Relevant bibliographies by topics / Eigenvalue-based detection

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  2. Dissertations / Theses
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Academic literature on the topic 'Eigenvalue-based detection'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Eigenvalue-based detection"

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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 (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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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 th
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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
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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 appli
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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
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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 théor
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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 najdôležit
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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. 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. 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. 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. 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. 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. 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. 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. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_83.

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Xiao, Dezhu, Xin Zhang, Shuaida Zhao, and Qiang Yang. "A Novel Direction Noise Detection Method of HFSWR Based on Space-Time Multi-eigenvalue Synthesis." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86203-8_3.

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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. 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
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Conference papers on the topic "Eigenvalue-based detection"

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Parihar, Nikhilsingh, Praful D. Mankar, and Sachin Chaudhari. "Maximum Eigenvalue Detection Based Spectrum Sensing in RIS-Aided System with Correlated Fading." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683023.

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Ebong, Frank E., Nicola Novello, and Andrea M. Tonello. "Human Detection Based on Learning and Classification of Radio Scattering Parameters and Para-Hermitian Eigenvalue Decomposition." In 2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2024. https://doi.org/10.1109/pimrc59610.2024.10817311.

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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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Camelo, Leonardo Yuto Suzuki, Bernardo Bentes Gatto, Ayrles Mendonça, Rafael Giusti, and Eulanda Miranda dos Santos. "Infant Movement Detection via Eigenvalue-Entropy Based Subspace Method." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbcas.2024.2764.

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The early identification of anomalous movements in infants is crucial for intervening in potential neuromotor development disorders. The clinical method General Movement Assessment (GMA) is devoted to this identification task. However, since GMA is intensive and requires experts, new machine learning-based approaches and keypoints extracted from videos have emerged. However, challenges such as the underrepresentation of infants with writhing movements (WM)—general movements presented by infants in their first weeks of life; the scarcity of public datasets; and the fact that only video segments
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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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