Long-period modulated structure and electric-field-induced structural transformation in Na0.5Bi0.5TiO3-based lead-free piezoelectrics

Na0.5Bi0.5TiO3- based lead-free piezoelectrics exhibiting giant piezostrain are technologically interesting materials for actuator applications. The lack of clarity with regard to the structure of the nonpolar phase of this system has hindered the understanding of the structural mechanism associated with the giant piezostrain and other related phenomena. In this paper, we have investigated the structure and field-induced phase transformation behavior of a model system (0.94 - x) Na0.5Bi0.5TiO3-0.06BaTiO(3)-xK(0.5)Na(0.5)NbO(3) (0.0 <= x <= 0.025). A detailed structural analysis using neutron powder diffraction revealed that the nonpolar phase is neither cubic nor a mixture of rhombohedral (R3c) and tetragonal (P4bm) phases as commonly reported in literature but exhibits a long-period modulated structure, which is most probably of the type root 2 x root 2 x n with n = 16. Our results suggest that the giant piezoelectric strain is associated with a field-induced phase transformation of the long-period modulated structure to rhombohedral R3c structure above a critical field. We also demonstrate that the giant piezostrain is lost if the system retains a fraction of the field-induced R3c phase. A possible correlation among depolarization temperature, giant piezostrain, and its electrical fatigue behavior has also been indicated.

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how sequential Monte Carlo methods provide a natural method for implementing our likelihood-based ABC procedures.

Bayesian learning of noisy Markov decision processes

This work addresses the problem of estimating the optimal value function in a Markov Decision Process from observed state-action pairs. We adopt a Bayesian approach to inference, which allows both the model to be estimated and predictions about actions to be made in a unified framework, providing a principled approach to mimicry of a controller on the basis of observed data. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from theposterior distribution over the optimal value function. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.

Markov Switching Risk Premium and the term structure of interest rates. Empirical evidence from US post-war interest rates

This paper considers the basic present value model of interest rates under rational expectations with two additional features. First, following McCallum (1994), the model assumes a policy reaction function where changes in the short-term interest rate are determined by the long-short spread. Second, the short-term interest rate and the risk premium processes are characterized by a Markov regime-switching model. Using US post-war interest rate data, this paper finds evidence that a two-regime switching model fits the data better than the basic model. The estimation results also show the presence of two alternative states displaying quite different features.

A model of the olfactory bulb and beyond

The olfactory bulb of mammals aids in the discrimination of odors. A mathematical model based on the bulbar anatomy and electrophysiology is described. Simulations of the highly non-linear model produce a 35-60 Hz modulated activity, which is coherent across the bulb. The decision states (for the odor information) in this system can be thought of as stable cycles, rather than as point stable states typical of simpler neuro-computing models. Analysis shows that a group of coupled non-linear oscillators are responsible for the oscillatory activities. The output oscillation pattern of the bulb is determined by the odor input. The model provides a framework in which to understand the transformation between odor input and bulbar output to the olfactory cortex. This model can also be extended to other brain areas such as the hippocampus, thalamus, and neocortex, which show oscillatory neural activities. There is significant correspondence between the model behavior and observed electrophysiology.

It has also been suggested that the olfactory bulb, the first processing center after the sensory cells in the olfactory pathway, plays a role in olfactory adaptation, odor sensitivity enhancement by motivation, and other olfactory psychophysical phenomena. The input from the higher olfactory centers to the inhibitory cells in the bulb are shown to be able to modulate the response, and thus the sensitivity, of the bulb to odor input. It follows that the bulb can decrease its sensitivity to a pre-existing and detected odor (adaptation) while remaining sensitive to new odors, or can increase its sensitivity to discover interesting new odors. Other olfactory psychophysical phenomena such as cross-adaptation are also discussed.

A uniform phase representation for the harmonic model in speech synthesis applications

Feature-based vocoders, e.g., STRAIGHT, offer a way to manipulate the perceived characteristics of the speech signal in speech transformation and synthesis. For the harmonic model, which provide excellent perceived quality, features for the amplitude parameters already exist (e.g., Line Spectral Frequencies (LSF), Mel-Frequency Cepstral Coefficients (MFCC)). However, because of the wrapping of the phase parameters, phase features are more difficult to design. To randomize the phase of the harmonic model during synthesis, a voicing feature is commonly used, which distinguishes voiced and unvoiced segments. However, voice production allows smooth transitions between voiced/unvoiced states which makes voicing segmentation sometimes tricky to estimate. In this article, two-phase features are suggested to represent the phase of the harmonic model in a uniform way, without voicing decision. The synthesis quality of the resulting vocoder has been evaluated, using subjective listening tests, in the context of resynthesis, pitch scaling, and Hidden Markov Model (HMM)-based synthesis. The experiments show that the suggested signal model is comparable to STRAIGHT or even better in some scenarios. They also reveal some limitations of the harmonic framework itself in the case of high fundamental frequencies.

Defining plausible migration rates based on historical tagging data: a Bayesian mark-recapture model applied to English sole (Parophrys vetulus)

A generalized Bayesian population dynamics model was developed for analysis of historical mark-recapture studies. The Bayesian approach builds upon existing maximum likelihood methods and is useful when substantial uncertainties exist in the data or little information is available about auxiliary parameters such as tag loss and reporting rates. Movement rates are obtained through Markov-chain Monte-Carlo (MCMC) simulation, which are suitable for use as input in subsequent stock assessment analysis. The mark-recapture model was applied to English sole (Parophrys vetulus) off the west coast of the United States and Canada and migration rates were estimated to be 2% per month to the north and 4% per month to the south. These posterior parameter distributions and the Bayesian framework for comparing hypotheses can guide fishery scientists in structuring the spatial and temporal complexity of future analyses of this kind. This approach could be easily generalized for application to other species and more data-rich fishery analyses.

Identificação de danos estruturais via método de Monte Carlo com cadeias de Markov

O presente trabalho apresenta um estudo referente à aplicação da abordagem Bayesiana como técnica de solução do problema inverso de identificação de danos estruturais, onde a integridade da estrutura é continuamente descrita por um parâmetro estrutural denominado parâmetro de coesão. A estrutura escolhida para análise é uma viga simplesmente apoiada do tipo Euler-Bernoulli. A identificação de danos é baseada em alterações na resposta impulsiva da estrutura, provocadas pela presença dos mesmos. O problema direto é resolvido através do Método de Elementos Finitos (MEF), que, por sua vez, é parametrizado pelo parâmetro de coesão da estrutura. O problema de identificação de danos é formulado como um problema inverso, cuja solução, do ponto de vista Bayesiano, é uma distribuição de probabilidade a posteriori para cada parâmetro de coesão da estrutura, obtida utilizando-se a metodologia de amostragem de Monte Carlo com Cadeia de Markov. As incertezas inerentes aos dados medidos serão contempladas na função de verossimilhança. Três estratégias de solução são apresentadas. Na Estratégia 1, os parâmetros de coesão da estrutura são amostrados de funções densidade de probabilidade a posteriori que possuem o mesmo desvio padrão. Na Estratégia 2, após uma análise prévia do processo de identificação de danos, determina-se regiões da viga potencialmente danificadas e os parâmetros de coesão associados à essas regiões são amostrados a partir de funções de densidade de probabilidade a posteriori que possuem desvios diferenciados. Na Estratégia 3, após uma análise prévia do processo de identificação de danos, apenas os parâmetros associados às regiões identificadas como potencialmente danificadas são atualizados. Um conjunto de resultados numéricos é apresentado levando-se em consideração diferentes níveis de ruído para as três estratégias de solução apresentadas.

Segmentação de nome e endereço por meio de modelos escondidos de Markov e sua aplicação em processos de vinculação de registros

A segmentação dos nomes nas suas partes constitutivas é uma etapa fundamental no processo de integração de bases de dados por meio das técnicas de vinculação de registros. Esta separação dos nomes pode ser realizada de diferentes maneiras. Este estudo teve como objetivo avaliar a utilização do Modelo Escondido de Markov (HMM) na segmentação nomes e endereços de pessoas e a eficiência desta segmentação no processo de vinculação de registros. Foram utilizadas as bases do Sistema de Informações sobre Mortalidade (SIM) e do Subsistema de Informação de Procedimentos de Alta Complexidade (APAC) do estado do Rio de Janeiro no período entre 1999 a 2004. Uma metodologia foi proposta para a segmentação de nome e endereço sendo composta por oito fases, utilizando rotinas implementadas em PL/SQL e a biblioteca JAHMM, implementação na linguagem Java de algoritmos de HMM. Uma amostra aleatória de 100 registros de cada base foi utilizada para verificar a correção do processo de segmentação por meio do modelo HMM.Para verificar o efeito da segmentação do nome por meio do HMM, três processos de vinculação foram aplicados sobre uma amostra das duas bases citadas acima, cada um deles utilizando diferentes estratégias de segmentação, a saber: 1) divisão dos nomes pela primeira parte, última parte e iniciais do nome do meio; 2) divisão do nome em cinco partes; (3) segmentação segundo o HMM. A aplicação do modelo HMM como mecanismo de segmentação obteve boa concordância quando comparado com o observador humano. As diferentes estratégias de segmentação geraram resultados bastante similares na vinculação de registros, tendo a estratégia 1 obtido um desempenho pouco melhor que as demais. Este estudo sugere que a segmentação de nomes brasileiros por meio do modelo escondido de Markov não é mais eficaz do que métodos tradicionais de segmentação.

The QBO, El Niño, and tidal resonance model

I tentatively conclude ... that the QBO may be either modulated or amplified by atmospheric tidal resonances. The generally strong correlation between the QBO and tropical Southern Oscillation, in turn, suggests that one or probably both of these stratospheric and tropospheric oscillations are modulated or amplified by the same tidal-resonance system. ... The strikingly weaker correlation between the Southern Oscillation and El Niño events strongly suggests that some critical parameters are missing in the GCMs specifically designed to predict El Niño occurrence.

System model for studying fibre grating dispersion compensators

A complete optical system model has been developed and used to assess chirped fibre Bragg grating dispersion compensators. Gratings suitable for dispersion compensation in both laser based and modulator based optical communications systems have been modelled. A grating 10 cm in length has been shown to permit virtually dispersion free transmission over 425 km, when used in an externally modulated system. Long haul dispersion compensation using several 2 cm gratings spaced at intervals along the fibre is also modelled, illustrating viable 10Gbit/s transmission over a distance in excess of 168 km.

Numerical modelling of dynamic response of loss modulated monolithic multisection mode-locked semiconductor lasers

Monolithic multisection mode-locked semiconductor lasers with an integrated distributed Bragg reflector (DBR) have recently been demonstrated to generate stable picosecond pulses at high repetition rates suitable for optical communication systems. However, there has been very little theoretical work on understanding the physical mechanisms of the device and on optimisation of the absorber modulator design. This article presents numerical modeling of the loss modulated mode-locking process in these lasers. The model predicts most aspects experimentally observed within this type of device, and the results show the output waveform, optical spectrum, instantaneous frequency chirp, and stable operating range.

On the relationship between hidden Markov Models and convex functional transforms for simulating scanning probe Microscopy

Models for simulating Scanning Probe Microscopy (SPM) may serve as a reference point for validating experimental data and practice. Generally, simulations use a microscopic model of the sample-probe interaction based on a first-principles approach, or a geometric model of macroscopic distortions due to the probe geometry. Examples of the latter include use of neural networks, the Legendre Transform, and dilation/erosion transforms from mathematical morphology. Dilation and the Legendre Transform fall within a general family of functional transforms, which distort a function by imposing a convex solution.In earlier work, the authors proposed a generalized approach to modeling SPM using a hidden Markov model, wherein both the sample-probe interaction and probe geometry may be taken into account. We present a discussion of the hidden Markov model and its relationship to these convex functional transforms for simulating and restoring SPM images.©2009 SPIE.

Colourless adaptively modulated optical OFDM transmitters using SOAs as intensity modulators.

The wavelength dependent transmission performance of adaptively modulated optical OFDM (AMOOFDM) signals is investigated, for the first time, over optical amplification- and chromatic dispersion compensation-free IMDD SMF systems using semiconductor optical amplifiers (SOAs) as intensity modulators. A theoretical SOA model describing both optical gain saturation and gain spectral dynamics is developed, based on which optimum SOA operating conditions are identified for various wavelengths varying in a broad range of 1510 nm- 1590 nm. Results show that, SOA intensity modulators operating at the identified optimum conditions enable the realization of colourless AMOOFDM transmitters within the aforementioned wavelength window. Such transmitters are capable of supporting >30 Gb/s signal transmission over 60 km SMFs.

Adaptively modulated optical OFDM modems utilizing RSOAs as intensity modulators in IMDD SMF transmission systems.

Detailed investigations of the transmission performance of adaptively modulated optical orthogonal frequency division multiplexed (AMOOFDM) signals converted using reflective semiconductor optical amplifiers (RSOAs) are undertaken over intensity-modulation and direct-detection (IMDD) single-mode fiber (SMF) transmission systems for WDM-PONs. The theoretical RSOA model adopted for modulating the AMOOFDM signals is experimentally verified rigorously in the aforementioned transmission systems incorporating recently developed real-time end-to-end OOFDM transceivers. Extensive performance comparisons are also made between RSOA and SOA intensity modulators. Optimum RSOA operating conditions are identified, which are independent of RSOA rear-facet reflectivity and very similar to those corresponding to SOAs. Under the identified optimum operating conditions, the RSOA and SOA intensity modulators support the identical AMOOFDM transmission performance of 30Gb/s over 60km SMFs. Under low-cost optical component-enabled practical operating conditions, RSOA intensity modulators with rear-facet reflectivity values of >0.3 outperform considerably SOA intensity modulators in transmission performance, which decreases significantly with reducing RSOA rear-facet reflectivity and optical input power. In addition, results also show that use can be made of the RSOA/SOA intensity modulation-induced negative frequency chirp to improve the AMOOFDM transmission performance in IMDD SMF systems.