Enhancing MPLS QoS routing algorithms by using the network protection degree paradigm

IP based networks still do not have the required degree of reliability required by new multimedia services, achieving such reliability will be crucial in the success or failure of the new Internet generation. Most of existing schemes for QoS routing do not take into consideration parameters concerning the quality of the protection, such as packet loss or restoration time. In this paper, we define a new paradigm to develop new protection strategies for building reliable MPLS networks, based on what we have called the network protection degree (NPD). This NPD consists of an a priori evaluation, the failure sensibility degree (FSD), which provides the failure probability and an a posteriori evaluation, the failure impact degree (FID), to determine the impact on the network in case of failure. Having mathematical formulated these components, we point out the most relevant components. Experimental results demonstrate the benefits of the utilization of the NPD, when used to enhance some current QoS routing algorithms to offer a certain degree of protection

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

An approach to vision-based station keeping for an unmanned underwater vehicle

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented

Surface registration from range image fusion

The registration of full 3-D models is an important task in computer vision. Range finders only reconstruct a partial view of the object. Many authors have proposed several techniques to register 3D surfaces from multiple views in which there are basically two aspects to consider. First, poor registration in which some sort of correspondences are established. Second, accurate registration in order to obtain a better solution. A survey of the most common techniques is presented and includes experimental results of some of them

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

Accuracy estimation of a new omnidirectional 3D vision sensor

We present a computer vision system that associates omnidirectional vision with structured light with the aim of obtaining depth information for a 360 degrees field of view. The approach proposed in this article combines an omnidirectional camera with a panoramic laser projector. The article shows how the sensor is modelled and its accuracy is proved by means of experimental results. The proposed sensor provides useful information for robot navigation applications, pipe inspection, 3D scene modelling etc

A system to reduce the effect of CCDs saturation

Colour image segmentation based on the hue component presents some problems due to the physical process of image formation. One of that problems is colour clipping, which appear when at least one of the sensor components is saturated. We have designed a system, that works for a trained set of colours, to recover the chromatic information of those pixels on which colour has been clipped. The chromatic correction method is based on the fact that hue and saturation are invariant to the uniform scaling of the three RGB components. The proposed method has been validated by means of a specific colour image processing board that has allowed its execution in real time. We show experimental results of the application of our method

A survey addressing the fundamental matrix estimation problem

Epipolar geometry is a key point in computer vision and the fundamental matrix estimation is the only way to compute it. This article surveys several methods of fundamental matrix estimation which have been classified into linear methods, iterative methods and robust methods. All of these methods have been programmed and their accuracy analysed using real images. A summary, accompanied with experimental results, is given

Identification and Semiactive Control of Smart Structures Equipped with Magnetorheological Actuators

This paper deals with the problem of identification and semiactive control of smart structures subject to unknown external disturbances such as earthquake, wind, etc. The experimental setup used is a 6-story test structure equipped with shear-mode semiactive magnetorheological actuators being installed in WUSCEEL. The experimental results obtained have verified the effectiveness of the proposed control algorithms

Studies about the atomic capabilities concept for linear control systems in physical multi-agent environments

This paper shows the impact of the atomic capabilities concept to include control-oriented knowledge of linear control systems in the decisions making structure of physical agents. These agents operate in a real environment managing physical objects (e.g. their physical bodies) in coordinated tasks. This approach is presented using an introspective reasoning approach and control theory based on the specific tasks of passing a ball and executing the offside manoeuvre between physical agents in the robotic soccer testbed. Experimental results and conclusions are presented, emphasising the advantages of our approach that improve the multi-agent performance in cooperative systems

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Imaging of human leukemic T-cell xenografts in nude mice by radiolabeled monoclonal antibodies and F(ab')2 fragments.

Monoclonal antibodies (MoAb) that react with the T-lymphocyte markers called cluster of differentiation CD5 and CD2 were labeled with iodine 131 (131I) and were injected intravenously in nude mice bearing solid subcutaneous xenografts derived from the human T-cell leukemia line Ichikawa. Both MoAb anti-CD5 and anti-CD2 yielded favorable mean tumor to whole-body ratios of 3.8 and 5.1, respectively. These ratios were further increased up to 10.0 for MoAb anti-CD5 and 15.5 for MoAb anti-CD2 by using their F(ab')2 fragments. The tumors could be imaged clearly by external scanning after injection of F(ab')2 fragments from both MoAb. F(ab')2 fragments from MoAb anti-CD2 and of a third MoAb recognizing the clonotypic determinant (Ti) of the antigen receptor expressed by the human T-cell line Jurkat were injected in mice bearing intrasplenic Jurkat xenografts. A selective localization of both fragments in tumor tissue was demonstrated with mean tumor to whole-body ratios of 7.5 and 4.1 for MoAb anti-CD2 and anti-Ti, respectively. These in vivo experimental results may provide useful information for the potential use of radiolabeled MoAb and fragments in the diagnosis and treatment of patients with T-cell lymphoma and different other forms of T-cell malignancies.

Asymmetric tunnels in P2MP LSPs as a label space reduction method

The objective of traffic engineering is to optimize network resource utilization. Although several works have been published about minimizing network resource utilization, few works have focused on LSR (label switched router) label space. This paper proposes an algorithm that takes advantage of the MPLS label stack features in order to reduce the number of labels used in LSPs. Some tunnelling methods and their MPLS implementation drawbacks are also discussed. The described algorithm sets up NHLFE (next hop label forwarding entry) tables in each LSR, creating asymmetric tunnels when possible. Experimental results show that the described algorithm achieves a great reduction factor in the label space. The presented works apply for both types of connections: P2MP (point-to-multipoint) and P2P (point-to-point)

A Label space reduction algorithm for P2MP LSPs using asymmetric tunnels

The aim of traffic engineering is to optimise network resource utilization. Although several works on minimizing network resource utilization have been published, few works have focused on LSR label space. This paper proposes an algorithm that uses MPLS label stack features in order to reduce the number of labels used in LSPs forwarding. Some tunnelling methods and their MPLS implementation drawbacks are also discussed. The algorithm described sets up the NHLFE tables in each LSR, creating asymmetric tunnels when possible. Experimental results show that the algorithm achieves a large reduction factor in the label space. The work presented here applies for both types of connections: P2MP and P2P

Semiactive backstepping control for vibration attenuation in structures equipped with magnetorheological actuatorscat

This paper deals with the problem of semiactive vibration control of civil engineering structures subject to unknown external disturbances (for example, earthquakes, winds, etc.). Two kinds of semiactive controllers are proposed based on the backstepping control technique. The experimental setup used is a 6-story test structure equipped with shear-mode semiactive magnetorheological dampers being installed in the Washington University Structural Control and Earthquake Engineering Laboratory (WUSCEEL). The experimental results obtained have verified the effectiveness of the proposed control algorithms