Nippon Telegraph and Telephone Corporation

Ntt Corporation

Yitu Wang

Takayuki Nakachi

Takafumi Tanaka

Tetsuro Inui

Takahiro Yamaguchi

Katsuhiro Shimano

The present disclosure is to perform beamforming corresponding to the influence of a dynamic environment in which a user moves. The present disclosure relates to a beamforming prediction device that includes: a storage unit that stores a dictionary D obtained by learning fingerprints based on trajectories, and a fingerprint database based on trajectories; a trajectory prediction unit that calculates a trajectory of a mobile terminal, using location information about the mobile terminal; a fingerprint estimation unit that applies the trajectory of the mobile terminal to an input of the dictionary D, and calculates the sparse coefficient X corresponding to the trajectory of the mobile terminal; and a beamforming calculation unit that calculates beamforming of the mobile terminal, using the sparse coefficient X calculated by the fingerprint estimation unit and the fingerprint database.

Beamforming prediction device, method and program

An object of the present invention is to provide an image recognition system, an image recognition server, and an image recognition method having a new high security framework that can achieve utilization of multi-device diversity. The image recognition system according to the present disclosure includes a computationally non-intensive encryption algorithm based on random unitary transformation and achieves a high level of security. In addition, the image recognition system achieves high recognition performance by using ensemble learning to integrate recognition results based on the dictionaries of different devices.

Image recognition system, image recognition server, and image recognition

The present disclosure has been made in view of such a problem, and an object of the present disclosure is to make it possible to predict fluctuation of unsteady traffic with a small amount of calculation. A traffic fluctuation prediction apparatus () according to the present disclosure includes: a data division unit () that divides time-series data X(t) in a certain period of the traffic into estimation and prediction data sets; a learning unit () that learns a dictionary D(t) using one of the two divided data sets, and learns a dictionary D(t) using the other of the two divided data sets; a prediction unit () that obtains a sparse code Y(t) in representing the time-series data using the learned dictionary D(t) and obtains a predicted value X{circumflex over ( )}(t) of future traffic using the obtained sparse code Y(t) and the dictionary D(t); and a prediction correction unit () that corrects a prediction error of the predicted value X{circumflex over ( )}(t) obtained by the prediction unit () using search for a stable region of a double queue of overestimation and underestimation.

Traffic fluctuation prediction device, method and program

The present disclosure has an object of proposing a method and a device for estimating the state of a transmission path or an optical transmitter capable of mechanically estimating a factor causing an error with a small amount of constellation data and a low computing amount. The present disclosure provides a device for estimating a state of optical communication, the device including: a data preprocessing unit that reduces the number of data using random sampling with respect to constellation data in which an amplitude and a phase of optical communication data are represented by a polar coordinate diagram and performs distribution calculation and a dimension reduction; a learning unit that learns a dictionary matrix in sparse coding using learning constellation data processed by the data preprocessing unit; and a recognition unit that calculates a sparse coefficient using recognition constellation data processed by the data preprocessing unit and the dictionary matrix learned by the learning unit and estimates a factor causing degradation of the optical communication using the calculated sparse coefficient.

Growing community of inventors

Musashino, Japan

Yitu Wang