A contour shape descriptor based on discrete Fourier transform (DFT) and a K-means al- gorithm modified self-organizing feature map (SOFM) neural network are established for shape clus- tering. The given shape is ...A contour shape descriptor based on discrete Fourier transform (DFT) and a K-means al- gorithm modified self-organizing feature map (SOFM) neural network are established for shape clus- tering. The given shape is first sampled uniformly in the polar coordinate. Then the discrete series is transformed to frequency domain and constructed to a shape characteristics vector. Firstly, sample set is roughly clustered using SOFM neural network to reduce the scale of samples. K-means algo- rithm is then applied to improve the performance of SOFM neural network and process the accurate clustering. K-means algorithm also increases the controllability of the clustering. The K-means algo- rithm modified SOFM neural network is used to cluster the shape characteristics vectors which is previously constructed. With leaf shapes as an example, the simulation results show that this method is effective to cluster the contour shapes.展开更多
The aim of this study was to explore the possibility of applying Fourier transform infrared(FTIR) spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholan...The aim of this study was to explore the possibility of applying Fourier transform infrared(FTIR) spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholangiocar- cinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position(P), intensity(/) and full width at half-maximum(FWHM), were measured, calculated and subsequently com- pared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function(RBF) net- work model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between can- cerous and normal tissue, including I1640, I1550, 11460,/1400, I1250, I1120,/10g0, Ii040 and P1040. In the RBF training classi- fication, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.展开更多
在通信环境日益密集、信号调制样式层出不穷的情况下,信号的调制识别变得愈加困难。寻求一种高精度、时效性好的自动调制识别新方法,对无线电通信应用领域有重大意义。对此,文中提出了一种结合通信信号时频特性的卷积神经网络(Convoluti...在通信环境日益密集、信号调制样式层出不穷的情况下,信号的调制识别变得愈加困难。寻求一种高精度、时效性好的自动调制识别新方法,对无线电通信应用领域有重大意义。对此,文中提出了一种结合通信信号时频特性的卷积神经网络(Convolutional Neural Network Based on Time-Frequency Characteristics,TFC-CNN)调制识别算法。首先,采集大量调制信号,将信号的时频特征通过短时傅里叶变换转换成图像特征,并将其作为网络的输入;然后,设计一种特征提取能力更强、参数更少的卷积神经网络,通过改进网络中不同层的连结方式来增加网络的特征提取能力,同时通过减小卷积核的尺度、使用全局均值池化层来减少模型参数,提高了模型的时效性;最后,在网络中添加批归一化(Batch Normalization,BN)层,在增加模型稳定性的同时防止模型出现过拟合。实验结果表明,所提算法在参数和训练时间上比传统方法明显减少,同时有更高的准确率,体现了所提算法的优越性。展开更多
基金Supported by Guangdong Province Key Science and TechnologyItem(2011A010801005,2010A080402015)the National NaturalScience Foundation of China(61171142)
文摘A contour shape descriptor based on discrete Fourier transform (DFT) and a K-means al- gorithm modified self-organizing feature map (SOFM) neural network are established for shape clus- tering. The given shape is first sampled uniformly in the polar coordinate. Then the discrete series is transformed to frequency domain and constructed to a shape characteristics vector. Firstly, sample set is roughly clustered using SOFM neural network to reduce the scale of samples. K-means algo- rithm is then applied to improve the performance of SOFM neural network and process the accurate clustering. K-means algorithm also increases the controllability of the clustering. The K-means algo- rithm modified SOFM neural network is used to cluster the shape characteristics vectors which is previously constructed. With leaf shapes as an example, the simulation results show that this method is effective to cluster the contour shapes.
文摘The aim of this study was to explore the possibility of applying Fourier transform infrared(FTIR) spec- troscopy as a medical diagnostic toot based on a neural network classifier for detecting and classifying cholangiocar- cinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position(P), intensity(/) and full width at half-maximum(FWHM), were measured, calculated and subsequently com- pared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function(RBF) net- work model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between can- cerous and normal tissue, including I1640, I1550, 11460,/1400, I1250, I1120,/10g0, Ii040 and P1040. In the RBF training classi- fication, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.
文摘在通信环境日益密集、信号调制样式层出不穷的情况下,信号的调制识别变得愈加困难。寻求一种高精度、时效性好的自动调制识别新方法,对无线电通信应用领域有重大意义。对此,文中提出了一种结合通信信号时频特性的卷积神经网络(Convolutional Neural Network Based on Time-Frequency Characteristics,TFC-CNN)调制识别算法。首先,采集大量调制信号,将信号的时频特征通过短时傅里叶变换转换成图像特征,并将其作为网络的输入;然后,设计一种特征提取能力更强、参数更少的卷积神经网络,通过改进网络中不同层的连结方式来增加网络的特征提取能力,同时通过减小卷积核的尺度、使用全局均值池化层来减少模型参数,提高了模型的时效性;最后,在网络中添加批归一化(Batch Normalization,BN)层,在增加模型稳定性的同时防止模型出现过拟合。实验结果表明,所提算法在参数和训练时间上比传统方法明显减少,同时有更高的准确率,体现了所提算法的优越性。