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The study of images in scientific fields such as remote sensing, medical imaging and astronomy comes naturally not only because pictures mimic one of the main sensory elements of humans, but also because they allow for the visualisation of wavelengths beyond the sensitive range of the human eye. However, accurate information extraction from images is only possible if the data are known to be free of noise, blur and artificial artifacts. In astronomical images, apart from hardware limitations, biases arise from image degradation caused by phenomena beyond one's control such as, for instance, atmospheric and ionospheric turbulence. Deconvolution attempts to undo such adverse effects and recover the true intensity values from measured ones. Having a robust and accurate deconvolution algorithm is very important especially for large-scale telescopes such as the Square Kilometre Array (SKA) through which sensitive investigations including gravitational lensing research and the detection of faint sources are to be made. In this work, we investigate the improvements gained if an ensemble of algorithms is used to minimise the overall restoration error. We present a blind deconvolution method that is able to process multiple frames and yields improved results when compared to the state-of-the-art., peer-reviewed

The formation of cosmological black holes is investigated using the functional Schr ̈odinger equation as observed by an asymptotic observer, assuming a spherical domain wall collapse process. The mass formula of the Sultana-Dyer black hole is derived using Israel’s domain wall mathematical framework. This is used to examine the semi-classical and quantum nature of the collapsing domain wall in the general scale of an arbitrary scale factor, while ignoring evaporation and back reaction mass losses. Particular FLRW scale factors are then explored for black hole horizon formation times, all yielding the same over-all classical result, namely that an arbitrary amount of time is required for an asymptotic observer to register the formation of this surface., N/A

Using f(R) gravity in the Palatini formularism, the metric for a charged spherically symmetric black hole is derived, taking the Ricci scalar curvature to be constant. The generalized uncertainty principle is then used to calculate the temperature of the resulting black hole; through this the entropy is found correcting the Bekenstein-Hawking entropy in this case. Using the entropy the tunneling probability and heat capacity are calculated up to the order of the Planck length, which produces an extra factor that becomes important as black holes become small, such as in the case of mini-black holes., peer-reviewed

Beamforming is central to the processing function of all phased arrays and becomes particularly challenging with a large number of antenna element (e.g. >100,000). The ability to beamform efficiently with reasonable power requirements is discussed in this paper. Whilst the most appropriate beamforming technology will change over time due to semiconductor and processing developments, we present a hierarchical structure which is technology agnostic and describe both Radio-Frequency (RF) and digital hierarchical beamforming approaches. We present implementations of both RF and digital beamforming systems on two antenna array demonstrators, namely the Electronic Multi Beam Radio Astronomy ConcEpt (EMBRACE) and the dual-polarisation all-digital array (2-PAD). This paper will compare and contrast both digital and analogue implementations without considering the deep system design of these arrays., peer-reviewed

In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and testing datasets is obtained from the Galaxy Zoo project while the corresponding photometric and spectra parameters are downloaded from the SDSS DR7 catalogue., peer-reviewed

This paper focuses on utilizing two different Bayesian methods to deal with a variety of toy problems which occur in data analysis. In particular we implement the Variational Bayesian and Nested Sampling methods to tackle the problems of polynomial selection and Gaussian Mixture Models, comparing the algorithms in terms of processing speed and accuracy. In the problems tackled here it is the Variational Bayesian algorithms which are the faster though both results give similar results., N/A

The aim of this paper is to present a new fast- convergent numerically stable space-time adaptive processing (STAP) algorithm derived using a novel technique of feedback orthogonalization. The main advantages of this approach lie in its perfected stability to computational errors and faults which makes its real-time implementation on substantially faster and cheaper regular fixed-point processors possible., N/A

We describe an hierarchical, frequency-domain beamforming architecture for synthesising a sky beam from the wideband antenna feeds of digital aperture arrays. The development of densely-packed, all-digital aperture arrays is an important area of research required for the Square Kilometre Array (SKA) radio telescope. The design of real-time signal processing systems for digital aperture arrays is currently a central challenge in pathfinder projects worldwide. In particular this work describes a specific implementation of the beamforming architecture to the 2-Polarisation All-Digital (2-PAD) aperture array demonstrator., N/A