Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Yangjie, Cao"'
Publikováno v:
Foundations of Computing and Decision Sciences, Vol 49, Iss 1, Pp 21-36 (2024)
Deep neural networks based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original input images. These crafted examples can fool sy
Externí odkaz:
https://doaj.org/article/55705007d66f480caaacbe5c6153442e
Publikováno v:
IEEE Access, Vol 12, Pp 28554-28563 (2024)
Fragment reassembly is widely used in fields such as archaeology and forensics. This paper introduces an algorithm for reassembling irregular fragments, enabling the reconstruction of arbitrarily segmented irregular fragments without any prerequisite
Externí odkaz:
https://doaj.org/article/77f7a5b349724bf09c1879a982158e27
Publikováno v:
MultiMedia Modeling ISBN: 9783031270765
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98391980e4282569469ed30f9e266129
https://doi.org/10.1007/978-3-031-27077-2_24
https://doi.org/10.1007/978-3-031-27077-2_24
Publikováno v:
Computer Science and Education ISBN: 9789819924424
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::834b7e4f63b91e6297006d1553852b8a
https://doi.org/10.1007/978-981-99-2443-1_17
https://doi.org/10.1007/978-981-99-2443-1_17
Publikováno v:
2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02bd7732e774d962b8207cf1500d19a2
https://doi.org/10.1109/hpcc-dss-smartcity-dependsys57074.2022.00125
https://doi.org/10.1109/hpcc-dss-smartcity-dependsys57074.2022.00125
Publikováno v:
Sun, L, Cao, Y, Yin, R, Wu, C, Zhu, Y & Chen, X 2022, Learning to optimize computation offloading performance in multi-access wireless networks . in AIIOT 2022-Proceedings of the 2022 1st Workshop on Digital Twin and Edge AI for Industrial IoT, Part of MobiCom 2022 . Association for Computing Machinery ACM, pp. 19-24, 2022 1st Workshop on Digital Twin and Edge AI for Industrial IoT, AIIOT 2022-Part of MobiCom 2022, Sydney, Australia, 21/10/22 . https://doi.org/10.1145/3566099.3569005
In this paper, we investigate computation offloading in a multi-access wireless network, which supports both cellular and WiFi connectivity between a mobile user (MU) and the edge server. The MU decides to process an arrived computation task locally
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3354eff989ee56c2d67d2244fca9c5ef
https://doi.org/10.1145/3566099.3569005
https://doi.org/10.1145/3566099.3569005
Publikováno v:
Journal of Computer Science and Technology
Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure when s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3fa68a0f6c62feab759f1e3f0c67847
https://doi.org/10.1007/s11390-021-0782-5
https://doi.org/10.1007/s11390-021-0782-5
Publikováno v:
Mobile Networks and Applications. 26:2024-2034
Traditional Wi-Fi based action recognition models often utilize only amplitude information from CSI, with phase information simply discarded due to phase error problems. Such a design decision inevitably limits the information utilization, thus the o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0153449f1e92040427752b1aaa8e5a98
https://doi.org/10.1007/s11036-021-01734-4
https://doi.org/10.1007/s11036-021-01734-4
Publikováno v:
ICC 2022 - IEEE International Conference on Communications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a1ba3e0f0647ae1b46142912bac0d4cd
https://doi.org/10.1109/icc45855.2022.9838612
https://doi.org/10.1109/icc45855.2022.9838612
Publikováno v:
IEEE Access, Vol 8, Pp 58672-58682 (2020)
In this paper, a calibration approach based on transfer function extraction for the Cartesian vector modulator (VM) is presented. Three kinds of VM models-the ideal VM model, the frequency-dependent VM model and the modified frequency-dependent VM mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c34b1f2592bcdf4d365cf1fe11965be5
https://ieeexplore.ieee.org/document/9044765/
https://ieeexplore.ieee.org/document/9044765/