Search

Purpose: The crystalline lens undergoes morphological and functional changes with age and may also play a role in eye emmetropisation. Both the geometry and the gradient index of refraction (GRIN) distribution contribute to the lens optical properties. We studied the lens GRIN in the guinea pig, a common animal model to study myopia., Methods: Lenses were extracted from guinea pigs (Cavia porcellus) at 18 days of age (n = 4, three monolaterally treated with negative lenses and one untreated) and 39 days of age (n = 4, all untreated). Treated eyes were myopic (-2.07 D on average) and untreated eyes hyperopic (+3.3 D), as revealed using streak retinoscopy in the live and cyclopeged animals. A custom 3D spectral domain optical coherence tomography (OCT) system (λ = 840 nm, Δλ = 50 nm) was used to image the enucleated crystalline lens at two orientations. Custom algorithms were used to estimate the lens shape and GRIN was modelled with four variables that were reconstructed using the OCT data and a minimisation algorithm. Ray tracing was used to calculate the optical power and spherical aberration assuming a homogeneous refractive index or the estimated GRIN., Results: Guinea pig lenses exhibited nearly parabolic GRIN profiles. When comparing the two age groups (18- and 39 day-old) there was a significant increase in the central thickness (from 3.61 to 3.74 mm), and in the refractive index of the surface (from 1.362 to 1.366) and the nucleus (from 1.443 to 1.454). The presence of GRIN shifted the spherical aberration (-4.1 µm on average) of the lens towards negative values., Conclusions: The guinea pig lens exhibits a GRIN profile with surface and nucleus refractive indices that increase slightly during the first days of life. GRIN plays a major role in the lens optical properties and should be incorporated into computational guinea pig eye models to study emmetropisation, myopia development and ageing., (© 2020 The Authors Ophthalmic & Physiological Optics © 2020 The College of Optometrists.)

Health Monitoring apps for smartphones have the potential to improve quality of life and decrease the cost of health services. However, they have failed to live up to expectation in the context of respiratory disease. This is in part due to poor objective measurements of symptoms such as cough. Real-time cough detection using smartphones faces two main challenges namely, the necessity of dealing with noisy input signals, and the need of the algorithms to be computationally efficient, since a high battery consumption would prevent patients from using them. This paper proposes a robust and efficient smartphone-based cough detection system able to keep the phone battery consumption below 25% (16% if only the detector is considered) during 24 h use. The proposed system efficiently calculates local image moments over audio spectrograms to feed an optimized classifier for final cough detection. Our system achieves 88.94% sensitivity and 98.64% specificity in noisy environments with a 5500× speed-up and 4× battery saving compared to the baseline implementation. Power consumption is also reduced by a minimum factor of 6 compared to existing optimized systems in the literature., (Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.)

This work proposes a modified version of an emerging nature-inspired technique, named flower pollination algorithm, for equalizing digital multiuser channels. This equalization involves two different tasks: (1) estimation of the channel impulse response, and (2) estimation of the users' transmitted symbols. The new algorithm is developed and applied in a direct sequence/code-division multiple-access multiuser communications system. Important issues such as robustness, convergence speed and population diversity control have been in deep investigated. A method based on the entropy of the flowers' fitness is proposed for in-service monitoring and adjusting population diversity. Numerical simulations analyze the performance, showing comparisons with well-known conventional multiuser detectors such as matched filter, minimum mean square error estimator or several Bayesian schemes, as well as with other nature-inspired strategies. Numerical analysis shows that the proposed algorithm enables transmission at higher symbol rates under stronger fading and interference conditions, constituting an attractive alternative to previous algorithms, both conventional and nature-inspired, whose performance is frequently sensible to near–far effects and multiple-access interference problems. These results have been validated by running hypothesis tests to confirm statistical significance. [ABSTRACT FROM AUTHOR]

Efficient digital filter design is an essential signal processing task. Finite Impulse Response (FIR) filters are used in many applications due to its properties of linear phase and frequency stability. Most traditional design methods suffer from the problem of insufficient control over the frequency response of the designed filter. For this reason, the use of a recently developed optimisation technique called flowers pollination algorithm (FPA), –based on the natural process of pollination of flowers– along with a novel multiple fitness function, is proposed in order to obtain optimised filter coefficients that best approximate ideal specifications. Results have been compared to both traditional methods (mainly windowing and the Parks-McClellan algorithm) as well as to several nature-inspired schemes. Finally, processing of a real EEG signal is used to quantitatively evaluate performance of designed filters. Numerical results show that our method achieves better fit to desired filter specifications, a 5 − 10 times larger attenuation in the stop band and a narrower transition band, at the expense of slightly increasing the pass-band ripple ( 5 − 15 % ) in 3 out of 4 of the cases. [ABSTRACT FROM AUTHOR]