Covariance Blind Detection Method Based on Eigenvector in Cognitive Radio Network by Yingxue Li, Jing Lei, Shiyuan Zhong, Chunming Huang, Chao Huang

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Covariance Blind Detection Method Based on Eigenvector in Cognitive Radio Network by Yingxue Li, Jing Lei, Shiyuan Zhong, Chunming Huang, Chao Huang

Subjects: “…cognitive radio;blind detection;wishart distribution;Haar distribution;eigenvector;covariance matrix…”
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Application Research of the Sparse Representation of Eigenvector on the PD Positioning in the Transformer Oil by Qing Xie, Dan Liu, Ying Zhang, Shuguo Gao, Tong Li, Xinjie Wang, Fangcheng Lü

“…Therefore, it is necessary to find new broadband signal processing methods to improve detection ability of the PD source. In this paper, the direction of arrival (DOA) estimation method based on sparse representation of eigenvector is proposed, and this method can further reduce the noise interference. …”
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Eigenvector biomarker for prediction of epileptogenic zones and surgical success from interictal data by Sayantika Roy, Armelle Varillas, Armelle Varillas, Emily A. Pereira, Patrick Myers, Patrick Myers, Golnoosh Kamali, Kristin M. Gunnarsdottir, Kristin M. Gunnarsdottir, Nathan E. Crone, Adam G. Rouse, Jennifer J. Cheng, Michael J. Kinsman, Patrick Landazuri, Patrick Landazuri, Utku Uysal, Carol M. Ulloa, Nathaniel Cameron, Sara Inati, Kareem A. Zaghloul, Varina L. Boerwinkle, Sarah Wyckoff, Niravkumar Barot, Jorge González-Martínez, Joon Y. Kang, Sridevi V. Sarma, Sridevi V. Sarma

“…In particular, the steady-state solution of the DNM highlights the sinks and is characterized by the leading eigenvector of the state-transition matrix of the DNM. …”
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Characteristically Near Stable Vector Fields in the Polar Complex Plane by Enze Cui, James F. Peters

“…All characteristic vectors (aka eigenvectors) emanate from the same fixed point in $\mathbb{C}$, namely, 0. …”
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Dynamic Neural Network States During Social and Non-Social Cueing in Virtual Reality Working Memory Tasks: A Leading Eigenvector Dynamics Analysis Approach by Pinar Ozel

“…LEiDA, conventionally utilized with fMRI, was creatively employed in EEG to detect swift alterations in brain network states, offering insights into cognitive processing dynamics. …”
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Enhanced Spectral Ensemble Clustering for Fault Diagnosis: Application to Photovoltaic Systems by Mohsen Zargarani, Claude Delpha, Demba Diallo, Anne Migan-Dubois, Chabakata Mahamat, Laurent Linguet

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Urban Area Function Zoning Based on User Relationships in Location-Based Social Networks by Fei Hao, Junzhe Zhang, Zongtao Duan, Liang Zhao, Lantian Guo, Doo-Soon Park

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Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters by Dagoberto R. Mares-Rincón, Josué J. Trejo-Alonso, José A. Guerrero-Díaz-de-León, Jorge E. Macías-Díaz

“…The methodology bridges statistical rigor with established astrophysical techniques, offering a promising avenue for advancing cluster detection in observational cosmology.…”
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Edge Detection with Hessian Matrix Property Based on Wavelet Transform by N. Aghazadeh, Y. Gholizade Atani

“…In this paper, we present an edge detection method based on wavelet transform and Hessian matrix of image at each pixel. …”
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Improved spectral clustering algorithm and its application in MCI detection by Jie XIANG, Dong-qin ZHAO

“…In order to detect mild cognitive impairment (MCI) using functional magnetic resonance imaging (fMRI)，a method based on fMRI clustering was proposed fMRI data were clustered to obtain the blood oxygen level dependence( BOLD) change model of MCI patients，then abnormal patterns were used to detect disease.The traditional spectral clustering algorithm needs to calculate all of the eigenvalue and eigenvector，so time and space complexity is higher.An improved spectral clustering method was proposed which modified the similar matrix construction method and the setting method of σ and k，and then this method was applied to clustering and detection of MCI patients.To verify the performance of the proposed method，the comparison of the clustering result，classification accuracy using traditional algorithm and Nyström is also done.The comparative experimental results show that the proposed method can get BOLD pattern more accurately，the accuracy of MCI detection is higher than the other two algorithms，and the time and space complexity are less than the traditional algorithm.…”
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Improved spectral clustering algorithm and its application in MCI detection by Jie XIANG, Dong-qin ZHAO

“…In order to detect mild cognitive impairment (MCI) using functional magnetic resonance imaging (fMRI)，a method based on fMRI clustering was proposed fMRI data were clustered to obtain the blood oxygen level dependence( BOLD) change model of MCI patients，then abnormal patterns were used to detect disease.The traditional spectral clustering algorithm needs to calculate all of the eigenvalue and eigenvector，so time and space complexity is higher.An improved spectral clustering method was proposed which modified the similar matrix construction method and the setting method of σ and k，and then this method was applied to clustering and detection of MCI patients.To verify the performance of the proposed method，the comparison of the clustering result，classification accuracy using traditional algorithm and Nyström is also done.The comparative experimental results show that the proposed method can get BOLD pattern more accurately，the accuracy of MCI detection is higher than the other two algorithms，and the time and space complexity are less than the traditional algorithm.…”
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A Community Detection Algorithm Based on Topology Potential and Spectral Clustering by Zhixiao Wang, Zhaotong Chen, Ya Zhao, Shaoda Chen

“…Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. …”
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A New Quotient and Iterative Detection Method in an Affine Krylov Subspace for Solving Eigenvalue Problems by Chein-Shan Liu, Jiang-Ren Chang, Jian-Hung Shen, Yung-Wei Chen

“…For both symmetric and nonsymmetric eigenvalue problems solved by the third algorithm, we develop a simple iterative detection method to maximize the Euclidean norm of the eigenvector in terms of the eigen-parameter, of which the peaks of the response curve correspond to the eigenvalues. …”
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Interval-based principal component analysis for reliable fault detection under data uncertainty by Raoudha Bel Hadj Ali, Anissa Ben Aicha, Belkhiria Kamel, Gilles Mourot, Majdi Mansouri

“…Despite these advancements, key challenges remain, particularly in the reliable estimation of interval eigenvalues and eigenvectors, and in the effective detection of faults when working with uncertain data. …”
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Enhanced insulator fault detection using optimized ensemble of deep learning models based on weighted boxes fusion by Stefano Frizzo Stefenon, Laio Oriel Seman, Gurmail Singh, Kin-Choong Yow

“…Based on that consideration, this paper proposes an optimized ensemble of deep learning models (OEDL) based on weighted boxes fusion (WBF), called OEDL-WBF, to enhance the fault detection of power grid insulators. The proposed model is hypertuned considering a tree-structured Parzen estimator (TPE), and interpretative results are provided using the eigenvector-based class activation map (Eigen-CAM) algorithm. …”
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Inverter Open-circuit Fault Diagnosis and Its Fault-tolerant Method Based on Three-phase Current Detection by WANG Zhi-yuan, ZHANG Jie, WANG Yu-qi, SONG Wen-sheng, GE Xing-lai

“…Signals of inverter three-phase current were taken as characteristic parameter, and based on wavelet principle, eigenvectors were extracted, and then inverter failure was detected through the support vector machine (SVM). …”
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Development of wireless electronic nose and using of remote monitoring by HU Ying, WANG Jun

“…The SVM analysis had a good recognition rate using eigenvector analysis after PCA. The experiments showed that the wireless electronic nose could be used for remote monitoring of fresh fruits.In all, wireless electronic nose system based on ZigBee is cost saving, convenient and flexible, also has good scalability and mobility with a good application prospect. …”
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Three-dimensional reconstruction and structural surface identification of high steep slopes based on UAV close-range photogrammetry by Linfeng WANG, Hui JIANG, Ning TANG, Xiaoming HUANG, Guojin TAN

“…Geological disaster investigations enable timely detection of hazards, issuance of early warnings, and prevention of loss of life and property. …”
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Fault diagnosis technology for three-level inverter based on ICEEMDAN-FE and SVM by CAO Ruijun, GUO Qiyi

“…In order to improve the accuracy to diagnose complex open-circuit faults for three-level inverters, a new fault diagnosis method of three-level inverters was proposed, combining improved complete ensemble empirical mode decomposition with adaptive noise-fuzzy entropy (ICEEMDAN-FE) and support vector machine (SVM). First, the detection signal is supplied at three-phase load voltage, which was converted into α-β phase voltage by Concordia to reduce the dimension of the eigenvector. …”
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