Presbyopic LASIK using hybrid bi-aspheric micro-monovision ablation profile for presbyopic corneal treatments

Purpose: To evaluate distance and near image quality after hybrid bi-aspheric multifocal central presbyLASIK treatments. Design: Consecutive case series. Methods: Sixty-four eyes of 32 patients consecutively treated with central presbyLASIK were assessed. The mean age of the patients was 51 ± 3 years with a mean spherical equivalent refraction of-1.08 ± 2.62 diopters (D) and mean astigmatism of 0.52 ± 0.42 D. Monocular corrected distance visual acuity (CDVA), corrected near visual acuity (CNVA), and distance corrected near visual acuity (DCNVA) of nondominant eyes; binocular uncorrected distance visual acuity (UDVA); uncorrected intermediate visual acuity (UIVA); distance corrected intermediate visual acuity (DCIVA); and uncorrected near visual acuity (UNVA) were assessed pre- and postoperatively. Subjective quality of vision and near vision was assessed using the 10-item Rasch-scaled Quality of Vision and Near Activity Visual Questionnaire, respectively. Results: At 1 year postoperatively, 93% of patients achieved 20/20 or better binocular UDVA; 90% and 97% of patients had J2 or better UNVA and UIVA, respectively; 7% lost 2 Snellen lines of CDVA; Strehl ratio reduced by ~-4% ± 14%. Defocus curves revealed a loss of half a Snellen line at best focus, with no change for intermediate vergence (-1.25 D) and a mean gain of 2 lines for near vergence (-3 D). Conclusions: Presbyopic treatment using a hybrid bi-aspheric micro-monovision ablation profile is safe and efficacious. The postoperative outcomes indicate improvements in binocular vision at far, intermediate, and near distances with improved contrast sensitivity. A 19% retreatment rate should be considered to increase satisfaction levels, besides a 3% reversal rate.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

Can aberrometry provide rapid and reliable measures of subjective depth of focus following multifocal intraocular lens implantation?

Purpose: To determine whether the ‘through-focus’ aberrations of a multifocal and accommodative intraocular lens (IOL) implanted patient can be used to provide rapid and reliable measures of their subjective range of clear vision. Methods: Eyes that had been implanted with a concentric (n = 8), segmented (n = 10) or accommodating (n = 6) intraocular lenses (mean age 62.9 ± 8.9 years; range 46-79 years) for over a year underwent simultaneous monocular subjective (electronic logMAR test chart at 4m with letters randomised between presentations) and objective (Aston open-field aberrometer) defocus curve testing for levels of defocus between +1.50 to -5.00DS in -0.50DS steps, in a randomised order. Pupil size and ocular aberration (a combination of the patient’s and the defocus inducing lens aberrations) at each level of blur was measured by the aberrometer. Visual acuity was measured subjectively at each level of defocus to determine the traditional defocus curve. Objective acuity was predicted using image quality metrics. Results: The range of clear focus differed between the three IOL types (F=15.506, P=0.001) as well as between subjective and objective defocus curves (F=6.685, p=0.049). There was no statistically significant difference between subjective and objective defocus curves in the segmented or concentric ring MIOL group (P>0.05). However a difference was found between the two measures and the accommodating IOL group (P<0.001). Mean Delta logMAR (predicted minus measured logMAR) across all target vergences was -0.06 ± 0.19 logMAR. Predicted logMAR defocus curves for the multifocal IOLs did not show a near vision addition peak, unlike the subjective measurement of visual acuity. However, there was a strong positive correlation between measured and predicted logMAR for all three IOLs (Pearson’s correlation: P<0.001). Conclusions: Current subjective procedures are lengthy and do not enable important additional measures such as defocus curves under differently luminance or contrast levels to be assessed, which may limit our understanding of MIOL performance in real-world conditions. In general objective aberrometry measures correlated well with the subjective assessment indicating the relative robustness of this technique in evaluating post-operative success with segmented and concentric ring MIOL.

The measurements of neutron and gamma dose rates in mixed radiation fields, using a liquid scintillation counter

Measurements of neutron and gamma dose rates in mixed radiation fields, and gamma dose rates from calibrated gamma sources, were performed using a liquid scintillation counter NE213 with a pulse shape discrimination technique based on the charge comparison method. A computer program was used to analyse the experimental data. The radiation field was obtained from a 241Am-9Be source. There was general agreement between measured and calculated neutron and gamma dose rates in the mixed radiation field, but some disagreement in the measurements of gamma dose rates for gamma sources, due to the dark current of the photomultiplier and the effect of the perturbation of the radiation field by the detector. An optical fibre bundle was used to couple an NE213 scintillator to a photomultiplier, in an attempt to minimise these effects. This produced an improvement in the results for gamma sources. However, the optically coupled detector system could not be used for neutron and gamma dose rate measurements in mixed radiation fields. The pulse shape discrimination system became ineffective as a consequence of the slower time response of the detector system.

The sensitivity of mapping methods to reference data quality:training supervised image classifications with imperfect reference data

The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.