Randomization can be a healer: consensus with dynamic omission failures

Wireless ad-hoc networks are being increasingly used in diverse contexts, ranging from casual meetings to disaster recovery operations. A promising approach is to model these networks as distributed systems prone to dynamic communication failures. This captures transitory disconnections in communication due to phenomena like interference and collisions, and permits an efficient use of the wireless broadcasting medium. This model, however, is bound by the impossibility result of Santoro and Widmayer, which states that, even with strong synchrony assumptions, there is no deterministic solution to any non-trivial form of agreement if n ? 1 or more messages can be lost per communication round in a system with n processes. In this paper we propose a novel way to circumvent this impossibility result by employing randomization. We present a consensus protocol that ensures safety in the presence of an unrestricted number of omission faults, and guarantees progress in rounds where such faults are bounded by ${f \,{\leq}\,\lceil \frac{n}{2} \rceil (n\,{-}\,k)\,{+}\,k\,{-}\,2}$ , where k is the number of processes required to decide, eventually assuring termination with probability 1.     

Interactive classification for pre-integrated volume rendering of high-precision volume data

The pre-integrated volume rendering technique is widely used for creating high quality images. It produces good images even though the transfer function is nonlinear. Because the size of the pre-integration lookup table is proportional to the square of data precision, the required storage and computation load steeply increase for rendering of high-precision volume data. In this paper, we propose a method that approximates the pre-integration function proportional to the data precision. Using the arithmetic mean instead of the geometric mean and storing opacity instead of extinction density, this technique reduces the size and the update time of the pre-integration lookup table so that it classifies high-precision volume data interactively. We demonstrate performance gains for typical renderings of volume datasets.     

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 influence of cooling rate during alloy casting on the oxidation behaviour of TiAl-based intermetallics

The oxidation behaviour of the intermetallic phase Ti-50 at% Al at 900 °C was studied. It was found that the oxidation behaviour strongly depends on the composition and distribution of the and ₂-phases in the alloy microstructure. As this microstructure strongly depends on the alloy cooling rate, the oxidation behaviour of TiAl-based intermetallics is significantly affected by the casting procedure and subsequent heat treatment. Rapid cooling from high temperature (T 1400 °C) has a beneficial effect on oxidation behaviour.On leave from the Academy of Mining and Metallurgy, Krakow, Poland.     

Exploring different technological platforms for supporting co-located collaborative games in the classroom

Computer Supported Collaborative Learning is a pedagogical approach that can be used for deploying educational games in the classroom. However, there is no clear understanding as to which technological platforms are better suited for deploying co-located collaborative games, nor the general affordances that are required. In this work we explore two different technological platforms for developing collaborative games in the classroom: one based on augmented reality technology and the other based on multiple-mice technology. In both cases, the same game was introduced to teach electrostatics and the results were compared experimentally using a real class.     

SpectralCAT: Categorical spectral clustering of numerical and nominal data

Data clustering is a common technique for data analysis, which is used in many fields, including machine learning, data mining, customer segmentation, trend analysis, pattern recognition and image analysis. Although many clustering algorithms have been proposed, most of them deal with clustering of one data type (numerical or nominal) or with mix data type (numerical and nominal) and only few of them provide a generic method that clusters all types of data. It is required for most real-world applications data to handle both feature types and their mix. In this paper, we propose an automated technique, called SpectralCAT, for unsupervised clustering of high-dimensional data that contains numerical or nominal or mix of attributes. We suggest to automatically transform the high-dimensional input data into categorical values. This is done by discovering the optimal transformation according to the Calinski–Harabasz index for each feature and attribute in the dataset. Then, a method for spectral clustering via dimensionality reduction of the transformed data is applied. This is achieved by automatic non-linear transformations, which identify geometric patterns in the data, and find the connections among them while projecting them onto low-dimensional spaces. We compare our method to several clustering algorithms using 16 public datasets from different domains and types. The experiments demonstrate that our method outperforms in most cases these algorithms.     

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.     

Constraint force design method for topology optimization of planar rigid-body mechanisms

This study develops a new design method called the constraint force design method, which allows topology optimization for planar rigid-body mechanisms. In conventional mechanism synthesis methods, the kinematics of a mechanism are analytically derived and the positions and types of joints of a fixed configuration (hereafter the topology) are optimized to obtain an optimal rigid-body mechanism tracking the intended output trajectory. Therefore, in conventional methods, modification of the configuration or topology of joints and links is normally considered impossible. In order to circumvent the fixed topology limitation in optimally designing rigid-body mechanisms, we present the constraint force design method. This method distributes unit masses simulating revolute or prismatic joints depending on the number of assigned degrees of freedom, analyzes the kinetics of unit masses coupled with constraint forces, and designs the existence of these constraint forces to minimize the root-mean-square error of the output paths of synthesized linkages and a target linkage using a genetic algorithm. The applicability and limitations of the newly developed method are discussed in the context of its application to several rigid-body synthesis problems.