NTT DoCoMo

About: NTT DoCoMo is a based out in . It is known for research contribution in the topics: Base station & Mobile station. The organization has 4032 authors who have published 8655 publications receiving 160533 citations.

Communication system employing automatic repeat request

Abstract: A communication method includes the steps of: a) transmitting an ACK signal indicating a received packet includes no error or a NACK signal indicating the received packet includes error from a reception end to a transmission end, and performing automatic repeat request; b) obtaining reliability of the received packet when demodulating it at the reception end; and c) reporting from the reception end to the transmission end the reliability of the received packet utilizing the ACK/NACK signal by using not less than three levels.

Gait analyzer based on a cell phone with a single three-axis accelerometer

Abstract: We propose a fuss-free gait analyzer based on a single three-axis accelerometer mounted on a cell phone for health care and presence services. It is not necessary for users not to wear sensors on any part of their bodies; all they need to do is to carry the cell phone. Our algorithm has two main functions; one is to extract feature vectors by analyzing sensor data in detail using wavelet packet decomposition. The other is to flexibly cluster personal gaits by combining a self-organizing algorithm with Bayesian theory. Not only does the three-axis accelerometer realize low cost personal devices, but we can track aging or situation changes through on-line learning. A prototype that implements the algorithm is constructed. Experiments on the prototype show that the algorithm can identify gaits such as walking, running, going up/down stairs, and walking fast with an accuracy of about 80%.

System for providing access control platform service for private networks

Abstract: A NSP network is disclosed for providing a platform service for providing data communication security in an IP-based communications network. The platform service includes connections to various private networks, a subscriber data management unit for managing information about the mobile terminals using the network, and a platform unit. The platform unit determines from the subscriber data management unit the status of the mobile terminals requesting connections to private networks and the status of the mobile terminals for which private networks target for connection. The platform unit includes applications for querying the subscriber data management unit for subscriber status, applications to command the transmission of authorization data, and applications to provide secure virtual private network communication lines for connecting the mobile terminals and the private networks.

I-centric communications: personalization, ambient awareness, and adaptability for future mobile services

Abstract: The acceptance of next-generation mobile communication systems depends to a large extent on the services and applications that can be offered to customers. Tailoring the services to actual user needs is considered to be crucial for the success of future wireless technology. The individual user, "I", has to be put in the center of service provisioning. In this article, we report the work developed by the Working Group 2 of the Wireless World Research Forum on a service infrastructure framework for the future wireless world. Major service capabilities such as personalization, ambient awareness, and adaptability are described along with a reference model focusing in I-centric communication.

Driver Identification Using Driving Behavior Signals

Abstract: In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, were measured using a driving simulator. We compared the identification rate obtained using different identification models. As a result, we found the Gaussian Mixture Model to be superior to the Helly model and the optimal velocity model. Also, the driver's operation signals were found to be better than road environment signals and car behavior signals for the Gaussian Mixture Model. The identification rate for thirty driver using actual vehicle driving in a city area was 73%.