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Hybrid Relaying Protocol for Joint Power and Subcarrier Allocation for OFDM Based Cognitive Radio Networks
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作者 Muthusamy Bhuvaneswari Shama Srinivasa Rao Madane 《Circuits and Systems》 2016年第10期3150-3161,共12页
This paper aims to avoid the interference imposed by the secondary user on a primary user in Cognitive Radio Network (CRN). In CRN, the interference from secondary user enforced on primary user mainly depends on spect... This paper aims to avoid the interference imposed by the secondary user on a primary user in Cognitive Radio Network (CRN). In CRN, the interference from secondary user enforced on primary user mainly depends on spectral interval between primary and secondary systems. Moreover, it also depends on the power allocated to the secondary user. In order to avoid interference imposed by secondary user on primary user, a Hybrid Relaying Protocol for Joint Power and Subcarrier Allocation for Orthogonal Frequency Division Multiplexing (OFDM) based Cognitive Radio Networks is proposed. In hybrid relaying protocol, a secondary user uses amplify and forward (AF) protocol and decode and forward (DF) protocol based on the requirement to maximize network throughput. A greedy algorithm is proposed for the selection of relay to get the optimal solution. Moreover, an efficient hybrid power and subcarrier algorithm is used by considering interference constraint imposed by cognitive network to the primary user. 展开更多
关键词 Orthogonal Frequency Division Multiplexing INTERFERENCE RELAY Power Amplify and Forward Decode and Forward Greedy Algorithm
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Feature extraction based on empirical mode decomposition for automatic mass classification of mammogram images 被引量:3
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作者 Vaijayanthi Nagarajan Elizabeth Caroline Britto Senthilvel Murugan Veeraputhiran 《Medicine in Novel Technology and Devices》 2019年第1期8-21,共14页
Breast cancer is one of the major health problems that leads to early mortality in women.To aid the radiologists,computer aided diagnosis provides a second opinion for the detection and classification of breast cancer... Breast cancer is one of the major health problems that leads to early mortality in women.To aid the radiologists,computer aided diagnosis provides a second opinion for the detection and classification of breast cancer.In this paper,two texture feature extraction methods using Empirical Mode Decomposition(EMD)have been proposed to classify the masses in mammogram images into benign or malignant.The first feature extraction method is based on Bi-dimensional Empirical Mode Decomposition(BEMD).On performing BEMD on Region of Interest(ROI)of mammogram image,the ROI is decomposed into a set of different frequency components called Bi-dimensional Intrinsic Mode Functions(BIMFs).Gray Level Co-occurrence Matrix(GLCM)and Gray Level Run Length Matrix(GLRM)features are extracted from these BIMFs and are given as input to the classifier for classification into benign or malignant.Due to the mode mixing problem that exists in BEMD,BIMFs obtained from BEMD are less orthogonal to each other.To overcome this drawback,the second feature extraction method called Modified Bidimensional Empirical Mode Decomposition(MBEMD)is proposed.The BIMFs are extracted by employing the proposed MBEMD on mammogram ROI.Features are extracted in a similar way as BEMD method.Support Vector Machine(SVM)and Linear Discriminant Analysis(LDA)classifiers are used for the classification of mammogram mass.The classification accuracy of 88.8%,96.2%and Area Under the Curve(AUC)of Receiver Operating Characteristics(ROC)of 0.9,0.96 are obtained with SVM classifier for BEMD,proposed MBEMD based features respectively.The results show that the proposed method yields consistent performance when applied across different databases. 展开更多
关键词 Image processing Image analysis Image classification Feature extraction MAMMOGRAPHY Computer-aided diagnosis Medical imaging Empirical mode decomposition
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