Supporting Unified Distributed Management and Autonomic Decisions: Design,Implementation and Deployment

Nowadays, the prevailing use of networks based on traditional centralized management systems reflects on a fast increase of the management costs. The growth in the number of network equipments and services reinforces the need to distribute the management responsibilities throughout the network devices. In this approach, each device executes common network management functionalities, being part of the overall network management platform. In this paper, we present a Unified Distributed Network Management (UDNM) framework that provides a unified (wired and wireless) management network solution, where further different network services can take part of this infrastructure, e.g., flow monitoring, accurate routing decisions, distributed policies dissemination, etc. This framework is divided in two main components: (A) Situation awareness, which sets up initial information through bootstrapping, discovery, fault-management process and exchange of management information; (B) Autonomic Decision System (ADS) that performs distributed decisions in the network with incomplete information. We deploy the UDNM framework in a testbed which involves two cities (\(\approx\)250 km between), different standards (IEEE 802.3, IEEE 802.11 and IEEE 802.16e) and network technologies, such as, wired virtual grid, wireless ad-hoc gateways, ad-hoc mobile access devices. The UDNM framework integrates management functionalities into the managed devices, proving to be a lightweight and easy-respond framework. The performance analysis shows that the UDNM framework is feasible to unify devices management functionalities and to take accurate decisions on top of a real network.     

Hand gesture recognition from depth and infrared Kinect data for CAVE applications interaction

This paper presents a real-time framework that combines depth data and infrared laser speckle pattern (ILSP) images, captured from a Kinect device, for static hand gesture recognition to interact with CAVE applications. At the startup of the system, background removal and hand position detection are performed using only the depth map. After that, tracking is started using the hand positions of the previous frames in order to seek for the hand centroid of the current one. The obtained point is used as a seed for a region growing algorithm to perform hand segmentation in the depth map. The result is a mask that will be used for hand segmentation in the ILSP frame sequence. Next, we apply motion restrictions for gesture spotting in order to mark each image as a ‘Gesture’ or ‘Non-Gesture’. The ILSP counterparts of the frames labeled as “Gesture” are enhanced by using mask subtraction, contrast stretching, median filter, and histogram equalization. The result is used as the input for the feature extraction using a scale invariant feature transform algorithm (SIFT), bag-of-visual-words construction and classification through a multi-class support vector machine (SVM) classifier. Finally, we build a grammar based on the hand gesture classes to convert the classification results in control commands for the CAVE application. The performed tests and comparisons show that the implemented plugin is an efficient solution. We achieve state-of-the-art recognition accuracy as well as efficient object manipulation in a virtual scene visualized in the CAVE.     

An adaptive learning approach for 3-D surface reconstruction from point clouds.

In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.     

3D posture estimation using geodesic distance maps

This paper presents a novel technique for three-dimensional (3D) human motion capture using a set of two non-calibrated cameras. The user’s five extremities (head, hands and feet) are extracted, labeled and tracked after silhouette segmentation. As they are the minimal number of points that can be used in order to enable whole body gestural interaction, we will henceforth refer to these features as crucial points. Features are subsequently labelled using 3D triangulation and inter-image tracking. The crucial point candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region that lie on the silhouette boundary. Due to its low computational complexity, the system can run at real-time paces on standard personal computers, with an average error rate range between 4% and 9% in realistic situations, depending on the context and segmentation quality.

New Research Results for Electronic Voting

Voting systems have become controversial in the years following the multiple election disasters that occurred in the United States during and after 2000. Of particular note were the electronic voting (e-voting) systems that were widely deployed to replace the pre-scored punch-card systems, systems that had have been frequently judged to be central to the problem in the 2000 Florida general election. The (premature) deployment of e-voting machines stimulated a new field of security and privacy research around the subject of elections, a subject that has turned out to be more complex and deeper than anyone would have predicted a decade ago. Here we present a sample of some of the finest recent work on election technology from authors on three continents.     

Robotic control in knee joint replacement surgery

A brief history of robotic systems in knee arthroplasty is provided. The place of autonomous robots is then discussed and compared to more recent 'hands-on' robotic systems that can be more cost effective. The case is made for robotic systems to have a clear justification, with improved benefits compared to those from cheaper navigation systems. A number of more recent, smaller, robot systems for knee arthroplasty are also described. A specific example is given of an active constraint medical robot, the ACROBOT system, used in a prospective randomized controlled trial of unicondylar robotic knee arthroplasty in which the robot was compared to conventional surgery. The results of the trial are presented together with a discussion of the need for measures of accuracy to be introduced so that the efficacy of the robotic surgery can be immediately identified, rather than have to wait for a number of years before long-term clinical improvements can be demonstrated.     

A Framework for Integrating Non-Functional Requirements into Conceptual Models

The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely non-functional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.       

Définition théorique des paramètres en fort niveau d’un multipôle actif non linéaire

In this paper, large signal non linear P parameters are rigorously defined, allowing the characterization of non linear active n-port circuits in the frequency domain with the aim of computer aided design of power microwave devices. No a priori conditions bind the parameters to the variables describing the input signal. However the following rules apply to the parameters: they may be function of any input variables of the n-port circuit; the expression of the large-signal parameters should become identical to the expression of the linear parameters characterizing the n-port circuit under linear operating conditions when the values of the input variables tend to zero. An application of the definition to afet is demonstrated.     

On the application of the Kramers-Kronig relations to problems concerning the frequency dependence of electrode impedance

It is emphasized that, under certain conditions, the frequency dependences of the real and imaginary parts of an electrical impedance are correlated by the Kramers-Kronig relations. The importance of these relations in electrochemistry is illustrated by the examples of certain electrochemical impedance types. Some misapprehensions appearing in the electrochemical literature are pointed out.     

Decision support system to divide a large network into suitable District Metered Areas

This paper presents a new approach to divide large Water Distribution Networks (WDN) into suitable District Metered Areas (DMAs). It uses a hydraulic simulator and two operational models to identify the optimal number of DMAs, their entry points and boundary valves, and the network reinforcement/replacement needs throughout the project plan. The first model divides the WDN into suitable DMAs based on graph theory concepts and some user-defined criteria. The second model uses a simulated annealing algorithm to identify the optimal number and location of entry points and boundary valves, and the pipes reinforcement/replacement, necessary to meet the velocity and pressure requirements. The objective function is the difference between the economic benefits in terms of water loss reduction (arising from the average pressure reduction) and the cost of implementing the DMAs. To illustrate the proposed methodology, the results from a hypothetical case study are presented and discussed.