Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

Subjects:

Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

Subjects:

Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

Subjects:

Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is a... by Owen M. O’Connor (11965460)

City driving speed query results. by Meilin Zhu (688698)

Cells are initially detected in frame t₀ and then tracked to frame t₁. Object queries are processed in the first layer of the decoder for object detection. In... by Owen M. O’Connor (11965460)

Number of tweets collected per query and type. by Sylvia Iasulaitis (8301189)

Validation results on the Tromsø Study accelerometer data subsample. by Marc Weitz (8131560)

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Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output al... by Owen M. O’Connor (11965460)

Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

Analysis of single features of vehicle movement. by Yaqin He (4627711)

Example of a knowledge query. by Bei Li (90587)

Bland-Altman plots for the predictions of our model compared to the total sleep duration measured by the Polar Vantage and Oura ring. by Marc Weitz (8131560)

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Overview over the different datasets and how they were used in this study. by Marc Weitz (8131560)

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Processing time for the different pipeline steps. by Marc Weitz (8131560)

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The accuracy of all employed learning algorithms. by Quan Shao (20148209)

Basic hand movements. by Yue Yuan (113839)

“…Finally, three machine learning methods are used to recognize hand movements. The proposed method was tested on the sEMG for Basic Hand movements Data Set and achieved an average accuracy of 99.14%. …”

Comparison of proposed model with default ML classifiers and recent existing studies. by Ankur Kumar (1357440)

Performance comparison for different algorithms reported in terms of bootstrapped canonical correlation of each component. by Shashwat Kumar (19120858)