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Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
TLDR
It is shown that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages, and these results are comparable to state-of-the-art methods with hand-engineered features. Expand
Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders
TLDR
A machine learning methodology for automatic sleep stage scoring that has both high overall accuracy and high mean $$F_1$$F1-score and mean accuracy across individual sleep stages over all subjects and is a suitable candidate for longitudinal monitoring using wearable EEG in real-world settings. Expand
Concinnity: A Generic Platform for Big Sensor Data Applications
TLDR
Concinnity takes sensor data from collection to final product via a cloud-based data repository and easy-to-use workflow system and supports rapid development of applications built on sensor data using data fusion and the integration and composition of models to form novel workflows. Expand
Building a generic platform for big sensor data application
TLDR
A sensor data platform “Concinnity” is provided which can take sensor data from collection to final product via a data repository and workflow system to enable rapid development of applications built on sensor data using data fusion and the integration and composition of models to form novel workflows. Expand
Elastic Information Management for Air Pollution Monitoring in Large-Scale M2M Sensor Networks
TLDR
Simulation results show that the EIMAP system has high performance in resource provision and scalability, and the experiment of pollution cloud dispersion tracking presents a case study of the system implementation. Expand
HUMAN ACTIVITY-BASED URBAN ANALYSIS USING COLLABORATIVE MOBILE SENSING
The widespread usage of mobile technologies alters the way we inhabit urban space. Therefore, new paradigms of mobile sensing are needed to capture the emerging adaptive spatial dynamics. To thisExpand