A new decoupling method for quadrature coils in magnetic resonance imaging

A powerful decoupling method is introduced to obtain decoupled signal voltages from quadrature coils in magnetic resonance imaging (MRI). The new method uses the knowledge of the position of the signal source in MRI, the active slice, to define a new mutual impedance which accurately quantifies the coupling voltages and enables them to be removed almost completely. Results show that by using the new decoupling method, the percentage errors in the decoupled voltages are of the order of 10(-7)% and isolations between two coils are more than 170 dB.

The emergence of order in random walk resource discovery protocols

While others have attempted to determine, by way of mathematical formulae, optimal resource duplication strategies for random walk protocols, this paper is concerned with studying the emergent effects of dynamic resource propagation and replication. In particular, we show, via modelling and experimentation, that under any given decay (purge) rate the number of nodes that have knowledge of particular resource converges to a fixed point or a limit cycle. We also show that even for high rates of decay - that is, when few nodes have knowledge of a particular resource - the number of hops required to find that resource is small.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Column connection detail, Kangaroo Point House

Hand-made spotted gum column on edge of outdoor room.

West elevation, Cleveland Point House, Cleveland

Hand-drawn elevation.

Cottage, Cleveland Point House, Cleveland

Beach elevation of renovated existing cottage.

Lattice to boat house, Cleveland Point House, Cleveland

Lattice behind pull-down blind to boat house.

Beachfront elevation, Cleveland Point House, Cleveland

Timber framing to new bath house (centre), with existing buildings to either side.

Bath house, Cleveland Point House, Cleveland

New bath house under construction (centre), with existing buildings to either side.

Bath house, Cleveland Point House, Cleveland

Bath house under construction, as seen looking towards water from deck area.

Bath house, Cleveland Point House, Cleveland

As seen looking towards water from deck area.

Beachfront elevation, Cleveland Point House, Cleveland

New bath house (centre), with existing buildings to either side.

Boat house, Cleveland Point House, Cleveland

Existing boat house undergoing renovations.

Bath house hip rafters, Cleveland Point House, Cleveland

Timber framing to new bath house.

Beachfront elevation, Cleveland Point House, Cleveland

Timber framing to new bath house (centre), with existing buildings to either side.