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506 results on '"Dai, Guojun"'

56. A Cluster Head Rotation Cooperative MIMO Scheme for Wireless Sensor Networks

Author

Hua, Huaida, Qiu, Jian, Song, Shiwei, Wang, Xizhe, Dai, Guojun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xu, Kuai, editor, and Zhu, Haojin, editor

Published
2015
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68. Energy Efficient Data Aggregation in Solar Sensor Networks

Author

Zhang, Jianhui, Tang, Shaojie, Shen, Xingfa, Dai, Guojun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Cheng, Yu, editor, Eun, Do Young, editor, Qin, Zhiguang, editor, Song, Min, editor, and Xing, Kai, editor

Published
2011
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72. A Lazy EDF Interrupt Scheduling Algorithm for Multiprocessor in Parallel Computing Environment

Author

Liu, Peng, Dai, Guojun, Fu, Tingting, Zeng, Hong, Zhang, Xiang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jin, Hai, editor, Rana, Omer F., editor, Pan, Yi, editor, and Prasanna, Viktor K., editor

Published
2007
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79. EEG-Based Index for Timely Detecting User's Drowsiness Occurrence in Automotive Applications

Author

Di Flumeri Gianluca, Ronca Vincenzo, Giorgi Andrea, Vozzi Alessia, Aricò Pietro, Sciaraffa Nicolina, Zeng Hong, Dai Guojun, Kong Wanzeng, Babiloni Fabio, Borghini Gianluca

Subjects
cognitive neuroscience, drowsiness, human factor, EEG, neurometrics, road safety, driving performance, neuroergonomics
Abstract

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic (EEG)-based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so-called “MDrow index”, was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements (self-reports).

Published
2022

80. Multi-Source Data Repairing: A Comprehensive Survey.

Author

Ye, Chen, Duan, Haoyang, Zhang, Hengtong, Zhang, Hua, Wang, Hongzhi, and Dai, Guojun

Subjects
BIG data, DATA quality, DATA compression
Abstract

In the era of Big Data, integrating information from multiple sources has proven valuable in various fields. To ensure a high-quality supply of multi-source data, repairing different types of errors in the multi-source data becomes critical. This paper categorizes errors in multi-source data into entity information overlapping, attribute value conflicts, and attribute value inconsistencies. We first summarize existing repairing methods for these errors and then examine and review the study of the detection and repair of compound-type errors in multi-source data. Finally, we indicate further research directions in multi-source data repair. [ABSTRACT FROM AUTHOR]

Published
2023
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