Analysis of the mechanisms for impaired high-temperature, high-speed performance of 1.3 μm InGaAsP lasers

In this paper, the static and dynamic performance of multi quantum-well (MQW) 1.3 μm InGaAsP Fabry Perot lasers is assessed experimentally and theoretically to identify the mechanisms responsible for impaired high speed performance at elevated temperature. Initially, threshold currents and spontaneous emission spectra are characterized for a range of temperatures from room temperature to 85 °C to indicate a significant increase in non-radiative current contributions. Preliminary estimates are made for the contributions of leakage and Auger recombination rates, found from the dependence of integrated spontaneous emission with carrier density. Drift-diffusion modelling is found to accurately predict the trend of threshold currents over temperature. Using gain modelling good agreement is found between the measured and predicted integrated spontaneous emission intensity. Gain measurements at 85 °C indicate a reduction in RIN frequency to 63% of the 25 °C value which matches well with experimental small signal performance.

Superfluorescence in semiconductor lasers

An analysis is made of the conditions for the generation of superfluorescence pulses in an inverted medium of electron-hole pairs in a semiconductor. It is shown that strong optical amplification in laser semiconductor amplifiers characterised by αL ≫ 1 (α is the small-signal gain and L is the amplifier length) leads to suppression of phase relaxation of the medium during the initial stages of evolution of superfluorescence and to formation of a macroscopic dipole from electron - hole pairs. Cooperative emission of radiation in this system results in generation of a powerful ultrashort pulse of the optical gain, which interacts coherently with the semiconductor medium. It is shown that coherent pulsations of the optical field, observed earlier by the author in Q-switched semiconductor lasers, are the result of superfluorescence and of the coherent interaction between the optical field and the medium.

Designing output-feedback predictive controllers by reverse-engineering existing LTI controllers

An approach to designing a constrained output-feedback predictive controller that has the same small-signal properties as a pre-existing output-feedback linear time invariant controller is proposed. Systematic guidelines are proposed to select an appropriate (non-unique) realization of the resulting state observer. A method is proposed to transform a class of offset-free reference tracking controllers into the combination of an observer, steady-state target calculator and predictive controller. The procedure is demonstrated with a numerical example. © 2013 IEEE.

MEMS-based mechanical AGC for oscillator circuits

This paper investigates a nonlinear amplitude saturation behavior in an electrostatically transduced, silicon MEMS disk resonator operating in its secondary elliptical bulk-mode (SEBM) at 3.932 MHz towards its implementation as an all-mechanical automatic gain control (AGC) element. The nonlinear vibration behavior of the SEBM mode is experimentally observed in open-loop testing such that above a threshold small signal drive voltage at a given polarization voltage, the vibration amplitude of the SEBM mode saturates. We also study this nonlinearity in an oscillator circuit designed such that the driving power level at the resonator input can be manually tuned as the circuit operates. The measurements of the voltage amplitudes show a clear transition from the linear to the nonlinear saturation region as the driving power is increased. Short-term frequency stability measurements were also conducted for different v ac and the resulting Allan deviation plots show an improvement in the short-term stability from 1.4 ppb in the linear region to 0.4 ppb in the amplitude saturation region. © 2013 IEEE.

Diode laser pumped, KERR phase-shifting and switching of a diode laser signal in a small area optical fibre

In this paper authors report the first demonstration of a diode laser powered Kerr effect device, consisting of a single birefringent fiber, able to phase-shift and switch an optical signal generated by a second laser diode. They have obtained fast, stable phase-shifting of 90° in a single fiber, at a coupled pump power of only 20 mW. Using this phase shift to induce polarization switching with resultant gating, 25% modulation of the diode laser signal has been observed, with a detection limited-rise time of 10ns.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics

Simulation of materials at the atomistic level is an important tool in studying microscopic structure and processes. The atomic interactions necessary for the simulation are correctly described by Quantum Mechanics. However, the computational resources required to solve the quantum mechanical equations limits the use of Quantum Mechanics at most to a few hundreds of atoms and only to a small fraction of the available configurational space. This thesis presents the results of my research on the development of a new interatomic potential generation scheme, which we refer to as Gaussian Approximation Potentials. In our framework, the quantum mechanical potential energy surface is interpolated between a set of predetermined values at different points in atomic configurational space by a non-linear, non-parametric regression method, the Gaussian Process. To perform the fitting, we represent the atomic environments by the bispectrum, which is invariant to permutations of the atoms in the neighbourhood and to global rotations. The result is a general scheme, that allows one to generate interatomic potentials based on arbitrary quantum mechanical data. We built a series of Gaussian Approximation Potentials using data obtained from Density Functional Theory and tested the capabilities of the method. We showed that our models reproduce the quantum mechanical potential energy surface remarkably well for the group IV semiconductors, iron and gallium nitride. Our potentials, while maintaining quantum mechanical accuracy, are several orders of magnitude faster than Quantum Mechanical methods.