Gian Franco Dalla Betta; Maurizio Boscardin; Luciano Bosisio,
A Comparative Evaluation of Integrated Capacitors for AC-Coupled Microstrip Detectors,
The electrical reliability of integrated capacitors to be used for AC-coupled microstrip detectors has been investigated by characterizing a specially designed test chip. Capacitors employing different stacked dielectrics, including double-layer oxide, oxide-nitride, and oxide-nitride-oxide insulators, have been compared in terms of intrinsic breakdown field and extrinsic failures distribution. Results obtained from two fabrication runs are presented and discussed. The impact of the different technological options adopted on diode leakage current and bulk- and surface-generation parameters is also reported,
Cesare Furlanello; Stefano Merler; C. Chemini,
A New Bootstrap Method for Risk Assessment ofExposure to Lyme Disease,
Lyme borreliosis, now the most common vector-borne illness in North America, may involve cardiac manifestations as atrioventricular block, myopericarditis, and rhythm disturbances. The overall prognosis of Lyme carditis is good, but temporary cardiac pacing may be required and late dilated cardiomyopathy may occur. Borreliosis should be suspected in all patients with unexplained cardiac symptoms (supraventricular tachiarrhythmia and especially in atrioventricular block of unknown origin in young patients) who have been exposed in regions invaded by the epidemic. Risk assessment of exposure to bites of infected ticks is thus needed for prevention and accurate diagnosis of borreliosis,
Paolo Avesani; Anna Perini,
Rassegna delle fonti del Dipartimento di Protezione Civile della Provincia Autonoma di Trento,
Questo documento intende dare un quadro il più completo possibile delle banche dati sviluppate o in corso di definizione all'interno del dipartimento di protezione civile della Provincia Autonoma di Trento che possono risultare utili alla costituzione dell'archivio su cui si basa il sistema informativo per la definizione e gestione dei piano di emergenza, sistema in corso di progettazione presso il dipartimento.
Per ciascuna base dati è stata compilata una scheda descrittiva contenente riferimenti ai dati gestiti, al tipo di archivio, al suo produttore, al gestiore e all'utilizzatore. Raccoglie inoltre informazioni dettagliate rispetto a tre aspetti fondamentali quali l'acquisizione, la manutenzione e l'utilizzo dell'archivio.
Le schede offrono un quadro riassuntivo funzionale ad una rapida individuazione del contenuto informativo di ciascun archivio e di caratteristiche quali omogeneità, completezza e affidabioità dei dati contenuti.
Questo documento è da considerarsi uno strumento di lavoro, quindi può essere soggetto a cambiamenti, anche rilevanti, sia nella forma che nei contenuti. In particolare potrà venire esteso ad archivi gestiti da altri dipartimenti P.A.T. che risultino utili al progetto del dipartimento di protezione civile,
Paolo Avesani; Anna Perini,
Sistemi informativi territoriali e applicazioni verticali,
In questo documento si presenta una breve rassegna di due categorie di prodotti software: un primo insieme che comprende gli strumenti per la realizzazione di sistem informativi territoriali, un secondo insieme che contempla le applicazioini verticali per supportare le attività di prevenzione in materia di protezione civile.
Tale rassegna è mirata a guidare la scelta del software più idoneo a realizzare un sistema informativo per la gestione dei piano di emergenza comunale per il rischio idrogeologico, sistema che è in corso di progettazione presso il dipartimento di protezione civile della Provincia Autonoma di Trento,
Un algoritmo distribuito per l'interrogazione di basi di dati federate,
A Semantics for Contextual Reasoning: Theory and Two Relevant Applications,
Since McCarthy`s Turing Award speech, in 1971, the notion of context has been used in Artificial Intelligence to localize reasoning, in the sense that intelligent agents reasoning depends on the situation agents are embedded in, and on their cognitive state. A typical example is McCarthy`s above theory, in which, depending on context, the `same` theory, describing a blocks world, can be represented in two different ways with a different degree of generality. The emphasis on the role of context in localizing reasoning does not mean that there is no relation between reasoning performed in different contexts. In many applications of contexts, e.g. reasoning about beliefs, reasoning about viewpoints, integration of heterogeneous information, and multiagent systems, reasoning may involve many interacting contexts. Therefore a certain form of compatibility must exist between facts described in different contexts. This thesis aims at defining a semantics for contextual reasoning, called Local Models Semantics, that formalizes the role of context in localizing reasoning and the relations (compatibilities) among different contexts. Additionally we use tis semantics to formalize two relevant applications, that is, reasoning about beliefs and the integration of heterogeneous information in a federated database. By developing the theory of Local Models Semantics we pursue two objectives. First we aim to illustrate the intuitions underlying the use of context in reasoning. In addition we define a formal semantics for contextual reasoning which formalizes these intuitions. These objectives are accomplished by giving the basic definitions of model, satisfiability, and logical consequence. By applying Local Models Semantics to reasoning about beliefs we intend to provide evidence that our semantics provides enough modularity and flexibility to formalize agents with various introspective reasoning capabilities. Finally, by applying Local Models Semantics to the integration of information coming from heterogeneous databases, we intend to show that a precise formal semantics of a federation of databases can be defined by considering each database in the federation as a context and interactions between different databases as relations between contexts,
Edmondo Trentin; Marco Gori,
A Survey of Hibrid ANN/HMM Models for Automatic Speech Recognition,
In spite of the advances accomplished throughout the last decades by a number of research teams, Automatic Speech Recognition (ASR) is still a challenging and difficult task. In particular, recognition systems based on hidden Markov models (HMMs) are effective under many circumstances, but do suffer from some major limitations that limit applicability of ASR technology in real-world environments. Attempts were made to overcome these limitations with the adoption of Artificial Neural Networks (ANN) as an alternative paradigm for ASR, but ANN were unsuccessful in dealing with long time-sequences of speech signals. Between the end of the Eighties and the beginning or the Nineties, some researchers began exploring a new research area, by combining HMMs and ANNs within a single, hybrid architecture. The goal in hybrid systems for ASR is to take advantage from the properties of both HMMs and ANNs, improving flexibility, hardware implementability and recognition performance. A variety of different architectures and novel training algorithms have been proposed in literature. This paper reviews a number of significant hybrid models for ASR. Early attempts to emulate HMMs by ANNs are briefly described. Them we focus on ANNs to estimate posterior probabilities of the states of an HMM and on 'global' optimization, where a single, overall training criterion is defined over the HMM and the ANNs, Connectionist Vector Quantization for discrete HMMs, and other more recent approaches are also reviewed, It is pointed out that, in addition to their theoretical interest, hybrid systems have been allowing for tangible improvements in recognition performance over the standard HMMs in difficult and significant benchmark tasks,
Marco Aste; Massimo Boninsegna; M. Della Torre,
Finding Perspective Projection and Stereo Localization mappings for a Multi-Camera System,
This paper investigates the problem of finding the perspective projection and the stereo localization transforms for a binocular imaging system with long baseline. Neural techniques are used to estimate the geometrical mappings from a set of associations between points spread in the 3D space and their corresponding image projections. Aim of this work is to explore how neural networks can deal with acquisition noise and optical distorsions without considering complex camera models. Three techniques are experimentally compared: one based on the simple pin-hole camera model, a second purely based on neural function approximation, and a third which uses neural networks only to account for the deviations of the actual data from the pin-hole model predictions. Experiments have been performed on real data collected by a four-camera system placed in a laboratory room. Performance has been measured in terms of Euclidean distance between actual targets and estimated outputs on a validation and a test set. The results show the effectiveness of the neural approach and validate the combined use of pin-hole model and neural nets to a large extent. The generalization ability of the neural architectures has been finally investigated on a empirical basis,
Roldano Cattoni; Alessandra Potrich,
Bayesian Belief Networks: Introduction and Learning,
Bayesian Belief Networks are graph-based representations of probability distributions. In the last decade they became popular for modeling and using uncertain knowledge in many and different contexts. In this paper an introduction to the framework and a review of the main issues related to learning Bayesian Belif Networks are presented. The first part focuses on the definition of the framework: the mathematical and representational properties are described and discussed as well as Belief Networks from data, a topic which received much attention recently. A large amount of works, approaches and methodologies proposed in the literature is surveyed,
Francesco Ricci; Paolo Avesani,
Workshop Italiano sul Ragionamento basato su Casi,