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Guojun DaiDoctor Professor | Doctoral supervisor Discipline: Post: |
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Graduate School: Research direction: Phone: Email: daigj@hdu.edu.cn Address: School of Computer Science , Hangzhou DIANZI University, China, Tel：+86 13906524548 |
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Mobile phone access |
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Prof. Guojun Dai is a renowned scholar in interdisciplinary research involving computer science and technology, biomedical engineering, and cognitive experimental psychology studying specific applications for the brain computer interfaces.
Prof. Dai developed his research activites in strict conjuction with several european laboratories across last two decades, by developing common research programs for laboratories implanted and active in Italy, UK, Spain, Switzerland, Hungary & Poland.
He firmly believes in international cooperation with european scientists and brings in his university (Hangzhou Dianzi University) several leading european professors for long stay visits in the last years, thus promoting cultural and scientific exchanges between continents. -1983.9 – 1988.6 Bachelor’s Degree in Biomedical Engineering & Instrumentation, Zhejiang University，Hangzhou,China; -1988.9 – 1991.6 Master’s Degree in Biomedical Engineering & Instrumentation , Zhejiang University, Hangzhou,China; -1994.9 – 1998.6 Doctoral Degree in Power Electronics Technology, Zhejiang University, Hangzhou, China; Current and Previous Positions： -2024 – present, Director, Zhejiang Provincial Key Technology Engineering Research Center for Brain Health Industry, Hangzhou, China; -2018 – present, Director, National International Joint Research Center for Brain-Machine Collaborative Intelligence Technology, Hangzhou, China; -2017 – present, Honorary Lifetime Professor, School of Computer Science, Hangzhou Dianzi University, Hangzhou, China; -2015 – 2016, Visiting Scholar, Sapienza University of Rome, Rome, Italy; -2010 – 2016, Associate Dean, School of Computer Science, Hangzhou Dianzi University, Hangzhou, China; -2005 – 2017, Lifetime Professor, School of Computer Science, Hangzhou Dianzi University, Hangzhou, China; -2000 – 2005, Associate Professor, School of Computer Science, Hangzhou Dianzi University, Hangzhou, China; -1998 – 2000, Lecturer, School of Computer Science, Hangzhou Dianzi University, Hangzhou, China. International Cooperation: -In the academic career in Hangzhou Dianzi University (HDU), Prof. Dai made possible 20 years of continuous cooperation between european academic institutions and China: in fact, he first promoted and signed the “BCI Technology Cooperative Research” agreement of HDU with the Sapienza University of Rome (UR), valid for 10 years since the 7 July 2009 up to July 2019. Then, when such treaty expired, pushed for the renewal of it for other 10 years with UR. Finally, in November 2019, HDU and UR signed the renewal of the scientific cooperation that will last up to November 2029, during a big cerimony in Rome attended, among others, by the vice-Rector of UR for research and the Head of Department of UR involved in the cooperation.
-In October 2012 prof. Dai signed a grant agreement for a scientific cooperation from the Minister of Science and Technology China and the Foreign Affairs Italian MInister with the University of Rome Sapienza for a project with the title “Research on Stroke Patient Rehabilitation Systems Based on BCI Technology”. The project lasted three years, from 2012 up to October 2015.
-In December 2014 he signed a grant agreement for a scientific and technology cooperation with the University of Bern (UB), Institute for Surgical Technology and Biomechanics, Switzerland. The research title was “Research on Low-Dose X-ray Image 3D Reconstruction Technology”, performed jointly by HDU and the UB. This scientific cooperation with UB in Switzerland lasted three years, from the end of 2014 up to december 2017.
-In May 2018 Prof. Dai with the HDU joined the EU Horizon2020 SimuSafe: Simulator of Behavioural aspects for safer transport project with eleven european partners from Spain, France, Germany, Sweden, Italy, Belgium, Slovakia, The Netherlands, UK, Portugal, Poland, in a real, pan-european cooperation. The SIMUSAFE cooperation project lasts three years from 2018 up to 2021.
-In July 2020 Prof. Dai signed the agreement for the generation of the “China-Spain AI technology joint laboratory for anxiety, stress and early diagnosis of cognitive dysfunction, with a gender dimension with the University of Murcia, Spain. This agreement lasts from July 2020 up to July 2025.
-During the same month, July 2020, he promoted for HDU an agreement with the Hungarian Academy of Sciences for the Joint Research on Driver Dynamic Cognitive State Assessment. The agreement lasts from July 2020 to July 2023.
-In December 2021 prof. Dai signed a “Multimodal Neurofeedback Motor BCI Technology for Rehabilitation” cooperative research agreement with John Paul II Catholic University of Lublin, Poland. The agreement lasts from December 2021 up to December 2024.
-In October 2023, he successfully get for HDU the support for the China-Spain CHINEKA program: “Key Technologies for Early Screening of AD”, with the University of Murcia, Spain. The project lasts from October 2023 up to October 2026.
-Moreover, in December 2023 prof. Dai continues the successful cooperation with the Spanish university of Murcia by signing another agreement to establish a “Joint Laboratory for Early Screening of Cognitive Disorders in the Elderly”. The agreement will last from December 2023 up to December 2026.
-In November 2023 with the HDU prof. Dai promoted and signed an agreement with the Sapienza University of Rome startup BrainSigns (Italy) for a project called “NeuroX Student Project Cooperation”. The agreement lasts from November 2023 to November 2026.
-One year later, in November 2024, he promoted and signed another agreement with the Sapienza University of Rome to establish the “Joint Laboratory for Cognitive Psychology Experimental Methodology”, at the Department of Physiology, University of Rome, Italy.
-Finally, in May 2025 prof. Dai received the Distinguished Contribution Award for International Scientific Research Cooperation by the Chinese Ministry of Cooperation as a sign of recognition from his country for his continous work for the establishment of international cooperation mainly with european countries. Research Areas : - Cognitive psychology experimental methodology based on Agent technology;
Prof. Dai pioneered a novel methodology for cognitive psychology experiments based on non-invasive brain–computer interface (BCI) technology. Supported by China–Italy science and technology cooperation programs, The research integrates AI agent, BME, and BCI technologies, proposing a revolutionary experimental paradigm that significantly enhances experimental efficiency and objective evaluation accuracy, thereby advancing cognitive psychology. The achievements in this research area obtained a very good recognition from international scholars and were highly praised by Professor Babiloni, Fellow of the European Academy of Sciences.
- Objective evaluation methods for cognitive activities based on human factors;
Prof. Dai, under the support of Chinese and EU flagship programs, promoted China–Europe cooperation in neuroscience and beyond. Participated in the H2020 “SimuSafe” project led by Spains ITCL Research Institute, by creating an evaluation methods for cognitive activities based on the estimation of human factors using the electroencephalographic activity. The achievements in this research area obtained a very good recognition from international scholars and were highly praised by Dr. Zhao Junjie, Science and Technology Counselor of the Chinese Embassy in Italy.
- Early screening methods for cognitive disorders based on wearable technology;
Prof. Dai investigated also recently the area of the Early Detection of Cognitive Impairment Diseases such as Alzheimers Disease (AD) with the state of the art signal processing techniques. Supported by the China–Spain CHINEKA Cooperative Research Program, prof. Dai conducted research on early-stage detection of cognitive impairment based on EMCI patients and he developed several wearable devices and computerized systems that have been validated in their use for AD patients through community trials in major Chinese cities such as Hangzhou and Guangzhou, achieving significant results. The achievements in this research area obtained a very good recognition from international scholars and were highly praised Professor Herrero, Fellow of the Royal Academy of Medicine of Spain. Currently collaborating with Neuro UP (Spain) for promotion in Europe. Research Achievements: -Prof. Dai supervised in the past more than 150 students for their master degree's thesis (each one of the master degree thesis lasts more than 1.5 years) and supervised more than 10 PhD students (each PhD thesis has an average duration of 3 years). He sponsored 3 PhD students of HDU for conducting research in Italy at the University of Rome Sapienza and he was also co-supervisor, together prof. Babiloni, of the PhD thesis of an HDU student in 2018.
-Prof. Dai signed several international China-Eu cooperation agreements: “BCI Technology Cooperative Research” (2009-2029) with Sapienza University, Italy, in 2020 the “eJoint Laboratory for Early Screening of Cognitive Disorders in the Elderly” with Murcia University, Spain in 2021 the Multimodal Neurofeedback Motor BCI Technology for Rehabilitation” with Paul II Catholic University of Lublin, Poland.
- He also founded the International Joint Research Center for Brain-Machine Collaborative Intelligence Technology (2018), based in HDU with an european established branches, aimed to foster international cooperation with european academics and international scholars on the theme of the use of Brain Computer Interfaces in several working contexts.
Longitudinal research
Transverse scientific research
Publications
Citations: 3063; H-Index: 27; i10-Index:62 Sources: https://scholar.google.com/citations?hl=zh-CN&user=EwVSbacAAAAJ
[1] Zhou, W., Qu, N., Yang, C., Li, Y., Mo, L., Lin, L., & Dai, G. (2025). Joint Temporal-Frequency-Channel Attention Learning for EEG-based Visual Object Classification. Journal of the Franklin Institute, 108128. [2] Xiang, X., Zhou, W., Zhu, H., Li, Y., Dai, G., & Lin, L. (2025). EEG-Driven Natural Image Reconstruction with Regional Semantic Awareness. Pattern Recognition, 112589. [3] Xiang, X., Zhou, W., & Dai, G. (2025). Electroencephalography-driven three-dimensional object decoding with multi-view perception diffusion. Engineering Applications of Artificial Intelligence, 156, 111180. [4] Shi, J., Zhao, Y., Wang, C., Zeng, H., & Dai, G. (2025). EEGSNet: A novel EEG cognitive recognition model using spiking neural network. Biomedical Signal Processing and Control, 105, 107610. [5] Ye, C., Duan, H., Zhang, H., Wu, Y., & Dai, G. (2024). Learned Query Optimization by Constraint-Based Query Plan Augmentation. Mathematics, 12(19), 3102. [6] Zeng, H., Zhao, Y., Babiloni, F., Tao, M., Kong, W., & Dai, G. (2024). A General DNA-Like Hybrid Symbiosis Framework: An EEG Cognitive Recognition Method. IEEE Journal of Biomedical and Health Informatics,volume: 28 Issue: 11. [7] Zhou, W., Ren, L., Yu, J., Qu, N., & Dai, G. (2024). Boosting rgb-d point cloud registration via explicit position-aware geometric embedding. IEEE Robotics and Automation Letters, 9(6), 5839-5846. [8] Wu, J., Dai, G., Zhou, W., Zhu, X., & Wang, Z. (2024). Multi-scale feature fusion with attention mechanism for crowded road object detection. Journal of Real-Time Image Processing, 21(2), 29. [9] Zhou, W., Wang, Y., Mo, L., Li, C., Xu, M., Kong, W., & Dai, G. (2024). Temporal-channel cascaded transformer for imagined handwriting character recognition. Neurocomputing, 573, 127243. [10] Zhao, Y., Zeng, H., Zheng, H., Wu, J., Kong, W., & Dai, G. (2023). A bidirectional interaction-based hybrid network architecture for eeg cognitive recognition. Computer Methods and Programs in Biomedicine, 238, 107593. [11] Ye, C., Xu, H., Zhang, H., Wu, Y., & Dai, G. (2023). Grier: graph repairing based on iterative embedding and rules. Knowledge and Information Systems, 65(8), 3273-3294. [12] Ye, C., Duan, H., Zhang, H., Zhang, H., Wang, H., & Dai, G. (2023). Multi-Source Data Repairing: A Comprehensive Survey. Mathematics, 11(10), 2314. [13] Ye, C., Zhi, H., Jiang, S., Zhang, H., Wu, Y., & Dai, G. (2023, April). TETA: text-enhanced tabular data annotation with multi-task graph convolutional network. In International Conference on Database Systems for Advanced Applications (pp. 523-533). Cham: Springer Nature Switzerland. [14] Zeng, H., Xia, N., Tao, M., Pan, D., Zheng, H., Wang, C., ... & Dai, G. (2023). DCAE: A dual conditional autoencoder framework for the reconstruction from EEG into image. Biomedical Signal Processing and Control, 81, 104440. [15] Yu, J., Ren, L., Zhang, Y., Zhou, W., Lin, L., & Dai, G. (2023). PEAL: Prior-embedded explicit attention learning for low-overlap point cloud registration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 17702-17711). [16] Ye, C., Jiang, S., Zhang, H., Wu, Y., Shi, J., Wang, H., & Dai, G. (2022). JointMatcher: Numerically-aware entity matching using pre-trained language models with attention concentration. Knowledge-Based Systems, 251, 109033. [17] Zeng, H., Fang, X., Zhao, Y., Wu, J., Li, M., Zheng, H., ... & Dai, G. (2022). EMCI: A novel EEG-based mental workload assessment index of mild cognitive impairment. IEEE Transactions on Biomedical Circuits and Systems, 16(5), 902-914. [18] Ye, C., Wang, H., & Dai, G. (2022). Pattern Discovery for Heterogeneous Data. In Knowledge Discovery from Multi-Sourced Data (pp. 53-67). Singapore: Springer Nature Singapore. [19] Ye, C., Wang, H., & Dai, G. (2022). Functional-Dependency-Based Truth Discovery for Isomorphic Data. In Knowledge Discovery from Multi-Sourced Data (pp. 13-31). Singapore: Springer Nature Singapore. [20] Ye, C., Wang, H., & Dai, G. (2022). Denial-constraint-based truth discovery for Isomorphic data. In Knowledge Discovery from Multi-Sourced Data (pp. 33-51). Singapore: Springer Nature Singapore. [21] Ye, C., Wang, H., & Dai, G. (2022). Fact Discovery for Text Data. In Knowledge Discovery from Multi-Sourced Data (pp. 69-83). Singapore: Springer Nature Singapore. [22] Ye, C., Wang, H., & Dai, G. (2022). Knowledge Discovery from Multi-Sourced Data. Springer. [23] Di Flumeri, G., Ronca, V., Giorgi, A., Vozzi, A., Aricò, P., Sciaraffa, N., ... & Borghini, G. (2022). EEG-based index for timely detecting user’s drowsiness occurrence in automotive applications. Frontiers in Human Neuroscience, 16, 866118. [24] Shen, F., Peng, Y., Dai, G., Lu, B., & Kong, W. (2022). Coupled projection transfer metric learning for cross-session emotion recognition from EEG. Systems, 10(2), 47. [25] Zhao, Y., Dai, G., Fang, X., Wu, Z., Xia, N., Jin, Y., & Zeng, H. (2022). E3GCAPS: Efficient EEG-based multi-capsule framework with dynamic attention for cross-subject cognitive state detection. China Communications, 19(2), 73-89. [26] Jin, R., Huang, H., Jiang, S., Zhang, H., Wu, Y., & Dai, G. (2021, November). Garbage classification method based on YOLOv3. In 2021 17th International Conference on Computational Intelligence and Security (CIS) (pp. 108-112). IEEE. [27] Ye, C., Wang, H., Lu, W., Gao, J., & Dai, G. (2021). Deep truth discovery for pattern-based fact extraction. Information Sciences, 580, 478-494. [28] Zhao, Y., Dai, G., Borghini, G., Zhang, J., Li, X., Zhang, Z., ... & Zeng, H. (2021). Label-based alignment multi-source domain adaptation for cross-subject EEG fatigue mental state evaluation. Frontiers in Human Neuroscience, 15, 706270. [29] Zhang, H., Yao, W., Huang, H., Wu, Y., & Dai, G. (2021). Adaptive coding unit size convolutional neural network for fast 3D-HEVC depth map intracoding. Journal of Electronic Imaging, 30(4), 041405-041405. [30] Zhang, H., Gou, R., Shang, J., Shen, F., Wu, Y., & Dai, G. (2021). Pre-trained deep convolution neural network model with attention for speech emotion recognition. Frontiers in Physiology, 12, 643202. [31] Shen, F., Peng, Y., Kong, W., & Dai, G. (2021). Multi-scale frequency bands ensemble learning for EEG-based emotion recognition. Sensors, 21(4), 1262. [32] Sun, L., Feng, S., Lyu, G., Zhang, H., & Dai, G. (2021). Partial multi-label learning with noisy side information. Knowledge and Information Systems, 63(2), 541-564. [33] Shen, F., Dai, G., Lin, G., Zhang, J., Kong, W., & Zeng, H. (2020). EEG-based emotion recognition using 4D convolutional recurrent neural network. Cognitive Neurodynamics, 14(6), 815-828. [34] Sun, L., Ye, P., Lyu, G., Feng, S., Dai, G., & Zhang, H. (2020). Weakly-supervised multi-label learning with noisy features and incomplete labels. Neurocomputing, 413, 61-71. [35] Xiang, L., Zhao, Y., Dai, G., Gou, R., Zhang, H., & Shi, J. (2020, October). The study of Chinese calligraphy font style based on edge-guided filter and convolutional neural network. In 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP) (pp. 883-887). IEEE. [36] Lyu, G., Feng, S., Li, Y., Jin, Y., Dai, G., & Lang, C. (2020). HERA: partial label learning by combining heterogeneous loss with sparse and low-rank regularization. ACM Transactions on Intelligent Systems and Technology (TIST), 11(3), 1-19. [37] Lyu, G., Feng, S., Huang, W., Dai, G., Zhang, H., & Chen, B. (2020). Partial label learning via low-rank representation and label propagation: G. Lyu et al. Soft Computing, 24(7), 5165-5176. [38] Ye, P., Feng, S., Feng, H., & Dai, G. (2019, November). Robust Multi-Label Learning with Corrupted Features and Incomplete Labels. In 2019 Chinese Automation Congress (CAC) (pp. 4411-4416). IEEE. [39] Zeng, H., Wu, Z., Zhang, J., Yang, C., Zhang, H., Dai, G., & Kong, W. (2019). EEG emotion classification using an improved SincNet-based deep learning model. Brain sciences, 9(11), 326. [40] Yu, S., Dai, G., Zhang, H., & Huang, H. (2019, October). Complexity Reduction for Depth Map Coding in 3D-HEVC. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (pp. 783-793). Cham: Springer International Publishing. [41] Dai, M., Dai, G., Wu, Y., Xia, Y., Shen, F., & Zhang, H. (2019, June). An improved feature fusion for speaker recognition. In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC) (pp. 183-187). IEEE. [42] Huang, J., Dai, G., Wu, Y., Zhang, H., & Shi, J. (2019, May). An IoT-supported small-scale liquefied natural gas distribution system using tank trucks in local areas. In 2019 IEEE 2nd International Conference on Electronics Technology (ICET) (pp. 20-27). IEEE. [43] Zeng, H., Yang, C., Zhang, H., Wu, Z., Zhang, J., Dai, G., ... & Kong, W. (2019). A LightGBM‐based EEG analysis method for driver mental states classification. Computational intelligence and neuroscience, 2019(1), 3761203. [44] Zeng, H., Yang, C., Dai, G., Qin, F., Zhang, J., & Kong, W. (2018). EEG classification of driver mental states by deep learning. Cognitive neurodynamics, 12(6), 597-606. [45] Yang, R., Dai, G., Zhang, H., Zhou, W., Yu, S., & Feng, J. (2018, November). Fast Depth Intra Mode Decision Based on DCT in 3D-HEVC. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (pp. 226-236). Cham: Springer International Publishing. [46] Liu, X., Zheng, K., Liu, X. Y., Wang, X., & Dai, G. (2018). Towards secure and energy-efficient CRNs via embracing interference: A stochastic geometry approach. IEEE Access, 6, 36757-36770. [47] Liu, X., Zheng, K., Fu, L., Liu, X. Y., Wang, X., & Dai, G. (2018). Energy efficiency of secure cognitive radio networks with cooperative spectrum sharing. IEEE Transactions on Mobile Computing, 18(2), 305-318. [48] Luo, Y., Zhou, W., Fang, J., Liang, L., Zhang, H., & Dai, G. (2017, October). Epi-patch based convolutional neural network for depth estimation on 4d light field. In International Conference on Neural Information Processing (pp. 642-652). Cham: Springer International Publishing. [49] Yang, N., Dai, G., Zhou, W., Zhang, H., & Yang, R. (2017, October). Distributed compressive sensing for light field reconstruction using structured random matrix. In CCF Chinese Conference on Computer Vision (pp. 222-233). Singapore: Springer Singapore. [50] Zeng, H., Dai, G., Kong, W., Chen, F., & Wang, L. (2017). A novel nonlinear dynamic method for stroke rehabilitation effect evaluation using eeg. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(12), 2488-2497. [51] Lei, X., Wang, L., Kong, W., Peng, Y., Hu, S., Zeng, H., ... & Tong, S. (2017, May). Identification of eeg features in stroke patients based on common spatial pattern and sparse representation classification. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 114-117). IEEE. [52] Kong, W., Liu, Y., Jiang, B., Dai, G., & Xu, L. (2016, November). A new EEG signal processing method based on low-rank and sparse decomposition. In International Conference on Cognitive Systems and Signal Processing (pp. 556-564). Singapore: Springer Singapore. [53] Hu, Y., Dai, G., Fan, J., Wu, Y., & Zhang, H. (2016, April). BlueAer: A fine-grained urban PM2. 5 3D monitoring system using mobile sensing. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications (pp. 1-9). IEEE. [54] Guo, H., Dai, G., Fan, J., Wu, Y., Shen, F., & Hu, Y. (2016). A mobile sensing system for urban PM2. 5 monitoring with adaptive resolution. Journal of Sensors, 2016(1), 7901245. [55] Hu, Y., Fan, J., Zhang, H., Chen, X., & Dai, G. (2016). An estimated method of urban PM2. 5 concentration distribution for a mobile sensing system. Pervasive and Mobile Computing, 25, 88-103. [56] Shen, X., Chen, Y., Zhang, J., Wang, L., Dai, G., & He, T. (2015, October). BarFi: Barometer-aided Wi-Fi floor localization using crowdsourcing. In 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems (pp. 416-424). IEEE. [57] Zhang, B., Fan, J., Dai, G., & Luan, T. H. (2015). A hybrid localization approach in 3D wireless sensor network. International Journal of Distributed Sensor Networks, 11(10), 692345. [58] Hua, H., Qiu, J., Song, S., Wang, X., & Dai, G. (2015, August). A cluster head rotation cooperative MIMO scheme for wireless sensor networks. In International Conference on Wireless Algorithms, Systems, and Applications (pp. 212-221). Cham: Springer International Publishing. [59] Wang, X., Qiu, J., Fan, J., & Dai, G. (2015, June). MDS-based localization scheme for large-scale WSNs within sparse anchor nodes. In 2015 IEEE International Conference on Communications (ICC) (pp. 6609-6614). IEEE. [60] Shen, X., Chen, Y., Zhang, Y., Zhang, J., Ge, Q., Dai, G., & He, T. (2015). OppCode: Correlated opportunistic coding for energy-efficient flooding in wireless sensor networks. IEEE Transactions on Industrial Informatics, 11(6), 1631-1642. [61] Fan, J., Zhang, B., & Dai, G. (2015). D3D-MDS: a distributed 3D localization scheme for an irregular wireless sensor network using multidimensional scaling. International Journal of Distributed Sensor Networks, 11(2), 103564. [62] Qiu, J., Mitchell, P., Grace, D., Lin, B., & Dai, G. (2015). An adaptive neighbour detection scheme for rapid configuration of wireless sensor networks. International Journal of Sensor Networks, 18(3-4), 130-139. [63] Wu, Y., Liu, P., Qiu, J., & Dai, G. (2015). Enable sustainable sensor networks with non-contact charging: efficient deployment of energy hubs. International Journal of Sensor Networks, 18(3-4), 172-181. [64] Zhang, Y., Shen, X., Chen, Y., Zhang, J., Dai, G., & He, T. (2014, October). Opportunistic coding for multi-packet flooding in wireless sensor networks with correlated links. In 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (pp. 371-379). IEEE. [65] Wang, N., Du, H., Xu, B., & Dai, G. (2014). Compact indexes based on core content in personal dataspace management system. Computing and Informatics, 33(2), 281-302. [66] Wang, X., Qiu, J., Ye, S., & Dai, G. (2014, June). An advanced fingerprint-based indoor localization scheme for WSNs. In 2014 9th IEEE Conference on Industrial Electronics and Applications (pp. 2164-2169). IEEE. [67] Kuicheu, N. C., Wang, N., Tchuissang, G. N. F., Xu, D., Dai, G., & Siewe, F. (2014). An iterative approach to managing uncertain mappings in dataspace support platforms. International Journal of Software Engineering and Knowledge Engineering, 24(04), 635-652. [68] Wan, P. J., Jia, X., Dai, G., Du, H., & Frieder, O. (2014, April). Fast and simple approximation algorithms for maximum weighted independent set of links. In IEEE INFOCOM 2014-IEEE Conference on Computer Communications (pp. 1653-1661). IEEE. [69] Qiu, J., Wang, X., & Dai, G. (2014). Improving the indoor localization accuracy for CPS by reorganizing the fingerprint signatures. International Journal of Distributed Sensor Networks, 10(3), 415710. [70] Wu, Y., Gao, Z., & Dai, G. (2014). Deadline and activation time assignment for partitioned real-time application on multiprocessor reservations. Journal of Systems Architecture, 60(3), 247-257. [71] Zeng, H., Zhang, J., Dai, G., Gao, Z., & Hu, H. (2014). Security visiting: RFID-based smartphone indoor guiding system. International Journal of Distributed Sensor Networks, 10(1), 212741. [72] Liu, P., Wu, Y., Qiu, J., Dai, G., & Fu, T. (2013). elighthouse: Enhance solar power coverage in renewable sensor networks. International Journal of Distributed Sensor Networks, 9(11), 256569. [73] Kong, W., Hu, S., Zhang, J., & Dai, G. (2013). Robust and smart spectral clustering from normalized cut. Neural Computing and Applications, 23(5), 1503-1512. [74] Kong, W., Zhou, Z., Hu, S., Zhang, J., Babiloni, F., & Dai, G. (2013). Automatic and direct identification of blink components from scalp EEG. Sensors, 13(8), 10783-10801. [75] Zhang, J., Shen, X., Zeng, H., Dai, G., Bo, C., Chen, F., & Lv, C. (2013). Energy‐efficient and localized lossy data aggregation in asynchronous sensor networks. International Journal of Communication Systems, 26(8), 989-1010. [76] Shen, X., Bo, C., Zhang, J., Tang, S., Mao, X., & Dai, G. (2013). EFCon: Energy flow control for sustainable wireless sensor networks. Ad Hoc Networks, 11(4), 1421-1431. [77] Wan, P. J., Jia, X., Dai, G., Du, H., Wan, Z., & Frieder, O. (2013, April). Scalable algorithms for wireless link schedulings in multi-channel multi-radio wireless networks. In 2013 Proceedings IEEE INFOCOM (pp. 2121-2129). IEEE. [78] Kuicheu, N. C., Wang, N., Tchuissang, G. N. F., Xu, D., Dai, G., & Siewe, F. (2013). Managing uncertain mediated schema and semantic mappings automatically in dataspace support platforms. Computing and Informatics, 32(1), 175-202. [79] Zeng, H., Zhang, J., & Dai, G. (2013). Construction of low weighted and fault–tolerant topology for wireless ad hoc and sensor network. International Journal of Sensor Networks, 14(4), 197-210. [80] Wang, T., Dai, G., Ni, B., Xu, D., & Siewe, F. (2012). A distance measure between labeled combinatorial maps. Computer Vision and Image Understanding, 116(12), 1168-1177.0. [81] Zhao, X., Hu, S., Zhang, J., Dai, G., Vecchiato, G., & Babiloni, F. (2012, May). The study of memorization index based on W-GFP during the observation of TV commercials. In 2012 IEEE International Conference on Systems and Informatics (ICSAI2012) (pp. 2198-2202). [82] Gao, Z., Wu, Y., Dai, G., & Xia, H. (2012). Energy-Efficient Scheduling for Hybrid Tasks in Control Devices for the Internet of Things. Sensors, 12(8), 11334-11359. [83] Dai, G., Qiu, J., Liu, P., Lin, B., & Zhang, S. (2012). Remaining energy-level-based transmission power control for energy-harvesting WSNs. International Journal of Distributed Sensor Networks, 8(5), 934240. [84] Wan, P. J., Chen, D., Dai, G., Wang, Z., & Yao, F. (2012, March). Maximizing capacity with power control under physical interference model in duplex mode. In 2012 Proceedings IEEE INFOCOM (pp. 415-423). IEEE. [85] He, Y., Liu, Y., Shen, X., Mo, L., & Dai, G. (2012). Noninteractive localization of wireless camera sensors with mobile beacon. IEEE Transactions on Mobile Computing, 12(2), 333-345. 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-2025, Government International Cooperation Contribution Award, Science and Technology Department; -2024, Outstanding International Cooperation Award, Sapienza University of Rome; -2018, Outstanding Academic Contributor, Hangzhou Dianzi University; -2017, Excellent International Scholar, University of Rome; -2015, Science and Technology Progress Award for Wearable Technology in Community Elderly Care, Science and Technology Department; -2014, Excellent Teaching Achievement Award for Apprenticeship-Based Innovative Practice Course, Education Department; -2011, Technology Progress Award for Multi-Physiological Parameter Synchronous Acquisition Technology, Chinese Institute of Electronics; |
