Effect of temporal separation on synchronization in rhythmic performance

A variety of short time delays inserted between pairs of subjects were found to affect their ability to synchronize a musical task. The subjects performed a clapping rhythm together from separate sound-isolated rooms via headphones and without visual contact. One-way time delays between pairs were manipulated electronically in the range of 3 to 78 ms. We are interested in quantifying the envelope of time delay within which two individuals produce synchronous per- formances. The results indicate that there are distinct regimes of mutually coupled behavior, and that `natural time delay'o¨delay within the narrow range associated with travel times across spatial arrangements of groups and ensembleso¨supports the most stable performance. Conditions outside of this envelope, with time delays both below and above it, create characteristic interaction dynamics in the mutually coupled actions of the duo. Trials at extremely short delays (corresponding to unnaturally close proximity) had a tendency to accelerate from anticipation. Synchronization lagged at longer delays (larger than usual physical distances) and produced an increasingly severe deceleration and then deterioration of performed rhythms. The study has implications for music collaboration over the Internet and suggests that stable rhythmic performance can be achieved by `wired ensembles' across distances of thousands of kilometers.

Phase separation and gap bowing in zinc-blende InGaN, InAlN, BGaN, and BAlN alloy layers

We present first-principles calculations of the thermodynamic and electronic properties of the zinc-blende ternary InxGa1-xN. InxAl1-xN, BxGa1-xN, and BxAl1-xN alloys. They are based on a generalized quasi-chemical approximation and a pseudopotential-plane-wave method. T-x phase diagrams for the alloys are obtained, We show that due to the large difference in interatomic distances between the binary compounds a significant phase miscibility gap for the alloys is found. In particular for the InxGa1-xN alloy, we show also experimental results obtained from X-ray and resonant Raman scattering measurements, which indicate the presence of an In-rich phase with x approximate to 0.8. For the boron-containing alloy layers we found a very high value for the critical temperature for miscibility. similar to9000 K. providing an explanation for the difficulties encountered to grow these materials with higher boron content. The influence of a biaxial strain on phase diagrams, energy gaps and gap bowing of these alloys is also discussed. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Organic complexation and translocation of ferric iron in podzols of the Negro River watershed. Separation of secondary Fe species from Al species

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Unfolding kernel embeddings of graphs:enhancing class separation through manifold learning

In this paper, we investigate the use of manifold learning techniques to enhance the separation properties of standard graph kernels. The idea stems from the observation that when we perform multidimensional scaling on the distance matrices extracted from the kernels, the resulting data tends to be clustered along a curve that wraps around the embedding space, a behavior that suggests that long range distances are not estimated accurately, resulting in an increased curvature of the embedding space. Hence, we propose to use a number of manifold learning techniques to compute a low-dimensional embedding of the graphs in an attempt to unfold the embedding manifold, and increase the class separation. We perform an extensive experimental evaluation on a number of standard graph datasets using the shortest-path (Borgwardt and Kriegel, 2005), graphlet (Shervashidze et al., 2009), random walk (Kashima et al., 2003) and Weisfeiler-Lehman (Shervashidze et al., 2011) kernels. We observe the most significant improvement in the case of the graphlet kernel, which fits with the observation that neglecting the locational information of the substructures leads to a stronger curvature of the embedding manifold. On the other hand, the Weisfeiler-Lehman kernel partially mitigates the locality problem by using the node labels information, and thus does not clearly benefit from the manifold learning. Interestingly, our experiments also show that the unfolding of the space seems to reduce the performance gap between the examined kernels.

Evaluation of separation management algorithms in Class G airspace

Use of Unmanned Aerial Vehicles (UAVs) in support of government applications has already seen significant growth and the potential for use of UAVs in commercial applications is expected to rapidly expand in the near future. However, the issue remains on how such automated or operator-controlled aircraft can be safely integrated into current airspace. If the goal of integration is to be realized, issues regarding safe separation in densely populated airspace must be investigated. This paper investigates automated separation management concepts in uncontrolled airspace that may help prepare for an expected growth of UAVs in Class G airspace. Not only are such investigations helpful for the UAV integration issue, the automated separation management concepts investigated by the authors can also be useful for the development of new or improved Air Traffic Control services in remote regions without any existing infrastructure. The paper will also provide an overview of the Smart Skies program and discuss the corresponding Smart Skies research and development effort to evaluate aircraft separation management algorithms using simulations involving realworld data communication channels, and verified against actual flight trials. This paper presents results from a unique flight test concept that uses real-time flight test data from Australia over existing commercial communication channels to a control center in Seattle for real-time separation management of actual and simulated aircraft. The paper also assesses the performance of an automated aircraft separation manager.

Non-adversarial advocates and gatekeepers : lawyers, FDR practitioners and co-operative post-separation parenting

The compulsory dispute resolution requirements in family law parenting cases create new roles and obligations for both lawyers and family dispute resolution (FDR) practitioners. This article will discuss how the legislative provisions impact on both sets of professionals in practice. It will also highlight the increased non-adversarial role of lawyers and a new role for FDR practitioners as “gatekeepers” to family courts in cases requiring FDR certificates.

High-performance ceramic membranes with a separation layer of metal oxide nanofibers

In conventional fabrication of ceramic separation membranes, the particulate sols are applied onto porous supports. Major structural deficiencies under this approach are pin-holes and cracks, and the dramatic losses of flux when pore sizes are reduced to enhance selectivity. We have overcome these structural deficiencies by constructing hierarchically structured separation layer on a porous substrate using lager titanate nanofibers and smaller boehmite nanofibers. This yields a radical change in membrane texture. The resulting membranes effectively filter out species larger than 60 nm at flow rates orders of magnitude greater than conventional membranes. This reveals a new direction in membrane fabrication.

Ceramic membranes for separation of proteins and DNA by in situ growth of alumina nanofibres with a nanomesh of metal oxide nanofibres

Ceramic membranes were fabricated by in situ synthesis of alumina nanofibres in the pores of an alumina support as a separation layer, and exhibited a high permeation selectivity for bovine serum albumin relative to bovine hemoglobin (over 60 times) and can effectively retain DNA molecules at high fluxes.

Porous PLGA microspheres effectively loaded with BSA protein by electrospraying combined with phase separation in liquid nitrogen

Polymer microspheres loaded with bioactive particles, biomolecules, proteins, and/or growth factors play important roles in tissue engineering, drug delivery, and cell therapy. The conventional double emulsion method and a new method of electrospraying into liquid nitrogen were used to prepare bovine serum albumin (BAS)-loaded poly(lactic-co-glycolic acid) (PLGA) porous microspheres. The particle size, the surface morphology and the internal porous structure of the microspheres were observed using scanning electron microscopy (SEM). The loading efficiency, the encapsulation efficiency, and the release profile of the BSA-loaded PLGA microspheres were measured and studied. It was shown that the microspheres from double emulsion had smaller particle sizes (3-50 m), a less porous structure, a poor loading efficiency (5.2 %), and a poor encapsulation efficiency (43.5%). However, the microspheres from the electrospraying into liquid nitrogen had larger particle sizes (400-600 m), a highly porous structure, a high loading efficiency (12.2%), and a high encapsulation efficiency (93.8%). Thus the combination of electrospraying with freezing in liquid nitrogen and subsequent freeze drying represented a suitable way to produce polymer microspheres for effective loading and sustained release of proteins.

Separation management approaches during periods of communication failure

New air traffic automated separation management concepts are constantly under investigation. Yet most of the automated separation management algorithms proposed over the last few decades have assumed either perfect communication or exact knowledge of all aircraft locations. In realistic environments, these idealized assumptions are not valid and any communication failure can potentially lead to disastrous outcomes. This paper examines the separation performance behavior of several popular algorithms during periods of information loss. This comparison is done through simulation studies. These simulation studies suggest that communication failure can cause the performance of these separation management algorithms to degrade significantly. This paper also describes some preliminary flight tests.

Automated centralised separation management with onboard decision support

This paper proposes a novel automated separation management concept in which onboard decision support is integrated within a centralised air traffic separation management system. The onboard decision support system involves a decentralised separation manager that can overrule air traffic management instructions under certain circumstances. This approach allows the advantages of both centralised and decentralised concepts to be combined (and disadvantages of each separation management approach to be mitigated). Simulation studies are used to illustrate the potential benefits of the combined separation management concept.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.