Abstract:
Web technologies are used more prominently in multimedia applications. HTML5 is the flagship of these technologies but other techologies such as the Scalable Vector Graphics (SVG) standard take a growing part. The SVG standard is about be released in its version 2, providing advanced graphical tools and more integration with HTML5, enabling richer multimedia applications. This talk will present the new features of the standard, as well as the research work being carried to further improve it. A particular focus will be put on the research work done in the areas of gradient technologies, such as Gradient Meshes and Diffusion Curves, and in the area of animations and streaming.
Biography:
Cyril Concolato is Associate Professor in the Multimedia Group at Telecom ParisTech, Paris, France, where he received his master and doctoral degree in Computer Science in 2000 and 2007, respectively. His interests lie in interactive multimedia applications, in particular for the mobile, television and web worlds. He has been involved several collaborative projects (including European projects) and has published more than 30 papers in this area. He is also an active participant to the standardization bodies of MPEG and W3C. Finally, he is one of the project leaders of the Open Source project GPAC.

Electromechanical sensors and actuators are important components appearing in all disciplines of engineering. Transducer is a commonly used generic term for those components. In this respect, two important classes of materials are discussed: firstly, ferroelectric and piezoelectric materials, and secondly, magnetic and magnetostrictive materials. In order to predict a transducer’s behavior, the utilized materials have to be characterized precisely. The two most decisive aspects in characterization of those materials are the physical models used to describe the material behavior as well as the measurement methods for the determination of the model parameters. Both aspects are closely linked and, therefore, have to be taken into account simultaneously.

This talk starts with an introduction of the material classes and their properties. The use of these materials for well known applications like piezoelectric stack actuators, ultrasonic transducers and electrical power transformers is presented. Furthermore, the talk highlights the most modern fields of applications like magnetostrictive thin film micro-actuators for micro electromechanical systems (MEMS) and piezoelectric smart materials. After this overview, the talk focuses on magnetic materials, especially on magnetic large signal hysteresis effects. The scalar Preisach model is discussed and compared to other approaches. The motivation for its vectorial extension is given. The methods and measurement setups for parameter identification are explained, with focus on the Vector Vibrating Sample Magnetometer. The talk closes with an outlook on future applications of large signal models in numerical simulation and control engineering.

Sensoren stellen die Schnittstellen zwischen der ”realen physikalischen” und der elektrischen/elektronischen (und digitalen) Welt dar. Nicht zuletzt aufgrund der stets steigenden Komplexität technischer Systeme nehmen nicht nur die Anzahl und die Aufgaben von Sensoren zu, sondern auch die Anforderungen an diese hinsichtlich Genauigkeit, Zuverlässigkeit und Sicherheit. Daneben sind auch zahlreiche weitere Anforderungen wie geringer Platz- und Energiebedarf oder geringe Kosten zu erfüllen

Um diesen hohen Anforderungen gerecht zu werden, werden oftmals „intelligente Sensoren“ eingesetzt, die neben einem (oder mehreren) Primärsensor(en) auch analoge und digitale Signalaufbereitung, Hard- und Software für Signalverarbeitung und Eigendiagnose sowie drahtgebundene oder drahtlose Kommunikationseinrichtungen aufweisen. Mitunter beinhalten derartige Sensoren eigene „Kraftwerke“, um sich aus der Umgebung mit Energie zu versorgen („Energy Harvesting“) und somit völlig autark arbeiten zu können. Für den Entwurf derartiger Systeme werden Methoden benötigt, die es den Designern ermöglichen, die Komplexität zu beherrschen.

In dem Vortrag werden Ansätze für den modellbasierten Design- und Optimierungsprozess von intelligenten Sensoren vorgestellt. Dabei spielen insbesondere die Themen Messunsicherheit, Zuverlässigkeit und Sicherheit eine große Rolle. Ein wesentlicher Aspekt ist die Berücksichtigung möglicher Störeinflüsse (Herstellungstoleranzen, Rauschen, Elektromagnetische Störer, Defekte, …) und deren Auswirkung während der gesamten Entwurfphase. Diese Vorgehensweise führt einerseits zu robusten Designs und erlaubt andererseits eine frühzeitige Erkennung von kritischen Faktoren, wodurch Fehlentwicklungen vermieden werden können

Electrical capacitance tomography (ECT) belongs to the class of so-called inverse problems which are the most challenging class of estimation problems due to their ill-posedness and their typically high-dimensional parameter spaces. In particular, ECT aims at determining electrical properties (the permittivity) of heterogeneous material distributions in inaccessible objects given sparse measurements. In order to obtain robust estimates as well as quantitative statements on parameter variability, additional information in form of prior knowledge about the unknown quantities is required. Such prior information might include system dynamics, empirical and analytical measurement models, the measurement noise mode, and certain constraints.

This talk addresses different numerical sensor modeling techniques and appropriate estimation approaches of determining process parameters in the presence of uncertainties by example. Uncertainty in recovered parameters arises from measurement noise, measurement sensitivities, model inaccuracy, discretization error, and a-priori uncertainty.

In this context, the ECT parameter estimation problem is formulated in a Bayesian inferential framework, by specifying a physically motivated prior distribution, and characterizing the statistics of measurement noise, to give a posterior distribution conditioned on measured data. The ECT sensor is simulated numerically by means of the finite element and the boundary element method. Markov chain Monte Carlo (MCMC) sampling with Metropolis-Hastings dynamics and particle filtering, notably useful for non-stationary problems, are investigated in terms of efficient exploration of the posterior distribution.

A key difference between the proposed statistical (Bayesian) approach and classical deterministic methods (e.g. Newton-type regularized optimization techniques) is that whereas regularization gives point estimates, typically using a data-misfit criterion, Bayesian methods present averages over all solutions consistent with the data. This leads to a marked difference in robustness of properties calculated from solutions. The proposed approaches are discussed for a reference problem of recovering the unknown shape and position of a constant permittivity inclusion in an otherwise uniform background. Statistics calculated in the reference problem give accurate estimates of inclusion area, and other properties, when using measured data.

Over the last decades monitoring of engineering structures has become more and more important. Besides traditional sensor systems, Smart Sensor Systems, which inmany cases are put into practice by embodying active materials into the structures to be monitored, have gained an ever increasing attention for Structural and Health Monitoring. The proper distribution of the active materials, which constitute the sensing authority, over the structure, is of fundamental importance in order to gain specific information concerning the state of the monitored structure. In particular, sensing mechanisms, which enable the direct measurement of strains (e.g. piezoelectric sensors) have proven to be suitable sensing authorities for distributed sensing.

Moreover, embodied smart sensor systems eliminate the need for additional external sensor systems and they may as well be used as actuator systems; this further enables passive/active control using embodied sensors and actuators. The first part of the present talk is focussed on the development of novel easy-to-use methods for computing the distribution of smart sensor systems to measure kinematically relevant structural entities (e.g. the relative displacement between two points or the slope of the displacement field of a structure). A simple solution for this Sensor Design Problem is presented and different types of practically important distributed smart sensor systems are discussed; e.g. modal sensors and nilpotent sensors. Based on these findings, practical implementations utilizing piezoelectric sensors are introduced and numerically and

experimentally verified. A specific problem, to which the design methods are applied, is discussed in detail in the second part of the talk – Structural Health Monitoring of frame structures using piezoelectric sensor networks. These networks are designed to be highly sensi tive to damage by using nilpotent sensor networks, which result into a trivial signal, if the structure is undamaged. For damaged frames this particular type of sensor network facilitates the detection, localisition and quantification of damage. Analytical, numerical and experimental results are presented for a three-storey frame structure.

In the last part of the talk extensions to using the smart piezoelectric sensor systems as actuators as well are introduced; in particular, methods such as Self-Sensing Actuators and passive Shunt Damping, for which no additional physical actuator system is needed. Practical problems concerned with active health monitoring of frame structures and with noise reduction for thin plates complete the present talk.