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Journal articles on the topic 'Eigenvalue-based detection'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

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 (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-Minimu

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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 (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 observe

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Xu, Yong, Yongfeng Zhu, and Zhiyong Song. "A New Extended Target Detection Method Based on the Maximum Eigenvalue of the Hermitian Matrix." Remote Sensing 16, no. 9 (2024): 1488. http://dx.doi.org/10.3390/rs16091488.

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In the field of radar target detection, the conventional approach is to employ the range profile energy accumulation method for detecting extended targets. However, this method becomes ineffective when dealing with non-stationary and non-uniform radar clutter scenarios, as well as long-distance targets with weak radar cross sections (RCSs). In such cases, the signal-to-noise ratio (SNR) of the target echo is severely degraded, rendering the energy accumulation detection algorithm unreliable. To address this issue, this paper presents a new extended target detection method based on the maximum

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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

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

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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

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

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XIE, Guangqi, Shuai YANG, Qihao CHEN, and Xiuguo LIU. "Oil spill detection based on compact polarimetric eigenvalue decomposition." National Remote Sensing Bulletin 23, no. 2 (2019): 303–12. http://dx.doi.org/10.11834/jrs.20197260.

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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 (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 s

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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 (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 decre

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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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Paramo, Gian, and Arturo S. Bretas. "WAMs Based Eigenvalue Space Model for High Impedance Fault Detection." Applied Sciences 11, no. 24 (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-base

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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 (2020): 1747–56. http://dx.doi.org/10.1049/iet-rsn.2020.0002.

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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 (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 in

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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 b

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R, Aswatha, Seethalakshmi V, Murugan K, Sathishkumar N, Reethika A, and Gunanandhini S. "Implementation of cooperative spectrum sensing using cognitive radio testbed." Indian Journal of Science and Technology 13, no. 13 (2020): 1355–66. https://doi.org/10.17485/IJST/v13i13.94.

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Abstract <strong>Objectives:</strong> To implement energy detection and eigenvalue based cooperative spectrum sensing in NI-USRP hardware platform and to obtain its performance.Cooperative spectrum sensing is to be implemented using O and AND fusion rules. <strong>Methodology:</strong> The hardware is implemented using one primary user transmitter and two cognitive radio users. The implementation is done using LABVIEW and detection performance is analyzed. In cooperative spectrum sensing (CSS), CR system shares its own sensing information with other sensing nodes and utilizes th

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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 (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 m

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Guo, Qianrui, Bin Guo, Xiangkun Li, and Weijiao Ma. "Cooperative Spectrum Sensing Algorithm Based on Eigenvalue Fusion." Journal of Physics: Conference Series 2637, no. 1 (2023): 012044. http://dx.doi.org/10.1088/1742-6596/2637/1/012044.

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Abstract A novel algorithm is introduced to improve collaborative spectrum sensing under low cognitive capabilities and insufficient signal-to-noise ratio. The algorithm is based on the difference of random matrix eigenvalues and uses the theory of random eigenvalues and the extreme distribution of the minimum eigenvalue. It makes use of the average, both arithmetic and geometric, as well as the minimum and maximum values of eigenvalues as the detection metric. It calculates the fusion power parameter through local energy spectrum sensing. Simulation results demonstrate that the algorithm outp

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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 (2018): 1294–306. http://dx.doi.org/10.1049/iet-rsn.2018.5229.

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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 (2005): 1094–101. http://dx.doi.org/10.1016/j.imavis.2005.07.014.

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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 (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 dete

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22

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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23

Zhou, Xiangyu, and Tao Li. "Ship Detection in PolSAR Images Based on a Modified Polarimetric Notch Filter." Electronics 12, no. 12 (2023): 2683. http://dx.doi.org/10.3390/electronics12122683.

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Ship detection based on synthetic aperture radar (SAR) imagery is one of the key applications for maritime security. Compared with single-channel SAR images, polarimetric SAR (PolSAR) data contains the fully-polarized information, which better facilitates better discriminating between targets, sea clutter, and interference. Therefore, many ship detection methods based on the polarimetric scattering mechanism have been studied. To deal with the false alarms caused by the existence of ghost targets, resulting from azimuth ambiguities and interference from side lobes, a modified polarimetric notc

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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 (2004): 1–13. http://dx.doi.org/10.1016/j.patrec.2003.08.007.

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Wang, Jin, Huajian Wu, Bing Gao, Ruijie Ma, and Lei Chen. "A wind power ramp event detection method based on eigenvalue correction and trend integration." Journal of Physics: Conference Series 2703, no. 1 (2024): 012085. http://dx.doi.org/10.1088/1742-6596/2703/1/012085.

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Abstract Wind power ramp events have the characteristic of small probability and big hazard, so it is significant to improve the recognition rate and accuracy of ramp events for the safe and stable operation of the power grid. In order to improve the efficiency of ramp event detection, a wind power ramp event detection method based on eigenvalue evaluation correction and trend integration is proposed by combining the feature information of ramp events. The original wind power data is extracted with extreme value feature points and corrected with eigenvalue evaluation to achieve the trend featu

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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

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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 (2012): 320–28. http://dx.doi.org/10.4156/aiss.vol4.issue22.39.

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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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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 (2018): 154834. http://dx.doi.org/10.4108/eai.27-6-2018.154834.

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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 (2020): 6413–33. http://dx.doi.org/10.1109/tit.2020.2998287.

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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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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 (2019): 7747–60. http://dx.doi.org/10.1109/tvt.2019.2923648.

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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 (2020): 04020010. http://dx.doi.org/10.1061/ajrua6.0001054.

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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

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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 th

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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 bl

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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

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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 gene

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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

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Lee, Yunseong, Chanhong Park, Taeyoung Kim, et al. "Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation." Applied Sciences 11, no. 4 (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 mach

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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 (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 uniformit

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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 (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,

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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 contri

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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 (1996): 4098–103. http://dx.doi.org/10.1016/s1474-6670(17)58322-6.

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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 (2016): 35–47. http://dx.doi.org/10.1109/tsp.2015.2474309.

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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 networ

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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 se

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Nguyen, Khac-Duy, Tommy HT Chan, David P. Thambiratnam, and Andy Nguyen. "Damage identification in a complex truss structure using modal characteristics correlation method and sensitivity-weighted search space." Structural Health Monitoring 18, no. 1 (2018): 49–65. http://dx.doi.org/10.1177/1475921718809471.

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Damage identification for complex structures is a challenging task due to the large amount of structural elements, limited number of measured modes and uncertainties in referenced numerical models. This article presents a study on enhancing the effectiveness of modal characteristics correlation methods for damage identification of complex structures. First, a correlation method using change in the ratio of modal strain energy to eigenvalue is introduced. Damage information is determined via a forward approach by optimizing the correlation level between the patterns of the analytical and measur

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Atamanskyi, D. V., V. P. Riabukha, V. M. Kartashov, A. V. Semeniaka, and L. V. Procopenco. "SPECTRAL ESTIMATION METHODS FOR A JOINT SYSTEM OF THE NON-NOISE-LIKE TARGETS DETECTION AND THE NOISE RADIATING SOURCES LOCALIZATION." Radio Electronics, Computer Science, Control, no. 1 (April 1, 2022): 7. http://dx.doi.org/10.15588/1607-3274-2022-1-1.

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Context. For many radars, the autonomous systems of the non-noise-like aerial targets (AT) detection and the noise radiating sources (NRS) localization (direction-of-arrival estimation) may be replaced with a single detection-localization system, which carries out the common operations of the AT-detection and the NRS-localization only once. For such a system, groups of noneigenvalue and eigenvalue decomposition based “super-resolving” spectral estimation (SE) methods are considered to substantiate efficient one for the NRS-localization. Objective. The comparative analysis efficiency of t

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