accuracy of the latter. The effectiveness of the proposed approach to context modeling is further demonstrated through a comparison to existing approaches on scene classification and image retrieval, on benchmark data sets. In all cases, the proposed approach achieves superior results.     

Load modeling at electric power distribution substations using dynamic load parameters estimation

This paper is focused on electric power distribution substations load modeling using dynamic load parameters estimation. The load parameters are estimated using two models: the exponential and the ZIP load models. Since the load bus voltage and parameters are known one can determine the active and reactive power injections of this bus and include these pseudo-measurements in the state estimation in order to improve observability and estimation accuracy. The dynamic parameter estimation is developed using the weighted least squares method in a recursive form and the tests are carried out based on actual measurements. It is shown that the estimated parameters (for both load models) at a distribution substation are valid, since the obtained active and reactive power residuals are very close to zero.     

Quantitative analysis of pore patterns on rat prostate nuclei using spatial statistics methods

Spatial statistics methods have been used to analyse the nuclear pore pattern in rat ventral prostate nuclei isolated from adult animals. The observed results show that: (1) pores on prostate nuclear membranes are not randomly distributed; (2) the data sets obtained from different micrographs are consistent with the same statistical model thus suggesting the existence of a typical pore distribution.     

Structural studies of pulsed-laser deposited nanocomposite metal-oxide films

Pulsed laser deposition in vacuum has been used to develop metal-oxide nanocomposite films with well controlled structural quality. Results for the copper–aluminium oxide (Cu:Al₂O₃) system are used to illustrate the main morphological and structural features of these films. High resolution transmission electron microscopy (TEM) analysis shows that the films consist of Cu nanocrystals with average dimensions that can be controlled between 2 nm and 10 nm embedded in an amorphous Al₂O₃ matrix. It is observed that the in-plane shape of the nanocrystals evolves from circular to elongated, and the number of nanocrystals per unit area decreases as their size increases. This evolution is explained in terms of nucleation at the substrate surface and coalescence during the later stages of growth. The thermal stability of the films has been studied by in situ TEM annealing and no transformation could be observed up to about 800 °C when partial crystallization of the Al₂O₃ starts.