Accelerating the phase demodulation process for heterodyne Φ-OTDR using spatial phase shifting.

An effective orthogonal signal generation method for heterodyne-detection-based phase-sensitive optical time-domain reflectometer systems is proposed to accelerate the phase demodulation process. The demodulation principle is based on the spatial phase shifting technique. By exploiting the relative phase difference between adjacent spatial sampling channels, the orthogonal signal is easily obtained from basic algebra calculations. The simulation and experimental results showed that the proposed method achieved >100% computation speed improvement compared with the conventional methods, with a slight trade-off in phase demodulation performance. Therefore, the proposed method is potentially beneficial for the distributed acoustic sensing technology for reducing the computation complexity of phase demodulation procedures.

EEG-based Emotion Recognition Using Similarity Measure of Brain Rhythm Sequencing.

The similarity is a fundamental measure from the homology theory in bioinformatics, and the biological sequence can be classified based on it. However, such an approach has not been utilized for electroencephalography (EEG)-based emotion recognition. To this end, the sequence generated by choosing the dominant brain rhythm owning maximum instantaneous power at each 0.2 s timestamp of the EEG signal has been proposed. Then, to recognize emotional arousal and valence, the similarity measures between pairwise sequences have been performed by dynamic time warping (DTW). After evaluations, the sequence that provides the highest accuracy has been obtained. Thus, the representative channel has been found. Besides, the appropriate time segment for emotion recognition has been estimated. Those findings helpfully exclude redundant data for assessing emotion. Results from the DEAP dataset displayed that the classification accuracies between 72%-75% can be realized by applying the single-channel data with a 5 s length, which is impressive when considering fewer data sources as the primary concern. Hence, the proposed idea would open a new way that uses the similarity measures of sequences for EEG-based emotion recognition.

ECG Parameter Extractor of Intelligent Home Healthcare Embedded System.

Automatic ECG QRS complex detection has been widely studied and used over the past decade. Although QRS complex is the most prominent feature in ECG and can provide useful information about the heart status, other parts of the ECG (P-wave, T-wave, etc) are also significant in determining the health status. Recently, researches for P-wave and T-wave detection algorithms started to appear but a parameter extractor in obtaining most essential ECG parameters (PQRST) is still not very popular. Considering that all these can be integrated together, we propose an Intelligent Home Health Care Embedded System (IHHCS) with essential ECG parameters extraction that can provide diagnosis about health status of patients directly at home. Inconvenient visits and precious time spent in health checking at hospitals or clinics can be saved.