A new method for temporal pattern matching of a time series is developed using pattern wavelets and genetic algorithms. The pattern wavelet is applied to the matching of an embedded time series. A problem-specific fitness factor is introduced in the new algorithm, which is useful to construct a fitness function of the feature space. A two-step process discovers the pattern wavelet that yields h...

Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. A notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures. These measures provide clinically applicable complexity analysis of behavioural patterns yielding scalar chara...

Based on accelerometer, we propose a 3D handwriting recognition system in this paper. The system is consists of 4 main parts: (1) data collection: a single tri-axis accelerometer is mounted on a handheld device to collect different handwriting data. A set of key patterns have to be written using the handheld device several times for consequential processing and training. (2) data preprocessing:...

Background Characterizing area-at-risk has become a sought-after clinical indicator for the treatment of cardiovascular disease. MRI can provide such information without contrast and radiation. Recently, cardiac phase-resolved blood-oxygen-level-dependent (CP-BOLD) MRI, has shown that this is possible at rest, without even stress agents. However, most of the necessary post-processing analysis r...

This paper examines how hot spots shift by hour of day and day of week. Hot spot analysis is more likely to have a substantial impact on crime patterns if spatiotemporal shifts are incorporated into the crime analysis process. Even in some of the highest crime neighborhoods in Bronx County (NY), not all micro-level geographies (e.g. street segments and property lots) contain substantial (if any...

A data mining method for synthesizing multiple time series is presented. Based on a single time series algorithm, the method embeds multiple time series into a phase space. The reconstructed state space allows temporal pattern extraction and local model development. Using an a priori data mining objective, an optimal local model is chosen for short-term forecasting. For the same sampling period...

Brain science is a frontier research area with great promise for understanding, preventing, and treating multiple diseases affecting millions of patients. Its key task of reconstructing neuronal brain connectivity poses unique Big Data Analysis challenges distinct from those in clinical or "-omics" domains. Our goal is to understand the strengths and limitations of reconstruction algorithms, me...

1 Singh, S. "Forecasting using a Fuzzy Nearest Neighbour Method", Proc. 6th International Conference on Fuzzy Theory and Technology , Fourth Joint Conference on Information Sciences (JCIS'98), North Carolina, vol. 1, pp.80-83, 1998 (23-28 October ,1998) ABSTRACT This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) base...

In intensive care physiological variables of the critical-ly ill are measured and recorded in short time intervals. The existing alarm systems based on fixed thresholds produce a large number of false alarms. Usually the change of a variable over time is more informative than one pathological value at a particular time point. Intelligent alarm systems which detect important changes within a phy...

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user's subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study inves...