Emission regimes in a distributed feedback tapered master-oscillator power-amplifier at 1.5 µm

Integrated master-oscillator power amplifiers driven under steady-state injection conditions are known to show a complex dynamics resulting in a variety of emission regimes. We present experimental results on the emission characteristics of a 1.5 µm distributed feedback tapered master-oscillator power-amplifier in a wide range of steady-state injection conditions, showing different dynamic behaviors. The study combines the optical and radio-frequency spectra recorded under different levels of injected current into the master oscillator and the power amplifier sections. Under low injection current of the master oscillator the correlation between the optical and radio-frequency spectral maps allows to identify operation regimes in which the device emission arises from either the master oscillator mode or from the compound cavity modes allowed by the residual reflectance of the amplifier front facet. The quasi-periodic occurrence of these emission regimes as a function of the amplifier current is interpreted in terms of a thermally tuned competition between the modes of the master oscillator and the compound cavity modes. Under high injection current of the masteroscillator, two different regimes alternate quasi-periodically as a function of the injected current in the power amplifier: a stable regime with a single mode emission at the master oscillator frequency, and an unstable and complex self-pulsating regime showing strong peaks in the radio-frequency spectra as well as multiple frequencies in the optical spectra.

Population inversion of a driven two-level system in a structureless bath

We derive a master equation for a driven double quantum dot damped by an unstructured phonon bath, and calculate the spectral density. We find that bath-mediated photon absorption is important at relatively strong driving, and may even dominate the dynamics, inducing population inversion of the double-dot system. This phenomenon is consistent with recent experimental observations.

Universality of quantum critical dynamics in a planar optical parametric oscillator

We analyze the critical quantum fluctuations in a coherently driven planar optical parametric oscillator. We show that the presence of transverse modes combined with quantum fluctuations changes the behavior of the quantum image critical point. This zero-temperature nonequilibrium quantum system has the same universality class as a finite-temperature magnetic Lifshitz transition.

Reciprocating power generation in a chemically driven synthetic muscle

A scalable synthetic muscle has been constructed that transducts nanoscale molecular shape changes into macroscopic motion. The working material, which deforms affinely in response to a pH stimulus, is a self-assembled block copolymer comprising nanoscopic hydrophobic domains in a weak polyacid matrix. A device has been assembled where the muscle does work on a cantilever and the force generated has been measured. When coupled to a chemical oscillator this provides a free running chemical motor that generates a peak power of 20 mW kg 1 by the serial addition of 10 nm shape changes that scales over 5 orders of magnitude. It is the nanostructured nature of the gel that gives rise to the affine deformation and results in a robust working material for the construction of scalable muscle devices.

Effect of surface conditions on flow of a micropolar fluid driven by a porous stretching sheet

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A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

Listening, sharing understanding and facilitating consumer, family and community empowerment through a priority driven partnership in Far North Queensland

Objectives: This paper provides an example of a mental health research partnership underpinned by empowerment principles that seeks to foster strength among community organizations to support better outcomes for consumers, families and communities. It aims to raise awareness among researchers and service providers that empowerment approaches to assist communities to address mental health problems are not too difficult to be practical but require long-term commitment and appropriate support. Methods: A collaborative research strategy that has become known as the Priority Driven Research (PDR) Partnership emerged through literature review,consultations, Family Wellbeing Program delivery with community groups and activities in two discrete Indigenous communities. Progress to date on three of the four components of the strategy is described. Results: The following key needs were identified in a pilot study and are now being addressed in a research-based implementation phase: (i) gaining two-way understanding of perspectives on mental health and promoting universal awareness; (ii) supporting the empowerment of carers, families, consumers and at-risk groups through existing community organizations to gain greater understanding and control of their situation; (iii) developing pathways of care at the primary health centre level to enable support of social and emotional wellbeing as well as more integrated mental health care; (iv) accessing data to enable an ongoing process of analysis/sharing/planning and monitoring to inform future activity. Conclusion: One of the key learnings to emerge in this project so far is that empowerment through partnership becomes possible when there is a concerted effort to strengthen grassroots community organizations. These include social health teams and men’s and women’s groups that can engage local people in an action orientation. Key words: Aboriginal, empowerment, Indigenous, mental health.

In Pursuit of Excellence : A Policy Driven Systemic Initiative to Embed Gifted Education

Embedding gifted education practices requires major professional development strategies supported by transparent, credible and enforceable policy. This paper describes an analysis of a state-wide initiative involving the establishment of a series of schools tasked to develop and disseminate gifted education principles. The authors have been involved with this initiative at a number of levels over a ten-year period. Their involvement culminated in a commissioned review of the program. Extensive qualitative data were purposively collected from all stakeholders and the effectiveness of the initiative is examined from a theoretical framework of policy development and excellence. The findings summarised in this proposal, indicate the achievement of excellence at a systemic level was constrained by lack of vision, leadership and commitment to long term achievements of excellence. At a local level evidence exists that excellence can be manifested when there is synchronicity of vision, purpose, decisions, and actions.

Improved GMM-based speaker verification using SVM-driven impostor dataset selection

The problem of impostor dataset selection for GMM-based speaker verification is addressed through the recently proposed data-driven background dataset refinement technique. The SVM-based refinement technique selects from a candidate impostor dataset those examples that are most frequently selected as support vectors when training a set of SVMs on a development corpus. This study demonstrates the versatility of dataset refinement in the task of selecting suitable impostor datasets for use in GMM-based speaker verification. The use of refined Z- and T-norm datasets provided performance gains of 15% in EER in the NIST 2006 SRE over the use of heuristically selected datasets. The refined datasets were shown to generalise well to the unseen data of the NIST 2008 SRE.