Non-stationary signal analysis by least mean fourth (LMF) adaptive algorithm

A modified least mean fourth (LMF) adaptive algorithm applicable to non-stationary signals is presented. The performance of the proposed algorithm is studied by simulation for non-stationarities in bandwidth, centre frequency and gain of a stochastic signal. These non-stationarities are in the form of linear, sinusoidal and jump variations of the parameters. The proposed LMF adaptation is found to have better parameter tracking capability than the LMS adaptation for the same speed of convergence.

Variable Size Population NSGA-II: VPNSGA-II

Multi-objective optimization is an active field of research with broad applicability in aeronautics. This report details a variant of the original NSGA-II software aimed to improve the performances of such a widely used Genetic Algorithm in finding the optimal Pareto-front of a Multi-Objective optimization problem for the use of UAV and aircraft design and optimsaiton. Original NSGA-II works on a population of predetermined constant size and its computational cost to evaluate one generation is O(mn^2 ), being m the number of objective functions and n the population size. The basic idea encouraging this work is that of reduce the computational cost of the NSGA-II algorithm by making it work on a population of variable size, in order to obtain better convergence towards the Pareto-front in less time. In this work some test functions will be tested with both original NSGA-II and VPNSGA-II algorithms; each test will be timed in order to get a measure of the computational cost of each trial and the results will be compared.

Extrapolation and series acceleration procedures: Hard spheres and discs as illustrations

Three new procedures - in the context of estimation of virial coefficients and summation of the partial virial series for hard discs and hard spheres - are proposed. They are based on the parametrised Euler transformation, a novel resummation, identity and the ε-convergence methods respectively. A comparison with other estimates (molecular dynamics, graph theory and empirical methods) reveals satisfactory agreement.

Agent-based modelling of worker interactions and related impacts on workplace dynamics

This study uses agent based modelling to simulate the worker interactions within a workplace and to investigate how the interactions can have impact on the workplace dynamics. Two new models (Bounded Confidence with Bias model and Relative Agreement with Bias model) are built based on the theoretical foundation of two existing models. A new factor, namely bias, is added into the new models which raises several issues to be studied.

Phylogeny of the Rhyparini and the new tribe Stereomerini, with descriptions of new genera and species (Coleoptera; Scarabaeidae; Aphodiinae)

A new tribe, the Stereomerini, is established for four unusual genera: Stereomera Arrow, Termitaxis Krikken, Australoxenella n.gen., and Bruneixenus n.gen. The previously described genera are monotypic, as is Bruneixenus, the type species being B. squamosus n.sp. from Brunei. Australoxenella contains two new species, A. humptydooensis, type species, and A. bathurstensis, both from the Northern Territory, Australia. The relationships of the new tribe are analyzed and compared with the most closely related tribe, the Rhyparini, in the Aphodiinae. The tribe Rhyparini is redefined, and the genus Notocaulus Quedenfeldt is transferred to the Eupariini. A key to genera in both the Stereomerini and the Rhyparini is presented, important characters are illustrated, a cladogram is given, and convergence is discussed.

Internationalizing school curriculum in Australasia: As niche, by test, or at heart?

Global citizenship has emerged as a pressing curricular priority which all educational systems are currently grappling with. The challenge is to negotiate how this orientation might sit alongside the more traditional mission of mass school curriculum in building collective ballast for a national identity through a common morality and shared narratives, or may conflict with efforts to protect and promote indigenous and minority identities. As a case study of how these agendas interact, this chapter will consider curricular responses to global imperatives in the variegated conditions across the Australasian region (defined as Australia, New Zealand and Papua New Guinea). The chapter will outline recent developments in the social, economic and political contexts surrounding curricular reforms in these settings, and demonstrate how these developments have changed the conditions of possibility and strength of purpose behind efforts to internationalise school curricula. Three types of systemic responses are then described: firstly, an appetite for globally branded curricula such as the International Baccalaureate, Montessori, and Cambridge University Certificates to distinguish some in a stratified market; secondly, convergence in curriculum to improve national performance on international standardised tests; and thirdly, the infusion of cosmopolitan sensibilities, regional identities and intercultural competencies as a core curricular goal for all. The chapter considers the various pragmatic interpretations of ‘internationalisation’ in these responses, and argues that the third response seems both the most difficult to enact, and the most vulnerable to political interference.

A hierarchical system of learning automata that can learn die globally optimal path

Systems of learning automata have been studied by various researchers to evolve useful strategies for decision making under uncertainity. Considered in this paper are a class of hierarchical systems of learning automata where the system gets responses from its environment at each level of the hierarchy. A classification of such sequential learning tasks based on the complexity of the learning problem is presented. It is shown that none of the existing algorithms can perform in the most general type of hierarchical problem. An algorithm for learning the globally optimal path in this general setting is presented, and its convergence is established. This algorithm needs information transfer from the lower levels to the higher levels. Using the methodology of estimator algorithms, this model can be generalized to accommodate other kinds of hierarchical learning tasks.

Visual demands in modern Australian primary school classrooms

BACKGROUND The visual demands of modern classrooms are poorly understood yet are relevant in determining the levels of visual function required to perform optimally within this environment. METHODS Thirty-three Year 5 and 6 classrooms from eight south-east Queensland schools were included. Classroom activities undertaken during a full school day (9 am to 3 pm) were observed and a range of measurements recorded, including classroom environment (physical dimensions, illumination levels), text size and contrast of learning materials, habitual working distances (distance and estimated for near) and time spent performing various classroom tasks. These measures were used to calculate demand-related minimum criteria for distance and near visual acuity, contrast and sustained use of accommodation and vergence. RESULTS The visual acuity demands for distance and near were 0.33 ± 0.13 and 0.72 ± 0.09 logMAR, respectively (using habitual viewing distances and smallest target sizes) or 0.33 ± 0.09 logMAR assuming a 2.5 times acuity reserve for sustained near tasks. The mean contrast levels of learning materials at distance and near were greater than 70 per cent. Near tasks (47 per cent) dominated the academic tasks performed in the classroom followed by distance (29 per cent), distance to near (15 per cent) and computer-based (nine per cent). On average, children engaged in continuous near fixation for 23 ± 5 minutes at a time and during distance-near tasks performed fixation changes 10 ± 1 times per minute. The mean estimated habitual near working distance was 23 ± 1 cm (4.38 ± 0.24 D accommodative demand) and the vergence demand was 0.86 ± 0.07Δ at distance and 21.94 ± 1.09Δ at near assuming an average pupillary distance of 56 mm. CONCLUSIONS Relatively high levels of visual acuity, contrast demand and sustained accommodative-convergence responses are required to meet the requirements of modern classroom environments. These findings provide an evidence base to inform prescribing guidelines and develop paediatric vision screening protocols and referral criteria.

A visual profile of Queensland Indigenous children

Purpose Little is known about the prevalence of refractive error, binocular vision, and other visual conditions in Australian Indigenous children. This is important given the association of these visual conditions with reduced reading performance in the wider population, which may also contribute to the suboptimal reading performance reported in this population. The aim of this study was to develop a visual profile of Queensland Indigenous children. Methods Vision testing was performed on 595 primary schoolchildren in Queensland, Australia. Vision parameters measured included visual acuity, refractive error, color vision, nearpoint of convergence, horizontal heterophoria, fusional vergence range, accommodative facility, AC/A ratio, visual motor integration, and rapid automatized naming. Near heterophoria, nearpoint of convergence, and near fusional vergence range were used to classify convergence insufficiency (CI). Results Although refractive error (Indigenous, 10%; non-Indigenous, 16%; p = 0.04) and strabismus (Indigenous, 0%; non-Indigenous, 3%; p = 0.03) were significantly less common in Indigenous children, CI was twice as prevalent (Indigenous, 10%; non-Indigenous, 5%; p = 0.04). Reduced visual information processing skills were more common in Indigenous children (reduced visual motor integration [Indigenous, 28%; non-Indigenous, 16%; p < 0.01] and slower rapid automatized naming [Indigenous, 67%; non-Indigenous, 59%; p = 0.04]). The prevalence of visual impairment (reduced visual acuity) and color vision deficiency was similar between groups. Conclusions Indigenous children have less refractive error and strabismus than their non-Indigenous peers. However, CI and reduced visual information processing skills were more common in this group. Given that vision screenings primarily target visual acuity assessment and strabismus detection, this is an important finding as many Indigenous children with CI and reduced visual information processing may be missed. Emphasis should be placed on identifying children with CI and reduced visual information processing given the potential effect of these conditions on school performance

Raman Spectroscopic study of valinomycin-metal complexes

Valinomycin, an ionophore of considerable interest for its ion selectivity, and its K+, Mg2+, Ba2+, and Ca2+ complexes were studied by Raman spectroscopy. Each complex has a characteristic spectrum which differs from that of uncomplexed valinomycin, suggesting several distinct structures for each of the metal-valinomycin complexes. The biologically active potassium complex shows the most significant changes in its spectrum, especially in the intensity of the symmetric C---H stretching vibration of CH3 and the convergence of the two ester carbonyl stretching vibration bands into one complex formation. These results are due to the unique orientation of the ester carbonyl groups toward the caged potassium ion and the resulting more free rotation of isopropyl side chains. The divalent cation-valinomycin complexes examined showed spectra which differed in each case uniquely from both valinomycin and its complex with potassium.

Invited paper Application of the least mean fourth (LMF) adaptive algorithm to signals associated with gaussian noise

This paper considers the applicability of the least mean fourth (LM F) power gradient adaptation criteria with 'advantage' for signals associated with gaussian noise, the associated noise power estimate not being known. The proposed method, as an adaptive spectral estimator, is found to provide superior performance than the least mean square (LMS) adaptation for the same (or even lower) speed of convergence for signals having sufficiently high signal-to-gaussian noise ratio. The results include comparison of the performance of the LMS-tapped delay line, LMF-tapped delay line, LMS-lattice and LMF-lattice algorithms, with the Burg's block data method as reference. The signals, like sinusoids with noise and stochastic signals like EEG, are considered in this study.

Optimal Parameter Trajectory Estimation in Parameterized SDEs: An Algorithmic Procedure

We consider the problem of estimating the optimal parameter trajectory over a finite time interval in a parameterized stochastic differential equation (SDE), and propose a simulation-based algorithm for this purpose. Towards this end, we consider a discretization of the SDE over finite time instants and reformulate the problem as one of finding an optimal parameter at each of these instants. A stochastic approximation algorithm based on the smoothed functional technique is adapted to this setting for finding the optimal parameter trajectory. A proof of convergence of the algorithm is presented and results of numerical experiments over two different settings are shown. The algorithm is seen to exhibit good performance. We also present extensions of our framework to the case of finding optimal parameterized feedback policies for controlled SDE and present numerical results in this scenario as well.

A novel approach to 6-DOF adaptive trajectory tracking control of an AUV in the presence of parameter uncertainties

In this paper, the trajectory tracking control of an autonomous underwater vehicle (AUVs) in six-degrees-of-freedom (6-DOFs) is addressed. It is assumed that the system parameters are unknown and the vehicle is underactuated. An adaptive controller is proposed, based on Lyapunov׳s direct method and the back-stepping technique, which interestingly guarantees robustness against parameter uncertainties. The desired trajectory can be any sufficiently smooth bounded curve parameterized by time even if consist of straight line. In contrast with the majority of research in this field, the likelihood of actuators׳ saturation is considered and another adaptive controller is designed to overcome this problem, in which control signals are bounded using saturation functions. The nonlinear adaptive control scheme yields asymptotic convergence of the vehicle to the reference trajectory, in the presence of parametric uncertainties. The stability of the presented control laws is proved in the sense of Lyapunov theory and Barbalat׳s lemma. Efficiency of presented controller using saturation functions is verified through comparing numerical simulations of both controllers.

A mechanism for the west-north-west movement of monsoon depressions

The monsoon depressions intensify over the Bay of Bengal, move in a west-north-west (WNW) direction and dissipate over the Indian continent. No convincing physical explanation for their observed movement has so far been arrived at, but here, I suggest why the maximum precipitation occurs in the western sector of the depression and propose a feedback mechanism for the WNW movement of the depressions. We assume that a heat source is created over the Bay of Bengal due to organization of cumulus convection by the initial instability. In a linear sense, heating at this latitude (20° N), produces an atmospheric response mainly in the form of a stationary Rossby–gravity wave to the west of the heat source. The low-level vorticity (hence the frictional convergence) and the vertical velocity associated with the steady-state response is such that the maximum moisture convergence (and precipitation) is expected to occur in the WNW sector at a later time. Thus, the heat source moves to the WNW sector at a later time and the feedback continues resulting in the WNW movement of the depressions.

Learning Optimal Discriminant Functions through a Cooperative Game of Automata

The problem of learning correct decision rules to minimize the probability of misclassification is a long-standing problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant functions is considered for the class of problems where the statistical properties of the pattern classes are completely unknown. The problem is posed as a game with common payoff played by a team of mutually cooperating learning automata. This essentially results in a probabilistic search through the space of classifiers. The approach is inherently capable of learning discriminant functions that are nonlinear in their parameters also. A learning algorithm is presented for the team and convergence is established. It is proved that the team can obtain the optimal classifier to an arbitrary approximation. Simulation results with a few examples are presented where the team learns the optimal classifier.