Academic literature on the topic 'Sampling Techniques'

The main topic of this thesis concerns t-SNE, a dimensionality reduction technique that has gained much popularity for showing great capability of preserving well-separated clusters from a high-dimensional space. Our goal with this thesis is twofold. Firstly we give an introduction to the use of dimensionality reduction techniques in visualization and, following recent research, show that t-SNE in particular is successful at preserving well-separated clusters. Secondly, we perform a thorough series of experiments that give us the ability to draw conclusions about the quality of embeddings from running t-SNE on samples of data using different sampling techniques. We are comparing pure random sampling, random walk sampling and so-called hubness sampling on a dataset, attempting to find a sampling method that is consistently better at preserving local information than simple random sampling. Throughout our testing, a specific variant of random walk sampling distinguished itself as a better alternative to pure random sampling.

In magnetic resonance imaging (MRI), samples of the object's spectrum are measured in the spatial frequency domain (k-space). For a number of reasons there is a desire to reduce the time taken to gather measurements. The approach considered is to sample below the Nyquist density, using prior knowledge of the object's support in the spatial domain to enable full reconstruction. The two issues considered are where to position the samples (sampling strategies) and how to form an image (reconstruction techniques). Particular attention is given to a special case of irregular sampling, referred to as Cartesian sampling, in which the samples are located on a Cartesian grid but only constitute a subset of the full grid. A further special case is considered where the sampling scheme repeats periodically, referred to as periodic Cartesian sampling. These types of sampling schemes are applicable to 3-D Cartesian MRI, MRSI, and other modalities that measure a single point in 2-D k-space per echo. The case of general irregular sampling is also considered, which is applicable to spiral sampling, for example. A body of theory concerning Cartesian sampling is developed that has practical implications for how to approach the problem and provides intuition about its nature. It is demonstrated that periodic Cartesian sampling effectively decomposes the problem into a number of much smaller subproblems, which leads to the development of a reconstruction algorithm that exploits these computational advantages. An additional algorithm is developed to predict the regions that could be reconstructed from a particular sampling scheme and support; it can be used to evaluate candidate sampling schemes before measurements are obtained. A number of practical issues are also discussed using illustrative examples. Sample selection algorithms for both Cartesian and periodic Cartesian sampling are developed using heuristic metrics that are fast to compute. The result is a significant reduction in selection time at the expense of a slightly worse conditioned system. The reconstruction problem for a general irregular sampling scheme is also analysed and a reconstruction algorithm developed that trades off computation time for better image quality.

ABSTRACT Heartwater, a tick-borne disease caused by Ehrlichia ruminantium, is considered to be a significant cause of mortality amongst domestic and wild ruminants in South Africa. The main vector is Amblyomma hebraeum and although previous epidemiological studies have outlined endemic areas based on mortalities, these have been limited by diagnostic methods which relied mainly on positive brain smears. The indirect fluorescent antibody test (IFA) has a low specificity for heartwater organisms as it cross-reacts with some other species. Since the advent of biotechnology and genomics, molecular epidemiology has evolved using the methodology of traditional epidemiology coupled with the new molecular techniques. A new quantitative real-time polymerase chain reaction (qPCR) test has been developed for rapid and accurate diagnosis of heartwater in the live animal. This method can also be used to survey populations of A. hebraeum ticks for heartwater. Sampling whole blood and ticks for this qPCR differs from routine serumsampling, which is used for many serological tests. Veterinary field staff, particularly animal health technicians, are involved in surveillance and monitoring of controlled and other diseases of animals in South Africa. However, it was found that the sampling of whole blood was not done correctly, probably because it is a new sampling technique specific for new technology, where the heartwater organism is much more labile than the serumantibodies required for other tests. This qPCR technique is highly sensitive and can diagnose heartwater in the living animal within 2 hours, in time to treat it. Poor sampling techniques that decrease the sensitivity of the test will, however, result in a false negative diagnosis. This paper describes the development of a skills training programme for para-veterinary field staff, to facilitate research into the molecular epidemiology of heartwater in ruminants and eliminate any sampling bias due to collection errors. Humane handling techniques were also included in the training, in line with the current focus on improved livestock welfare.