Nippon Telegraph and Telephone Corporation

Takayuki Nakachi

Takahiro Yamaguchi

Yitu Wang

Yoshihide Tonomura

Daisuke Shirai

Tatsuya Fujii

Takafumi Tanaka

Tetsuro Inui

Katsuhiro Shimano

Masanori Ogawara

Masahiko Kitamura

Tetsurou Fuji

Hiroyuki Ishihara

Tomoko Sawabe

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.

Method, device and program for estimating optical communication status

An objective of the present disclosure is to enable estimation of an attention target of communication parties even if there are a plurality of objects having the same appearance in a space. An attention target estimation deviceaccording to the present disclosure acquires a first-person point-of-view video IMcaptured from a perspective of a person and a line-of-sight position gof a person when the first-person point-of-view video IMis captured, identifies positions in a 3D space of objects, andextracted from the first-person point-of-view video IM, and determines an object close to the line-of-sight position gof the person among the objects, andincluded in the first-person point-of-view video IMas the attention target of the person.

Attention target estimating device, and attention target estimating method

A selective PEG algorithm, creating a sparse matrix while maintaining row weight/column weight at arbitrary multi-levels, and in the process, inactivating an arbitrary edge so that a minimum loop formed between arbitrary nodes is enlarged or performing constrained interleaving, so that encoding efficiency in the case where a matrix space is narrow is improved.

Sparse graph creation device and sparse graph creation method

This method and device makes it possible to implement maximum likelihood decoding of a sparse graph code at low computational complexity in the maximum likelihood decoding of the sparse graph code. This is, in the maximum likelihood of decoding of the sparse graph code, a lost data decoding process by a trivial decoding method and a lost data decoding process by a Gauss elimination method are performed repeatedly and alternately.

Channel decoding method and channel decoding device

A encoding apparatus decomposes an original image into M (M is an integer and M&#x3e;2) uniform subbands, and encodes the decomposed signals by using an embedded type entropy encoding method. A decoding apparatus receives the coded data encoded by the encoding apparatus, extracts N signals from the coded data from a low frequency component side in decomposed signals, decodes the N signals by using an entropy decoding method, and synthesizes the N decoded signals to obtain a decoded image of a resolution of N/M times (M and N are integers, and 1&#x2266;N&#x2266;M and M&#x3e;2) that of an original image.

Growing community of inventors

Yokosuka, Japan

Takayuki Nakachi