Fast-to-fed shift in glucose homeostasis: clues to an earlier detection of human prediabetic states

FCM: UC Bioquímica I - PhD Thesis

Massive shift in the pneumococcal nasopharyngeal flora after the 7-valent conjugate vaccine: epidemiological studies and testing pathogenic potential in animal models

Dissertation presented to obtain the PhD degree in Biology/Molecular Biology by Universidade Nova de Lisboa, Instituto de Tecnologia Química e Biológica

Modelos de previsão de deterioração de pontes existentes: processos de Markov

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Civil

THE U.S. and European M&A cycles: A Markov switching and state space approach

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Credit cycle identification: A Markov-switching application

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.

Quadrature generators based on ring oscillators and shift registers

Quadrature oscillators are key elements in modern radio frequency (RF) transceivers and very useful nowadays in wireless communications, since they can provide: low quadrature error, low phase-noise, and wide tuning range (useful to cover several bands). RC oscillators can be fully integrated without the need of external components (external high Q-inductors), optimizing area, cost, and power consumption. The conventional structure of ring oscillator offers poor frequency stability and phasenoise, low quality factor (Q), and besides being vulnerable to process, voltage and temperature (PVT) variations, its performance degrades as the frequency of operation increases. This thesis is devoted to quadrature oscillators and presents a detailed comparative study of ring oscillator and shift register (SR) approaches. It is shown that in SRs both phase-noise and phase error are reduced, while ring oscillators have the advantage of occupying less area and less consumption due to the reduced number of components in the circuit. Thus, although ring oscillators are more suitable for biomedical applications, SRs are more appropriate for wireless applications, especially when specification requirements are more stringent and demanding. The first architecture studied consists in a simple CMOS ring oscillator employing an odd number of static single-ended inverters as delay cells. Subsequently, the quadrature 4-stage ring oscillator concept is shown and post-layout simulations are presented. The 3 and 4-phase single-frequency local oscillator (LO) generators employing SRs are presented, the latter with 50% and 25% duty-cycles. The circuits operate at 600 MHz and 900 MHz, and were designed in a 130 nm standard CMOS technology with a voltage supply of 1.2 V.