Regression analysis of ranked segment parameters for optic nerve head classification:a pilot study

Purpose: To determine whether curve-fitting analysis of the ranked segment distributions of topographic optic nerve head (ONH) parameters, derived using the Heidelberg Retina Tomograph (HRT), provide a more effective statistical descriptor to differentiate the normal from the glaucomatous ONH. Methods: The sample comprised of 22 normal control subjects (mean age 66.9 years; S.D. 7.8) and 22 glaucoma patients (mean age 72.1 years; S.D. 6.9) confirmed by reproducible visual field defects on the Humphrey Field Analyser. Three 10°-images of the ONH were obtained using the HRT. The mean topography image was determined and the HRT software was used to calculate the rim volume, rim area to disc area ratio, normalised rim area to disc area ratio and retinal nerve fibre cross-sectional area for each patient at 10°-sectoral intervals. The values were ranked in descending order, and each ranked-segment curve of ordered values was fitted using the least squares method. Results: There was no difference in disc area between the groups. The group mean cup-disc area ratio was significantly lower in the normal group (0.204 ± 0.16) compared with the glaucoma group (0.533 ± 0.083) (p < 0.001). The visual field indices, mean deviation and corrected pattern S.D., were significantly greater (p < 0.001) in the glaucoma group (-9.09 dB ± 3.3 and 7.91 ± 3.4, respectively) compared with the normal group (-0.15 dB ± 0.9 and 0.95 dB ± 0.8, respectively). Univariate linear regression provided the best overall fit to the ranked segment data. The equation parameters of the regression line manually applied to the normalised rim area-disc area and the rim area-disc area ratio data, correctly classified 100% of normal subjects and glaucoma patients. In this study sample, the regression analysis of ranked segment parameters method was more effective than conventional ranked segment analysis, in which glaucoma patients were misclassified in approximately 50% of cases. Further investigation in larger samples will enable the calculation of confidence intervals for normality. These reference standards will then need to be investigated for an independent sample to fully validate the technique. Conclusions: Using a curve-fitting approach to fit ranked segment curves retains information relating to the topographic nature of neural loss. Such methodology appears to overcome some of the deficiencies of conventional ranked segment analysis, and subject to validation in larger scale studies, may potentially be of clinical utility for detecting and monitoring glaucomatous damage. © 2007 The College of Optometrists.

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained.

Probabilistic control for uncertain systems

In this paper a new framework has been applied to the design of controllers which encompasses nonlinearity, hysteresis and arbitrary density functions of forward models and inverse controllers. Using mixture density networks, the probabilistic models of both the forward and inverse dynamics are estimated such that they are dependent on the state and the control input. The optimal control strategy is then derived which minimizes uncertainty of the closed loop system. In the absence of reliable plant models, the proposed control algorithm incorporates uncertainties in model parameters, observations, and latent processes. The local stability of the closed loop system has been established. The efficacy of the control algorithm is demonstrated on two nonlinear stochastic control examples with additive and multiplicative noise.

A probabilistic indirect adaptive control for systems with input-dependent noise

A probabilistic indirect adaptive controller is proposed for the general nonlinear multivariate class of discrete time system. The proposed probabilistic framework incorporates input–dependent noise prediction parameters in the derivation of the optimal control law. Moreover, because noise can be nonstationary in practice, the proposed adaptive control algorithm provides an elegant method for estimating and tracking the noise. For illustration purposes, the developed method is applied to the affine class of nonlinear multivariate discrete time systems and the desired result is obtained: the optimal control law is determined by solving a cubic equation and the distribution of the tracking error is shown to be Gaussian with zero mean. The efficiency of the proposed scheme is demonstrated numerically through the simulation of an affine nonlinear system.

Control of cascaded DC-DC converter based hybrid battery energy storage systems - Part II:Lyapunov approach

A cascaded DC-DC boost converter is one of the ways to integrate hybrid battery types within a grid-tie inverter. Due to the presence of different battery parameters within the system such as, state-of-charge and/or capacity, a module based distributed power sharing strategy may be used. To implement this sharing strategy, the desired control reference for each module voltage/current control loop needs to be dynamically varied according to these battery parameters. This can cause stability problem within the cascaded converters due to relative battery parameter variations when using the conventional PI control approach. This paper proposes a new control method based on Lyapunov Functions to eliminate this issue. The proposed solution provides a global asymptotic stability at a module level avoiding any instability issue due to parameter variations. A detailed analysis and design of the nonlinear control structure are presented under the distributed sharing control. At last thorough experimental investigations are shown to prove the effectiveness of the proposed control under grid-tie conditions.

Control of cascaded DC-DC converter-based hybrid battery energy storage systems - Part I:stability Issue

There is an emerging application which uses a mixture of batteries within an energy storage system. These hybrid battery solutions may contain different battery types. A DC-side cascaded boost converters along with a module based distributed power sharing strategy has been proposed to cope with variations in battery parameters such as, state-of-charge and/or capacity. This power sharing strategy distributes the total power among the different battery modules according to these battery parameters. Each module controller consists of an outer voltage loop with an inner current loop where the desired control reference for each control loop needs to be dynamically varied according to battery parameters to undertake this sharing. As a result, the designed control bandwidth or stability margin of each module control loop may vary in a wide range which can cause a stability problem within the cascaded converter. This paper reports such a unique issue and thoroughly investigates the stability of the modular converter under the distributed sharing scheme. The paper shows that a cascaded PI control loop approach cannot guarantee the system stability throughout the operating conditions. A detailed analysis of the stability issue and the limitations of the conventional approach are highlighted. Finally in-depth experimental results are presented to prove the stability issue using a modular hybrid battery energy storage system prototype under various operating conditions.

Perception of global image contrast is predicted by the same spatial integration model of gain control as detection and discrimination

How are the image statistics of global image contrast computed? We answered this by using a contrast-matching task for checkerboard configurations of ‘battenberg’ micro-patterns where the contrasts and spatial spreads of interdigitated pairs of micro-patterns were adjusted independently. Test stimuli were 20 × 20 arrays with various sized cluster widths, matched to standard patterns of uniform contrast. When one of the test patterns contained a pattern with much higher contrast than the other, that determined global pattern contrast, as in a max() operation. Crucially, however, the full matching functions had a curious intermediate region where low contrast additions for one pattern to intermediate contrasts of the other caused a paradoxical reduction in perceived global contrast. None of the following models predicted this: RMS, energy, linear sum, max, Legge and Foley. However, a gain control model incorporating wide-field integration and suppression of nonlinear contrast responses predicted the results with no free parameters. This model was derived from experiments on summation of contrast at threshold, and masking and summation effects in dipper functions. Those experiments were also inconsistent with the failed models above. Thus, we conclude that our contrast gain control model (Meese & Summers, 2007) describes a fundamental operation in human contrast vision.

Multiple roles for executive control in belief-desire reasoning:distinct neural networks are recruited for self perspective inhibition and complexity of reasoning

Belief-desire reasoning is a core component of 'Theory of Mind' (ToM), which can be used to explain and predict the behaviour of agents. Neuroimaging studies reliably identify a network of brain regions comprising a 'standard' network for ToM, including temporoparietal junction and medial prefrontal cortex. Whilst considerable experimental evidence suggests that executive control (EC) may support a functioning ToM, co-ordination of neural systems for ToM and EC is poorly understood. We report here use of a novel task in which psychologically relevant ToM parameters (true versus false belief; approach versus avoidance desire) were manipulated orthogonally. The valence of these parameters not only modulated brain activity in the 'standard' ToM network but also in EC regions. Varying the valence of both beliefs and desires recruits anterior cingulate cortex, suggesting a shared inhibitory component associated with negatively valenced mental state concepts. Varying the valence of beliefs additionally draws on ventrolateral prefrontal cortex, reflecting the need to inhibit self perspective. These data provide the first evidence that separate functional and neural systems for EC may be recruited in the service of different aspects of ToM.

Control of the in-cavity dynamics in mode-locked fibre lasers

At the level of fundamental research, fibre lasers provide convenient and reproducible experimental settings for the study of a variety of nonlinear dynamical processes, while at the applied research level, pulses with different and optimised features – e.g., in terms of pulse duration, temporal and/or spectral intensity profile, energy, repetition rate and emission bandwidth – are sought with the general constraint of developing efficient cavity architectures. In this talk, we review our recent progress on the realisation of different regimes of pulse generation in passively mode-locked fibre lasers through control of the in-cavity propagation dynamics. We report on the possibility to achieve both parabolic self-similar and triangular pulse shaping in a mode-locked fibre laser via adjustment of the net normal dispersion and integrated gain of the cavity [1]. We also show that careful control of the gain/loss parameters of a net-normal dispersion laser cavity provides the means of achieving switching among Gaussian pulse, dissipative soliton and similariton pulse solutions in the cavity [2,3]. Furthermore, we report on our recent theoretical and experimental studies of pulse shaping by inclusion of an amplitude and phase spectral filter into the cavity of a laser. We numerically demonstrate that a mode-locked fibre laser can operate in dif- ferent pulse-generation regimes, including parabolic, flattop and triangular waveform generations, depending on the amplitude profile of the in-cavity spectral filter [4]. An application of technique using a flat-top spectral filter is demonstrated to achieve the direct generation of sinc-shaped optical Nyquist pulses of high quality and of a widely tuneable bandwidth from the laser [5]. We also report on a recently-developed versa- tile erbium-doped fibre laser, in which conventional soliton, dispersion-managed soli- ton (stretched-pulse) and dissipative soliton mode-locking regimes can be selectively and reliably targeted by programming different group-velocity dispersion profiles and bandwidths on an in-cavity programmable filter [6]. References: 1. S. Boscolo and S. K. Turitsyn, Phys. Rev. A 85, 043811 (2012). 2. J. Peng et al., Phys. Rev. A 86, 033808 (2012). 3. J. Peng, Opt. Express 24, 3046-3054 (2016). 4. S. Boscolo, C. Finot, H. Karakuzu, and P. Petropoulos, Opt. Lett. 39, 438-441 (2014). 5. S. Boscolo, C. Finot, and S. K. Turitsyn, IEEE Photon. J. 7, 7802008 (2015). 6. J. Peng and S. Boscolo, Sci. Rep. 6, 25995 (2016).

Microparticle surface layering through dry coating:impact of moisture content and process parameters on the properties of orally disintegrating tablets

OBJECTIVES: The aim of this study was to investigate the influence of process parameters during dry coating on particle and dosage form properties upon varying the surface adsorbed moisture of microcrystalline cellulose (MCC), a model filler/binder for orally disintegrating tablets (ODTs). METHODS: The moisture content of MCC was optimised using the spray water method and analysed using thermogravimetric analysis. Microproperty/macroproperty assessment was investigated using atomic force microscopy, nano-indentation, scanning electron microscopy, tablet hardness and disintegration testing. KEY FINDINGS: The results showed that MCC demonstrated its best flowability at a moisture content of 11.2% w/w when compared to control, comprising of 3.9% w/w moisture. The use of the composite powder coating process (without air) resulted in up to 80% increase in tablet hardness, when compared to the control. The study also demonstrated that surface adsorbed moisture can be displaced upon addition of excipients during dry processing circumventing the need for particle drying before tabletting. CONCLUSIONS: It was concluded that MCC with a moisture content of 11% w/w provides a good balance between powder flowability and favourable ODT characteristics.