Longo et al. [1] described three possible sources of train location data for analysis: sensors embedded in the infrastructure to detect trains, on-board sensors to record the distance travelled and positioning equipment such as GPS.
The system was based on a combination of amperometric multielectrode biosensors with chemometric data analysis of sensor outputs using artificial neural networks (ANN).
Currently, the CP decomposition is gaining importance in several applications, for example, in exploratory data analysis [9], sensor array processing [10], telecommunications [11, 12], ICA [13], and in multiple-input multiple-output radar systems [14].
The filtering process uses the scheme stated in [49] that is based on statistical data analysis for periodically aggregates sensors' survival massages to detect failure nodes and their corrupted data.
In addition, there are promising ventures in the area of data analysis collected by smart sensors in both rural and urban areas as well as ad-hoc connectivity networks with broad reach that permit monitoring surface conditions, finding cattle and most significantly, smart thrift shopping and on-demand services for supplying urban centers.
Based on the analysis of the demand of big data processing in wireless sensor network, the data analysis and processing platform for wireless sensor network are designed based on the combination with the cloud computing.
In this paper, we propose a novel and important query in the context of uncertain databases, namely probabilistic group subspace skyline (PGSS) query, which is useful in applications like sensor data analysis.
In our study, spatial statistical application for optimal band selection, multi-sensor data analysis and image fuse were demonstrated for mapping reliable geologically induced lineament patterns.
All of the above is implemented in a modular way allowing easy integration of additional functionality (e.g. new sensor data analysis and visualization schemes).
The study of distributed top-k monitoring is significant in a variety of application scenarios, such as network monitoring, sensor data analysis, web usage logs, and market surveillance.
This mobility detection system combined a sensor mat (using a minimum number of sensors) with data analysis software to monitor turning ability.
