Abstract: The present disclosure relates to a method for feeding back reliable downlink channel state information from a receiver to a transmitter in a multi-user massive MIMO system based on a reconfigurable intelligent surface, and more particularly, to a method for the receiver to transform acquired downlink channel state information into downlink channel state information in an angle domain and compress the downlink channel state information through a compressed-sensing scheme, and then feed the compressed result back to the transmitter, and for the transmitter to acquire the reliable downlink channel state information through a recovery algorithm. According to the present disclosure, the downlink channel state information is compressed according to a compression and recovery algorithm in an mmWave environment, and only a codeword index suitable for the compressed result is fed back to the transmitter to obtain the effect of reduction in feedback overhead in the entire communication system.

Abstract: The present disclosure relates to a method for feeding back reliable downlink channel state information from a receiver to a transmitter in a multi-user massive MIMO system based on a reconfigurable intelligent surface, and more particularly, to a method for the receiver to transform acquired downlink channel state information into downlink channel state information in an angle domain and compress the downlink channel state information through a compressed-sensing scheme, and then feed the compressed result back to the transmitter, and for the transmitter to acquire the reliable downlink channel state information through a recovery algorithm. According to the present disclosure, the downlink channel state information is compressed according to a compression and recovery algorithm in an mmWave environment, and only a codeword index suitable for the compressed result is fed back to the transmitter to obtain the effect of reduction in feedback overhead in the entire communication system.

Abstract: Embodiments of the present invention proposes a method to automatically identify the flying targets by physical information (coordinates, heading, speed), time, and identification information (3/A code). This method includes two steps: features extraction and building machine learning model. In the features extraction step, features which are extracted include: cell indexes corresponding to coordinates, information of flight path, time in day/night format, heading, speed, and 3/A code, constructing n-dimensional vector. This vector is used as input for training a Random Forest model, to automatically identify class label of flying targets.

Abstract: Provided are a method of screening for novel therapeutic targets to develop therapeutic agents for colon cancer, and prognostic biomarkers for colon cancer treatment which are screened using the same.

Abstract: Embodiments of the present invention proposes a method to automatically identify the flying targets by physical information (coordinates, heading, speed), time, and identification information (3/A code). This method includes two steps: features extraction and building machine learning model. In the features extraction step, features which are extracted include: cell indexes corresponding to coordinates, information of flight path, time in day/night format, heading, speed, and 3/A code, constructing n-dimensional vector. This vector is used as input for training a Random Forest model, to automatically identify class label of flying targets.

Abstract: Provided are a method of screening for novel therapeutic targets to develop therapeutic agents for colon cancer, and prognostic biomarkers for colon cancer treatment which are screened using the same.