La Cultura de la caritat: inventari dels béns de l'Hospital de la Seu (Girona, 31 de gener 1342)

L'any 1342 el rector de l'Hospital de la Seu de Girona, Pere Miquel, encarrega la redacció d'un inventari detallat dels béns d'aquesta institució, relació que s'acompanya de la valoració d'aquests per a un possible encant o subhasta pública; el seu càrrec, nomenat pel capítol catedralici, l'obliga a vetllar per la conservació d'aquests béns. És per aquest motiu que ordena aquesta escripturació davant notari i testimonis: registra el patrimoni moble i immoble de l'hospital i jura davant els Evangelis guardar- lo i mantenir-lo fins que s'esgoti el seu mandat; promet protegir aquestes possessions dels actu malorum hominum i respondre de qualsevol pèrdua. [...]

Entorn del projecte de "l'obra nova" del Palau de la Generalitat. El Memorial de 1603

Quan el 1602 els diputats ordenen una nova visura de les obres d'ampliació del Palau de la Generalitat, s'està de fet qüestionant la primitiva traça de Pere Blai. Hom posa en joc tot un seguit de categories, d'acord amb un nou estil visual d'arrel renaixentista que prima la mirada. En són requeriments: els costos econòmics, la visualització de l'edifici –valor de la imatge d'acord amb una intencionada representació del poder civil– i la seva individualització en el teixit urbà –d'aquí, el propòsit d'alliberar l'espai d'una plaça. El Memorial de 1603 s'insereix en una llarga i intricada seqüència. El document, d'interès pel cabal intrínsec d'informació projectual i constructiva, ho és també per a l'exegesi del model clàssic, del seu grau de comprensió i utilització al llindar del segle XVII en l'àmbit del Principat

Projecte Universitat - Escoles: la implementació d'estratègies de pràctica reflexiva en la formació inicial dels mestres d'educació infantil

L’experiència formativa que presentem a continuació s’ha dut a terme el curs acadèmic 2008-2009, en l’àmbit de la formació inicial de mestres d’educació infantil, amb un grup d’estudiants de 1er i en el marc de l’assignatura Didàctica General i Atenció a la Diversitat (DGAD), de caràcter troncal, obligatòria i anual. Per dur-la a terme hem comptat amb la col·laboració de mestres en actiu de l’Etapa d’Educació Infantil de dues escoles de Sant Andreu (La Maquinista i Eulàlia Bota), a partir de la creació d’una comissió mixta de treball entre Universitat i Escoles. Aquesta experiència ens ha facilitat la incorporació d’estratègies de pràctica reflexiva a partir de la participació directa dels estudiants en entorns d’aprenentatge propis de l’exercici docent, les aules d’educació infantil. La intervenció dels nostres estudiants ha estat possible gràcies a l’organització i planificació curricular de les escoles en diferents espais i ambients d’aprenentatge que han permès un treball col·laboratiu entre mestres, estudiants i professorat. El resultat d’aquesta experiència ha respòs a l’objectiu comú d’implimentar noves metodologies per a contribuir a la millora de l’acció docent a través de la participació conjunta de dos col·lectius: universitat i escola

Multicast extension of unicast charging for QoS Services

In order to successfully deploy multicast services in QoS-aware networks, pricing architectures must take into account the particular characteristics of multicast sessions. With this objective, we propose a charging scheme for QoS multicast services, assuming that the unicast cost of each interconnecting link is determined and that such cost is expressed in terms of quality of service (QoS) parameters. Our scheme allows determining the cost distribution of a multicast session along a cost distribution tree (CDT), and basing such distribution in those pre-existing unicast cost functions. The paper discusses in detail the main characteristics of the problem in a realistic interdomain scenario and how the proposed scheme would contribute to its solution

A quality-based cost distribution charging scheme for QoS multicast networks

This paper presents a new charging scheme for cost distribution along a point-to-multipoint connection when destination nodes are responsible for the cost. The scheme focus on QoS considerations and a complete range of choices is presented. These choices go from a safe scheme for the network operator to a fair scheme to the customer. The in-between cases are also covered. Specific and general problems, like the incidence of users disconnecting dynamically is also discussed. The aim of this scheme is to encourage the users to disperse the resource demand instead of having a large number of direct connections to the source of the data, which would result in a higher than necessary bandwidth use from the source. This would benefit the overall performance of the network. The implementation of this task must balance between the necessity to offer a competitive service and the risk of not recovering such service cost for the network operator. Throughout this paper reference to multicast charging is made without making any reference to any specific category of service. The proposed scheme is also evaluated with the criteria set proposed in the European ATM charging project CANCAN

On the use of QoS requirements as base of multicast charging

We propose a charging scheme for cost distribution along a multicast tree when cost is the responsibility of the receivers. This scheme focuses on QoS considerations and it does not depend on any specific type of service. The scheme has been designed to be used as a bridge between unicast and multicast services, solving the problem of charging multicast services by means of unicast charging and existing QoS routing mechanisms. We also include a numerical comparison and discussions of the case of non-numerical or relative QoS and on the application to some service examples in order to give a better understanding of the proposal

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

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Vision-based localization of an underwater robot in a structured environment

This paper presents a vision-based localization approach for an underwater robot in a structured environment. The system is based on a coded pattern placed on the bottom of a water tank and an onboard down looking camera. Main features are, absolute and map-based localization, landmark detection and tracking, and real-time computation (12.5 Hz). The proposed system provides three-dimensional position and orientation of the vehicle along with its velocity. Accuracy of the drift-free estimates is very high, allowing them to be used as feedback measures of a velocity-based low-level controller. The paper details the localization algorithm, by showing some graphical results, and the accuracy of the system

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

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task