John Abela

Mutually Compatible and Incompatible Merges for the Search of the Smallest Consistent DFA

State Merging algorithms, such as Rodney Price’s EDSM (Evidence-Driven State Merging) algorithm, have been reasonably successful at solving DFA-learning problems. EDSM, however, often does not converge to the target DFA and, in the case of sparse training data, does not converge at all. In this paper we argue that is partially due to the particular heuristic used in EDSM and also to the greedy search strategy employed in EDSM. We then propose a new heuristic that is based on minimising the risk involved in making merges. In other words, the heuristic gives preference to merges, whose evidence is supported by high compatibility with other merges. Incompatible merges can be trivially detected during the computation of the heuristic. We also propose a new heuristic limitation of the set of candidates after a backtrack to these incompatible merges, allowing to introduce diversity in the search.

A Machine Learning approach for automatic land cover mapping from DSLR images over the Maltese Islands

Abstract High resolution raster data for land cover mapping or change analysis are normally acquired through satellite or aerial imagery. Apart from the incurred costs, the available files might not have the required temporal resolution. Moreover, cloud cover and atmospheric absorptions may limit the applicability of existing algorithms or reduce their accuracy. This paper presents a novel technique that is capable of mapping garrigue and/or phrygana vegetation as well as karst or ground-armour terrain in photos captured by a digital camera. By including a reference pattern in every frame, the automated method estimates the total area covered by each land type. Pixel based classification is performed by supervised decision tree methods. Although the intention is not to replace traditional surface cover analysis, the proposed technique allows accurate land cover mapping with quantitative estimates to be obtained. Since no expensive hardware or specialised personnel are required, vegetation monitoring of local sites can be carried out cheaply and frequently. The developed proof of concept is tested on photos taken in thirteen different sites across the Maltese Islands.

Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.

Optimization of RF On-Chip Inductors Using Genetic Algorithms

This chapter discusses the optimization of the geometry of RF on-chip inductors by means of a genetic algorithm in order to achieve adequate performance. Necessary background theory together with the modeling of these inductors is included in order to aid the discussion. A set of guidelines for the design of such inductors with a good quality factor in a standard CMOS process is also provided. The optimization process is initialized by using a set of empirical formulae in order to estimate the physical parameters of the required structure as constrained by the technology. Then, automated design optimization is executed to further improve its performance by means of dedicated software packages. The authors explain how to use state-of-the-art computer-aided design tools in the optimization process and how to efficiently simulate the inductor performance using electromagnetic simulators.

Optimal SKA antenna configuration using genetic algorithms

The Square Kilometre Array (SKA) is a radio telescope designed to operate between 70MHz and 10GHz. Due to this large bandwidth, the SKA will be built out of different collectors, namely antennas and dishes to cover the frequency range adequately. In order to deal with this bandwidth, innovative feeds and detectors must be designed and introduced in the initial phases of development. Moreover, the required level of resolution may only be achieved through a novel configuration of dishes and antennas. Due to the large collecting area and the specifications required for the SKA to deliver the promised science, the configuration of the dishes and the antennas within stations is an important question. This research investigates the applicability of machine learning techniques to determine an optimum configuration for the elements within an aperture array station. Genetic algorithms are primarily used to search a large space of optimum layouts. Fitness functions based on estimates of the main lobe to maximum side lobe ratio, the side lobes fall off rate, the main lobe area to side lobes area ratio as well as the kurtosis of residuals from polynomial fits of the main beam, are employed.

Multi-frame blind image deconvolution through split frequency - phase recovery

Accurate information extraction from images can only be realised if the data is blur free and contains no artificial artefacts. In astronomical images, apart from hardware limitations, biases are introduced by phenomena beyond control such as atmospheric turbulence. The induced blur function does vary in both time and space depending on the astronomical “seeing” conditions as well as the wavelengths being recorded. Multi-frame blind image deconvolution attempts to recover a sharp latent image from an image sequence of blurry and noisy observations without knowledge of the blur applied to each image within the recorded sequence. Finding a solution to this inverse problem that estimates the original scene from convolved data is a heavily ill-posed problem. In this paper we describe a novel multi-frame blind deconvolution algorithm, that performs image restoration by recovering the frequency and phase information of the latent sharp image in two separate steps. For every given image in the sequence a point-spread function (PSF) is estimated that allows iterative refinement of our latent sharp image estimate. The datasets generated for testing purposes assume Moffat or complex Kolmogorov blur kernels. The results from our implemented prototype are promising and encourage further research.

Aperture arrays for radio astronomy

Ultra-wideband capacitively coupled phased array antennas for low and mid frequency radio astronomy are presented. Two distinct and novel broadband techniques are employed for the candidate current sheet models. One of the arrays is designed to operate from 250 MHz to 1.3 GHz while the other functions from 50 MHz to 400 MHz. The radiating arrays comprise synthetic periodic aperture of fractal plates, each subdivided into a number of scaled down octagon rings. Both structures are dual-polarised and serve as low profile conformal arrays. A 16 × 16 mid-band finite array was constructed to demonstrate its wideband performance. The tightly coupled arrays are limited to bandwidths around 4:1 (VSWR <; 2) without using dielectric substrates. However, a 4 × 4 prototype of the low frequency antenna array validates the purpose of using a conductive resistive frequency-selective surfaces to extend the bandwidth towards higher frequencies. This feature leads to unique array properties, expanding the bandwidth to double. This paper also studies and analyses sensitivity and system noise contribution from such sub-arrays embedded in an infinite periodic array.

Dictionary based encoding of cosmological images

The pioneering theory of Compressed Sensing (CS) provides a framework for ill-posed inverse problems and allows for the recovery of sparse signals from a set of measurements. Its applicability to astronomy datasets was recognised from its infancy. In this work, CS techniques are used to aid in the construction of an optimised dictionary that is capable of encoding cosmological images. A learning algorithm that automatically determines and adapts the size of the repository according to the provided training set, is presented. Use of the robust and fast StOMP ℓ1 minimization method is made for the recovery of sparse one dimensional signals. The results suggest that accurate reconstructions with very low residual errors can be obtained.