deep neural network
Using acoustic waves to estimate fluid concentration is a promising technology due to its practicality and non-intrusive aspect, especially for medical applications. The existing approaches are exclusively based on the correlation between the reflection coefficient and the concentration. However, these techniques are limited by the high sensitivity of the reflection coefficient to environmental conditions changes, even slight ones. This introduces inaccuracies that cannot be tolerated in medical applications.
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Four independently built Twitter JSON files containing between 40,000 - 50,000 tweets with a common Hashtag to be used with network discovery algorithms.
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A synthetic signal dataset of 12 different modulations (including PSK, QPSK, 8PSK, QFSK, 8FSK, 16APSK, 16QAM, 64QAM, 4PAM, LFM, DSB-SC, and SSBSC) with different DOAs (discrete angles ranging from -60° to 60° with the step size of 1°) is generated using MATLAB 2021a. Regarding the signal model configuration for the data generation, we specify a uniform linear antenna array of M = 5 elements to acquire incoming signals having N = 1024 envelope complex samples, thus conducting an I/Q data array of size 1024 × 2 × 5.
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Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution.
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This dataset contains three benchmark datasets as part of the scholarly output of an ICDAR 2021 paper:
Meng Ling, Jian Chen, Torsten Möller, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Robert S. Laramee, Han-Wei Shen, Jian Wu, and C. Lee Giles, Document Domain Randomization for Deep Learning Document Layout Extraction, 16th International Conference on Document Analysis and Recognition (ICDAR) 2021. September 5-10, Lausanne, Switzerland.
This dataset contains nine class lables: abstract, algorithm, author, body text, caption, equation, figure, table, and title.
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