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This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.

The dataset includes experiments with 1, 2, and 3 people, covering various positions, walking trajectories, and intermediate locations to evaluate model generalization. Ground truth information is embedded in the folder names, indicating the true distance and angle of the targets.

Categories:

This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.

The dataset includes experiments with 1, 2, and 3 people, covering various positions, walking trajectories, and intermediate locations to evaluate model generalization. Ground truth information is embedded in the folder names, indicating the true distance and angle of the targets.

Categories:

This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.

The dataset includes experiments with 1, 2, and 3 people, covering various positions, walking trajectories, and intermediate locations to evaluate model generalization. Ground truth information is embedded in the folder names, indicating the true distance and angle of the targets.

Categories:

This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.

The dataset includes experiments with 1, 2, and 3 people, covering various positions, walking trajectories, and intermediate locations to evaluate model generalization. Ground truth information is embedded in the folder names, indicating the true distance and angle of the targets.

Categories:
Mirror arrays have been applied to indoor visible light communication (VLC) as a passive the reconfigurable intelligent surface (RIS) which has no signal processing to solve the problem of indoor visible light line-of-sight obstruction, however, after channel modeling, it is found that the reflected channel in this scenario has a serious multipath effect, to this end, we introduce deep learning techniques into channel estimation of VLC systems with mirror arrays for the first time, and propose a hybrid new model of Transformer and the bidirectional longshort-term memory model (Transformer-BiL
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