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Journal articles on the topic 'Eigenvalue-based detection'

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Author: Grafiati

Published: 14 March 2023

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1

Patil, Kishor P., Ashwini S. Lande, and Mudassar H. Naikwadi. "A Review on the Evolution of Eigenvalue Based Spectrum Sensing Algorithms for Cognitive Radio." Network Protocols and Algorithms 8, no. 2 (July 21, 2016): 58. http://dx.doi.org/10.5296/npa.v8i2.9349.

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Spectrum scarcity has been encountered as a leading problem when launching new wireless services. To overcome this problem, cognitive radio is an optimistic solution. Spectrum sensing is a prominent task of cognitive radio. Over the past decade, numerous spectrum sensing algorithms have been proposed. In this paper, we present a comprehensive survey ofevolutionary achievements of eigenvalue based spectrum sensing algorithms. The correlation between signal samples due to oversampling, multipath or multiple receivers gets reflected on the eigenvalues of the covariance matrix. It has been observed that different combinations ofeigenvalues are used as test statistics and the distribution of eigenvalues and derivation of probability of detection is based on RMT (Random Matrix Theory). The main advantage offered by these algorithms is their robustness to noise uncertainty which severely affect other methods. Furthermore, they do not require accurate synchronization. These detections can be used for different signal detection applications without any prior information of signal or noise. To evaluate the performance of eigenvalue based spectrum sensing techniques under fading channels, we have simulated maximum to minimum eigenvalue based Detection(MME) and maximum eigenvalue based detection (MED) estimation for Rician fading channel. Simulation results shows that MME is much better than MED.

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A.Tag El-Dien, Heba, Rokaia M. Zaki, Mohsen M. Tantawy, and Hala M. Abdel-Kader. "Noise Uncertainty Effect on a Modified Two-Stage Spectrum Sensing Technique." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 2 (February 1, 2016): 341. http://dx.doi.org/10.11591/ijeecs.v1.i2.pp341-348.

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Detecting the presence or absence of primary user is the key task of cognitive radio networks. However, relying on single detector reduces the probability of detection and increases the probability of missed detection. Combining two conventional spectrum sensing techniques by integrating their individual features improves the probability of detection especially under noise uncertainty. This paper introduces a modified two-stage detection technique that depends on the energy detection as a first stage due to its ease and speed of detection, and the proposed Modified Combinational Maximum-Minimum Eigenvalue based detection as a second stage under noise uncertainty and comperes it with the case of using Maximum-Minimum Eigenvalue and Combinational Maximum-Minimum Eigenvalue as a second stage.

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3

Ge, Zhiqiang, and Zhihuan Song. "Process structure change detection by eigenvalue-based method." Computers & Chemical Engineering 35, no. 2 (February 2011): 284–95. http://dx.doi.org/10.1016/j.compchemeng.2010.05.011.

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4

Ali, Syed Sajjad, Chang Liu, Jialong Liu, Minglu Jin, and Jae Moung Kim. "On the Eigenvalue Based Detection for Multiantenna Cognitive Radio System." Mobile Information Systems 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3848734.

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Eigenvalue based spectrum sensing can make detection by catching correlation features in space and time domains, which can not only reduce the effect of noise uncertainty, but also achieve high detection probability. Hence, the eigenvalue based detection is always a hot topic in spectrum sensing area. However, most existing algorithms only consider part of eigenvalues rather than all the eigenvalues, which does not make full use of correlation of eigenvalues. Motivated by this, this paper focuses on multiantenna system and makes all the eigenvalues weighted for detection. Through the analysis of system model, we transfer the eigenvalue weighting issue to an optimal problem and derive the theoretical expression of detection threshold and probability of false alarm and obtain the close form expression of optimal solution. Finally, we propose new weighting schemes to give promotions of the detection performance. Simulations verify the efficiency of the proposed algorithms.

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Peng, Ziran, and Guojun Wang. "A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform." BioMed Research International 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5168346.

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This study investigated an electrocardiogram (ECG) eigenvalue automatic analysis and detection method; ECG eigenvalues were used to reverse the myocardial action potential in order to achieve automatic detection and diagnosis of heart disease. Firstly, the frequency component of the feature signal was extracted based on the wavelet transform, which could be used to locate the signal feature after the energy integral processing. Secondly, this study established a simultaneous equations model of action potentials of the myocardial membrane, using ECG eigenvalues for regression fitting, in order to accurately obtain the eigenvalue vector of myocardial membrane potential. The experimental results show that the accuracy of ECG eigenvalue recognition is more than 99.27%, and the accuracy rate of detection of heart disease such as myocardial ischemia and heart failure is more than 86.7%.

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Lo, Edisanter. "Hyperspectral anomaly detection based on constrained eigenvalue–eigenvector model." Pattern Analysis and Applications 20, no. 2 (September 22, 2015): 531–55. http://dx.doi.org/10.1007/s10044-015-0519-6.

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7

Du, Liping, Yuting Fu, Yueyun Chen, Xiaojian Wang, and Xiaoyan Zhang. "Eigenvalue-Based Spectrum Sensing with Small Samples Using Circulant Matrix." Symmetry 13, no. 12 (December 5, 2021): 2330. http://dx.doi.org/10.3390/sym13122330.

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In cognitive radio (CR) networks, eigenvalue-based detectors (EBDs) have attracted much attention due to their good performance of detecting secondary users (SUs). In order to further improve the detection performance of EBDs with short samples, we propose two new detectors: average circulant matrix-based Roy’s largest root test (ACM-RLRT) and average circulant matrix-based generalized likelihood ratio test (ACM-GLRT). In the proposed method, the circulant matrix of samples at each time instant from SUs is calculated, and then, the covariance matrix of the circulant matrix is averaged over a short period of time. The eigenvalues of the achieved average circulant matrix (ACM) are used to build our proposed detectors. Using a circulant matrix can improve the dominant eigenvalue of covariance matrix of signals and also the detection performance of EBDs even with short samples. The probability distribution functions of the detectors undernull hypothesis are analyzed, and the asymptotic expressions for the false-alarm and thresholds of two proposed detectors are derived, respectively. The simulation results verify the effectiveness of the proposed detectors.

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Yan, Binpeng, Sanyi Yuan, Shangxu Wang, Yonglin OuYang, Tieyi Wang, and Peidong Shi. "Improved eigenvalue-based coherence algorithm with dip scanning." GEOPHYSICS 82, no. 2 (March 1, 2017): V95—V103. http://dx.doi.org/10.1190/geo2016-0149.1.

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Detection and identification of subsurface anomalous structures are key objectives in seismic exploration. The coherence technique has been successfully used to identify geologic abnormalities and discontinuities, such as faults and unconformities. Based on the classic third eigenvalue-based coherence ([Formula: see text]) algorithm, we make several improvements and develop a new method to construct covariance matrix using the original and Hilbert transformed seismic traces. This new covariance matrix more readily converges to the main effective signal energy on the largest eigenvalue by decreasing all other eigenvalues. Compared with the conventional coherence algorithms, our algorithm has higher resolution and better noise immunity ability. Next, we incorporate this new eigenvalue-based algorithm with time-lag dip scanning to relieve the dip effect and highlight the discontinuities. Application on 2D synthetic data demonstrates that our coherence algorithm favorably alleviates the low-valued artifacts caused by linear and curved dipping strata and clearly reveals the discontinuities. The coherence results of 3D real field data also commendably suppress noise, eliminate the influence of large dipping strata, and highlight small hidden faults. With the advantages of higher resolution and robustness to random noise, our strategy successfully achieves the goal of detecting the distribution of discontinuities.

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Paramo, Gian, and Arturo S. Bretas. "WAMs Based Eigenvalue Space Model for High Impedance Fault Detection." Applied Sciences 11, no. 24 (December 20, 2021): 12148. http://dx.doi.org/10.3390/app112412148.

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High impedance faults present unique challenges for power system protection engineers. The first challenge is the detection of the fault, given the low current magnitudes. The second challenge is to locate the fault to allow corrective measures to be taken. Corrective actions are essential as they mitigate safety hazards and equipment damage. The problem of high impedance fault detection and location is not a new one, and despite the safety and reliability implications, relatively few efforts have been made to find a general solution. This work presents a hybrid data driven and analytical-based model for high impedance fault detection in distribution systems. The first step is to estimate a state space model of the power line being monitored. From the state space model, eigenvalues are calculated, and their dynamic behavior is used to develop zones of protection. These zones of protection are generated analytically using machine learning tools. High impedance faults are detected as they drive the eigenvalues outside of their zones. A metric called eigenvalue drift coefficient was formulated in this work to facilitate the generalization of this solution. The performance of this technique is evaluated through case studies based on the IEEE 5-Bus system modeled in Matlab. Test results are encouraging indicating potential for real-life applications.

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Xu, Huaping, Siyuan Wang, Shuo Li, Guobing Zeng, Zhenwan You, and Wei Li. "Multibaseline InSAR Layover Detection Based on Local Frequency and Eigenvalue." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 10571–82. http://dx.doi.org/10.1109/jstars.2021.3120007.

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Jiang, Yuan, Yan-Hua Wang, Yang Li, and Xing Chen. "Eigenvalue-based ground target detection in high-resolution range profiles." IET Radar, Sonar & Navigation 14, no. 11 (November 1, 2020): 1747–56. http://dx.doi.org/10.1049/iet-rsn.2020.0002.

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12

Omondi, Gevira, and Vitalis K. Oduol. "An Optimal Eigenvalue Based Spectrum Sensing Algorithm for Cognitive Radio." International Journal for Innovation Education and Research 3, no. 10 (October 31, 2015): 45–54. http://dx.doi.org/10.31686/ijier.vol3.iss10.444.

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Spectrum is a scarce resource, and licensed spectrum is intended to be used only by the spectrum owners. Various measurements of spectrum utilization have shown unused resources in frequency, time and space. Cognitive radio is a new concept of reusing licensed spectrum in an unlicensed manner. The unused resources are often referred to as spectrum holes or white spaces. These spectrum holes could be reused by cognitive radios, sometimes called secondary users. All man-made signals have some structure that can be potentially exploited to improve their detection performance. This structure is intentionally introduced for example by the channel coding, the modulation and by the use of space-time codes. This structure, or correlation, is inherent in the sample covariance matrix of the received signal. In particular the eigenvalues of the sample covariance matrix have some spread, or in some cases some known features that can be exploited for detection. This work aims to implement, evaluate, and eventually improve on algorithms for efficient computation of eigenvalue-based spectrum sensing methods. The computations will be based on power methods for computation of the dominant eigenvalue of the covariance matrix of signals received at the secondary users. The proposed method endeavors to overcome the noise uncertainty problem, and perform better than the ideal energy detection method. The method should be used for various signal detection applications without requiring the knowledge of the signal, channel and noise power.

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Li, Jing, and Pei Jun Wei. "A Coupled Sensitivity Method for Structural Damage Detection." Advanced Materials Research 681 (April 2013): 271–75. http://dx.doi.org/10.4028/www.scientific.net/amr.681.271.

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Based on the vibration information, a mixed sensitivity method is presented to identify structural damage by combining the eigenvalue sensitivity with the generalized flexibility sensitivity. The sensitivity of structural generalized flexibility matrix is firstly derived by using the first frequency and the corresponding mode shape only and then the eigenvalue sensitivity together with the generalized flexibility sensitivity are combined to calculate the elemental damage parameters. The presented mixed perturbation approach is demonstrated by a numerical example concerning a simple supported beam structure. It has been shown that the proposed procedure is simple to implement and may be useful for structural damage identification.

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14

Zhao, Wenjing, Chang Liu, Wenlong Liu, and Minglu Jin. "Maximum eigenvalue‐based target detection for the K‐distributed clutter environment." IET Radar, Sonar & Navigation 12, no. 11 (September 4, 2018): 1294–306. http://dx.doi.org/10.1049/iet-rsn.2018.5229.

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15

Tsai, Du-Ming, and Ron-Hwa Yang. "An eigenvalue-based similarity measure and its application in defect detection." Image and Vision Computing 23, no. 12 (November 2005): 1094–101. http://dx.doi.org/10.1016/j.imavis.2005.07.014.

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16

Maali, Asmaa, Hayat Semlali, Sara Laafar, Najib Boumaaz, and Abdallah Soulmani. "An Overview of the Different Principal Spectrum Sensing Techniques in Cognitive Radio Systems." Advanced Science, Engineering and Medicine 12, no. 3 (March 1, 2020): 342–47. http://dx.doi.org/10.1166/asem.2020.2503.

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Cognitive radio is a technology proposed to increase the effective use of the spectrum. This can be done through the main function of cognitive radio technology, which is the spectrum sensing. In our work, we propose an analysis of the following spectrum sensing techniques: the matched filter detector, the cyclostationary feature detector, the energy detector and the maximum eigenvalue detector. More attention is given to blind sensing techniques that they do not need any knowledge of the primary user signal characteristics, namely the energy detection and maximum eigenvalue detection. These methods are evaluated in terms of Receiver Operational Characteristic curves and detection probability for various values of Signal to Noise Ratio based on Monte Carlo simulations, using MATLAB. As a result of this study, we found that the energy detection offers a good performance only for high SNR. Furthermore, with the maximum eigenvalue detector, the noise uncertainty problem encountered by the energy detection is solved when the value of the smoothing factor L ≥ 8 and. Finally, a summary of the comparative analysis is presented.

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17

Guru, D. S., B. H. Shekar, and P. Nagabhushan. "A simple and robust line detection algorithm based on small eigenvalue analysis." Pattern Recognition Letters 25, no. 1 (January 2004): 1–13. http://dx.doi.org/10.1016/j.patrec.2003.08.007.

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18

Moawad, Azza, Koffi-Clément Yao, Ali Mansour, and Roland Gautier. "A Cepstrum-Based Spectrum Sensing Approach for Detecting Spread Spectrum Signals." Journal of Physics: Conference Series 2128, no. 1 (December 1, 2021): 012003. http://dx.doi.org/10.1088/1742-6596/2128/1/012003.

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Abstract In this manuscript, we introduce a semi-blind spectrum sensing technique based on cepstral analysis for interweave cognitive systems. The misdetection problem of spread spectrum signals leads to erroneous sensing results, which affect the quality-of-service of a legitimate user. The simplicity and accuracy of cepstral analysis approaches make them reliable for signals detection. Therefore, we formulate the averaged autocepstrum detection technique that utilizes the strength of the autocepstral features of spread spectrum signals. The proposed technique is compared with the energy detection and eigenvalue-based detection techniques and shows reliability and efficacy in terms of detection accuracy.

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19

Wang, Chang, and Jing Jing Gao. "The Detection of Surface Quality On-Line Based on Machine Vision in the Production of Bearings." Applied Mechanics and Materials 319 (May 2013): 523–27. http://dx.doi.org/10.4028/www.scientific.net/amm.319.523.

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The combining of digital image processing technique and pattern recognition technique, it can be wild used in the products of industry classification and recognition Line bearing assembly defects in this article for the detection and identification of needs, Automatic detection system based on machine vision, contrast measurement plane array camera on a different surfaceImage acquisition, binarization processing for subsequent pretreatment image pattern recognition, feature extraction and eigenvalue comparison, product line surface defect detection and identification.

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20

Zhaogen Zhong, Limin Zhang, Shutao Xia, and Hengzhou Wu. "Blind Detection of Multi-satellite Signals in Single-channel Based on Eigenvalue Analysis." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 22 (December 31, 2012): 320–28. http://dx.doi.org/10.4156/aiss.vol4.issue22.39.

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21

Nguyen, Quy Thue, and Ramazan Livaoğlu. "Damage detection of high-rise buildings using an eigenvalue problem-based inverse solution." Soil Dynamics and Earthquake Engineering 152 (January 2022): 107019. http://dx.doi.org/10.1016/j.soildyn.2021.107019.

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22

Kortun, Ayse. "Eigenvalue-based Detection Techniques Using Finite Dimensional Complex Random Matrix Theory: A Review." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 5, no. 14 (June 27, 2018): 154834. http://dx.doi.org/10.4108/eai.27-6-2018.154834.

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23

Chamain, Lahiru D., Prathapasinghe Dharmawansa, Saman Atapattu, and Chintha Tellambura. "Eigenvalue-Based Detection of a Signal in Colored Noise: Finite and Asymptotic Analyses." IEEE Transactions on Information Theory 66, no. 10 (October 2020): 6413–33. http://dx.doi.org/10.1109/tit.2020.2998287.

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24

Xia, Huizhu, Weiqiong Song, Rui Li, Xiaolin Wu, and Yizhao Luo. "Low-voltage power line broadband carrier communication signal detection based on eigenvalue analysis." IOP Conference Series: Materials Science and Engineering 677 (December 10, 2019): 042003. http://dx.doi.org/10.1088/1757-899x/677/4/042003.

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25

Liu, Chang, Jie Wang, Xuemeng Liu, and Ying-Chang Liang. "Maximum Eigenvalue-Based Goodness-of-Fit Detection for Spectrum Sensing in Cognitive Radio." IEEE Transactions on Vehicular Technology 68, no. 8 (August 2019): 7747–60. http://dx.doi.org/10.1109/tvt.2019.2923648.

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26

Futamura, Yasunori, Xiucai Ye, Akira Imakura, and Tetsuya Sakurai. "Spectral Anomaly Detection in Large Graphs Using a Complex Moment-Based Eigenvalue Solver." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 6, no. 2 (June 2020): 04020010. http://dx.doi.org/10.1061/ajrua6.0001054.

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27

Jiang, Jianfeng, Wenjun Zhu, Xingang Wang, and Chong Zhang. "Abnormal Power Consumption Detection Based on Data-Driven." E3S Web of Conferences 261 (2021): 01029. http://dx.doi.org/10.1051/e3sconf/202126101029.

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Based on high dimensional random matrix theory and machine learning algorithm, a method to detect abnormal power consumption behaviour of users is proposed. Firstly, the K-means clustering algorithm is used to divide the power loads into load types that obey specific distribution law or with random fluctuation. Then the linear eigenvalue statistics (LES) index can be used to detect the abnormal power consumption behaviour for the former such as unimodal load or bimodal load. And the difference between the actual and predicted value of regression model based on XGBoost algorithm can be used as the basis for judging abnormal power consumption behaviour of the latter. The method proposed in this paper is applicable to different types of loads and can implement a good discriminant effect.

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28

Liu, Fu Shun, Hua Jun Li, Guang Ming Yu, Yan Chun Liu, and Wei Ying Wang. "Initial Damage Detection Based on Elastic Modulus Randomness." Key Engineering Materials 324-325 (November 2006): 109–12. http://dx.doi.org/10.4028/www.scientific.net/kem.324-325.109.

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A damage detection method reflecting initial damage of the elastic structure is presented. It is based on the idea that the damage will decrease the structure stiffness. From eigenvalue issue, regard Poison ratio and elastic modulus as a constant and a random variable, respectively, and in terms of known data and a combination of the FEM, then equations solving random modulus of the undamaged and the damaged structure are obtained, respectively. Based on the element damage indicator, initial damage of the structure can be detected. At last, a five-storey shearing structure is simulated, and the results indicate that initial damage situation can be accurately calculated, and initial damage locations can be estimated based on the proposed method, which is simple and effective, and contributes to the application in Engineering.

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29

Wang, Tailin, Hua Zheng, Fangshu Li, Nian Jia, and Zengliang Cai. "Evaluation Algorithm of Volleyball Players’ Competitive Ability Based on the Random Matrix Model." Mathematical Problems in Engineering 2022 (July 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/6967379.

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It is the trend of the development of modern competitive sports to put scientific and technological analysis methods and means into the study of volleyball, and it is also one of the powerful guarantee ways to promote the competitive level of all countries. The random matrix model algorithm has unique advantages to construct the team’s collective technical and tactical ability structure model. The quantitative relationship of the model describes the relationship between the technical and tactical ability structure and the result of victory and defeat and makes the advantages and disadvantages of the team clear, which is conducive to the subsequent targeted training and improvement. The technical and tactical abilities of the teams in different seasons were input to verify the prediction accuracy of the model for the teams in different seasons. In the face of the rapidly changing game situation, the coach team timely transmits the adjusted technical and tactical strategies to the players on the field and deals with the changes accurately and effectively. After the game, the opponent’s strengths and weaknesses should be clarified, and the team’s daily training details should be summarized to provide reference for the cultivation of collective technical and tactical consciousness. The random sample covariance matrix of the random monitoring matrix is constructed and the maximum and minimum eigenvalues of the sample covariance matrix are solved. The ratio of characteristic values is used to construct the detection index of characteristic values, and the detection threshold algorithm of characteristic values is determined to judge the competitive ability of volleyball players. In the case of false alarm rate and matrix size, based on Tracy-Widom distribution characteristics, the maximum eigenvalue and minimum eigenvalue approximations of sample covariance matrix are used to improve the eigenvalue index detection threshold algorithm, and the influence of false alarm rate, matrix size, and other parameters on the improved eigenvalue index detection threshold is further studied. Then, Iris data set was used to verify the effectiveness of the algorithm in terms of accuracy, recall rate, and comprehensive effective value, and the validation results proved that the accuracy of the algorithm reached more than 90%.

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30

Zhang, Rui Fang, Han Min Ye, Zi Hang Song, and Min Wang. "Blind Detection of Copy-Move Forgery in Digital Images Based on Dyadic Wavelet Transform." Advanced Materials Research 989-994 (July 2014): 4127–31. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4127.

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This paper proposed a detection algorithm for copy-move in same image based on dyadic wavelet transform. First of all, four sub images could be got through the decomposition of detecting image by dyadic wavelet transform. Secondly, high-frequency and low-frequency sub image were decomposed into blocks without any overlap and two sub image’s dyadic wavelet coefficients were regarded as the eigenvalue of the image block. At Last, both the high similarity among the low-frequency sub image blocks and the low similarity among the high-frequency sub image blocks were selected as a distorted image block. A kind of image edge processing methods was used to improve the tampering region at the same time. Through the experiments, it shows that the algorithm got the higher detection rate and lower error rates.

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31

Liu, Weijian, Haoyuan Chang, Yang Xiao, Shuisheng Yu, Chuanbo Huang, and Yuntian Yao. "Automated Detection of Microseismic Arrival Based on Convolutional Neural Networks." Shock and Vibration 2022 (December 3, 2022): 1–14. http://dx.doi.org/10.1155/2022/8000477.

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It is difficult to accurately and efficiently detect seismic wave signals at the time of arrival for automatic positioning from microseismic waves. A U-net model to detect the arrival time of seismic waves is constructed based on the convolutional neural network (CNN) theory. The original data for 1555 segments and synthetic data of 7764 segments were detected using Akaike’s information criterion (AIC) algorithm, the time window energy eigenvalue algorithm, and the U-net model. During uniaxial compression of the test block, acoustic emission equipment is used to collect the vibration wave generated by the rupture of the test block. Source imaging images are drawn using the Origin software, the arrival time error is counted, and the advantages and disadvantages of the three arrival time methods are discussed. Similarities between the source image and the actual fracture image are observed. There is a high similarity between the source imaging map and the physical trajectory map when the U-net model is used. Thus, it is feasible to use the U-net model to detect the arrival time of seismic waves. Its accuracy is greater than that of the time window energy eigenvalue algorithm but lower than that of the AIC algorithm for high signal-to-noise ratios. After reducing the signal-to-noise ratio, the stability and accuracy of the U-net model to detect the arrival time have improved over the other two algorithms.

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He, Yanhu, Rongyang Wang, and Yanfeng Wang. "Fault Detection and Health Assessment of Equipment Based on Fuzzy DPCA Spatial Eigenvalue Similarity." Mathematical Problems in Engineering 2021 (July 1, 2021): 1–17. http://dx.doi.org/10.1155/2021/9983497.

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To improve the fault recognition rate of the dynamic principal component spatial data drive method, a fault diagnosis and equipment health status assessment method based on similarity fuzzy dynamics principal component analysis was proposed. First, the data are fuzzified according to the error function, and an augmented matrix is constructed. The eigenvalues are decomposed to obtain a score matrix and residual matrix of the fuzzy principal component. Further, the similarity between fault data and normal data is calculated. Meanwhile, a health assessment of the equipment is realized. The contribution rate of the observed variables is calculated. Finally, general Tennessee Eastman data and health assessment of a hydraulic press are used to validate the algorithm. The results show that the SFDPCA has a 100 % fault recognition rate for some faults, and the recognition rate for other faults is also higher than that of DPCA-Diss, DPCA-SPE, and PCA-SPE. The SDDPCA accurately identifies abnormal phenomena. It can determine the health level of prefilling and effectively make up for the shortcomings of PCA − T 2 , PCA-SPE, DPCA-Diss, and other methods and also can be applied to data-driven fault diagnosis to improve the fault recognition rate.

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Stoica, Petre, and Mats Cedervall. "An Eigenvalue-Based Detection Test for Array Signal Processing in Unknown Correlated Noise Fields." IFAC Proceedings Volumes 29, no. 1 (June 1996): 4098–103. http://dx.doi.org/10.1016/s1474-6670(17)58322-6.

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34

Yousif, Ebtihal H. G., Tharmalingam Ratnarajah, and Mathini Sellathurai. "A Frequency Domain Approach to Eigenvalue-Based Detection With Diversity Reception and Spectrum Estimation." IEEE Transactions on Signal Processing 64, no. 1 (January 2016): 35–47. http://dx.doi.org/10.1109/tsp.2015.2474309.

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35

Wu, Na, Ke Wang, Liangtian Wan, and Ning Liu. "A Source Number Estimation Algorithm Based on Data Local Density and Fuzzy C-Means Clustering." Wireless Communications and Mobile Computing 2021 (February 20, 2021): 1–7. http://dx.doi.org/10.1155/2021/6658785.

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An advanced source number estimation (SNE) algorithm based on both fuzzy C-means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one-dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal-to-noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.

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Zhao, Enzhong, Lili Dong, and Hao Dai. "Infrared Maritime Small Target Detection Based on Multidirectional Uniformity and Sparse-Weight Similarity." Remote Sensing 14, no. 21 (October 31, 2022): 5492. http://dx.doi.org/10.3390/rs14215492.

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Infrared maritime target detection is a key technology in the field of maritime search and rescue, which usually requires high detection accuracy. Despite the promising progress of principal component analysis methods, it is still challenging to detect small targets of unknown polarity (bright or dark) with strong edge interference. Using the partial sum of tubal nuclear norm to estimate low-rank background components and weighted l1 norm to estimate sparse components is an effective method for target extraction. In order to suppress the strong edge interference, considering that the uniformity of the target scattering field is significantly higher than that of the background scattering field in the eigenvalue of the structure tensor, a prior weight based on the multidirectional uniformity of structure tensor eigenvalue was proposed and applied to the optimization model. In order to detect targets with unknown polarity, the images with opposite polarity were substituted into the optimization model, respectively, and the sparse-weight similarity is used to judge the polarity of the target. In order to make the method more efficient, the polarity judgment is made in the second iteration, and then, the false iteration will stop. The proposed method is compared with nine advanced baseline methods on 14 datasets and shows significant strong robustness, which is beneficial to engineering applications.

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Wang, Jing Fang. "Toeplitz Robust Noisy Speech Endpoint Detection." Applied Mechanics and Materials 198-199 (September 2012): 1462–68. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1462.

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In this paper, under the conditions of low SNR speech endpoint detection, a feature based on the maximum value of Toeplitz Noise endpoint detection methods. Terms of the method of spectrum from the corresponding sequences with a symmetric Toeplitz matrix constructed using the maximum eigenvalue of the matrix information on the voice signal for dual endpoint detection threshold. New algorithm has been tested to effectively distinguish between speech and noise, low-noise in different environmental conditions has good robustness. With the recent recursive signal analysis methods, the accuracy is higher. The algorithm to calculate the cost of a small, real good, simple and easy to implement.

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Xia, Xianzhao, Rui Chen, Pinquan Wang, and Yiqiang Zhao. "Robust Noise Suppression Technique for a LADAR System via Eigenvalue-Based Adaptive Filtering." Sensors 19, no. 10 (May 19, 2019): 2311. http://dx.doi.org/10.3390/s19102311.

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The laser detection and ranging system (LADAR) is widely used in various fields that require 3D measurement, detection, and modeling. In order to improve the system stability and ranging accuracy, it is necessary to obtain the complete waveform of pulses that contain target information. Due to the inevitable noise, there are distinct deviations between the actual and expected waveforms, so noise suppression is essential. To achieve the best effect, the filters’ parameters that are usually set as empirical values should be adaptively adjusted according to the different noise levels. Therefore, we propose a novel noise suppression method for the LADAR system via eigenvalue-based adaptive filtering. Firstly, an efficient noise level estimation method is developed. The distributions of the eigenvalues of the sample covariance matrix are analyzed statistically after one-dimensional echo data are transformed into matrix format. Based on the boundedness and asymptotic properties of the noise eigenvalue spectrum, an estimation method for noise variances in high dimensional settings is proposed. Secondly, based on the estimated noise level, an adaptive guided filtering algorithm is designed within the gradient domain. The optimized parameters of the guided filtering are set according to an estimated noise level. Through simulation analysis and testing experiments on echo waves, it is proven that our algorithm can suppress the noise reliably and has advantages over the existing relevant methods.

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Lee, Yunseong, Chanhong Park, Taeyoung Kim, Yeongyoon Choi, Kiseon Kim, Dongho Kim, Myung-Sik Lee, and Dongkeun Lee. "Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation." Applied Sciences 11, no. 4 (February 23, 2021): 1942. http://dx.doi.org/10.3390/app11041942.

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Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

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Lixia Ji, Lixia Ji, Xiao Zhang Lixia Ji, Yao Zhao Xiao Zhang, and Zongkun Li Yao Zhao. "Anomaly Detection of Dam Monitoring Data based on Improved Spectral Clustering." 網際網路技術學刊 23, no. 4 (July 2022): 749–59. http://dx.doi.org/10.53106/160792642022072304010.

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<p>In response to the abnormal data mining in dam safety monitoring, and based on the traditional spectral clustering, this paper presents an anomaly detection method based on improved spectral clustering. This method applies a distance and density adaptive similarity measure. The natural eigenvalue is introduced to adaptively select the neighbors of data points, and the similarity is redefined to be combined with the natural k-nearest neighbor. Furthermore, the shared neighbor is introduced to adjust the similarity between the monitoring data samples according to the regional density. Moreover, considering the distribution of dam monitoring data, the initialization of clustering centers is optimized according to both the density and distance feature. This method can prevent the algorithm from local optimum, better adapt to the density of non-convex dataset, reduce the number of iterations, and enhance the efficiencies of clustering and anomaly detection. Taking the dam slab monitoring data as the research object, experimental datasets are formed. Experiments on these datasets further verify that the method of this paper can effectively adapt to discrete distribution datasets and is superior to the classical spectral clustering method in both clustering and anomaly detection.</p> <p> </p>

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Bai, Pengcheng, Yunxiu Yang, Fengtao Xue, Rong Yang, and Qin Shu. "Underdetermined mixing matrix estimation based on time-frequency single source points detection and eigenvalue decomposition." Signal, Image and Video Processing 16, no. 4 (January 4, 2022): 1061–69. http://dx.doi.org/10.1007/s11760-021-02055-5.

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Pillay, N., and H. J. Xu. "Eigenvalue-based spectrum ‘hole’ detection for Nakagami-m fading channels with Gaussian and impulse noise." IET Communications 6, no. 13 (September 5, 2012): 2054–64. http://dx.doi.org/10.1049/iet-com.2011.0758.

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Nadakuditi, R. R., and A. Edelman. "Sample Eigenvalue Based Detection of High-Dimensional Signals in White Noise Using Relatively Few Samples." IEEE Transactions on Signal Processing 56, no. 7 (July 2008): 2625–38. http://dx.doi.org/10.1109/tsp.2008.917356.

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Zhong, Li Jun, and Wen Wen Li. "A Method of Online Color-Difference Detecting Based on Image Processing and its Application." Applied Mechanics and Materials 37-38 (November 2010): 14–17. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.14.

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A method of the classifying of ceramic tiles’ color difference is proposed, and the online detection system based on linear array color CCD sensors is designed. After the image of tile grabbed by CCD is transformed to the HIS color model, a series of image processing and analyzing methods are used to calculate the eigenvalue of sample. The minimum distance classifier is used to carry out tiles’ classifying. Experimental results show the method is effective.

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Zhang, Shi Ding, Hai Lian Wang, and Jing Ping Mei. "Sensing Confidence Level-Based Cooperative Spectrum Sensing Algorithm." Applied Mechanics and Materials 380-384 (August 2013): 1499–504. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1499.

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Cooperative spectrum sensing is a key technology to tackle the challenges such as fading or hidden terminal problem in local spectrum sensing of cognitive radio system. Conventional cooperative method can improve the detection performance in some sense, but increase overhead of control channel. In order to reduce the overhead, a new cooperative spectrum sensing algorithm based on confidence level is proposed. In this algorithm, the maximum-eigenvalue-based detection scheme is carried out to obtain the local spectrum detection and the detection probability and false alarm probability of each secondary user are used to estimate the reliability of the sensing decision. The test statistic of the secondary users with high reliability are chosen and sent to fusion center. Then weighted factors of chosen secondary users are derived from creditability values, and the global decision is made by weighted fusion at fusion center. The simulation results show that the proposed algorithm improves the detection probability in the guarantee of the false-alarm probability close to 0 and saves half of the overhead in the control channel.

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Shuang, Huang, Cao Shaozhong, Zhu Weijun, and Bao Chenyang. "Surface Defect Segmentation and Detection of Printing Roller Based on Improved FT Algorithm." Journal of Physics: Conference Series 2278, no. 1 (May 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2278/1/012007.

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Abstract The method based on machine vision is one of the important ways of printing roller defect detection. It has the advantage of intuitively reflecting the surface state of printing roller, but the effect of detecting the position where the surface defect of printing roller is not obvious is not ideal. Aiming at the problems of different printing roller surface defects and large interference of background texture, a printing roller surface defect detection algorithm based on improved frequency tuning significance and threshold segmentation is proposed. Firstly, the nonlocal mean filter method is used to preprocess the image to solve the influence of noise on the image; Then, the saliency map is extracted by converting the lab color space of FT algorithm to HSV color space, normalizing and weighting each eigenvalue respectively; Finally, the salient image is binarized by iterative threshold segmentation to obtain the final defect image. The experimental results show that the accuracy of the algorithm is 97.1%, which is better than other similar algorithms. It can accurately detect the roller image with different size of surface defects and texture background interference, and the comprehensive performance is outstanding.

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Fan, Ya Qin, Ge Zhang, Miao Liu, and Xin Zhang. "The Study Found that the Intelligent Mobile Phone Technology of Malicious Code." Advanced Materials Research 765-767 (September 2013): 1263–66. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1263.

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This paper studies the development trend of intelligent mobile phone, confirmed the necessity of research on intelligent mobile phone malicious code. Study on the detection technology, proposed intelligent mobile phone regular networks and random networks based on malicious code propagation model, propagation mechanism is studied. Set up a perfect malicious code discovery and defense system model, at different levels is put forward that different, prove the necessity of scanning algorithm and Semantic Detection Algorithm for eigenvalue. To improve the security of the whole communication network.

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ROBBIO, FEDERICO I., DIEGO M. ALONSO, and JORGE L. MOIOLA. "DETECTION OF LIMIT CYCLE BIFURCATIONS USING HARMONIC BALANCE METHODS." International Journal of Bifurcation and Chaos 14, no. 10 (October 2004): 3647–54. http://dx.doi.org/10.1142/s0218127404011491.

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In this paper, bifurcations of limit cycles close to certain singularities of the vector fields are explored using an algorithm based on the harmonic balance method, the theory of nonlinear feedback systems and the monodromy matrix. Period-doubling, pitchfork and Neimark–Sacker bifurcations of cycles are detected close to a Gavrilov–Guckenheimer singularity in two modified Rössler systems. This special singularity has a zero eigenvalue and a pair of pure imaginary eigenvalues in the linearization of the flow around its equilibrium. The presented results suggest that the proposed technique can be promising in analyzing limit cycle bifurcations arising in the unfoldings of other complex singularities.

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Lay-Ekuakille, Aimé, Carlo Pariset, and Amerigo Trotta. "Leak detection of complex pipelines based on the filter diagonalization method: robust technique for eigenvalue assessment." Measurement Science and Technology 21, no. 11 (September 21, 2010): 115403. http://dx.doi.org/10.1088/0957-0233/21/11/115403.

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Zhang, Jian Qiu. "An eigenvalue residuum-based criterion for detection of the number of sinusoids in white Gaussian noise." Digital Signal Processing 13, no. 2 (April 2003): 275–83. http://dx.doi.org/10.1016/s1051-2004(02)00029-5.

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