TiO2-hydroxyapatite （HA） nanostructured coatings were produced by atmospheric plasma spray method. The effects of starting powder composition and grain size on their mechanical properties were investigated. The micr...TiO2-hydroxyapatite （HA） nanostructured coatings were produced by atmospheric plasma spray method. The effects of starting powder composition and grain size on their mechanical properties were investigated. The microstructure and morphology were characterized by X-ray diffraction and scanning electron microscopy （SEM）. It is found that the coating with 10% HA has the best mechanical properties. Based on Rietveld refinement method, the mean grain size of the as-received powder （212 nm） extensively decreases to 66.4 nm after 20 h of high-energy ball milling. In spite of grain growth, the deposited coatings maintain their nanostructures with the mean grain size of 112 nm. SEM images show that there is a lower porosity in the coating with a higher HA content. Optical microscopy images show that uniform thickness is obtained for all the coatings.展开更多
Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detec-tion has been the subject of considerable re-search efforts in recent years. Intelligent com-puting tools such as artificial n...Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detec-tion has been the subject of considerable re-search efforts in recent years. Intelligent com-puting tools such as artificial neural network (ANN) and fuzzy logic approaches are demon-strated to be competent when applied individu-ally to a variety of problems. Recently, there has been a growing interest in combining both of these approaches, and as a result, adaptive neural fuzzy filters (ANFF) [1] have been evolved. This study presents a comparative study of the classification accuracy of ECG signals using (MLP) with back propagation training algorithm, and a new adaptive neural fuzzy filter architec-ture (ANFF) for early diagnosis of ECG ar-rhythmia. ANFF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules [1]. In this paper we used an adap-tive neural fuzzy filter as an ECG beat classifier. We combined 3 famous wavelet transforms and used them mid 4 the order AR model coefficient as features. Our results suggest that a new proposed classifier (ANFF) with these features can generalize better than ordinary MLP archi-tecture and also learn better and faster. The results of proposed method show high accu-racy in ECG beat classification (97.6%) with 100% specificity and high sensitivity.展开更多
The brain is a highly complex system. Under-standing the behavior and dynamics of billions of interconnected neurons from the brain signal requires knowledge of several signal- process-ing techniques, from the linear ...The brain is a highly complex system. Under-standing the behavior and dynamics of billions of interconnected neurons from the brain signal requires knowledge of several signal- process-ing techniques, from the linear and non-linear domains. The analysis of EEG signals plays an important role in a wide range of applications, such as psychotropic drug research, sleep studies, seizure detection and hypnosis proc-essing. In this paper we accomplish to analyze and explore the nature of hypnosis in Right, Left, Back and Frontal hemisphere in 3 groups of hypnotizable subjects by means of Fuzzy Simi-larity Index method.展开更多
文摘TiO2-hydroxyapatite （HA） nanostructured coatings were produced by atmospheric plasma spray method. The effects of starting powder composition and grain size on their mechanical properties were investigated. The microstructure and morphology were characterized by X-ray diffraction and scanning electron microscopy （SEM）. It is found that the coating with 10% HA has the best mechanical properties. Based on Rietveld refinement method, the mean grain size of the as-received powder （212 nm） extensively decreases to 66.4 nm after 20 h of high-energy ball milling. In spite of grain growth, the deposited coatings maintain their nanostructures with the mean grain size of 112 nm. SEM images show that there is a lower porosity in the coating with a higher HA content. Optical microscopy images show that uniform thickness is obtained for all the coatings.
文摘Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detec-tion has been the subject of considerable re-search efforts in recent years. Intelligent com-puting tools such as artificial neural network (ANN) and fuzzy logic approaches are demon-strated to be competent when applied individu-ally to a variety of problems. Recently, there has been a growing interest in combining both of these approaches, and as a result, adaptive neural fuzzy filters (ANFF) [1] have been evolved. This study presents a comparative study of the classification accuracy of ECG signals using (MLP) with back propagation training algorithm, and a new adaptive neural fuzzy filter architec-ture (ANFF) for early diagnosis of ECG ar-rhythmia. ANFF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules [1]. In this paper we used an adap-tive neural fuzzy filter as an ECG beat classifier. We combined 3 famous wavelet transforms and used them mid 4 the order AR model coefficient as features. Our results suggest that a new proposed classifier (ANFF) with these features can generalize better than ordinary MLP archi-tecture and also learn better and faster. The results of proposed method show high accu-racy in ECG beat classification (97.6%) with 100% specificity and high sensitivity.
文摘The brain is a highly complex system. Under-standing the behavior and dynamics of billions of interconnected neurons from the brain signal requires knowledge of several signal- process-ing techniques, from the linear and non-linear domains. The analysis of EEG signals plays an important role in a wide range of applications, such as psychotropic drug research, sleep studies, seizure detection and hypnosis proc-essing. In this paper we accomplish to analyze and explore the nature of hypnosis in Right, Left, Back and Frontal hemisphere in 3 groups of hypnotizable subjects by means of Fuzzy Simi-larity Index method.
