Nippon Telegraph and Telephone Corporation

Takuma Isoda

Hirofumi Noguchi

Misao Kataoka

Yoji Yamato

Kyota Hattori

Tatsuya Demizu

Akinori Shiraga

Seisuke Arai

An imaging range estimation device includes an image data processor configured to acquire image data imaged by a camera device and generate image data with an object name label added, a reference data generator configured to set, by using geographic information, a region within a predetermined distance that is imagable from an estimated position at which the camera device is installed and generate reference data with an object name label added, and an imaging range estimator configured to calculate a concordance rate by comparing a feature indicated by a region of an object name label of the image data with a feature indicated by a region of an object name label of the reference data, and estimate the imaging range of the camera device to be a region of the reference data that corresponds to the image data.

Imaging range estimation device, imaging range estimation method, and program

An inter-device positional relationship estimation apparatus includes an analysis information acquisition unit that acquires time-series analysis information, a recommendation processing unit that estimates unanalyzed detailed information in time-series analysis information applying a predetermined recommendation algorithm, an initial map creation unit that calculates a neighborhood determination point indicating a degree of correlation of detailed information between fixed devices to create an initial map, and a device relationship map creation unit that calculates neighborhood points indicating a degree of correlation of detailed information between respective devices including a moving device or/and a new fixed device to calculate a detection probability and a movement time between devices and create a device relationship map.

Inter-device positional relationship estimation device, inter-device positional relationship estimation method, and program

A sensor device allocation apparatus that allocates a sensor device to an IoT service of the present invention includes a scoring unit that performs ranking of sensor devices satisfying a service requirement by an evaluation function based on specification conditions of the sensor devices and a scheduling unit that determines a control law of the sensor devices ranked by the scoring unit, in which the scheduling unit determines whether a service is satisfied based on collected data of the sensor device collected based on the control law, and the scoring unit corrects the evaluation function and repeats ranking of the sensor devices when the scheduling unit determines that the service is not satisfied.

Sensor device allocation apparatus, sensor device allocation method, and sensor device allocation program

A multi device coordination control device includes: a degree-of-service-achievement calculator configured to acquire sensor information from one or more sensor devices disposed on the same physical space, and convert the acquired sensor information to a degree of service achievement; a target control amount calculation part configured to make a learning device allow an input of the degree of service achievement and compute therefrom an actuator control amount for each of the actuator devices, to thereby obtain the actuator control amount as an output value; and an actuator controller configured to convert the actuator control amount obtained as the output value to a control instruction in accordance with each of the actuator devices, and transmit the control instruction to each of the actuator devices, to thereby make the each of the actuator devices execute the control instruction.

Multi-device coordination control device, multi-device coordinaton control method, and multi-device coordination control program, and learning device, learning method, and learning program

A device identification apparatus includes: a communication information collection unit configured to acquire communication information of existing devices and an identification target device; a feature amount generation unit configured to make the communication information of the existing devices' feature amounts and assign labels to generate first training data, make the communication information of the identification target device feature amounts and assign a dummy label to generate second training data, and further acquire communication information of the identification target device to generate identification data; a machine learning unit configured to cause a learning engine to learn the training data, and input the identification data to classify the identification data into the labels; a degree-of-similarity calculation unit configured to calculate a degree of similarity for each label; and a device identification unit configured to use a new type determination threshold to determine whether the device is a new type device.

Device identification device, device identification method, and device identification program

Provided is an optimally deploying an application on a node of a network. An application deployment apparatusis an apparatus that deploys an application in a node N in a network, the networkincluding a plurality of the nodes N communicably connected by links L, and includes a deployment determination unitconfigured to determine, as a deployment destination of the application, a node of the nodes N having a minimum cost including a computation cost of the node N and a communication cost of a links of the links L, and a deployment execution unitconfigured to distribute the application to the determined node N.

Application placement device and application placement program

When selecting a combination of application software, a computer, and a device in executing various services, even in a situation in which an environment greatly varies, the probability to achieve the purpose of the service is increased to shorten the duration time required to achieve the purpose of the service. Data effectiveness expectation value J(x, z) regarding a combination of a specific application x and a specific device z on a communication network is calculated, an evaluation value F(x, y, z) including the data effectiveness expectation value and a specific computer y as parameters is calculated, and an appropriate combination (x, y, z) is selected based on the result. The data effectiveness expectation value is calculated from actual record data. The data effectiveness expectation value is calculated by weighting old data and new data. The data effectiveness expectation value is calculated by using actual record values of other applications similar to the specific application x.

Service allocation selection method and service allocation selection program

An offloading server includes: a data transfer designation section configured to analyze reference relationships of variables used in loop statements in an application and designate, for data that can be transferred outside a loop, a data transfer using an explicit directive that explicitly specifies a data transfer outside the loop; a parallel processing designation section configured to identify loop statements in the application and specify a directive specifying application of parallel processing by an accelerator and perform compilation for each of the loop statements; and a parallel processing pattern creation section configured to exclude loop statements causing a compilation error from loop statements to be offloaded and create a plurality of parallel processing patterns each of which specifies whether to perform parallel processing for each of the loop statements not causing a compilation error.

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Tokyo, Japan

Takuma Isoda