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With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
M. Bādoiu, S. Har-Peled, and P. Indyk, “Approximate clustering via core-sets,” in Proceedings of the thiry-fourth annual ACM symposium on Theory of computing , 2002, pp. 250–257
2002
Earlier work this paper cites.
G. Venayagamoorthy et al. , “Unmanned vehicle navigation using swarm intelligence,” in International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of . IEEE, 2004, pp. 249–253
2004
Earlier work this paper cites.
N. Provos et al. , “A virtual honeypot framework.” in USENIX Security Symposium , vol. 173, no. 2004, 2004, pp. 1–14
2004
Earlier work this paper cites.
J. Kennedy, “Swarm intelligence,” in Handbook of nature-inspired and innovative computing: integrating classical models with emerging technologies . Springer, 2006, pp. 187–219
2006
Earlier work this paper cites.
A. Ahmadzadeh, A. Jadbabaie, V. Kumar, and G. J. Pappas, “Multi-UAV cooperative surveillance with spatio-temporal specifications,” in Proceedings of the 45th IEEE Conference on Decision and Control , 2006, pp. 5293–5298
2006
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, pp. 1345–1359, 2009
2009
Earlier work this paper cites.
R.-j. Yan, S. Pang, H.-b. Sun, and Y.-j. Pang, “Development and missions of unmanned surface vehicle,” Journal of Marine Science and Application , vol. 9, pp. 451–457, 2010
2010
Earlier work this paper cites.
N. Nigam, S. Bieniawski, I. Kroo, and J. Vian, “Control of multiple UAVs for persistent surveillance: Algorithm and flight test results,” IEEE Transactions on Control Systems Technology , vol. 20, no. 5, pp. 1236–1251, 2012
2012
Earlier work this paper cites.
L. J. Ratliff, S. A. Burden, and S. S. Sastry, “Characterization and computation of local nash equilibria in continuous games,” in 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2013, pp. 917–924
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
M. Abdelkader, M. Shaqura, M. Ghommem, N. Collier, V. Calo, and C. Claudel, “Optimal multi-agent path planning for fast inverse modeling in UAV-based flood sensing applications,” in 2014 international conference on unmanned aircraft systems (ICUAS) . IEEE, 2014, pp. 64–71
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
C. A. Thiels, J. M. Aho, S. P. Zietlow, and D. H. Jenkins, “Use of unmanned aerial vehicles for medical product transport,” Air medical journal , vol. 34, no. 2, pp. 104–108, 2015
2015
Earlier work this paper cites.
M. Volger, “Human detection and recognition in visual data from a swarm of unmanned aerial and ground vehicles through dynamic navigation,” Ph.D. dissertation, Faculty of Science and Engineering, 2015
2015
Earlier work this paper cites.
N. H. Motlagh, T. Taleb, and O. Arouk, “Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives,” IEEE Internet of Things Journal , vol. 3, no. 6, pp. 899–922, 2016
2016
Earlier work this paper cites.
Z. Liu, Y. Zhang, X. Yu, and C. Yuan, “Unmanned surface vehicles: An overview of developments and challenges,” Annual Reviews in Control , vol. 41, pp. 71–93, 2016
2016
Earlier work this paper cites.
Z. Liu, Y. Zhang, X. Yu, and C. Yuan, “Unmanned surface vehicles: An overview of developments and challenges,” Annual Reviews in Control , vol. 41, pp. 71–93, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1367578816300219
2016
Earlier work this paper cites.
H. Qin, J. Q. Cui, J. Li, Y. Bi, M. Lan, M. Shan, W. Liu, K. Wang, F. Lin, Y. Zhang et al. , “Design and implementation of an unmanned aerial vehicle for autonomous firefighting missions,” in 2016 12th IEEE International Conference on Control and Automation (ICCA) . IEEE, 2016, pp. 62–67
2016
Earlier work this paper cites.
A. M. Hayajneh, S. A. R. Zaidi, D. C. McLernon, and M. Ghogho, “Drone empowered small cellular disaster recovery networks for resilient smart cities,” in 2016 IEEE international conference on sensing, communication and networking (SECON Workshops) . IEEE, 2016, pp. 1–6
2016
Earlier work this paper cites.
J. Fleureau, Q. Galvane, F.-L. Tariolle, and P. Guillotel, “Generic drone control platform for autonomous capture of cinema scenes,” in Proceedings of the 2nd workshop on micro aerial vehicle networks, systems, and applications for civilian use , 2016, pp. 35–40
2016
Earlier work this paper cites.
A. Claesson, D. Fredman, L. Svensson, M. Ringh, J. Hollenberg, P. Nordberg, M. Rosenqvist, T. Djarv, S. Österberg, J. Lennartsson et al. , “Unmanned aerial vehicles (drones) in out-of-hospital-cardiac-arrest,” Scandinavian journal of trauma, resuscitation and emergency medicine , vol. 24, pp. 1–9, 2016
2016
Earlier work this paper cites.
A. Pulver, R. Wei, and C. Mann, “Locating aed enabled medical drones to enhance cardiac arrest response times,” Prehospital Emergency Care , vol. 20, no. 3, pp. 378–389, 2016
2016
Earlier work this paper cites.
A. Merwaday, A. Tuncer, A. Kumbhar, and I. Guvenc, “Improved throughput coverage in natural disasters: Unmanned aerial base stations for public-safety communications,” IEEE Vehicular Technology Magazine , vol. 11, no. 4, pp. 53–60, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 961–971
2016
Earlier work this paper cites.
J. Lyu, Y. Zeng, R. Zhang, and T. J. Lim, “Placement optimization of UAV-mounted mobile base stations,” IEEE Communications Letters , vol. 21, no. 3, pp. 604–607, 2016
2016
Earlier work this paper cites.
M. Carpentiero, L. Gugliermetti, M. Sabatini, and G. B. Palmerini, “A swarm of wheeled and aerial robots for environmental monitoring,” in 2017 IEEE 14th international conference on networking, sensing and control (ICNSC) . IEEE, 2017, pp. 90–95
2017
Earlier work this paper cites.
D. Kim and P. Y. Oh, “Skywriting unmanned aerial vehicle proof-of-concept design,” in 2017 International Conference on Unmanned Aircraft Systems (ICUAS) . IEEE, 2017, pp. 1398–1403
2017
Earlier work this paper cites.
T. K. Amukele, J. Hernandez, C. L. Snozek, R. G. Wyatt, M. Douglas, R. Amini, and J. Street, “Drone transport of chemistry and hematology samples over long distances,” American journal of clinical pathology , vol. 148, no. 5, pp. 427–435, 2017
2017
Earlier work this paper cites.
A. Claesson, L. Svensson, P. Nordberg, M. Ringh, M. Rosenqvist, T. Djarv, J. Samuelsson, O. Hernborg, P. Dahlbom, A. Jansson et al. , “Drones may be used to save lives in out of hospital cardiac arrest due to drowning,” Resuscitation , vol. 114, pp. 152–156, 2017
2017
Earlier work this paper cites.
P. H. Heins, B. L. Jones, and D. J. Taunton, “Design and validation of an unmanned surface vehicle simulation model,” Applied Mathematical Modelling , vol. 48, pp. 749–774, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0307904X17301245
2017
Earlier work this paper cites.
Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh, “Making the v in vqa matter: Elevating the role of image understanding in visual question answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 6904–6913
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
X. Yang, “Understanding the variational lower bound,” variational lower bound, ELBO, hard attention , vol. 22, pp. 1–4, 2017
2017
Earlier work this paper cites.
A. Srivastava, L. Valkov, C. Russell, M. U. Gutmann, and C. Sutton, “Veegan: Reducing mode collapse in gans using implicit variational learning,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
T. D. Barfoot, State estimation for robotics . Cambridge University Press, 2017
2017
Earlier work this paper cites.
Q. Gao, F. Zhou, K. Zhang, G. Trajcevski, X. Luo, and F. Zhang, “Identifying human mobility via trajectory embeddings.” in Proceedings of International Joint Conference on Artificial Intelligence , vol. 17, 2017, pp. 1689–1695
2017
Earlier work this paper cites.
M. A. Mehmood, M. N. A. Khan, and W. Afzal, “Transforming context-aware application development model into a testing model,” in Proceedding of 2017 8th IEEE International Conference on Software Engineering and Service Science , 2017, pp. 177–182
2017
Earlier work this paper cites.
B. Li, Z. Fei, and Y. Zhang, “UAV communications for 5G and beyond: Recent advances and future trends,” IEEE Internet of Things Journal , vol. 6, no. 2, pp. 2241–2263, 2018
2018
Earlier work this paper cites.
T. Nägeli, “Intelligent drone cinematography,” Ph.D. dissertation, ETH Zurich, 2018
2018
Earlier work this paper cites.
K. Z. Ang, X. Dong, W. Liu, G. Qin, S. Lai, K. Wang, D. Wei, S. Zhang, S. K. Phang, X. Chen et al. , “High-precision multi-UAV teaming for the first outdoor night show in singapore,” Unmanned Systems , vol. 6, no. 01, pp. 39–65, 2018
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. He, C. Luo, X. Tian, and W. Zeng, “A twofold siamese network for real-time object tracking,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4834–4843
2018
Earlier work this paper cites.
R. Krajewski, T. Moers, D. Nerger, and L. Eckstein, “Data-driven maneuver modeling using generative adversarial networks and variational autoencoders for safety validation of highly automated vehicles,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , 2018, pp. 2383–2390
2018
Earlier work this paper cites.
M. Zhang, Y. Zhang, L. Zhang, C. Liu, and S. Khurshid, “Deeproad: GAN-based metamorphic testing and input validation framework for autonomous driving systems,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering , 2018, pp. 132–142
2018
Earlier work this paper cites.
J. Song, H. Ren, D. Sadigh, and S. Ermon, “Multi-agent generative adversarial imitation learning,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
G. Kobeaga, M. Merino, and J. A. Lozano, “An efficient evolutionary algorithm for the orienteering problem,” Computers & Operations Research , vol. 90, pp. 42–59, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0305054817302241
2018
Earlier work this paper cites.
X. Liu, H. Chen, and C. Andris, “trajgans: Using generative adversarial networks for geo-privacy protection of trajectory data (vision paper),” in Location privacy and security workshop , 2018, pp. 1–7
2018
Earlier work this paper cites.
H. Shakhatreh, A. H. Sawalmeh, A. Al-Fuqaha, Z. Dou, E. Almaita, I. Khalil, N. S. Othman, A. Khreishah, and M. Guizani, “Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges,” IEEE Access , vol. 7, pp. 48 572–48 634, 2019
2019
Earlier work this paper cites.
K. Kuru, D. Ansell, W. Khan, and H. Yetgin, “Analysis and optimization of unmanned aerial vehicle swarms in logistics: An intelligent delivery platform,” IEEE Access , vol. 7, pp. 15 804–15 831, 2019
2019
Earlier work this paper cites.
A. Tahir, J. Böling, M.-H. Haghbayan, H. T. Toivonen, and J. Plosila, “Swarms of unmanned aerial vehicles — A survey,” Journal of Industrial Information Integration , vol. 16, p. 100106, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2452414X18300086
2019
Earlier work this paper cites.
J. Li, D. Lu, G. Zhang, J. Tian, and Y. Pang, “Post-disaster unmanned aerial vehicle base station deployment method based on artificial bee colony algorithm,” IEEE Access , vol. 7, pp. 168 327–168 336, 2019
2019
Earlier work this paper cites.
F. Malandrino, C. Rottondi, C.-F. Chiasserini, A. Bianco, and I. Stavrakakis, “Multiservice UAVs for emergency tasks in post-disaster scenarios,” in Proceedings of the ACM MobiHoc workshop on innovative aerial communication solutions for FIrst REsponders network in emergency scenarios , 2019, pp. 18–23
2019
Earlier work this paper cites.
S. G. Fernandez, K. Vijayakumar, R. Palanisamy, K. Selvakumar, D. Karthikeyan, D. Selvabharathi, S. Vidyasagar, and V. Kalyanasundhram, “Unmanned and autonomous ground vehicle,” International Journal of Electrical and Computer Engineering , vol. 9, no. 5, p. 4466, 2019
2019
Earlier work this paper cites.
V. A. M. Jorge, R. Granada, R. G. Maidana, D. A. Jurak, G. Heck, A. P. F. Negreiros, D. H. dos Santos, L. M. G. Gonçalves, and A. M. Amory, “A survey on unmanned surface vehicles for disaster robotics: Main challenges and directions,” Sensors , vol. 19, no. 3, 2019. [Online]. Available: https://www.mdpi.com/1424-8220/19/3/702
2019
Earlier work this paper cites.
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” in International conference on machine learning . PMLR, 2019, pp. 7354–7363
2019
Earlier work this paper cites.
D. P. Kingma, M. Welling et al. , “An introduction to variational autoencoders,” Foundations and Trends® in Machine Learning , vol. 12, no. 4, pp. 307–392, 2019
2019
Earlier work this paper cites.
M. Westerlund, “The emergence of deepfake technology: A review,” Technology innovation management review , vol. 9, no. 11, 2019
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Cited alongside, same era.
J. Ho, X. Chen, A. Srinivas, Y. Duan, and P. Abbeel, “Flow++: Improving flow-based generative models with variational dequantization and architecture design,” in International Conference on Machine Learning . PMLR, 2019, pp. 2722–2730
2019
Cited alongside, same era.
A. Abdelhamed, M. A. Brubaker, and M. S. Brown, “Noise flow: Noise modeling with conditional normalizing flows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 3165–3173
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah, “Transformers in vision: A survey,” ACM computing surveys (CSUR) , vol. 54, no. 10s, pp. 1–41, 2022
2022
Later among the works it cites.
G. Marco, J. Gonzalo, and L. Rello, “A systematic evaluation of the creative writing skills of transformer deep neural networks,” Available at SSRN 4042578 , 2022
2022
Later among the works it cites.
Y. Shi, L. Han, L. Han, S. Chang, T. Hu, and D. Dancey, “A latent encoder coupled generative adversarial network (LE-GAN) for efficient hyperspectral image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–19, 2022
2022
Later among the works it cites.
X. Li, H. Duan, Y. Tian, and F.-Y. Wang, “Exploring image generation for UAV change detection,” IEEE/CAA Journal of Automatica Sinica , vol. 9, no. 6, pp. 1061–1072, 2022
2022
Later among the works it cites.
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alphaXiv is searching for related work…
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Y. He, S. Chai, and Z. Xu, “A novel approach for state estimation using generative adversarial network,” in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) , 2019, pp. 2248–2253
2019
Cited alongside, same era.
X. Dai, R. Fu, E. Zhao, Z. Zhang, Y. Lin, F.-Y. Wang, and L. Li, “Deeptrend 2.0: A light-weighted multi-scale traffic prediction model using detrending,” Transportation Research Part C: Emerging Technologies , vol. 103, pp. 142–157, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0968090X1830648X
2019
Cited alongside, same era.
R. Krajewski, T. Moers, A. Meister, and L. Eckstein, “Béziervae: Improved trajectory modeling using variational autoencoders for the safety validation of highly automated vehicles,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) , 2019, pp. 3788–3795
2019
Cited alongside, same era.
R. Uittenbogaard, C. Sebastian, J. Vijverberg, B. Boom, D. M. Gavrila et al. , “Privacy protection in street-view panoramas using depth and multi-view imagery,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 10 581–10 590
2019
Cited alongside, same era.
S. Gao, J. Rao, X. Liu, Y. Kang, Q. Huang, and J. App, “Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of twitter users,” Journal of Spatial Information Science , no. 19, pp. 105–129, 2019
2019
Cited alongside, same era.
Y. Tan, J. Wang, J. Liu, and Y. Zhang, “Unmanned systems security: Models, challenges, and future directions,” IEEE Network , vol. 34, no. 4, pp. 291–297, 2020
2020
Cited alongside, same era.
X. Yu, W. Liao, C. Qu, Q. Bao, and Z. Xu, “UAV cooperative search based on multi-agent generative adversarial imitation learning,” in 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) , 2022, pp. 441–446
2022
Later among the works it cites.
M. Ružička, M. Vološin, J. Gazda, T. Maksymyuk, L. Han, and M. Dohler, “Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization,” International Journal of Distributed Sensor Networks , vol. 18, no. 3, p. 15501477221075544, 2022. [Online]. Available: https://doi.org/10.1177/15501477221075544
2022
Later among the works it cites.
O. Adeboye, T. Dargahi, M. Babaie, M. Saraee, and C.-M. Yu, “Deepclean: a robust deep learning technique for autonomous vehicle camera data privacy,” IEEE Access , vol. 10, pp. 124 534–124 544, 2022
2022
Later among the works it cites.
Y. Tian, J. Wang, Y. Wang, C. Zhao, F. Yao, and X. Wang, “Federated vehicular transformers and their federations: Privacy-preserving computing and cooperation for autonomous driving,” IEEE Transactions on Intelligent Vehicles , vol. 7, no. 3, pp. 456–465, 2022
2022
Later among the works it cites.
H. Huang, G. Zhu, Z. Fan, H. Zhai, Y. Cai, Z. Shi, Z. Dong, and Z. Hao, “Vision-based distributed multi-UAV collision avoidance via deep reinforcement learning for navigation,” in Proceedding of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 13 745–13 752
2022
Later among the works it cites.
L. Tong, X. Gan, L. Yu, and H. Zhang, “Evaluation of safety target level of unmanned aerial vehicle system in fusion airspace,” in Proceedding of IEEE International Conference on Artificial Intelligence and Computer Applications . IEEE, 2022, pp. 375–379
2022
Later among the works it cites.
H. Kurunathan, H. Huang, K. Li, W. Ni, and E. Hossain, “Machine learning-aided operations and communications of unmanned aerial vehicles: A contemporary survey,” IEEE Communications Surveys & Tutorials , 2023
2023
Later among the works it cites.
S. Sai, A. Garg, K. Jhawar, V. Chamola, and B. Sikdar, “A comprehensive survey on artificial intelligence for unmanned aerial vehicles,” IEEE Open Journal of Vehicular Technology , 2023
2023
Later among the works it cites.
N. Cheng, S. Wu, X. Wang, Z. Yin, C. Li, W. Chen, and F. Chen, “AI for UAV-assisted iot applications: A comprehensive review,” IEEE Internet of Things Journal , 2023
2023
Later among the works it cites.
J. Li, G. Zhang, C. Jiang, and W. Zhang, “A survey of maritime unmanned search system: theory, applications and future directions,” Ocean Engineering , vol. 285, p. 115359, 2023
2023
Later among the works it cites.
I. Bae and J. Hong, “Survey on the developments of unmanned marine vehicles: Intelligence and cooperation,” Sensors , vol. 23, no. 10, 2023. [Online]. Available: https://www.mdpi.com/1424-8220/23/10/4643
2023
Later among the works it cites.
N. Ogorelysheva, A. Vasileva, and N. Gramse, “On troubleshooting in agv-based autonomous systems,” in 2023 International Conference on Control, Automation and Diagnosis (ICCAD) . IEEE, 2023, pp. 1–6
2023
Later among the works it cites.
Z. Xin, J. Li, J. Li, and C. Liu, “Collaborative search and package delivery strategy for UAV swarms under area restrictions,” Journal of Advanced Computational Intelligence and Intelligent Informatics , vol. 27, no. 5, pp. 932–941, 2023
2023
Later among the works it cites.
A. Wibisono, M. J. Piran, H.-K. Song, and B. M. Lee, “A survey on unmanned underwater vehicles: Challenges, enabling technologies, and future research directions,” Sensors , vol. 23, no. 17, 2023. [Online]. Available: https://www.mdpi.com/1424-8220/23/17/7321
2023
Later among the works it cites.
M. Byrne, “The disruptive impacts of next generation generative artificial intelligence,” CIN: Computers, Informatics, Nursing , vol. 41, no. 7, pp. 479–481, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Bahroun, C. Anane, V. Ahmed, and A. Zacca, “Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis,” Sustainability , vol. 15, no. 17, p. 12983, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
D. K. Kanbach, L. Heiduk, G. Blueher, M. Schreiter, and A. Lahmann, “The genai is out of the bottle: generative artificial intelligence from a business model innovation perspective,” Review of Managerial Science , pp. 1–32, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M.-H. Yang, “Diffusion models: A comprehensive survey of methods and applications,” ACM Computing Surveys , vol. 56, no. 4, pp. 1–39, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
J. Betker, G. Goh, L. Jing, T. Brooks, J. Wang, L. Li, L. Ouyang, J. Zhuang, J. Lee, Y. Guo et al. , “Improving image generation with better captions,” Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
R. Zhang, K. Xiong, H. Du, D. Niyato, J. Kang, X. Shen, and H. V. Poor, “Generative AI-enabled vehicular networks: Fundamentals, framework, and case study,” 2023
2023
Later among the works it cites.
A. Karapantelakis, P. Alizadeh, A. Alabassi, K. Dey, and A. Nikou, “Generative AI in mobile networks: a survey,” Annals of Telecommunications , pp. 1–19, 2023
2023
Later among the works it cites.
S. Gao, C. Gao, Y. He, J. Zeng, L. Nie, X. Xia, and M. Lyu, “Code structure–guided transformer for source code summarization,” ACM Transactions on Software Engineering and Methodology , vol. 32, no. 1, pp. 1–32, 2023
2023
Later among the works it cites.
T. Singh, J. Prakash, T. Bharti, and A. K. Mandpura, “Time series approach for visual servoing using transformers,” in 2023 12th Mediterranean Conference on Embedded Computing (MECO) . IEEE, 2023, pp. 1–6
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Reyes-González and R. Torre, “Testing the boundaries: Normalizing flows for higher dimensional data sets,” in Journal of Physics: Conference Series , vol. 2438, no. 1. IOP Publishing, 2023, p. 012155
2023
Later among the works it cites.
Y. Ma, M. N. Al Islam, J. Cleland-Huang, and N. V. Chawla, “Detecting anomalies in small unmanned aerial systems via graphical normalizing flows,” IEEE Intelligent Systems , 2023
2023
Later among the works it cites.
Y. Xie, Z. Shao, F. Chen, and H. Lin, “Harmonic state estimation based on network equivalence and closed loop GAN,” in 2023 8th Asia Conference on Power and Electrical Engineering (ACPEE) , 2023, pp. 1688–1693
2023
Later among the works it cites.
N. K. Jha and V. K. N. Lau, “Temporally correlated compressed sensing using generative models for channel estimation in unmanned aerial vehicles,” IEEE Transactions on Wireless Communications , pp. 1–1, 2023
2023
Later among the works it cites.
M. Arvinte and J. I. Tamir, “Mimo channel estimation using score-based generative models,” IEEE Transactions on Wireless Communications , vol. 22, no. 6, pp. 3698–3713, 2023
2023
Later among the works it cites.
H. Delecki, L. A. Kruse, M. R. Schlichting, and M. J. Kochenderfer, “Deep normalizing flows for state estimation,” 2023
2023
Later among the works it cites.
D. Duplevska, V. Medvedevs, D. Surmacs, and A. Aboltins, “The synthetic data application in the UAV recognition systems development,” in 2023 IEEE 10th Jubilee Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) , 2023, pp. 1–6
2023
Later among the works it cites.
D. Xing and A. Tzes, “Synthetic aerial dataset for UAV detection via text-to-image diffusion models,” in 2023 IEEE Conference on Artificial Intelligence (CAI) , 2023, pp. 51–52
2023
Later among the works it cites.
J. de Curtò, I. de Zarzà, and C. T. Calafate, “Semantic scene understanding with large language models on unmanned aerial vehicles,” Drones , vol. 7, no. 2, 2023. [Online]. Available: https://www.mdpi.com/2504-446X/7/2/114
2023
Later among the works it cites.
Z. Zhang and M. Fu, “Research on unmanned system environment perception system methodology,” in International Workshop on Advances in Civil Aviation Systems Development . Springer, 2023, pp. 219–233
2023
Later among the works it cites.
S. Bandela and Y. Cao, “Drone navigation in unreal engine using generative adversarial imitation learning,” in AIAA SCITECH 2023 Forum , 2023, p. 0506
2023
Later among the works it cites.
P. Bentley, S. L. Lim, P. Arcaini, and F. Ishikawa, “Using a variational autoencoder to learn valid search spaces of safely monitored autonomous robots for last-mile delivery,” in Proceedings of the Genetic and Evolutionary Computation Conference , ser. GECCO ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 1303–1311. [Online]. Available: https://doi.org/10.1145/3583131.3590459
2023
Later among the works it cites.
D. Fuertes, C. R. del Blanco, F. Jaureguizar, J. J. Navarro, and N. García, “Solving routing problems for multiple cooperative unmanned aerial vehicles using transformer networks,” Engineering Applications of Artificial Intelligence , vol. 122, p. 106085, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0952197623002695
2023
Later among the works it cites.
T. Rathod and S. Tanwar, “Autoencoder-based efficient resource allocation in device-to-device communication,” Physical Communication , vol. 60, p. 102133, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1874490723001362
2023
Later among the works it cites.
2023
Later among the works it cites.
B. Du, H. Du, H. Liu, D. Niyato, P. Xin, J. Yu, M. Qi, and Y. Tang, “Yolo-based semantic communication with generative AI-aided resource allocation for digital twins construction,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Duan, S. Zhang, P. Yin, X. Li, and J. Luo, “Scma-tpgan: A new perspective on sparse codebook multiple access for UAV system,” Computer Communications , vol. 200, pp. 161–170, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
T. P. Nguyen, H. Nam, and D. Kim, “Transformer-based attention network for in-vehicle intrusion detection,” IEEE Access , vol. 11, pp. 55 389–55 403, 2023
2023
Later among the works it cites.
R. Dhakal, C. Bosma, P. Chaudhary, and L. N. Kandel, “UAV fault and anomaly detection using autoencoders,” in Proceedding of IEEE/AIAA 42nd Digital Avionics Systems Conference . IEEE, 2023, pp. 1–8
2023
Later among the works it cites.
V. Sadhu, K. Anjum, and D. Pompili, “On-board deep-learning-based unmanned aerial vehicle fault cause detection and classification via fpgas,” IEEE Transactions on Robotics , 2023
2023
Later among the works it cites.
J. Zhao, X. Feng, J. Wang, Y. Lian, M. Ouyang, and A. F. Burke, “Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks,” Applied Energy , vol. 352, p. 121949, 2023
2023
Later among the works it cites.
S. LING, N. WANG, J. LI, and L. DING, “Optimization of VAE-CGAN structure for missing time-series data complementation of uav jujube garden aerial surveys,” Turkish Journal of Agriculture and Forestry , vol. 47, no. 5, pp. 746–760, May 2023
2023
Later among the works it cites.
J. Zhao, Q. Zhai, P. Zhao, R. Huang, and H. Cheng, “Co-visual pattern-augmented generative transformer learning for automobile geo-localization,” Remote Sensing , vol. 15, no. 9, 2023. [Online]. Available: https://www.mdpi.com/2072-4292/15/9/2221
2072
Closest in time.