Experimental demonstration of a record high 11.25Gb/s real-time optical OFDM transceiver supporting 25km SMF end-to-end transmission in simple IMDD systems.

The fastest ever 11.25Gb/s real-time FPGA-based optical orthogonal frequency division multiplexing (OOFDM) transceivers utilizing 64-QAM encoding/decoding and significantly improved variable power loading are experimentally demonstrated, for the first time, incorporating advanced functionalities of on-line performance monitoring, live system parameter optimization and channel estimation. Real-time end-to-end transmission of an 11.25Gb/s 64-QAM-encoded OOFDM signal with a high electrical spectral efficiency of 5.625bit/s/Hz over 25km of standard and MetroCor single-mode fibres is successfully achieved with respective power penalties of 0.3dB and -0.2dB at a BER of 1.0 x 10(-3) in a directly modulated DFB laser-based intensity modulation and direct detection system without in-line optical amplification and chromatic dispersion compensation. The impacts of variable power loading as well as electrical and optical components on the transmission performance of the demonstrated transceivers are experimentally explored in detail. In addition, numerical simulations also show that variable power loading is an extremely effective means of escalating system performance to its maximum potential.

Electrical actuation and readout in a nanoelectromechanical resonator based on a laterally suspended zinc oxide nanowire.

In this paper, we present experimental results describing enhanced readout of the vibratory response of a doubly clamped zinc oxide (ZnO) nanowire employing a purely electrical actuation and detection scheme. The measured response suggests that the piezoelectric and semiconducting properties of ZnO effectively enhance the motional current for electromechanical transduction. For a doubly clamped ZnO nanowire resonator with radius ~10 nm and length ~1.91 µm, a resonant frequency around 21.4 MHz is observed with a quality factor (Q) of ~358 in vacuum. A comparison with the Q obtained in air (~242) shows that these nano-scale devices may be operated in fluid as viscous damping is less significant at these length scales. Additionally, the suspended nanowire bridges show field effect transistor (FET) characteristics when the underlying silicon substrate is used as a gate electrode or using a lithographically patterned in-plane gate electrode. Moreover, the Young's modulus of ZnO nanowires is extracted from a static bending test performed on a nanowire cantilever using an AFM and the value is compared to that obtained from resonant frequency measurements of electrically addressed clamped–clamped beam nanowire resonators.

Seismic model parameter estimation using least-squares migration

We describe a method for verifying seismic modelling parameters. It is equivalent to performing several iterations of unconstrained least-squares migration (LSM). The approach allows the comparison of modelling/imaging parameter configurations with greater confidence than simply viewing the migrated images. The method is best suited to determining discrete parameters but can be used for continuous parameters albeit with greater computational expense.

Parameter extraction and calorimetric validation for a silicon carbide jfet pspice model

This paper focuses on the PSpice model of SiC-JFET element inside a SiCED cascode device. The device model parameters are extracted from the I-V and C-V characterization curves. In order to validate the model, an inductive test rig circuit is designed and tested. The switching loss is estimated both using oscilloscope and calorimeter. These results are found to be in good agreement with the simulated results.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

Parameter estimation for agenda-based user simulation

This paper presents an agenda-based user simulator which has been extended to be trainable on real data with the aim of more closely modelling the complex rational behaviour exhibited by real users. The train-able part is formed by a set of random decision points that may be encountered during the process of receiving a system act and responding with a user act. A sample-based method is presented for using real user data to estimate the parameters that control these decisions. Evaluation results are given both in terms of statistics of generated user behaviour and the quality of policies trained with different simulators. Compared to a handcrafted simulator, the trained system provides a much better fit to corpus data and evaluations suggest that this better fit should result in improved dialogue performance. © 2010 Association for Computational Linguistics.

Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

The parameter space of graphene chemical vapor deposition on polycrystalline Cu

A systematic study of the parameter space of graphene chemical vapor deposition (CVD) on polycrystalline Cu foils is presented, aiming at a more fundamental process rationale in particular regarding the choice of carbon precursor and mitigation of Cu sublimation. CH 4 as precursor requires H 2 dilution and temperatures ≥1000 °C to keep the Cu surface reduced and yield a high-quality, complete monolayer graphene coverage. The H 2 atmosphere etches as-grown graphene; hence, maintaining a balanced CH 4/H 2 ratio is critical. Such balance is more easily achieved at low-pressure conditions, at which however Cu sublimation reaches deleterious levels. In contrast, C 6H 6 as precursor requires no reactive diluent and consistently gives similar graphene quality at 100-150 °C lower temperatures. The lower process temperature and more robust processing conditions allow the problem of Cu sublimation to be effectively addressed. Graphene formation is not inherently self-limited to a monolayer for any of the precursors. Rather, the higher the supplied carbon chemical potential, the higher the likelihood of film inhomogeneity and primary and secondary multilayer graphene nucleation. For the latter, domain boundaries of the inherently polycrystalline CVD graphene offer pathways for a continued carbon supply to the catalyst. Graphene formation is significantly affected by the Cu crystallography; i.e., the evolution of microstructure and texture of the catalyst template form an integral part of the CVD process. © 2012 American Chemical Society.

System studies of the superconducting fault current limiter in electrical distribution grids

A superconducting fault current limiter (SFCL) in series with a downstream circuit breaker could provide a viable solution to controlling fault current levels in electrical distribution networks. In order to integrate the SFCL into power grids, we need a way to conveniently predict the performance of the SFCL in a given scenario. In this paper, short circuit analysis based on the electromagnetic transient program was used to investigate the operational behavior of the SFCL installed in an electrical distribution grid. System studies show that the SFCL can not only limit the fault current to an acceptable value, but also mitigate the voltage sag. The transient recovery voltage (TRV) could be remarkably damped and improved by the presence of the SFCL after the circuit breaker is opened to clear the fault. © 2007 British Crown Copyright.

Electrical and optical properties of ion implanted SOI-based photonic crystals

We investigate the electrical properties of Silicon-on-Insulator photonic crystals as a function of doping level and air filling factor. A very interesting trade-off between conductivity and optical losses in L3 cavities is also found. © 2011 IEEE.