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In this paper, a new method is presented for defects classification by ultrasonic cylindrical phased array. Firstly, a finite element model is conducted to simulate the defects identification by the cylindrical phased array transducer. A series of simulation are done for 4 types of defects with different sizes by a 64-element cylindrical phased transducer with the center frequency of 500 kHz. Then,...
Good features are critical for the research of speech emotion recognition. This paper based on the theory of deep learning, and phonetic features were extracted by using the method of deep auto-encoder (DAE). In this paper, a deep auto-encoder containing five hidden layers was designed. To get the input data, we divided the audio into short frames, each frame of speech emotion signal was then decomposed...
Research on automated facial expression analysis (FEA) has been focused on applying different feature extraction methods on texture space and geometric space, using holistic or local facial regions based on regular grids or facial anatomical structure. Not much work has been investigated by taking human perception into account. In this paper, we propose to study the facial expressive regions using...
Automatic pain expression recognition is a challenging task for pain assessment and diagnosis. Conventional 2D-based approaches to automatic pain detection lack robustness to the moderate to large head pose variation and changes in illumination that are common in real-world settings and with few exceptions omit potentially informative temporal information. In this paper, we propose an innovative 3D...
In this paper, we propose a new, compact, 4D spatio-temporal “Nebula” feature to improve expression and facial movement analysis performance. Given a spatio-temporal volume, the data is voxelized and fit to a cubic polynomial. A label is assigned based on the principal curvature values, and the polar angles of the direction of least curvature are computed. The labels and angles for each feature are...
As P2P-TV applications are widely used on the Internet, its disadvantages shows up with its conventions, particular the easier spread of erotic, violent or pirated content. To address this problem, this paper presents a system that can accurately identify and closely monitor the P2P-TV traffic passing through a campus network. It consists of five core modules: data collection, traffic identification,...
This paper presents a method to determine music-motion correspondence.For specific type of dance and music,system extract low-level features,calculate correspondence ,then select muisc-motion correspondence using genetic algorithms,and get a correspondence satisfing match accuracy and operation speed in the end.The experimental results indicate that system fully express the changes between music and...
Retinal image registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate registration of low-quality retinal images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This...
This paper presents a real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias. This detection algorithm focuses on two life-threatening arrhythmias ventricular tachycardia and fibrillation (VT/VF), which are detected through the application of pre-detection processing and main detection processing. In pre-detection processing, applies a statistical method...
This paper compares the forecasting performance of the feature extraction using the principal component analysis (PCA) that is one of the oldest and best known techniques in multivariate analysis with the feature selection using the non overlap area distribution measurement method based on the neural network with weighted fuzzy membership functions (NEWFM). This paper proposes CPPn,m (current price...
The ventricular arrhythmias including ventricular tachycardia (VT) and ventricular fibrillation (VF) are life-threatening heart diseases. This paper presents an approach to detect normal sinus rhythm (NSR) and VF/VT using the neural network with weighted fuzzy membership functions (NEWFM). NEWFM classifies NSR and VF/VT beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs)...
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