Monitoring switch-type sensors and powering autonomous sensors via inductive coupling: application to removable seats in vehicles

Aquesta tesi explora la possibilitat de fer servir enllaços inductius per a una aplicació de l’automòbil on el cablejat entre la centraleta (ECU) i els sensors o detectors és difícil o impossible. S’han proposat dos mètodes: 1) el monitoratge de sensors commutats (dos possibles estats) via acoblament inductiu i 2) la transmissió mitjançant el mateix principi físic de la potència necessària per alimentar els sensors autònoms remots. La detecció d'ocupació i del cinturó de seguretat per a seients desmuntables pot ser implementada amb sistemes sense fils passius basats en circuits ressonants de tipus LC on l'estat dels sensors determina el valor del condensador i, per tant, la freqüència de ressonància. Els canvis en la freqüència són detectats per una bobina situada en el terra del vehicle. S’ha conseguit provar el sistema en un marge entre 0.5 cm i 3 cm. Els experiments s’han dut a terme fent servir un analitzador d’impedàncies connectat a una bobina primària i sensors comercials connectats a un circuit remot. La segona proposta consisteix en transmetre remotament la potència des d’una bobina situada en el terra del vehicle cap a un dispositiu autònom situat en el seient. Aquest dispositiu monitorarà l'estat dels detectors (d'ocupació i de cinturó) i transmetrà les dades mitjançant un transceptor comercial de radiofreqüència o pel mateix enllaç inductiu. S’han avaluat les bobines necessàries per a una freqüència de treball inferior a 150 kHz i s’ha estudiat quin és el regulador de tensió més apropiat per tal d’aconseguir una eficiència global màxima. Quatre tipus de reguladors de tensió s’han analitzat i comparat des del punt de vista de l’eficiència de potència. Els reguladors de tensió de tipus lineal shunt proporcionen una eficiència de potència millor que les altres alternatives, els lineals sèrie i els commutats buck o boost. Les eficiències aconseguides han estat al voltant del 40%, 25% i 10% per les bobines a distàncies 1cm, 1.5cm, i 2cm. Les proves experimentals han mostrat que els sensors autònoms han estat correctament alimentats fins a distàncies de 2.5cm.

Prediction of dynamical, nonlinear, and unstable process behavior

Process scheduling techniques consider the current load situation to allocate computing resources. Those techniques make approximations such as the average of communication, processing, and memory access to improve the process scheduling, although processes may present different behaviors during their whole execution. They may start with high communication requirements and later just processing. By discovering how processes behave over time, we believe it is possible to improve the resource allocation. This has motivated this paper which adopts chaos theory concepts and nonlinear prediction techniques in order to model and predict process behavior. Results confirm the radial basis function technique which presents good predictions and also low processing demands show what is essential in a real distributed environment.

Remarks on the Nagata Conjecture

2000 Mathematics Subject Classification: 14C20, 14E25, 14J26.

Deformations of canonical triple covers

In this paper, we show that if X is a smooth variety of general type of dimension m≥3 for which the canonical map induces a triple cover onto Y, where Y is a projective bundle over P1 or onto a projective space or onto a quadric hypersurface, embedded by a complete linear series (except Q3 embedded in P4), then the general deformation of the canonical morphism of X is again canonical and induces a triple cover. The extremal case when Y is embedded as a variety of minimal degree is of interest, due to its appearance in numerous situations. For instance, by looking at threefolds Y of minimal degree we find components of the moduli of threefolds X of general type with KX3=3pg−9,KX3≠6, whose general members correspond to canonical triple covers. Our results are especially interesting as well because they have no lower dimensional analogues.

Deformations of canonical double covers

In this paper we show that if X is a smooth variety of general type of dimension m≥2 for which its canonical map induces a double cover onto Y, where Y is the projective space, a smooth quadric hypersurface or a smooth projective bundle over P1, embedded by a complete linear series, then the general deformation of the canonical morphism of X is again canonical and induces a double cover. The second part of the article proves the non-existence of canonical double structures on the rational varieties above mentioned. Our results have consequences for the moduli of varieties of general type of arbitrary dimension, since they show that infinitely many moduli spaces of higher dimensional varieties of general type have an entire “hyperelliptic” component. This is in sharp contrast with the case of curves or surfaces of lower Kodaira dimension.

Non-linear time series models

The last three decades have seen quite dramatic changes the way we modeled time dependent data. Linear processes have been in the center stage in modeling time series. As far as the second order properties are concerned, the theory and the methodology are very adequate.However, there are more and more evidences that linear models are not sufficiently flexible and rich enough for modeling purposes and that failure to account for non-linearities can be very misleading and have undesired consequences.

Forecasting economic and financial time series with non-linear models

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

Model updating of the non-linear vibrating structures through Volterra series and proper orthogonal decomposition

Classical procedures for model updating in non-linear mechanical systems based on vibration data can fail because the common linear metrics are not sensitive for non-linear behavior caused by gaps, backlash, bolts, joints, materials, etc. Several strategies were proposed in the literature in order to allow a correct representative model of non-linear structures. The present paper evaluates the performance of two approaches based on different objective functions. The first one is a time domain methodology based on the proper orthogonal decomposition constructed from the output time histories. The second approach uses objective functions with multiples convolutions described by the first and second order discrete-time Volterra kernels. In order to discuss the results, a benchmark of a clamped-clamped beam with an pre-applied static load is simulated and updated using proper orthogonal decomposition and Volterra Series. The comparisons and discussions of the results show the practical applicability and drawbacks of both approaches.

Non-parametric identification of a non-linear buckled beam using discrete-time Volterra Series

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Uniform approach to linear and nonlinear interrelation patterns in multivariate time series

Currently, a variety of linear and nonlinear measures is in use to investigate spatiotemporal interrelation patterns of multivariate time series. Whereas the former are by definition insensitive to nonlinear effects, the latter detect both nonlinear and linear interrelation. In the present contribution we employ a uniform surrogate-based approach, which is capable of disentangling interrelations that significantly exceed random effects and interrelations that significantly exceed linear correlation. The bivariate version of the proposed framework is explored using a simple model allowing for separate tuning of coupling and nonlinearity of interrelation. To demonstrate applicability of the approach to multivariate real-world time series we investigate resting state functional magnetic resonance imaging (rsfMRI) data of two healthy subjects as well as intracranial electroencephalograms (iEEG) of two epilepsy patients with focal onset seizures. The main findings are that for our rsfMRI data interrelations can be described by linear cross-correlation. Rejection of the null hypothesis of linear iEEG interrelation occurs predominantly for epileptogenic tissue as well as during epileptic seizures.

Series combination of a switched dc-dc converter and a linear regulator for high frequency RF envelope amplifier

Classical linear amplifiers such as A, AB and B offer very good linearity suitable for RF power amplifiers. However, its inherent low efficiency limits its use especially in base-stations that manage tens or hundreds of Watts. The use of linearization techniques such as Envelope Elimination and Restoration (EER) allow an increase of efficiency keeping good linearity. This technique requires a very fast dc-dc power converter to provide variable voltage supply to the power amplifier. In this paper, several alternatives are analyzed to implement the envelope amplifier based on a cascade association of a switched dc-dc converter and a linear regulator. A simplified version of this approach is also suitable to operate with Envelope Tracking technique.