Interactive multi-objective particle swarm optimisation using decision space interaction

The most common approach to decision making in multi-objective optimisation with metaheuristics is a posteriori preference articulation. Increased model complexity and a gradual increase of optimisation problems with three or more objectives have revived an interest in progressively interactive decision making, where a human decision maker interacts with the algorithm at regular intervals. This paper presents an interactive approach to multi-objective particle swarm optimisation (MOPSO) using a novel technique to preference articulation based on decision space interaction and visual preference articulation. The approach is tested on a 2D aerofoil design case study and comparisons are drawn to non-interactive MOPSO. © 2013 IEEE.

Measuring the composition of AlGaN layers in GaN based structures grown on 150 mm Si substrates using (2 0 5) reciprocal space maps

A critical element for the successful growth of GaN device layers on Si is accurate control of the AlGaN buffer layers used to manage strain. Here we present a method for measuring the composition of the AlGaN buffer layers in device structures which makes use of a one-dimensional x-ray detector to provide efficient measurement of a reciprocal space map which covers the full compositional range from AlN to GaN. Combining this with a suitable x-ray reflection with low strain sensitivity it is possible to accurately determine the Al fraction of the buffer layers independent of their relaxation state. © 2013 IOP Publishing Ltd.

Scalable, low-energy hybrid photonic space switch

A scalable monolithically integrated photonic space switch is proposed which uses a combination of Mach-Zehnder modulators and semiconductor optical amplifiers (SOAs) for improved crosstalk performance and reduced switch loss. This architecture enables the design of high-capacity, high-speed, large-port count, low-energy switches. Extremely low crosstalk of better than -50 dB can be achieved using a 2 × 2 dilated hybrid switch module. A 'building block' approach is applied to make large port count optical switches possible. Detailed physical layer multiwavelength simulations are used to investigate the viability of a 64 × 64 port switch. Optical signal degradation is estimated as a function of switch size and waveguide induced crosstalk. A comparison between hybrid and SOA switching fabrics highlights the power-efficient, high-performance nature of the hybrid switch design, which consumes less than one-third of the energy of an equivalent SOA-based switch. The significantly reduced impairments resulting from this switch design enable scaling of the port count, compared to conventional SOA-based switches. © 1983-2012 IEEE.

Modelling of free-space optoelectronic switching with MPLS for storage area networks (SANs)

Low-cost optical switches based on SLMs have conventionally been considered unsuitable for packet switching due to slow reconfiguration time. In this paper, we demonstrate that the constraint of SLM reconfiguration time in a hybrid three-stage electronic/optical switching node architecture can be compensated through the utilization of MPLS label switching mechanism to achieve the best performance for SAN applications. © 2012 SEE.

Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models

We consider the smoothing problem for a class of conditionally linear Gaussian state-space (CLGSS) models, referred to as mixed linear/nonlinear models. In contrast to the better studied hierarchical CLGSS models, these allow for an intricate cross dependence between the linear and the nonlinear parts of the state vector. We derive a Rao-Blackwellized particle smoother (RBPS) for this model class by exploiting its tractable substructure. The smoother is of the forward filtering/backward simulation type. A key feature of the proposed method is that, unlike existing RBPS for this model class, the linear part of the state vector is marginalized out in both the forward direction and in the backward direction. © 2013 IEEE.

Effects of high-order laser distortion products in radio over free-space optical links

RoFSO links are found to be susceptible to high-order laser distortion making conventional SFDR ineffective as a performance indicator. For the first time, peak input power is demonstrated as a service-independent bound on dynamic range. © OSA/ CLEO 2011.

Aberration correction for free space optical communications using rectangular zernike modal wavefront sensing

A time multiplexed rectangular Zernike modal wavefront sensor based on a nematic phase-only liquid crystal spatial light modulator and specially designed for a high power two-electrode tapered laser diode which is a compact and novel free space optical communication source is used in an adaptive beam steering free space optical communication system, enabling the system to have 1.25 GHz modulation bandwidth, 4.6° angular coverage and the capability of sensing aberrations within the system and caused by atmosphere turbulence up to absolute value of 0.15 waves amplitude and correcting them in one correction cycle. Closed-loop aberration correction algorithm can be implemented to provide convergence for larger and time varying aberrations. Improvement of the system signal-to-noise-ratio performance is achieved by aberration correction. To our knowledge, it is first time to use rectangular orthonormal Zernike polynomials to represent balanced aberrations for high power rectangular laser beam in practice. © 2014 IEEE.

Radio over free space optical link using a directly modulated two-electrode high power tapered laser

The analog modulation performance of a high-power two-electrode tapered laser is investigated. A 25dB dynamic range for 2.4GHz 802.11g signals is achieved with a 26dB loss budget, showing a >1km free space range is possible. © 2010 Optical Society of America.

A simple closed aquatic ecosystem (CAES) for space

A closed aquatic ecosystem (CAES) was developed to stud), the effects of microgravity on the function of closed ecosystems aboard the Chinese retrieved satellite and on the spacecraft SHENZHOU-II. These systems housed a small freshwater snail (Bulinus australianus) and an autotrophic green algae (Chlorella pyrenoidosa). The results of the test on the satellite were that the concentration of algae changed little, but that the snails died during the experiments. We then sought to optimize the function of the control system, the cultural conditions and the data acquisition system and carried out an experiment on the spacecraft SHENZHOU-II. Using various sensors to monitor the CAES, real-time data regarding the operation of the CAES in microgravity was acquired. In addition, all on-board Ig centrifuge was included to identify gravity-related factors. It was found that microgravity is the major factor affecting the operation of the CAES in space. The change in biomass of the primary producer during each day in microgravity was larger than that of the control groups. The mean biomass concentration per day in the microgravity group decreased, but that of the control groups increased for several days and then leveled off. Space effects on the biomass of a primary producer may be a result of microgravity effects leading to increasing metabolic rates of the consumer combined with decreases in photosynthesis. (c) 2007 COSPAR. Published by Elsevier Ltd. All rights reserved.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.