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We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using… Continue Reading
We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as… Continue Reading
The drive toward smart cities alongside the increasing adoption of personal sensors is leading to big sensor data, which is so large and complex that traditional methods for utilizing it are… Continue Reading
The drive toward smart cities alongside the rising adoption of personal sensors is leading to a torrent of sensor data. While systems exist for storing and managing sensor data, the real value of… Continue Reading
In large-scale machine-to-machine sensor networks, the applications such as urban air pollution monitoring require information management over widely distributed sensors under restricted power,… Continue Reading
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 this… Continue Reading