Multi-wavelength erbium/Raman gain based random distributed feedback fiber laser

A multiwavelength generation in a random distributed feedback fiber laser based on hybrid Raman and erbium gain and a Lyot all-fiber spectral filter is demonstrated for the first time. The use of erbium-doped fiber allows a multi-wavelength generation to be achieved at lower pump powers in comparison with random fiber lasers based on Raman gain only. The operating bandwidth and flatness of power distribution between different lines in generation are also improved in the hybrid gain configuration.

Meta level component-based framework for distributed computing applications

Adaptability for distributed object-oriented enterprise frameworks is a critical mission for system evolution. Today, building adaptive services is a complex task due to lack of adequate framework support in the distributed computing environment. In this thesis, we propose a Meta Level Component-Based Framework (MELC) which uses distributed computing design patterns as components to develop an adaptable pattern-oriented framework for distributed computing applications. We describe our novel approach of combining a meta architecture with a pattern-oriented framework, resulting in an adaptable framework which provides a mechanism to facilitate system evolution. The critical nature of distributed technologies requires frameworks to be adaptable. Our framework employs a meta architecture. It supports dynamic adaptation of feasible design decisions in the framework design space by specifying and coordinating meta-objects that represent various aspects within the distributed environment. The meta architecture in MELC framework can provide the adaptability for system evolution. This approach resolves the problem of dynamic adaptation in the framework, which is encountered in most distributed applications. The concept of using a meta architecture to produce an adaptable pattern-oriented framework for distributed computing applications is new and has not previously been explored in research. As the framework is adaptable, the proposed architecture of the pattern-oriented framework has the abilities to dynamically adapt new design patterns to address technical system issues in the domain of distributed computing and they can be woven together to shape the framework in future. We show how MELC can be used effectively to enable dynamic component integration and to separate system functionality from business functionality. We demonstrate how MELC provides an adaptable and dynamic run time environment using our system configuration and management utility. We also highlight how MELC will impose significant adaptability in system evolution through a prototype E-Bookshop application to assemble its business functions with distributed computing components at the meta level in MELC architecture. Our performance tests show that MELC does not entail prohibitive performance tradeoffs. The work to develop the MELC framework for distributed computing applications has emerged as a promising way to meet current and future challenges in the distributed environment.

Vibration analysis of submerged rectangular microplates with distributed mass loading

This paper investigates the vibration characteristics of the coupling system of a microscale fluid-loaded rectangular isotropic plate attached to a uniformly distributed mass. Previous literature has, respectively, studied the changes in the plate vibration induced by an acoustic field or by the attached mass loading. This paper investigates the issue of involving these two types of loading simultaneously. Based on Lamb's assumption of the fluid-loaded structure and the Rayleigh–Ritz energy method, this paper presents an analytical solution for the natural frequencies and mode shapes of the coupling system. Numerical results for microplates with different types of boundary conditions have also been obtained and compared with experimental and numerical results from previous literature. The theoretical model and novel analytical solution are of particular interest in the design of microplate-based biosensing devices.

32 km distributed temperature sensor based on Brillouin loss in an optical fibre

We present a novel distributed temperature sensor that uses the temperature dependence of the frequency at which the loss is maximized in the interaction between a cw laser and a pulsed laser. With a 32-km sensing length, a temperature resolution of 1°C has been achieved; it is also shown that a spatial resolution of 5 m may be obtained.

Selective localization of organoclay and effects on the morphology and mechanical properties of LDPE/PA11 blends with distributed and co-continuous morphology

A study was made on the effect of small amounts of organically modified clay on the morphology and mechanical properties of blends of low-density polyethylene and polyamide 11 at different compositions. The influence of the filler on the blend morphology was investigated using wide angle X-ray diffractometry, scanning and transmission electron microscopy and selective extraction experiments. The filler was found to locate predominantly in the more hydrophilic polyamide phase. Although such uneven distribution does not have a significant effect on the onset of phase co-continuity of the polymer components, it brings about a drastic refinement of the microstructure for the blends both with droplets/matrix and co-continuous morphologies. In addition to the expected reinforcing action of the filler, the resulting fine microstructure plays an important role in enhancing the mechanical properties of the blends. This is essentially because of a good quality of stress transfer across the interface between the constituents, which also seems to benefit for a good interfacial adhesion promoted by the filler. Our results provide the experimental evidence for the capabilities of nanoparticles added to multiphase polymer systems to act selectively as a reinforcing agent for specific domains of the material and as a medium able to assist the refinement of the polymer phases during mixing.

The effect of load on agent-based algorithms for distributed task allocation

Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

Learning task specific distributed paragraph representations using a 2-tier convolutional neural network

We introduce a type of 2-tier convolutional neural network model for learning distributed paragraph representations for a special task (e.g. paragraph or short document level sentiment analysis and text topic categorization). We decompose the paragraph semantics into 3 cascaded constitutes: word representation, sentence composition and document composition. Specifically, we learn distributed word representations by a continuous bag-of-words model from a large unstructured text corpus. Then, using these word representations as pre-trained vectors, distributed task specific sentence representations are learned from a sentence level corpus with task-specific labels by the first tier of our model. Using these sentence representations as distributed paragraph representation vectors, distributed paragraph representations are learned from a paragraph-level corpus by the second tier of our model. It is evaluated on DBpedia ontology classification dataset and Amazon review dataset. Empirical results show the effectiveness of our proposed learning model for generating distributed paragraph representations.

All-optical OFDM and distributed Raman amplification : challenges to enable high capacities and extend reach

We review recent advances in all-optical OFDM technologies and discuss the performance of a field trial of a 2 Tbit/s Coherent WDM over 124 km with distributed Raman amplification. The results indicate that careful optimisation of the Raman pumps is essential. We also consider how all-optical OFDM systems perform favourably against energy consumption when compared with alternative coherent detection schemes. We argue that, in an energy constrained high-capacity transmission system, direct detected all-optical OFDM with `ideal' Raman amplification is an attractive candidate for metro area datacentre interconnects with ~100 km fibre spans, with an overall energy requirement at least three times lower than coherent detection techniques.

"Magic" dispersion maps with distributed Raman amplification

We analyze pulse propagation in an optical fiber with a periodic dispersion map and distributed amplification. Using an asymptotic theory and a momentum method, we identify a family of dispersion management schemes that are advantageous for massive multichannel soliton transmission. For the case of two-step dispersion maps with distributed Raman amplification to compensate for the fiber loss, we find special schemes that have optimal (chirp-free) launch point locations that are independent of the fiber dispersion. Despite the variation of dispersion with wavelength due to the fiber dispersion slope, the transmission in several different channels can be optimized simultaneously using the same optimal launch point. The theoretical predictions are verified by direct numerical simulations. The obtained results are applied to a practical multichannel transmission system.

Optimal dispersion maps for multichannel soliton transmission with distributed Raman amplification

Using an asymptotic theory and a momentum method, we identify a family of dispersion management schemes with distributed Raman amplification, which are advantageous for massive multichannel soliton transmission. For the case of two-step dispersion maps, special schemes are found that have optimal (chirp-free) launch point locations that are independent of the fibre dispersion. Despite the variation of dispersion with wavelength due to the fibre dispersion slope, the transmission in several different channels can be optimized simultaneously using the same optimal launch point. The theoretical results are verified by direct numerical simulations.

The influence of memory in a threshold model for distributed task assignment

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task selection in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without a priori knowledge of the available mail at the cities or inter-agent communication. In order to process a different mail type than the previous one, agents must undergo a change-over during which it remains inactive. We propose a threshold based algorithm in order to maximise the overall efficiency (the average amount of mail collected). We show that memory, i.e. the possibility for agents to develop preferences for certain cities, not only leads to emergent cooperation between agents, but also to a significant increase in efficiency (above the theoretical upper limit for any memoryless algorithm), and we systematically investigate the influence of the various model parameters. Finally, we demonstrate the flexibility of the algorithm to changes in circumstances, and its excellent scalability.

Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback

Distributed network utility maximization (NUM) is receiving increasing interests for cross-layer optimization problems in multihop wireless networks. Traditional distributed NUM algorithms rely heavily on feedback information between different network elements, such as traffic sources and routers. Because of the distinct features of multihop wireless networks such as time-varying channels and dynamic network topology, the feedback information is usually inaccurate, which represents as a major obstacle for distributed NUM application to wireless networks. The questions to be answered include if distributed NUM algorithm can converge with inaccurate feedback and how to design effective distributed NUM algorithm for wireless networks. In this paper, we first use the infinitesimal perturbation analysis technique to provide an unbiased gradient estimation on the aggregate rate of traffic sources at the routers based on locally available information. On the basis of that, we propose a stochastic approximation algorithm to solve the distributed NUM problem with inaccurate feedback. We then prove that the proposed algorithm can converge to the optimum solution of distributed NUM with perfect feedback under certain conditions. The proposed algorithm is applied to the joint rate and media access control problem for wireless networks. Numerical results demonstrate the convergence of the proposed algorithm. © 2013 John Wiley & Sons, Ltd.