In this paper we consider data on underwater sounds of differing types. Our objective isto filter background noise and achieve an acceptable vel of reduction in the raw data, whilst at the same time maintaining the main features of the original signal. In particular, we consider data compression

in the time domain. The experimental result shows that the MCC algorithm is comparable to C4.5. Using MCC as a mining algorithm to predict the `upward' or `downward' trend of k-day stock returns, the average hit rate on pre-processed data is 20.55% higher than that on the original data. This means

-effectiveness of pre-processing with singleton consistency algorithms. Our experiments use both random and structured problems. Whilst pre-processing with singleton consistencies is not in general beneficial for random problems, it starts to pay off when randomness and structure are combined, and it is very worthwhile

, representing carbonyl group stretching in the protein backbone) is known to depend on protein secondary structures. Bloodmeal showed a broad, convoluted peak in this region, with a maximum in the range 1648 – 1658 cm-1, associated with α-helices. With processing additives, a dip was seen in the α-helix region

Abstract Detecting event related potentials (ERPs) from single trials is crit-ical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging

Previous behavioural research has consistently found enhanced cognitive function whilst listening to classical music; the so called Mozart Effect (Rauscher et al, 1993). However, to date it has proven difficult to draw firm conclusions from this literature because of the diversity of methods used

pre-processing the data in some way, and can often be computationally complex. The Value Difference Metric (VDM) is a symbolic distance metric used by a number of different nearest neighbour learning algorithms. This paper demonstrates how the VDM can be used to reduce the impact of irrelevant

We describe a dynamic face tracking system based on an integrated motion-based object tracking and model-based face detection framework. The motion-based tracker focuses attention for the face detector whilst the latter aids the tracking process. The system produces segmented face sequences from

This paper presents pre-processing of input features to artificial neural network (NN). This is for preparation of reliable reference templates for the set of words to be recognized. The processed features are pitch and Linear Predictive Coefficients (LPC) for input and reference templates, based

are applied to pre-process each clip into a video object representation that we augment with object classification and colour infor-mation. The result is a system capable of searching videos based on the desired colour, motion path, and semantic labels of the objects present. We evaluate the performance