A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications

Keywords: Drone detection, Deep Learning detection, Machine Learning classification, sound sensors, video sensors, radiofrequency sensors

Abstract

Unmanned Aerial Vehicles (UAVs), also known as drones, have had an exponential evolution in recent times due in large part to the development of technologies that enhance the development of these devices. This has resulted in increasingly affordable and better-equipped artifacts, which implies their application in new fields such as agriculture, transport, monitoring, and aerial photography. However, drones have also been used in terrorist acts, privacy violations, and espionage, in addition to involuntary accidents in high-risk zones such as airports. In response to these events, multiple technologies have been introduced to control and monitor the airspace in order to ensure protection in risk areas. This paper is a review of the state of the art of the techniques, methods, and algorithms used in video, radiofrequency, and audio-based applications to detect UAVs and Unmanned Aircraft Systems (UAS). This study can serve as a starting point to develop future drone detection systems with the most convenient technologies that meet certain requirements of optimal scalability, portability, reliability, and availability.

Author Biographies

Jimmy Flórez, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Colombia

PhD. in Engineer, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro- Colombia, jimmy.florez@fac.mil.co

José Ortega, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Colombia

MSc. in Information and Communication Technologies, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro - Colombia, jose.ortega@fac.mil.co

Andrés Betancourt, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa, Fuerza Aérea Colombiana, Colombia

Project management specialist, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa, Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro- Colombia, abetancur.cetad@epfac.co

Andrés García, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa

MSc. in Information and Communication Technologies, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa, Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro - Colombia, andres.garciares@upb.edu.co

Marlon Bedoya, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa Fuerza Aérea Colombiana, Colombia

Electronic Engineer, Centro de Desarrollo Tecnológico Aeroespacial para la Defensa, Fuerza Aérea Colombiana, Comando Aéreo de Combate Número 5, Rionegro - Colombia, marlon.bedoya@epfac.edu.co

Juan S. Botero*, Instituto Tecnológico Metropolitano, Colombia

PhD. in Electronic Engineer, Grupo Automática, Electrónica y Ciencias Computacionales, Instituto Tecnológico Metropolitano, Medellín- Colombia, juanbotero@itm.edu.co

References

C. Guillot, “Drone’s-eye view,” in Image Testimonies Witnessing in Times of Social Mediavol., M. Richardson, Ed. Routledge, 2018, pp. 72–86. Available: https://www.taylorfrancis.com/books/e/9780429434853/chapters/10.4324/9780429434853-6

B. Hearing and J. Franklin, “Drone detection and classification methods and apparatus,” US 9.275,645 B2, Mar. 2016. Available: https://patentimages.storage.googleapis.com/fa/28/8e/24456959a181c1/US9275645.pdf

Ministry of Transport, Ministry of Business, Innovation and Employment, “Drones : Benefits study High level findings,” report no. MOT009.18 2019. Available: https://www.coursehero.com/file/52540225/04062019-Drone-Benefit-Studypdf/

L. E. Davis, M. J. McNerney, J. Chow, T. Hamilton, S. Harting, and D. Byman, Armed and Dangerous?: UAVs and U.S. Security, RAND Corporation, pp. 37. 2014. Available: https://www.jstor.org/stable/10.7249/j.ctt6wq880

M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, “Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques,” in 2019 IEEE Aerospace Conference, Big Sky, MT, 2019. Available: https://ieeexplore.ieee.org/abstract/document/8741970

B. Custers, The Future of Drone Use, vol. 27. The Hague: T.M.C. Asser Press, 2016. https://doi.org/10.1007/978-94-6265-132-6

P. Schober, C. Boer, and L. A. Schwarte, “Correlation Coefficients: Appropriate Use and Interpretation,” Anesth. Analg., vol. 126, no. 5, pp. 1763–1768, May 2018. https://doi.org/10.1213/ANE.0000000000002864

K. Hartman, J. Krois and B. Waske, “E-Learning Project SOGA: Statistics and Geospatial Data Analysis. Department of Earth Sciences,” Correlation, 2018. Available:https://www.geo.fu-berlin.de/en/v/soga/Basics-of-statistics/Descriptive-Statistics/Measures-of-Relation-Between-Variables/Correlation/index.html

J. Mezei and A. Molnar, “Drone sound detection by correlation,” in 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, 2016, pp. 509–518. https://doi.org/10.1109/SACI.2016.7507430

E. Obilor , and E. Amadi “Test for Significance of Pearson’s correlation coefficient(r),” 2018. Available: https://www.researchgate.net/publication/323522779_Test_for_Significance_of_Pearson's_Correlation_Coefficient

R. Artusi, P. Verderio, and E. Marubini, “Bravais-Pearson and Spearman correlation coefficients: Meaning, test of hypothesis and confidence interval,” Int. J. Biol. Markers, vol. 17, no. 2, pp. 148–151, Apr. 2002. https://doi.org/10.1177/172460080201700213

J. C. F. de Winter, S. D. Gosling, and J. Potter, “Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data.,” Psychol. Methods, vol. 21, no. 3, pp. 273–290, May. 2016. http://dx.doi.org/10.1037/met0000079

J. H. Zar, “Spearman Rank Correlation,” in Encyclopedia of Biostatistics, Chichester, UK: John Wiley & Sons, Ltd, 2005, pp. 47–57. https://doi.org/10.1002/0470011815.b2a15150

E. Szmidt and J. Kacprzyk, “The Spearman and Kendall rank correlation coefficients between intuitionistic fuzzy sets,” in Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011), Aug. 2011, vol. 1, no. 1, pp. 521–528. https://doi.org/10.2991/eusflat.2011.85

H. Abdi, “Kendall Rank Correlation Coefficient,” in The Concise Encyclopedia of Statistics, New York, NY: Springer New York, 2008, pp. 278–281. https://doi.org/10.1007/978-0-387-32833-1_211

C. Yau, “R Tutorial and introduction to statistics,” Kendall Rank Coefficient, 2019. [Online]. Available: http://www.r-tutor.com/gpu-computing/correlation/kendall-rank-coefficient

L. Hauzenberger and E. H. Ohlsson, “Drone Detection using Audio Analysis,” (Master Thesis), LUND university libraries, 2015.. [Online]. Available: https://lup.lub.lu.se/student-papers/search/publication/7362609

D. O’Shaughnessy, “Linear predictive coding,” IEEE Potentials, vol. 7, no. 1, pp. 29–32, Feb. 1988. https://doi.org/10.1109/45.1890

A. R. Madane, Z. Shah, R. Shah, and S. Thakur, “Speech Compression Using Linear Predictive Coding,” in proceedings International workshop on Machine Intelligence Research MIR labs., Mumbai. 2009, pp. 119–122. Available: https://www.semanticscholar.org/paper/Speech-Compression-Using-Linear-Predictive-Coding-Madane-Shah/985a532c0dcef1bf4526354faac41e6814b100bc

M. W. Spratling, “A review of predictive coding algorithms,” Brain Cogn., vol. 112, pp. 92–97, Mar. 2017. https://doi.org/10.1016/j.bandc.2015.11.003

J. Kim, C. Park, J. Ahn, Y. Ko, J. Park, and J. C. Gallagher, “Real-time UAV sound detection and analysis system,” in 2017 IEEE Sensors Applications Symposium (SAS), Glassboro, 2017. https://doi.org/10.1109/SAS.2017.7894058

J. Diz, G. Marreiros, and A. Freitas, “Applying Data Mining Techniques to Improve Breast Cancer Diagnosis,” J. Med. Syst., vol. 40, no. 203, Aug. 2016. https://doi.org/10.1007/s10916-016-0561-y

W. Cherif, “Optimization of K-NN algorithm by clustering and reliability coefficients: Application to breast-cancer diagnosis,” Procedia Comput. Sci., vol. 127, pp. 293–299, 2018. https://doi.org/10.1016/j.procs.2018.01.125

Z. Zhang, “Introduction to machine learning: K-nearest neighbors,” Ann. Transl. Med., vol. 4, no. 11, Jun. 2016. https://doi.org/10.21037/atm.2016.03.37

R. Sonnleitner, “Audio Identication via Fingerprinting Achieving Robustness to Severe Signal Modications,” (Doctoral Thesis), Departament of computational percepcion, Johanes Kleper University Linz. pp. 196, 2017. Available: https://es.scribd.com/document/384118954/Sonnleitner-Audio-Identification-via-FIngerprinting-achieving-robustness-to-severe-signal-modifications

A. Bernardini, F. Mangiatordi, E. Pallotti, and L. Capodiferro, “Drone detection by acoustic signature identification,” Electron. Imaging, no. 5, pp. 60–64, Jan. 2017. https://doi.org/10.2352/ISSN.2470-1173.2017.10.IMAWM-168

V. Jakkula, “Tutorial on Support Vector Machine (SVM),” Sch. EECS, Washingt. State Univ., pp. 1–13, 2011. Available: https://www.semanticscholar.org/paper/Tutorial-on-Support-Vector-Machine-(-SVM-)-Jakkula/7cc83e98367721bfb908a8f703ef5379042c4bd9

Z. Rafii, B. Coover and J. Han, An audio fingerprinting system for live version identification using image processing techniques," 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, 2014, pp. 644-64. https://doi.org/10.1109/ICASSP.2014.6853675

T. Edwina Alias, N. Naveen, and D. Mathew, “A novel acoustic fingerprint method for audio signal pattern detection,” Proc. - 2014 4th Int. Conf. Adv. Comput. Commun. ICACC 2014, no. 1, Cochin, 2014, pp. 64–68. https://doi.org/10.1109/ICACC.2014.21

E. Páli, K. Máthé, L. Tamás, and L. Buşoniu, “Railway track following with the AR.Drone using vanishing point detection,” Proc. 2014 IEEE Int. Conf. Autom. Qual. Testing, Robot. AQTR 2014, Cluj-Napoca, 2014, pp. 1-6. https://doi.org/10.1109/AQTR.2014.6857870

D. Zeng, X. Chen, M. Zhu, M. Goesele, and A. Kuijper, “Background Subtraction with Real-time Semantic Segmentation,” IEEE Access, vol. 10, pp. 1–1, Feb. 2019. https://doi.org/10.1109/ACCESS.2019.2899348

A. Sobral, “BGSLibrary: An OpenCV C++ Background Subtraction Library,” IX Work. Visao Comput., 2013, pp. 1–3, 2013. Available: https://www.semanticscholar.org/paper/Tutorial-on-Support-Vector-Machine-(-SVM-)-Jakkula/7cc83e98367721bfb908a8f703ef5379042c4bd9

S. Noh and M. Jeon, “A new framework for background subtraction using multiple cues,” in (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg 2013. https://doi.org/10.1007/978-3-642-37431-9_38

F. Christnacher et al., “Optical and acoustical UAV detection,” Electro-Optical Remote Sens. X, vol. 9988, Edinburgh 2016. https://doi.org/10.1117/12.2240752

S. R. Ganti and Y. Kim, “Implementation of Detection and Tracking Mechanism For Small UAS,” in 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, 2016, pp. 1254–1260. https://doi.org/10.1109/ICUAS.2016.7502513

A. Schumann, L. Sommer, J. Klatte, T. Schuchert, and J. Beyerer, “Deep cross-domain flying object classification for robust UAV detection,” in 2017 14th IEEE Int. Conf. Adv. Video Signal Based Surveillance, AVSS 2017, Lecce, 2017. https://doi.org/10.1109/avss.2017.8078558

E. Unlu, E. Zenou, N. Riviere, and P.-E. Dupouy, “Deep learning-based strategies for the detection and tracking of drones using several cameras,” IPSJ Trans. Comput. Vis. Appl., vol. 11, no. 7, Jul. 2019. https://doi.org/10.1186/s41074-019-0059-x

X. Wang, P. Cheng, X. Liu, and B. Uzochukwu, “Fast and accurate, convolutional neural network based approach for object detection from UAV,” in Proc. IECON 2018 - 44th Annu. Conf. IEEE Ind. Electron. Soc., Washington, 2018, pp. 3171–3175. https://doi.org/10.1109/IECON.2018.8592805

M. A. Akhloufi, S. Arola, and A. Bonnet, “Drones Chasing Drones: Reinforcement Learning and Deep Search Area Proposal,” Drones, vol. 3, no. 3, pp. 58, Jul. 2019. https://doi.org/10.3390/drones3030058

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol 39, no. 16, pp. 1137–1149, Jun. 2017. https://doi.org/10.1109/TPAMI.2016.2577031

Liu. W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C. and Berg A., “SSD: Single Shot Multibox Detector”, European Conference on Computer Vision (ECCV), pp. 21–37, 2016. https://doi.org/10.1007/978-3-319-46448-0_2

R. Girshick, “Fast R-CNN,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2015 Inter, Santiago, 2015, pp. 1440–1448. Available: http://openaccess.thecvf.com/content_iccv_2015/html/Girshick_Fast_R-CNN_ICCV_2015_paper.html

A. Flodell and C. Christensson, “Wildlife Surveillance Using a UAV and Thermal Imagery,” (Master Thesis), Department of Electrical Engineering, Linköping University pp. 132, 2016. Available: http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A941275&dswid=6107

R. Stolkin, D. Rees, M. Talha, and I. Florescu, “Bayesian fusion of thermal and visible spectra camera data for region based tracking with rapid background adaptation,” in IEEE Int. Conf. Multisens. Fusion Integr. Intell. Syst., Hamburg, 2012, pp. 192–199. https://doi.org/10.1109/MFI.2012.6343021

P. Andraši, T. Radišić, M. Muštra, and J. Ivošević, “Night-time Detection of UAVs using Thermal Infrared Camera,” Transp. Res. Procedia, vol. 28, pp. 183–190, 2017. https://doi.org/10.1016/j.trpro.2017.12.184

Bavak Beveiligingsgroep BV.” “RF-Sensors. 2019. Available: https://www.bavak.com/integrated-security-solutions/drone-detection-systems/rf-sensors/

Robin radas systems “9 Counter-Drone Technologies To Detect And Stop Drones Today.” 2019. [Online]. Available: https://www.robinradar.com/press/blog/9-counter-drone-technologies-to-detect-and-stop-drones-today

W. D. Scheller, “Detecting Drones Using Machine Learning,” (Master Thesis) Electrical and Computer Engineering, Iowa State University, 2017. Available: https://lib.dr.iastate.edu/etd/16210/

T. F. Wong, “Chapter 2 Introduction to Spread Spectrum Communications,” Univ. Florida, vol. 1, no. Spread Spectrum & CDMA, pp. 1–25, 2014. Available: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=51467B980E5A5CFA33E44548DDEDA9A6?doi=10.1.1.117.8806&rep=rep1&type=pdf

Asian Military Review “RF Techniques for Detection, Classification and Location of Commercial Drone Controllers - Asian Military Review.” [Online]. Available: https://asianmilitaryreview.com/2019/05/rf-techniques-for-detection-classification-and-location-of-commercial-drone-controllers/

Y. K. Kwag, J. S. Jung, I. S. Woo, and M. S. Park, “Multi-band multi-mode SDR radar platform,” Proc. 2015 IEEE 5th Asia-Pacific Conf. Synth. Aperture Radar, APSAR 2015, Singapore, 2015, pp. 46–49. https://doi.org/10.1109/APSAR.2015.7306151

H. Fu, S. Abeywickrama, L. Zhang, and C. Yuen, “Low-Complexity Portable Passive Drone Surveillance via SDR-Based Signal Processing,” IEEE Commun. Mag., vol. 56, no. 4, pp. 112–118, Apr. 2018. https://doi.org/10.1109/MCOM.2018.1700424

P. Nguyen, H. Truong, M. Ravindranathan, A. Nguyen, R. Han, and T. Vu, “Drone Presence Detection by Identifying Physical Signatures in the Drone’s RF Communication,” Proc. 15th Annu. Int. Conf. Mob. Syst. Appl. Serv. - MobiSys ’17, New York, 2017. pp. 211–224. https://doi.org/10.1145/3081333.3081354

W. Feng, G. Cherniak, J.-M. Friedt, and M. Sato, “Software defined radio implementation of passive RADAR using low-cost DVB-T receivers.” Available: https://pdfs.semanticscholar.org/0fd0/91cdc131a29aa0a3bdb50039e83d3de8addd.pdf

S. Sciancalepore, O. A. Ibrahim, G. Oligeri, and R. Di Pietro, “Picking a Needle in a Haystack: Detecting Drones via Network Traffic Analysis,” Jan. 2019. Available: https://www.researchgate.net/profile/Savio_Sciancalepore/publication/330357671_Picking_a_Needle_in_a_Haystack_Detecting_Drones_via_Network_Traffic_Analysis/links/5c3f15c7299bf12be3cc1f89/Picking-a-Needle-in-a-Haystack-Detecting-Drones-via-Network-Traffic-Analysis.pdf

N. P. Bhatta and M. Geethapriya, “RADAR and its Applications,” Jan. 2017. Available: https://www.researchgate.net/publication/316696944_RADAR_and_its_applications

I. Güvenç, O. Ozdemir, Y. Yapici, H. Mehrpouyan, and D. Matolak, “Detection, localization, and tracking of unauthorized UAS and Jammers,” in 2017 AIAA/IEEE Digit. Avion. Syst. Conf. - Proc. (DASC), St, Petersburg 2017. https://doi.org/10.1109/DASC.2017.8102043

V. Demirev, “Drone detection in urban environment – The new challenge for the Radar Systems Designers,” Security & Future, vol. 116, no. 3, pp. 114–116, 2017. Available: https://stumejournals.com/journals/confsec/2017/3/114

F. Fioranelli, “Radar detection and classification of small UAVs and micro-drones.” The University of Glasgow, charity number SC004401. Available: https://www.gla.ac.uk/media/Media_480052_smxx.pdf

C. Iovescu and S. Rao, “The Fundamentals of Millimeter Wave Sensors,” Texas Instruments, pp. 1–8, 2017. Available: https://www.mouser.ee/pdfdocs/mmwavewhitepaper.pdf

National Instruments Corporation “¿Qué es el Sistema de Transceptor de Onda Milimétrica?”, 2020. Available: https://www.ni.com/es-co/shop/wireless-design-test/what-is-mmwave-transceiver-system.html

J. A. Nanzer and V. C. Chen, “Microwave interferometric and Doppler radar measurements of a UAV,” in 2017 IEEE Radar Conf. RadarConf 2017, Seattle, 2017, pp. 1628–1633. https://doi.org/10.1109/RADAR.2017.7944468

“Sistema de Transceptor de Onda Milimétrica - National Instruments.” [Online]. Available: http://sine.ni.com/np/app/main/p/docid/nav-116/lang/es/fmid/12027/

Ancortek Inc “SDR-KIT 2400AD2” 2019. [Online]. Available: https://ancortek.com/sdr-kit-2400ad2

Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR “Detection of small drones with millimeter wave radar” [Online]. Available: https://www.fhr.fraunhofer.de/en/businessunits/security/Detection-of-small-drones-with-millimeter-wave-radar.html

G. Rankin, A. Tirkel, and A. Leukhin, “Millimeter wave array for UAV imaging MIMO radar,” in 2015 16th International Radar Symposium (IRS), Dresden, 2015. pp. 499–504. https://doi.org/10.1109/IRS.2015.7226217

T. Debatty, “Software defined RADAR a state of the art,” 2010 2nd Int. Work. Cogn. Inf. Process. CIP2010, Elba, 2010, pp. 253–257. https://doi.org/10.1109/CIP.2010.5604241

Y.-K. Kwag, I.-S. Woo, H.-Y. Kwak, and Y.-H. Jung, “Multi-Mode SDR Radar Platform for Smann Air-Vehicle Drone Detection,” in CIE International Conference on Radar, Guangzhou, 2017, pp. 4–7. https://doi.org/10.1109/RADAR.2016.8059254

M. Jahangir, C. J. Baker, and G. A. Oswald, “Doppler characteristics of micro-drones with L-Band multibeam staring radar,” 2017 IEEE Radar Conf. RadarConf 2017, Seattle 2017, pp. 1052–1057. https://doi.org/10.1109/RADAR.2017.7944360

A. Laučys et al., “Investigation of detection possibility of uavs using low cost marine radar,” Aviation, vol. 23, no. 2, pp. 48–53, May. 2019. https://doi.org/10.3846/aviation.2019.10320

CERBAIR “Drone Detection & Neutralization- Part I | CERBAIR.” [Online].

Available:https://www.cerbair.com/drone-detection-and-neutralization-technologies-parti-blog/

Squarehead Technology “Acoustic Drone Detection System Discovair – Rapid Deployable CUAS solution –.” [Online]. Available: https://www.sqhead.com/drone-detection/

C. Huang, P. Chen, X. Yang, and K. T. T. Cheng, “REDBEE: A visual-inertial drone system for real-time moving object detection,” in IEEE Int. Conf. Intell. Robot. Syst., vol. 2017–Septe, , Vancouver, 2017, pp. 1725–1731. https://doi.org/10.1109/IROS.2017.8205985

Help Net Security “Drone detection: What works and what doesn’t - Help Net Security.” 2015. [Online]. Available: https://www.helpnetsecurity.com/2015/05/28/drone-detection-what-works-and-what-doesnt/

GCN “Drones: Findable but not stoppable.” [Online]. Available: https://gcn.com/articles/2015/06/03/drone-detection.aspx?m=1

J. S. Patel, F. Fioranelli, and D. Anderson, “Review of radar classification and RCS characterisation techniques for small UAVs or drones,” IET Radar, Sonar Navig., vol. 12, no. 9, pp. 911–919, 2018. https://doi.org10.1049/iet-rsn.2018.0020

L. Ziemba “Observe and Report: Considerations for Evaluating Drone Detection Systems.” Security Industry Association SIA, 2019. [Online]. Available: https://www.securityindustry.org/2019/09/06/observe-and-report-considerations-for-evaluating-drone-detection-systems/

I. Bisio, C. Garibotto, F. Lavagetto, A. Sciarrone, and S. Zappatore, “Unauthorized Amateur UAV Detection Based on WiFi Statistical Fingerprint Analysis,” IEEE Commun. Mag., vol. 56, no. 4, pp. 106–111, Apr. 2018. https://doi.org/10.1109/MCOM.2018.1700340

How to Cite
Flórez, J., Ortega, J., Betancourt, A., García, A., Bedoya, M., & Botero , J. S. (2020). A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications. TecnoLógicas, 23(48), 269-285. https://doi.org/10.22430/22565337.1408

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Published
2020-05-15
Section
Review Article