Enrutament i control de flux en xarxes híbrides satèl·lit-terrestre

Les xarxes híbrides satèl·lit-terrestre ofereixen connectivitat a zones remotes i aïllades i permeten resoldre nombrosos problemes de comunicacions. No obstant, presenten diversos reptes, ja que realitzen la comunicació per un canal mòbil terrestre i un canal satèl·lit contigu. Un d'aquests reptes és trobar mecanismes per realitzar eficientment l'enrutament i el control de flux, de manera conjunta. L'objectiu d'aquest projecte és simular i estudiar algorismes existents que resolguin aquests problemes, així com proposar-ne de nous, mitjançant diverses tècniques d'optimització convexa. A partir de les simulacions realitzades en aquest estudi, s'han analitzat àmpliament els diversos problemes d'enrutament i control de flux, i s'han avaluat els resultats obtinguts i les prestacions dels algorismes emprats. En concret, s'han implementat de manera satisfactòria algorismes basats en el mètode de descomposició dual, el mètode de subgradient, el mètode de Newton i el mètode de la barrera logarítmica, entre d'altres, per tal de resoldre els problemes d'enrutament i control de flux plantejats.

Impact of an intervention to control risk associated with patient transfer.

QUESTION UNDER STUDY: Hospitals transferring patients retain responsibility until admission to the new health care facility. We define safe transfer conditions, based on appropriate risk assessment, and evaluate the impact of this strategy as implemented at our institution. METHODS: An algorithm defining transfer categories according to destination, equipment monitoring, and medication was developed and tested prospectively over 6 months. Conformity with algorithm criteria was assessed for every transfer and transfer category. After introduction of a transfer coordination centre with transfer nurses, the algorithm was implemented and the same survey was carried out over 1 year. RESULTS: Over the whole study period, the number of transfers increased by 40%, chiefly by ambulance from the emergency department to other hospitals and private clinics. Transfers to rehabilitation centres and nursing homes were reassigned to conventional vehicles. The percentage of patients requiring equipment during transfer, such as an intravenous line, decreased from 34% to 15%, while oxygen or i.v. drug requirement remained stable. The percentage of transfers considered below theoretical safety decreased from 6% to 4%, while 20% of transfers were considered safer than necessary. A substantial number of planned transfers could be "downgraded" by mutual agreement to a lower degree of supervision, and the system was stable on a short-term basis. CONCLUSION: A coordinated transfer system based on an algorithm determining transfer categories, developed on the basis of simple but valid medical and nursing criteria, reduced unnecessary ambulance transfers and treatment during transfer, and increased adequate supervision.

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

Synchronous and baroceptor-sensitive oscillations in skin microcirculation: evidence for central autonomic control.

To determine whether skin blood flow is local or takes part in general regulatory mechanisms, we recorded laser-Doppler flowmetry (LDF; left and right index fingers), blood pressure, muscle sympathetic nerve activity (MSNA), R-R interval, and respiration in 10 healthy volunteers and 3 subjects after sympathectomy. We evaluated 1) the synchronism of LDF fluctuations in two index fingers, 2) the relationship with autonomically mediated fluctuations in other signals, and 3) the LDF ability to respond to arterial baroreflex stimulation (by neck suction at frequencies from 0.02 to 0.20 Hz), using spectral analysis (autoregressive uni- and bivariate, time-variant algorithms). Synchronous LDF fluctuations were observed in the index fingers of healthy subjects but not in sympathectomized patients. LDF fluctuations were coherent with those obtained for blood pressure, MSNA, and R-R interval. LDF fluctuations were leading blood pressure in the low-frequency (LF; 0.1 Hz) band and lagging in the respiratory, high-frequency (HF; approximately 0.25 Hz) band, suggesting passive "downstream" transmission only for HF and "upstream" transmission for LF from the microvessels. LDF fluctuations were responsive to sinusoidal neck suction up to 0.1 Hz, indicating response to sympathetic modulation. Skin blood flow thus reflects modifications determined by autonomic activity, detectable by frequency analysis of spontaneous fluctuations.

Endoleak and in-stent thrombus detection with CT angiography in a thoracic aortic aneurysm phantom at different tube energies using filtered back projection and iterative algorithms.

PURPOSE: To determine the lower limit of dose reduction with hybrid and fully iterative reconstruction algorithms in detection of endoleaks and in-stent thrombus of thoracic aorta with computed tomographic (CT) angiography by applying protocols with different tube energies and automated tube current modulation. MATERIALS AND METHODS: The calcification insert of an anthropomorphic cardiac phantom was replaced with an aortic aneurysm model containing a stent, simulated endoleaks, and an intraluminal thrombus. CT was performed at tube energies of 120, 100, and 80 kVp with incrementally increasing noise indexes (NIs) of 16, 25, 34, 43, 52, 61, and 70 and a 2.5-mm section thickness. NI directly controls radiation exposure; a higher NI allows for greater image noise and decreases radiation. Images were reconstructed with filtered back projection (FBP) and hybrid and fully iterative algorithms. Five radiologists independently analyzed lesion conspicuity to assess sensitivity and specificity. Mean attenuation (in Hounsfield units) and standard deviation were measured in the aorta to calculate signal-to-noise ratio (SNR). Attenuation and SNR of different protocols and algorithms were analyzed with analysis of variance or Welch test depending on data distribution. RESULTS: Both sensitivity and specificity were 100% for simulated lesions on images with 2.5-mm section thickness and an NI of 25 (3.45 mGy), 34 (1.83 mGy), or 43 (1.16 mGy) at 120 kVp; an NI of 34 (1.98 mGy), 43 (1.23 mGy), or 61 (0.61 mGy) at 100 kVp; and an NI of 43 (1.46 mGy) or 70 (0.54 mGy) at 80 kVp. SNR values showed similar results. With the fully iterative algorithm, mean attenuation of the aorta decreased significantly in reduced-dose protocols in comparison with control protocols at 100 kVp (311 HU at 16 NI vs 290 HU at 70 NI, P ≤ .0011) and 80 kVp (400 HU at 16 NI vs 369 HU at 70 NI, P ≤ .0007). CONCLUSION: Endoleaks and in-stent thrombus of thoracic aorta were detectable to 1.46 mGy (80 kVp) with FBP, 1.23 mGy (100 kVp) with the hybrid algorithm, and 0.54 mGy (80 kVp) with the fully iterative algorithm.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

On Modelling, System Identification and Control of Servo-Systems with a Flexible Load

The present study was done with two different servo-systems. In the first system, a servo-hydraulic system was identified and then controlled by a fuzzy gainscheduling controller. The second servo-system, an electro-magnetic linear motor in suppressing the mechanical vibration and position tracking of a reference model are studied by using a neural network and an adaptive backstepping controller respectively. Followings are some descriptions of research methods. Electro Hydraulic Servo Systems (EHSS) are commonly used in industry. These kinds of systems are nonlinearin nature and their dynamic equations have several unknown parameters.System identification is a prerequisite to analysis of a dynamic system. One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) for solving global optimization problems. In the study, the DE algorithm is proposed for handling nonlinear constraint functionswith boundary limits of variables to find the best parameters of a servo-hydraulic system with flexible load. The DE guarantees fast speed convergence and accurate solutions regardless the initial conditions of parameters. The control of hydraulic servo-systems has been the focus ofintense research over the past decades. These kinds of systems are nonlinear in nature and generally difficult to control. Since changing system parameters using the same gains will cause overshoot or even loss of system stability. The highly non-linear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. The study is concerned with a second order model reference to positioning control of a flexible load servo-hydraulic system using fuzzy gainscheduling. In the present research, to compensate the lack of dampingin a hydraulic system, an acceleration feedback was used. To compare the results, a pcontroller with feed-forward acceleration and different gains in extension and retraction is used. The design procedure for the controller and experimental results are discussed. The results suggest that using the fuzzy gain-scheduling controller decrease the error of position reference tracking. The second part of research was done on a PermanentMagnet Linear Synchronous Motor (PMLSM). In this study, a recurrent neural network compensator for suppressing mechanical vibration in PMLSM with a flexible load is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system respectively. The proposed control method is firstly designed by using non-linear simulation model built in Matlab Simulink and then implemented in practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully. In the last part of research, a nonlinear load control method is developed and implemented for a PMLSM with a flexible load. The purpose of the controller is to track a flexible load to the desired position reference as fast as possible and without awkward oscillation. The control method is based on an adaptive backstepping algorithm whose stability is ensured by the Lyapunov stability theorem. The states of the system needed in the controller are estimated by using the Kalman filter. The proposed controller is implemented and tested in a linear motor test drive and responses are presented.

Proposal of methodology for the modeling and control of manipulators

Industrial applications demand that robots operate in agreement with the position and orientation of their end effector. It is necessary to solve the kinematics inverse problem. This allows the displacement of the joints of the manipulator to be determined, to accomplish a given objective. Complete studies of dynamical control of joint robotics are also necessary. Initially, this article focuses on the implementation of numerical algorithms for the solution of the kinematics inverse problem and the modeling and simulation of dynamic systems. This is done using real time implementation. The modeling and simulation of dynamic systems are performed emphasizing off-line programming. In sequence, a complete study of the control strategies is carried out through the study of several elements of a robotic joint, such as: DC motor, inertia, and gearbox. Finally a trajectory generator, used as input for a generic group of joints, is developed and a proposal of the controller's implementation of joints, using EPLD development system, is presented.

Cost-effective analysis of different algorithms for the diagnosis of hepatitis C virus infection

We compared the cost-benefit of two algorithms, recently proposed by the Centers for Disease Control and Prevention, USA, with the conventional one, the most appropriate for the diagnosis of hepatitis C virus (HCV) infection in the Brazilian population. Serum samples were obtained from 517 ELISA-positive or -inconclusive blood donors who had returned to Fundação Pró-Sangue/Hemocentro de São Paulo to confirm previous results. Algorithm A was based on signal-to-cut-off (s/co) ratio of ELISA anti-HCV samples that show s/co ratio ³95% concordance with immunoblot (IB) positivity. For algorithm B, reflex nucleic acid amplification testing by PCR was required for ELISA-positive or -inconclusive samples and IB for PCR-negative samples. For algorithm C, all positive or inconclusive ELISA samples were submitted to IB. We observed a similar rate of positive results with the three algorithms: 287, 287, and 285 for A, B, and C, respectively, and 283 were concordant with one another. Indeterminate results from algorithms A and C were elucidated by PCR (expanded algorithm) which detected two more positive samples. The estimated cost of algorithms A and B was US$21,299.39 and US$32,397.40, respectively, which were 43.5 and 14.0% more economic than C (US$37,673.79). The cost can vary according to the technique used. We conclude that both algorithms A and B are suitable for diagnosing HCV infection in the Brazilian population. Furthermore, algorithm A is the more practical and economical one since it requires supplemental tests for only 54% of the samples. Algorithm B provides early information about the presence of viremia.

Transmission dynamics and tuberculosis control among HIV/AIDS patients

Introduction: Les efforts globaux pour contrôler la tuberculose sont présentement restreints par la prévalence croissante du VIH/SIDA. Quoique les éclosions de la tuberculose multi résistante (TB-MDR) soient fréquemment rapportées parmi les populations atteintes du SIDA, le lien entre VIH/SIDA et le développement de résistance n’est pas clair. Objectifs: Cette recherche visait à : (1) développer une base de connaissances concernant les facteurs associés à des éclosions de la TB-MDR parmi les patients atteints du VIH/SIDA; (2) utiliser ce cadre de connaissances pour accroître des mesures préliminaires pour mieux contrôler la tuberculose pulmonaire chez les patients atteints du VIH/SIDA; et (3) afin d’améliorer l’application des ces mesures, affiner les techniques bactériologiques existantes pour Mycobacterium tuberculosis. Méthodologie: Quatre études ont été réalisées : (1) Une étude longitudinale pour identifier les facteurs associés avec une éclosion de la TB-MDR parmi les patients atteints du SIDA qui ont reçu le traitement directement supervisé de courte durée (DOTS) pour la tuberculose pulmonaire au Lima et au Pérou entre 1999 et 2005; (2) Une étude transversale pour décrire différentes étapes de l’histoire naturelle de la tuberculose, la prévalence et les facteurs associés avec la mycobactérie qu’on retrouve dans les selles des patients atteints du SIDA; (3) Un projet pilote pour développer des stratégies de dépistage pour la tuberculose pulmonaire parmi les patients hospitalisés atteints du SIDA, en utilisant l’essaie Microscopic Observation Drug Susceptibility (MODS); et (4) Une étude laboratoire pour identifier les meilleures concentrations critiques pour détecter les souches MDR de M. tuberculosis en utilisant l’essaie MODS. Résultats : Étude 1 démontre qu’une épidémie de TB-MDR parmi les patients atteints du SIDA qui ont reçu DOTS pour la tuberculose pulmonaire ait été causée par la superinfection du clone de M. tuberculosis plutôt que le développement de la résistance secondaire. Bien que ce clone ait été plus commun parmi la cohorte de patients atteints du SIDA, il n’avait aucune différence de risque pour superinfection entre les patients avec ou sans SIDA. Ces résultats suggèrent qu’un autre facteur, possiblement associé à la diarrhée, peu contribuer à la prévalence élevée de ce clone chez les patients atteints du SIDA. Étude 2 suggère que chez la plupart des patients atteints du SIDA il a été retrouvé une mycobactérie dans leurs selles alors qu’ils étaient en phase terminale au niveau de la tuberculose pulmonaire. Or, les patients atteints du SIDA ayant été hospitalisés pendant les deux dernières années pour une autre condition médicale sont moins à risque de se retrouver avec une mycobactérie dans leurs selles. Étude 3 confirme que la tuberculose pulmonaire a été commune à tous les patients hospitalisés atteints du SIDA, mais diagnostiquée incorrectement en utilisant les critères cliniques présentement recommandés pour la tuberculose. Or, l’essaie MODS a détecté pour la plupart de ces cas. De plus, MODS a été également efficace quand la méthode a été dirigée aux patients soupçonnés d’avoir la tuberculose, à cause de leurs symptômes. Étude 4 démontre les difficultés de détecter les souches de M. tuberculosis avec une faible résistance contre ethambutol et streptomycine en utilisant l’essai MODS avec les concentrations de drogue présentement recommandées pour un milieu de culture. Cependant, l’utilité diagnostique de MODS peut être améliorée ; modifier les concentrations critiques et utiliser deux plaques et non une, pour des tests réguliers. Conclusion: Nos études soulèvent la nécessité d’améliorer le diagnostic et le traitement de la tuberculose parmi les patients atteints du SIDA, en particulier ceux qui vivent dans des régions avec moins de ressources. Par ailleurs, nos résultats font ressortir les effets indirects que les soins de santé ont sur les patients infectés par le VIH et qu’ils peuvent avoir sur le développement de la tuberculose.

Explorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methods

There has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.

On the Convergence of Stochastic Iterative Dynamic Programming Algorithms

Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD(lambda) and Q-learning belong.

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

Control Structures

These slides describe how control structures (if/else and loops) are used within algorithms. It includes a description of conditionals (>, ==, etc.) and logic (AND, OR, etc).