Relevant bibliographies by topics / Eigenvalue-based detection

Academic literature on the topic 'Eigenvalue-based detection'

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Published: 14 March 2023

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Journal articles on the topic "Eigenvalue-based detection"

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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 (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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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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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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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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Dissertations / Theses on the topic "Eigenvalue-based detection"

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TESHOME, ABIY TEREFE. "FPGA based Eigenvalue Detection Algorithm for Cognitive Radio." Thesis, Högskolan i Gävle, Radio Center Gävle, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-7855.

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Radio Communication technologies are undergoing drastic demand over the past two decades. The precious radio resource, electromagnetic radio spectrum, is in vain as technology advances. It is required to come up with a solution to improve its wise uses. Cognitive Radio enabled by Software-Defined Radio brings an intelligent solution to efficiently use the Radio Spectrum. It is a method to aware the radio communication system to be able to adapt to its radio environment like signal power and free spectrum holes. The approach will pose a question on how to efficiently detect a signal. In this thesis different spectrum sensing algorithm will be explained and a special concentration will be on new sensing algorithm based on the Eigenvalues of received signal. The proposed method adapts blind signal detection approach for applications that lacks knowledge about signal, noise and channel property. There are two methods, one is ratio of the Maximum Eigenvalue to Minimum Eigenvalue and the second is ratio of Signal Power to Minimum Eigenvalue. Random Matrix theory (RMT) is a branch of mathematics and it is capable in analyzing large set of data or in a conclusive approach it provides a correlation points in signals or waveforms. In the context of this thesis, RMT is used to overcome both noise and channel uncertainties that are common in wireless communication. Simulations in MATLAB and real-time measurements in LabVIEW are implemented to test the proposed detection algorithms. The measurements were performed based on received signal from an IF-5641R Transceiver obtained from National Instruments.
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Just, Frederick A. "Damage Detection Based on the Geometric Interpretation of the Eigenvalue Problem." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29555.

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A method that can be used to detect damage in structures is developed. This method is based on the convexity of the geometric interpretation of the eigenvalue problem for undamped positive definite systems. The damage detection scheme establishes various damage scenarios which are used as failure sets. These scenarios are then compared to the structure's actual response by measuring the natural frequencies of the structure and using a Euclideian norm. Mathematical models were developed for application of the method on a cantilever beam. Damage occurring at a single location or in multiple locations was estalished and studied. Experimental verification was performed on serval prismatic beams in which the method provided adequate results. The exact location and extent of damage for several cases was predicted. When the method failed the prediction was very close to the actual condition in the structure. This method is easy to use and does not require a rigorous amount of instrumentation for obtaining the experimental data required in the detection scheme.
Ph. D.
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Ayeh, Eric. "Statistical Strategies for Efficient Signal Detection and Parameter Estimation in Wireless Sensor Networks." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc407740/.

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This dissertation investigates data reduction strategies from a signal processing perspective in centralized detection and estimation applications. First, it considers a deterministic source observed by a network of sensors and develops an analytical strategy for ranking sensor transmissions based on the magnitude of their test statistics. The benefit of the proposed strategy is that the decision to transmit or not to transmit observations to the fusion center can be made at the sensor level resulting in significant savings in transmission costs. A sensor network based on target tracking application is simulated to demonstrate the benefits of the proposed strategy over the unconstrained energy approach. Second, it considers the detection of random signals in noisy measurements and evaluates the performance of eigenvalue-based signal detectors. Due to their computational simplicity, robustness and performance, these detectors have recently received a lot of attention. When the observed random signal is correlated, several researchers claim that the performance of eigenvalue-based detectors exceeds that of the classical energy detector. However, such claims fail to consider the fact that when the signal is correlated, the optimal detector is the estimator-correlator and not the energy detector. In this dissertation, through theoretical analyses and Monte Carlo simulations, eigenvalue-based detectors are shown to be suboptimal when compared to the energy detector and the estimator-correlator.
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Arts, Martijn Verfasser], Rudolf [Akademischer Betreuer] [Mathar, and Anja [Akademischer Betreuer] Klein. "Eigenvalue-Based Spectrum Sensing for Cognitive Radio: Change Detection Problems and Fundamental Performance Limits / Martijn Arts ; Rudolf Mathar, Anja Klein." Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/1162450436/34.

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RIVIELLO, DANIEL GAETANO. "Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2641328.

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The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities.
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Kobeissi, Hussein. "Eigenvalue Based Detector in Finite and Asymptotic Multi-antenna Cognitive Radio Systems." Thesis, CentraleSupélec, 2016. http://www.theses.fr/2016SUPL0011/document.

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La thèse aborde le problème de la détection d’un signal dans une bande de fréquences donnée sans aucune connaissance à priori sur la source (détection aveugle) dans le contexte de la radio intelligente. Le détecteur proposé dans la thèse est basé sur l’estimation des valeurs propres de la matrice de corrélation du signal reçu. A partir de ces valeurs propres, plusieurs critères ont été développés théoriquement (Standard Condition Number, Scaled Largest Eigenvalue, Largest Eigenvalue) en prenant pour hypothèse majeure un nombre fini d’éléments, contrairement aux hypothèses courantes de la théorie des matrices aléatoires qui considère un comportement asymptotique de ces critères. Les paramètres clés des détecteurs ont été formulés mathématiquement (probabilité de fausse alarme, densité de probabilité) et une correspondance avec la densité GEV a été explicitée. Enfin, ce travail a été étendu au cas multi-antennes (MIMO) pour les détecteurs SLE et SCN
In Cognitive Radio, Spectrum Sensing (SS) is the task of obtaining awareness about the spectrum usage. Mainly it concerns two scenarios of detection: (i) detecting the absence of the Primary User (PU) in a licensed spectrum in order to use it and (ii) detecting the presence of the PU to avoid interference. Several SS techniques were proposed in the literature. Among these, Eigenvalue Based Detector (EBD) has been proposed as a precious totally-blind detector that exploits the spacial diversity, overcome noise uncertainty challenges and performs adequately even in low SNR conditions. The first part of this study concerns the Standard Condition Number (SCN) detector and the Scaled Largest Eigenvalue (SLE) detector. We derived exact expressions for the Probability Density Function (PDF) and the Cumulative Distribution Function (CDF) of the SCN using results from finite Random Matrix Theory; In addition, we derived exact expressions for the moments of the SCN and we proposed a new approximation based on the Generalized Extreme Value (GEV) distribution. Moreover, using results from the asymptotic RMT we further provided a simple forms for the central moments of the SCN and we end up with a simple and accurate expression for the CDF, PDF, Probability of False-Alarm, Probability of Detection, of Miss-Detection and the decision threshold that could be computed and hence provide a dynamic SCN detector that could dynamically change the threshold value depending on target performance and environmental conditions. The second part of this study concerns the massive MIMO technology and how to exploit the large number of antennas for SS and CRs. Two antenna exploitation scenarios are studied: (i) Full antenna exploitation and (ii) Partial antenna exploitation in which we have two options: (i) Fixed use or (ii) Dynamic use of the antennas. We considered the Largest Eigenvalue (LE) detector if noise power is perfectly known and the SCN and SLE detectors when noise uncertainty exists
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Knapo, Peter. "Vývoj algoritmů pro digitální zpracování obrazu v reálním čase v DSP procesoru." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217872.

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Rozpoznávanie tvárí je komplexný proces, ktorého hlavným ciežom je rozpoznanie žudskej tváre v obrázku alebo vo video sekvencii. Najčastejšími aplikáciami sú sledovacie a identifikačné systémy. Taktiež je rozpoznávanie tvárí dôležité vo výskume počítačového videnia a umelej inteligencií. Systémy rozpoznávania tvárí sú často založené na analýze obrazu alebo na neurónových sieťach. Táto práca sa zaoberá implementáciou algoritmu založeného na takzvaných „Eigenfaces“ tvárach. „Eigenfaces“ tváre sú výsledkom Analýzy hlavných komponent (Principal Component Analysis - PCA), ktorá extrahuje najdôležitejšie tvárové črty z originálneho obrázku. Táto metóda je založená na riešení lineárnej maticovej rovnice, kde zo známej kovariančnej matice sa počítajú takzvané „eigenvalues“ a „eigenvectors“, v preklade vlastné hodnoty a vlastné vektory. Tvár, ktorá má byť rozpoznaná, sa premietne do takzvaného „eigenspace“ (priestor vlastných hodnôt). Vlastné rozpoznanie je na základe porovnania takýchto tvárí s existujúcou databázou tvárí, ktorá je premietnutá do rovnakého „eigenspace“. Pred procesom rozpoznávania tvárí, musí byť tvár lokalizovaná v obrázku a upravená (normalizácia, kompenzácia svetelných podmienok a odstránenie šumu). Existuje mnoho algoritmov na lokalizáciu tváre, ale v tejto práci je použitý algoritmus lokalizácie tváre na základe farby žudskej pokožky, ktorý je rýchly a postačujúci pre túto aplikáciu. Algoritmy rozpoznávania tváre a lokalizácie tváre sú implementované do DSP procesoru Blackfin ADSP-BF561 od Analog Devices.
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Book chapters on the topic "Eigenvalue-based detection"

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Azmoodeh, Amin, Ali Dehghantanha, Reza M. Parizi, Sattar Hashemi, Bahram Gharabaghi, and Gautam Srivastava. "Active Spectral Botnet Detection Based on Eigenvalue Weighting." In Handbook of Big Data Privacy, 385–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38557-6_19.

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Maali, Asmaa, Sara Laafar, Hayat Semlali, Najib Boumaaz, and Abdallah Soulmani. "Maximum Eigenvalue Based Detection Using Jittered Random Sampling." In Lecture Notes on Data Engineering and Communications Technologies, 183–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11437-4_14.

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Zhang, Chunjie, Shanshuang Li, Zhian Deng, and Yingjun Hao. "An Improved Eigenvalue-Based Channelized Sub-band Spectrum Detection Method." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 244–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19086-6_27.

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Feng, Sai-sai, Yue-bin Chen, and Fei Gao. "Signal Detection Based on Maximum-Minimum Eigenvalue in Rician Fading Channel." In Lecture Notes in Computer Science, 160–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38227-7_19.

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Guan, Gangqiang, Deyong Xian, Liu Shi, Jia Mu, and Xinshu Zhao. "Analysis of Threshold Setting for Eigenvalue Ratio Based Interference Detection Under Constant Missed Detection Probability." In China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume I, 997–1007. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4588-2_84.

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Jiang, Jingjing, Xinming Liu, Wenzhuang Chen, and Aikun Mao. "Cluster Analysis Based Eigenvalue Extraction and Dynamic Time Regulation for Electricity Anomaly Detection." In Lecture Notes in Electrical Engineering, 1130–38. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1528-4_115.

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Charan, Chhagan, and Rajoo Pandey. "Cooperative Spectrum Sensing Using Eigenvalue-Based Double-Threshold Detection Scheme for Cognitive Radio Networks." In Advances in Intelligent Systems and Computing, 189–99. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1822-1_18.

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Wang, Miao, Xiao-xia Cai, and Ke-fan Zhu. "Underdetermined Mixed Matrix Estimation of Single Source Point Detection Based on Noise Threshold Eigenvalue Decomposition." In Lecture Notes in Electrical Engineering, 704–11. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_83.

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Elleuch, Ines, Fatma Abdelkefi, and Mohamed Siala. "Complexity Issues within Eigenvalue-Based Multi-Antenna Spectrum Sensing." In Advances in Wireless Technologies and Telecommunication, 603–17. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6571-2.ch023.

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This chapter provides a deep insight into multiple antenna eigenvalue-based spectrum sensing algorithms from a complexity perspective. A review of eigenvalue-based spectrum-sensing algorithms is provided. The chapter presents a finite computational complexity analysis in terms of Floating Point Operations (flop) and a comparison of the Maximum-to-Minimum Eigenvalue (MME) detector and a simplified variant of the Multiple Beam forming detector as well as the Approximated MME method. Constant False Alarm Performances (CFAR) are presented to emphasize the complexity-reliability tradeoff within the spectrum-sensing problem, given the strong requirements on the sensing duration and the detection performance.
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Bhatti, Farrukh A., Gerard B. Rowe, and Kevin W. Sowerby. "Spectrum Sensing Using Principal Components for Multiple Antenna Cognitive Radios." In Advances in Wireless Technologies and Telecommunication, 179–99. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6571-2.ch007.

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This chapter presents an experimental comparative analysis of the well-known Covariance-Based Detection (CBD) techniques, which include Covariance Absolute Value (CAV), Maximum-Minimum Eigenvalue (MME), Energy with Minimum Eigenvalue (EME), and Maximum Eigenvalue Detection (MED). CBD techniques overcome the noise uncertainty issue of the Energy Detector (ED) and can even outperform ED in the case of correlated signals. They can perform accurate blind detection given sufficient number of signal samples. This chapter also presents a novel CBD algorithm that is based on Principal Component (PC) analysis. A Software-Defined Radio (SDR)-based multiple antenna system is used to evaluate the detection performance of the considered algorithms. The PC algorithm significantly outperforms the MED and EME algorithms and it also outperforms MME and CAV algorithms in certain cases.
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Conference papers on the topic "Eigenvalue-based detection"

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Maali, Asmaa, Hayat Semlali, Najib Boumaaz, and Abdallah Soulmani. "Energy detection versus maximum eigenvalue based detection: A comparative study." In 2017 14th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2017. http://dx.doi.org/10.1109/ssd.2017.8166914.

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Getu, Tilahun M., Wessam Ajib, and Rene Landry. "An Eigenvalue-Based Multi-Antenna RFI Detection Algorithm." In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall). IEEE, 2018. http://dx.doi.org/10.1109/vtcfall.2018.8690863.

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Zi-li, WANG, SONG Xiao-ou, and WANG Xiao-rong. "Spectrum Sensing Detection Algorithm Based on Eigenvalue Variance." In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2019. http://dx.doi.org/10.1109/itaic.2019.8785807.

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Swamy, Tata Jagannadha, Srinivas Avasarala, Thaskani Sandhya, and Garimella Ramamurthy. "Spectrum sensing: Approximations for Eigenvalue ratio based detection." In 2012 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2012. http://dx.doi.org/10.1109/iccci.2012.6158914.

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Zhengzheng Sun. "On the performance of eigenvalue-based signal detection." In 2012 12th International Conference on ITS Telecommunications (ITST). IEEE, 2012. http://dx.doi.org/10.1109/itst.2012.6425193.

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Yuan Sun, Bo Zhang, Chao Wang, and Fan Wu. "Ship detection based on eigenvalue-eigenvector decomposition and OS-CFAR detector." In 2012 International Conference on Computer Vision in Remote Sensing (CVRS). IEEE, 2012. http://dx.doi.org/10.1109/cvrs.2012.6421288.

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Miranda, Joao Paulo, Boris Galkin, Giuseppe Abreu, and Luiz DaSilva. "Experimental assessment of eigenvalue-based detection for cognitive radio." In 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2014. http://dx.doi.org/10.1109/sam.2014.6882364.

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Zhang, Lamei, Bin Zou, and Wenyan Tang. "Building detection based on Polarimetric Interferometric Eigenvalue Similarity Parameter." In 2011 IEEE Radar Conference (RadarCon). IEEE, 2011. http://dx.doi.org/10.1109/radar.2011.5960619.

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9

Wei, Lu, and Olav Tirkkonen. "Spectrum sensing with Gaussian approximated eigenvalue ratio based detection." In 2010 7th International Symposium on Wireless Communication Systems (ISWCS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iswcs.2010.5624271.

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10

Bhagath, Parabattina, and Pradip K. Das. "Graph Eigenvalue based Structural Method towards Phonetic Boundary Detection." In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON). IEEE, 2021. http://dx.doi.org/10.1109/tencon54134.2021.9707281.

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