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Accurately obtaining the position of active transmitters within an indoor wireless network has promising applications in future wireless networks. However, due to the complex propagation phenomena experienced by signals indoors, classical model-based localization techniques present poor accuracy, and machine learning (ML) based positioning has a promising potential to deliver high accuracy localization services indoors. Hence, datasets containing real-world measurands available represent an important step to better understand the achievable performance of ML-based positioning schemes.

This dataset is gathered for quadrotor tail-sitter UAV system identification and modeling by PolyU MAV/UAV Lab. Various operating conditions are recorded during the UAV hovering phase.

The picture shows the operation result of image security retrieval.  The experiment was validated on five common data sets.

The following database contains over 25,000 records collected using the author's data glove while researching 16 static letters of the Polish Sign Language alphabet. 

Data are readings from 10 piezoresistive sensors placed over the fingers of the hand expressed in ADC values.

Data are from 15 test subjects, each of whom performed each letter ten times

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