Wavelength reused bidirectional transmission of adaptively modulated optical OFDM signals in WDM-PONs incorporating SOA and RSOA intensity modulators.

Detailed numerical investigations are undertaken of wavelength reused bidirectional transmission of adaptively modulated optical OFDM (AMOOFDM) signals over a single SMF in a colorless WDM-PON incorporating a semiconductor optical amplifier (SOA) intensity modulator and a reflective SOA (RSOA) intensity modulator in the optical line termination and optical network unit, respectively. A comprehensive theoretical model describing the performance of such network scenarios is, for the first time, developed, taking into account dynamic optical characteristics of SOA and RSOA intensity modulators as well as the effects of Rayleigh backscattering (RB) and residual downstream signal-induced crosstalk. The developed model is rigorously verified experimentally in RSOA-based real-time end-to-end OOFDM systems at 7.5 Gb/s. It is shown that the RB noise and crosstalk effects are dominant factors limiting the maximum achievable downstream and upstream transmission performance. Under optimum SOA and RSOA operating conditions as well as practical downstream and upstream optical launch powers, 10 Gb/s downstream and 6 Gb/s upstream over 40 km SMF transmissions of conventional double sideband AMOOFDM signals are feasible without utilizing in-line optical amplification and chromatic dispersion compensation. In particular, the aforementioned transmission performance can be improved to 23 Gb/s downstream and 8 Gb/s upstream over 40 km SMFs when single sideband subcarrier modulation is adopted in the downstream systems.

Wavelength reused bidirectional transmission of adaptively modulated optical OFDM signals in SOA/RSOA intensity modulator-based WDM-PONs

Detailed numerical investigations are undertaken of wavelength reused bidirectional transmission of adaptively modulated optical OFDM (AMOOFDM) signals over a single SMF in a WDM-PON incorporating a SOA intensity modulator and a RSOA intensity modulator in the OLT and ONU, respectively. A comprehensive theoretical model describing the performance of such network scenarios is, for the first time, developed, taking into account dynamic optical characteristics of SOA and RSOA intensity modulators as well as the effects of Rayleigh backscattering (RB) and residual downstream signal-induced crosstalk. The developed model is rigorously verified experimentally in RSOA-based real-time end-to-end OOFDM systems at 7.5Gb/s. It is shown that the RB noise and crosstalk effects are the dominant factors limiting the maximum achievable downstream and upstream transmission performance. Under optimum SOA and RSOA operating conditions as well as practical downstream and upstream optical launch powers, 10Gb/s downstream and 6Gb/s upstream over 40km SMF transmissions of conventional double sideband AMOOFDM signals are feasible without utilizing inline optical amplification and chromatic dispersion compensation. In particular, the transmission performance can be improved to 23Gb/s downstream and 8Gb/s upstream over 40 km SMFs when single sideband subcarrier modulation is adopted in the downstream systems. Copyright © 2010 The authors.

Convex approaches to model wavelet sparsity patterns

Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-convex optimizations. Past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods. In this paper, we propose new modeling approaches based on group-sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently. We show that the methods we develop perform significantly better in de-convolution and compressed sensing applications, while being as computationally efficient as standard coefficient-wise approaches such as lasso. © 2011 IEEE.

Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment

In this paper, we consider Bayesian interpolation and parameter estimation in a dynamic sinusoidal model. This model is more flexible than the static sinusoidal model since it enables the amplitudes and phases of the sinusoids to be time-varying. For the dynamic sinusoidal model, we derive a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment of lost audio and speech packets. © EURASIP, 2010.

Partially Observable Markov Decision Processes with continuous observations for dialogue management

This work shows how a dialogue model can be represented as a Partially Observable Markov Decision Process (POMDP) with observations composed of a discrete and continuous component. The continuous component enables the model to directly incorporate a confidence score for automated planning. Using a testbed simulated dialogue management problem, we show how recent optimization techniques are able to find a policy for this continuous POMDP which outperforms a traditional MDP approach. Further, we present a method for automatically improving handcrafted dialogue managers by incorporating POMDP belief state monitoring, including confidence score information. Experiments on the testbed system show significant improvements for several example handcrafted dialogue managers across a range of operating conditions.

State estimation schemes for independent component coupled hidden Markov models

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood EM algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based EM algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results. © Taylor & Francis Group, LLC.

Bayesian Learning of Noisy Markov Decision Processes

We consider the inverse reinforcement learning problem, that is, the problem of learning from, and then predicting or mimicking a controller based on state/action data. We propose a statistical model for such data, derived from the structure of a Markov decision process. Adopting a Bayesian approach to inference, we show how latent variables of the model can be estimated, and how predictions about actions can be made, in a unified framework. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from the posterior distribution. 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.

Analytical model of spin filtering effect and its experimental verification in a forward-biased iron-metal-Al2O3-n-type GaAs tunneling structure under optical spin orientation

An analytical model for the spin filtering transport in a ferromagnetic-metal - Al2O3 - n-type semiconductor tunneling structure has been developed, and demonstrated that the ratio of the helicity-modulated photo-response to the chopped one is proportional to the sum of the relative asymmetry in conductance of two opposite spin-polarized tunneling channels and the MCD effect of the ferromagnetic metal film. The performed measurement in an iron-metal/Al2O3/n-type GaAs tunneling structure under the optical spin orientation has verified that all the aspects of the experimental results are very well in accordance with our model in the regime of the spin filtering. After the MCD effect of the iron film is calibrated by an independent measurement, the physical quantity of Delta G(t)/G(t) (Delta G(t) = G(t)(up arrow) - G(t)(down arrow) is the difference of the conductance between two opposite spin tunneling channels, G(t) =( G(t)(up arrow) + G(t)(down arrow))/2 the averaged tunneling conductance), which concerns us most, can be determined quantitatively with a high sensitivity in the framework of our analytical model. Copyright (c) EPLA, 2008.

Stochastic resonance for modulated gain in a single-mode laser

Stochastic resonance (SR) induced by the signal modulation is investigated, by introducing the signal-modulated gain into a single-mode laser system. Using the linear approximation method, we detailedly calculate the signal-to-noise ratio (SNR) of a gain-noise model of the single-mode laser, taking the cross-correlation between the quantum noise and pump noise into account. We find that, SR appears in the dependence of the SNR on the intensities of the quantum and the pump noises when the correlation coefficient between both the noises is negative; moreover, when the cross-correlation between the two noises is strongly negative, SR exhibits a resonance and a suppression versus the gain coefficient, meanwhile, the single-peaked SR and multi-peaked SR occur in the behaviors of the SNR as functions of the loss coefficient and the deterministic steady-state intensity. (c) 2005 Elsevier B.V. All rights reserved.

Spin splitting modulated by uniaxial stress in InAs nanowires

We theoretically study the electronic structure, spin splitting, effective mass, and spin orientation of InAs nanowires with cylindrical symmetry in the presence of an external electric field and uniaxial stress. Using an eight-band k center dot p theoretical model, we deduce a formula for the spin splitting in the system, indicating that the spin splitting under uniaxial stress is a nonlinear function of the momentum and the electric field. The spin splitting can be described by a linear Rashba model when the wavevector and the electric field are sufficiently small. Our numeric results show that the uniaxial stress can modulate the spin splitting. With the increase of wavevector, the uniaxial tensile stress first restrains and then amplifies the spin splitting of the lowest electron state compared to the no strain case. The reverse is true under a compression. Moreover, strong spin splitting can be induced by compression when the top of the valence band is close to the bottom of the conductance band, and the spin orientations of the electron stay almost unchanged before the overlap of the two bands.

Co-assembly of ferrocene-terminated and alkylthiophene thiols on gold and its redox chemistry modulated by surfactant adsorption

Coadsorption of ferrocene-terminated alkanethiols (FcCO(2)(CH2)(8)SH, Fc=(mu(5)-C5H5)Fe(mu(5)-C5H4)) with alkylthiophene thiols (2-mercapto-3-n-octylthiophene) yields stable, electroactive self-assembled monolayers on gold. The resulting mixed monolayer provides an energetically favorable hydrophobic surface for the adsorption of the surfactant aggregates in aqueous solution. The adsorptions have been characterized via their effect on the redox properties of ferrocenyl alkanethiols immobilized as minority components in the monolayers and on the interfacial capacitance of the electrode. Surfactant adsorption causes a decrease in the overall capacitance at the electrode and dramatically shifts the redox potential for ferrocene oxidation in a positive or negative direction depending on the identity of the surfactant employed. A structural model is proposed in which the alkane chains of the adsorbed surfactants interdigitate with those of the underlying self-assembled monolayer, leading to the formation of a hybrid bilayer membrane.

Formalizing Triggers: A Learning Model for Finite Spaces

In a recent seminal paper, Gibson and Wexler (1993) take important steps to formalizing the notion of language learning in a (finite) space whose grammars are characterized by a finite number of parameters. They introduce the Triggering Learning Algorithm (TLA) and show that even in finite space convergence may be a problem due to local maxima. In this paper we explicitly formalize learning in finite parameter space as a Markov structure whose states are parameter settings. We show that this captures the dynamics of TLA completely and allows us to explicitly compute the rates of convergence for TLA and other variants of TLA e.g. random walk. Also included in the paper are a corrected version of GW's central convergence proof, a list of "problem states" in addition to local maxima, and batch and PAC-style learning bounds for the model.

Learning Fine Motion by Markov Mixtures of Experts

Compliant control is a standard method for performing fine manipulation tasks, like grasping and assembly, but it requires estimation of the state of contact between the robot arm and the objects involved. Here we present a method to learn a model of the movement from measured data. The method requires little or no prior knowledge and the resulting model explicitly estimates the state of contact. The current state of contact is viewed as the hidden state variable of a discrete HMM. The control dependent transition probabilities between states are modeled as parametrized functions of the measurement We show that their parameters can be estimated from measurements concurrently with the estimation of the parameters of the movement in each state of contact. The learning algorithm is a variant of the EM procedure. The E step is computed exactly; solving the M step exactly would require solving a set of coupled nonlinear algebraic equations in the parameters. Instead, gradient ascent is used to produce an increase in likelihood.

Equivalence and Reduction of Hidden Markov Models

This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.

A Phylogenetic Mixture Model for Detecting Pattern-Heterogeneity in Gene Sequence or Character-State Data

Mark Pagel, Andrew Meade (2004). A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Systematic Biology, 53(4), 571-581. RAE2008