Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device

A Hidden Markov Model (HMM) modified to work in combination with a Fuzzy System is utilised to determine the current behavioural state of the user from information obtained with specialised hardware. Due to the high dimensionality and not-linearly-separable nature of the Fuzzy System and the sensor data obtained with the hardware which informs the state decision, a new method is devised to update the HMM and replace the initial Fuzzy System such that subsequent state decisions are based on the most recent information. The resultant system first reduces the dimensionality of the original information by using a manifold representation in the high dimension which is unfolded in the lower dimension. The data is then linearly separable in the lower dimension where a simple linear classifier, such as the perceptron used here, is applied to determine the probability of the observations belonging to a state. Experiments using the new system verify its applicability in a real scenario

Dimension reduction for linear separation with curvilinear distances

Any high dimensional data in its original raw form may contain obviously classifiable clusters which are difficult to identify given the high-dimension representation. In reducing the dimensions it may be possible to perform a simple classification technique to extract this cluster information whilst retaining the overall topology of the data set. The supervised method presented here takes a high dimension data set consisting of multiple clusters and employs curvilinear distance as a relation between points, projecting in a lower dimension according to this relationship. This representation allows for linear separation of the non-separable high dimensional cluster data and the classification to a cluster of any successive unseen data point extracted from the same higher dimension

Discovery of a Significant Magnetic CV Population in the Limiting Window

[Abridged] We have discovered 10 periodic X-ray sources from the 1 Ms Chandra ACIS observation of the Limiting Window (LW), a low extinction region (A_V~3.9) at 1.4 Deg south of the Galactic center. The observed periods (~1.3 to 3.4 hours) and the X-ray luminosities (10^{31.8-32.9} erg s^-1 at 8 kpc) of the 10 periodic sources, combined with the lack of bright optical counterparts and thus high X-ray-to-optical flux ratios, suggest that they are likely accreting binaries, in particular, magnetic cataclysmic variables (MCVs). All of the 10 sources exhibit a relatively hard X-ray spectrum (PLI<2 for a power law model) and X-ray spectra of at least five show an extinction larger than the field average expected from the interstellar medium in the region. The discovery of these periodic X-ray sources in the LW further supports the current view that MCVs constitute the majority of low luminosity hard X-ray sources (~10^{30-33} erg s^-1) in the Bulge. The period distribution of these sources resembles those of polars, whereas the relatively hard spectra suggest that they could be intermediate polars (IPs). These puzzling properties can be explained by unusual polars with buried magnetic fields or a rare sub-class of MCVs, nearly synchronous MCVs. These unusual MCVs may provide important clues in the evolutionary path of MCVs from IPs to polars. The completeness simulation indicates >~40% of the hard X-ray sources in the LW are periodic. Therefore, this discovery provides a first direct evidence of a large MCV population in the Bulge.Comment: 15 pages, 10 figures, 5 tables, submitted to ApJ, revised in response to the referee's revie