1 |
CHENS Z, LIANGY C, SUNS H, et al. Vision, requirements, and technology trend of 6G: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed[J]. IEEE Wireless Communications, 2020, 27(2): 218-228.
|
2 |
张宏科, 冯博昊, 权伟. 智融标识网络基础研究[J]. 电子学报, 2019, 47(5): 977-982.
|
|
ZHANGH K, FENGB H, QUANW. Fundamental research on smart integration identifier networking[J]. Acta Electronica Sinica, 2019, 47(5): 977-982. (in Chinese)
|
3 |
CARLTONB. Nissan partners with HaptX to bring realistic touch to vehicle design[EB/OL]. (2019-03-08)[2022-04-26]. .
|
4 |
ZHOUL, WUD, CHENJ X, et al. Cross-modal collaborative communications[J]. IEEE Wireless Communications, 2020, 27(2): 112-117.
|
5 |
MOSKVITCHK. Tactile Internet: 5G and the cloud on steroids[J]. Engineering & Technology, 2015, 10(4): 48-53.
|
6 |
YUANZ, WEIX, CHENJ X, et al. Ultra-reliability connectivity with redundant D2D transmission scheme for tactile Internet[C]//2019 IEEE Globecom Workshops. Waikoloa, HI: IEEE, 2019: 1-6.
|
7 |
ZHOUL. On data-driven delay estimation for media cloud[J]. IEEE Transactions on Multimedia, 2016, 18(5): 905-915.
|
8 |
JANKOWSKIM, GÜNDÜZD, MIKOLAJCZYKK, et al. Wireless image retrieval at the edge[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(1): 89-100.
|
9 |
JANKOWSKIM, GÜNDÜZD, MIKOLAJCZYKK. Deep joint source-channel coding for wireless image retrieval[C]//2020 IEEE International Conference on Acoustics, Speech and Signal Processing. Barcelona: IEEE, 2020: 5070-5074.
|
10 |
ZHOUL, WUD, WEIX, et al. Cross-modal stream scheduling for eHealth[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(2): 426-437.
|
11 |
GAOY, WEIX, KANGB, et al. Edge intelligence empowered cross-modal streaming transmission[J]. IEEE Network, 2021, 35(2): 236-243.
|
12 |
LIUC F, HUANGW B, SUNF C, et al. LDS-FCM: A linear dynamical system based fuzzy C-means method for tactile recognition[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(1): 72-83.
|
13 |
LUOS, MOUW X, ALTHOEFERK, et al. Novel tactile-SIFT descriptor for object shape recognition[J]. IEEE Sensors Journal, 2015, 15(9): 5001-5009.
|
14 |
CHUV, MCMAHONI, RIANOL, et al. Robotic learning of haptic adjectives through physical interaction[J]. Robotics and Autonomous Systems, 2015, 63(3): 279-292.
|
15 |
WARD-CHERRIERB, PESTELLN, LEPORAN F. NeuroTac: A neuromorphic optical tactile sensor applied to texture recognition[C]//2020 IEEE International Conference on Robotics and Automation. Paris: IEEE, 2020: 2654-2660.
|
16 |
李志欣, 凌锋, 张灿龙, 等. 融合两级相似度的跨媒体图像文本检索[J]. 电子学报, 2021, 49(2): 268-274.
|
|
LIZ X, LINGF, ZHANGC L, et al. Cross-media image-text retrieval with two level similarity[J]. Acta Electronica Sinica, 2021, 49(2): 268-274. (in Chinese)
|
17 |
HARDOOND R, SZEDMAKS, SHAWE-TAYLORJ. Canonical correlation analysis: An overview with application to learning methods[J]. Neural Computation, 2004, 16(12): 2639-2664.
|
18 |
AKAHOS. A kernel method for canonical correlation analysis[J]. Tsukuba, Japan, 2006: 263-269.
|
19 |
周沛, 陈后金, 于泽宽, 等. 跨模态医学图像预测综述[J]. 电子学报, 2019, 47(1):220-226.
|
|
ZHOUP, CHENH J, YUZ K, et al. Review of cross-modality medical image prediction[J]. Acta Electronica Sinica, 2019, 47(1): 220-226. (in Chinese)
|
20 |
SHANGX D, ZHANGH W, CHUAT S. Deep learning generic features for cross-media retrieval[C]//MMM 2016: Proceedings, Part I, of the 22nd International Conference on MultiMedia Modeling. Miami, FL: Springer, 2016: 264-275.
|
21 |
WANGC, YANGH J, MEINELC. Deep semantic mapping for cross-modal retrieval[C]//2015 IEEE 27th International Conference on Tools with Artificial Intelligence. Vietri sul Mare: IEEE, 2015: 234-241.
|
22 |
FAYEKH. Speech processing for machine learning: Filter banks, mel-frequency cepstral coefficients(MFCCs) and what's in-between[Z/OL]. (2016-04-21)[2022-04-26]. .
|
23 |
SALVADORA, HYNESN, AYTARY, et al. Learning cross-modal embeddings for cooking recipes and food images[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 3068-3076.
|
24 |
HORIGUCHIS, KANDAN, NAGAMATSUK. Face-voice matching using cross-modal embeddings[C]//Proceedings of the 26th ACM international conference on Multimedia. Seoul: ACM, 2018: 1011-1019.
|
25 |
STRESEM, SCHUWERKC, IEPUREA, et al. Multimodal feature-based surface material classification[J]. IEEE Transactions on Haptics, 2017, 10(2): 226-239.
|
26 |
张峰, 钟宝江. 基于兴趣目标的图像检索[J]. 电子学报, 2018, 46(8):1915-1923.
|
|
ZHANGF, ZHONGB J. Image retrieval based on interested objects[J]. Acta Electronica Sinica, 2018, 46(8): 1915-1923. (in Chinese)
|
27 |
LUC Y, FENGJ S, CHENY D, et al. Tensor robust principal component analysis with a new tensor nuclear norm[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(4): 925-938.
|
28 |
ECKERTM A, KAMDARN V, CHANGC E, et al. A cross-modal system linking primary auditory and visual cortices: Evidence from intrinsic fMRI connectivity analysis[J]. Human Brain Mapping, 2008, 29(7): 848-857.
|
29 |
VINCENTP, LAROCHELLEH, LAJOIEI, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(12): 3371-3408.
|
30 |
WUY L, WANGS H, HUANGQ M. Multi-modal semantic autoencoder for cross-modal retrieval[J]. Neurocomputing, 2019, 331: 165-175.
|
31 |
秦姣华, 黄家华, 向旭宇, 等. 基于卷积神经网络和注意力机制的图像检索[J]. 电讯技术, 2021, 61(3): 304-310.
|
|
QINJ H, HUANGJ H, XIANGX Y, et al. Image retrieval based on convolutional neural network and attention mechanism[J]. Telecommunication Engineering, 2021, 61(3): 304-310. (in Chinese)
|