Data Cleaning and Outlier Removal: Application in Human Skin Detection

An outlier removal based data cleaning technique is proposed to
clean manually pre-segmented human skin data in colour images.
The 3-dimensional colour data is projected onto three 2-dimensional
planes, from which outliers are removed. The cleaned 2 dimensional
data projections are merged to yield a 3D clean RGB data. This data
is finally used to build a look up table and a single Gaussian classifier
for the purpose of human skin detection in colour images.

Wireless data encoding and decoding using OAM modes

A means of encoding and decoding data using wireless orbital angular momentum (OAM) modes is proposed and analysed. Source data symbols are used to select an OAM mode, which is generated using an 8-element circular array. A 2-element array is used to detect the mode by estimating the phase gradient of the received signal, and hence identifying the transmitted data symbol. The results are presented in terms of mode estimation error.

Address inaccuracy in health registration data: determinants and impacts

Access to demographic data that are complete, accurate and up-to-date is fundamental to many aspects of public health, government and academic work and for accurate interpretation of other databases. Health registration data are the prime source of demographic information for health and social care systems; for example, as an indicator of need, as a source of denominators to convert number of events into rates, or in the case of the residential address information as the basis for generating the call-recall invitation letters that are used for most screening programs (e.g. breast, colo-rectal and AAA screening). However, list inflation (ghosts, duplicates or emigrants) and a degree of address inaccuracy are recognised caveats with the health registration data and a recent NILS-related study on breast screening suggests that improved address accuracy might be a fast and efficient means of increasing screening uptake rates in cities and amongst deprived populations. In NI these data are collated by the BSO who uniquely in the UK also have access to data relating to prescribing, dental registrations and use of A&E services. These can be used to supplement the standard demographic and address information by (i) indicating patients who are alive and resident in NI and (ii) providing an independent source of probably improved address information. This study will use the NI Unique Property Reference Number (UPRN), rather than the addresses per se which are difficult to work with, to compare the addresses registered in the BSO with those addresses in the enumerated 2011 census. Assuming that the census is a more accurate source of address information for individuals, a comparison of the health registration addresses with those recorded at the census, the aim of the proposed study will be to (i) characterise the amount and distributions of these differences, (ii) to see what proportion of those who do not attend for screening did not actually receive an invitation letter because the addresses were incorrect, (iii) to determine how much of the social gradient (and urban/rural differences) in screening uptake are due to address inaccuracies, (iv) a comparison of timing of address changes at the BSO will provide information on the delays in updating of addresses.