An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Estimation of evolutionary spectrum based on short time Fourier transform and modified group delay
Signal Processing - Signal processing in communications
Fuzzy sets in pattern recognition and machine intelligence
Fuzzy Sets and Systems
Pattern Recognition Letters
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The Doppler ultrasound technique is commonly used to detect emboli in the cerebral circulation. Here an automated feature extraction and emboli detection system is proposed based on the principal components analysis (PCA) and fuzzy sets. In the system, two features, R"r"y and k, are extracted by the PCA method. Meanwhile, MMR and @s"f"m"i"n are obtained with the traditional temporal processing and spectrogram analysis, respectively. Normal blood flow signals are firstly distinguished from abnormal signals by MMR. Then signals containing emboli and disturbance noises are further differentiated by other features based on fuzzy sets. From experiments with computer-simulated and clinical Doppler ultrasound signals, it is shown that features extracted from the PCA method achieve better classification performance than those of traditional methods. The fuzzy-based detection system not only obtains high classification accuracy but is more applicable in clinical diagnosis.