An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints

We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of cross- entropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications. Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator. Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.

Relative entropy rate based model selection for linear hybrid system filters of uncertain nonlinear systems

Hybrid system representations have been exploited in a number of challenging modelling situations, including situations where the original nonlinear dynamics are too complex (or too imprecisely known) to be directly filtered. Unfortunately, the question of how to best design suitable hybrid system models has not yet been fully addressed, particularly in the situations involving model uncertainty. This paper proposes a novel joint state-measurement relative entropy rate based approach for design of hybrid system filters in the presence of (parameterised) model uncertainty. We also present a design approach suitable for suboptimal hybrid system filters. The benefits of our proposed approaches are illustrated through design examples and simulation studies.

An examination of the psychologists' attitudes towards complementary and alternative therapies scale within a practitioner sample

This study assessed the validity of a scale measuring psychologists' attitudes towards complementary and alternative therapies and compared the attitudes of psychologists with a previous sample of psychology students. The scale, derived from existing measures for medical professionals and previously tested on a sample of psychology students, was completed by practising psychologists (N = 122). The data were factor analysed, and three correlated subscales were identified, assessing the perceived importance of knowledge about available therapies, attitudes towards integration with psychological practice, and concerns about associated risks of use. This structure was similar, but not identical, to that found in a previous sample of psychology students; however, psychologists expressed more concern for risks associated with integration and were less likely to hold a positive attitude towards integration. This scale will be useful in gauging changes in psychologists' attitudes towards integrative practice over time.

Are you telling the truth? Psychopathy assessment and impression management in a community sample

Objectives: Researchers have suggested that approximately 1% of individuals within the community have psychopathic tendencies (Neumann and Hare, 2008), although confirmatory evidence is scant. Design: The current study aimed to extend previous research beyond university student samples to explore the effect of impression management and self-deception on the identification of psychopathic traits. Methods: A non-incarcerated community sample comprising of 300 adults completed the Self-Reported Psychopathy scale – version 3 (SRP-III; Paulhus, Hemphill & Hare, in press) as well as the Paulhus Deception Scales (PDS; Paulhus, 1998). Results: Results indicated that at least 1% of the current community sample had clear psychopathic tendencies, and that such tendencies were found in younger males who mis-used alcohol. Conclusions: Importantly, individuals with psychopathic traits did not present with an inflated propensity to distort assessment responses, which provides support for future research endeavours that aim to conduct larger-scale psychopathy assessments within the community. This paper further outlines the study implications in regards to the practical assessment of psychopathy.

Optimal image sample size for corneal nerve morphometry

Purpose Arbitrary numbers of corneal confocal microscopy images have been used for analysis of corneal subbasal nerve parameters under the implicit assumption that these are a representative sample of the central corneal nerve plexus. The purpose of this study is to present a technique for quantifying the number of random central corneal images required to achieve an acceptable level of accuracy in the measurement of corneal nerve fiber length and branch density. Methods Every possible combination of 2 to 16 images (where 16 was deemed the true mean) of the central corneal subbasal nerve plexus, not overlapping by more than 20%, were assessed for nerve fiber length and branch density in 20 subjects with type 2 diabetes and varying degrees of functional nerve deficit. Mean ratios were calculated to allow comparisons between and within subjects. Results In assessing nerve branch density, eight randomly chosen images not overlapping by more than 20% produced an average that was within 30% of the true mean 95% of the time. A similar sampling strategy of five images was 13% within the true mean 80% of the time for corneal nerve fiber length. Conclusions The “sample combination analysis” presented here can be used to determine the sample size required for a desired level of accuracy of quantification of corneal subbasal nerve parameters. This technique may have applications in other biological sampling studies.