Design Strategies and Modelling of Low-Power Sigma-Delta Analog-to-Digital Converters

Oggi, i dispositivi portatili sono diventati la forza trainante del mercato consumer e nuove sfide stanno emergendo per aumentarne le prestazioni, pur mantenendo un ragionevole tempo di vita della batteria. Il dominio digitale è la miglior soluzione per realizzare funzioni di elaborazione del segnale, grazie alla scalabilità della tecnologia CMOS, che spinge verso l'integrazione a livello sub-micrometrico. Infatti, la riduzione della tensione di alimentazione introduce limitazioni severe per raggiungere un range dinamico accettabile nel dominio analogico. Minori costi, minore consumo di potenza, maggiore resa e una maggiore riconfigurabilità sono i principali vantaggi dell'elaborazione dei segnali nel dominio digitale. Da più di un decennio, diverse funzioni puramente analogiche sono state spostate nel dominio digitale. Ciò significa che i convertitori analogico-digitali (ADC) stanno diventando i componenti chiave in molti sistemi elettronici. Essi sono, infatti, il ponte tra il mondo digitale e analogico e, di conseguenza, la loro efficienza e la precisione spesso determinano le prestazioni globali del sistema. I convertitori Sigma-Delta sono il blocco chiave come interfaccia in circuiti a segnale-misto ad elevata risoluzione e basso consumo di potenza. I tools di modellazione e simulazione sono strumenti efficaci ed essenziali nel flusso di progettazione. Sebbene le simulazioni a livello transistor danno risultati più precisi ed accurati, questo metodo è estremamente lungo a causa della natura a sovracampionamento di questo tipo di convertitore. Per questo motivo i modelli comportamentali di alto livello del modulatore sono essenziali per il progettista per realizzare simulazioni veloci che consentono di identificare le specifiche necessarie al convertitore per ottenere le prestazioni richieste. Obiettivo di questa tesi è la modellazione del comportamento del modulatore Sigma-Delta, tenendo conto di diverse non idealità come le dinamiche dell'integratore e il suo rumore termico. Risultati di simulazioni a livello transistor e dati sperimentali dimostrano che il modello proposto è preciso ed accurato rispetto alle simulazioni comportamentali.

Numerical modelling of unsteady mixed flows

This thesis concerns mixed flows (which are characterized by the simultaneous occurrence of free-surface and pressurized flow in sewers, tunnels, culverts or under bridges), and contributes to the improvement of the existing numerical tools for modelling these phenomena. The classic Preissmann slot approach is selected due to its simplicity and capability of predicting results comparable to those of a more recent and complex two-equation model, as shown here with reference to a laboratory test case. In order to enhance the computational efficiency, a local time stepping strategy is implemented in a shock-capturing Godunov-type finite volume numerical scheme for the integration of the de Saint-Venant equations. The results of different numerical tests show that local time stepping reduces run time significantly (between −29% and −85% CPU time for the test cases considered) compared to the conventional global time stepping, especially when only a small region of the flow field is surcharged, while solution accuracy and mass conservation are not impaired. The second part of this thesis is devoted to the modelling of the hydraulic effects of potentially pressurized structures, such as bridges and culverts, inserted in open channel domains. To this aim, a two-dimensional mixed flow model is developed first. The classic conservative formulation of the 2D shallow water equations for free-surface flow is adapted by assuming that two fictitious vertical slots, normally intersecting, are added on the ceiling of each integration element. Numerical results show that this schematization is suitable for the prediction of 2D flooding phenomena in which the pressurization of crossing structures can be expected. Given that the Preissmann model does not allow for the possibility of bridge overtopping, a one-dimensional model is also presented in this thesis to handle this particular condition. The flows below and above the deck are considered as parallel, and linked to the upstream and downstream reaches of the channel by introducing suitable internal boundary conditions. The comparison with experimental data and with the results of HEC-RAS simulations shows that the proposed model can be a useful and effective tool for predicting overtopping and backwater effects induced by the presence of bridges and culverts.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce two novel techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar catterometer data gathered by a remote-sensing satellite.

Modelling conditional probability distributions for periodic variables

Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and investigate their performance using synthetic data. We then apply these techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Regularised mixture density networks for modelling wind direction

This technical report contains all technical information and results from experiments where Mixture Density Networks (MDN) using an RBF network and fixed kernel means and variances were used to infer the wind direction from satellite data from the ersII weather satellite. The regularisation is based on the evidence framework and three different approximations were used to estimate the regularisation parameter. The results were compared with the results by `early stopping'.

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

Modelling frontal discontinuities in wind fields

A Bayesian procedure for the retrieval of wind vectors over the ocean using satellite borne scatterometers requires realistic prior near-surface wind field models over the oceans. We have implemented carefully chosen vector Gaussian Process models; however in some cases these models are too smooth to reproduce real atmospheric features, such as fronts. At the scale of the scatterometer observations, fronts appear as discontinuities in wind direction. Due to the nature of the retrieval problem a simple discontinuity model is not feasible, and hence we have developed a constrained discontinuity vector Gaussian Process model which ensures realistic fronts. We describe the generative model and show how to compute the data likelihood given the model. We show the results of inference using the model with Markov Chain Monte Carlo methods on both synthetic and real data.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

Modelling financial time series with switching state space models

The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space model (SSSM) and discuss the practical details of training such models with a variational EM algorithm due to [Ghahramani and Hilton,1998]. The performance of the SSSM is evaluated on several financial data sets and it is shown to improve on a number of existing benchmark methods.

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Modelling foreign direct investment in China

This thesis consists of three empirical and one theoretical studies. While China has received an increasing amount of foreign direct investment (FDI) and become the second largest host country for FDI in recent years, the absence of comprehensive studies on FDI inflows into this country drives this research. In the first study, an econometric model is developed to analyse the economic, political, cultural and geographic determinants of both pledged and realised FDI in China. The results of this study suggest that China's relatively cheaper labour force, high degree of international integration with the outside world (represented by its exports and imports) and bilateral exchange rates are the important economic determinants of both pledged FDI and realised FDI in China. The second study analyses the regional distribution of both pledged and realised FDI within China. The econometric properties of the panel data set are examined using a standardised 't-bar' test. The empirical results indicate that provinces with higher level of international trade, lower wage rates, more R&D manpower, more preferential policies and closer ethnic links with overseas Chinese attract relatively more FDI. The third study constructs a dynamic equilibrium model to study the interactions among FDI, knowledge spillovers and long run economic growth in a developing country. The ideas of endogenous product cycles and trade-related international knowledge spillovers are modified and extended to FDI. The major conclusion is that, in the presence of FDI, economic growth is determined by the stock of human capital, the subjective discount rate and knowledge gap, while unskilled labour can not sustain growth. In the fourth study, the role of FDI in the growth process of the Chinese economy is investigated by using a panel of data for 27 provinces across China between 1986 and 1995. In addition to FDI, domestic R&D expenditure, international trade and human capital are added to the standard convergence regressions to control for different structural characteristics in each province. The empirical results support endogenous innovation growth theory in which regional per capita income can converge given technological diffusion, transfer and imitation.

Simulation process templates for e-business modelling

This paper argues the use of reusable simulation templates as a tool that can help to predict the effect of e-business introduction on business processes. First, a set of requirements for e-business modelling is introduced and modelling options described. Traditional business process mapping techniques are examined as a way of identifying potential changes. Whilst paper-based process mapping may not highlight significant differences between traditional and e-business processes, simulation does allow the real effects of e-business to be identified. Simulation has the advantage of capturing the dynamic characteristics of the process, thus reflecting more accurately the changes in behaviour. This paper shows the value of using generic process maps as a starting point for collecting the data that is needed to build the simulation and proposes the use of reusable templates/components for the speedier building of e-business simulation models.