In this paper, we show that there is an excellent possibility that 5G can be operated as a spectrum-sharing system in the 15 GHz band of the frequency range 3 (FR3). First, we consider how much spectrum resource can be secured in the 15 GHz band and show that it can ensure a 550 MHz bandwidth in many places and a 190–510 MHz bandwidth in urban areas. Next, we propose a phase noise model for the 15 GHz band, evaluate the block error rate characteristics of various sub carrier spacings (SCSs) using link-level simulation, and show no effect of phase noise if the SCS is 30 kHz or more. Finally, the interference immunity of the 5G new radio is evaluated, and it is found that the required SIR is approximately 10 dB and that each 5G base station could achieve coverage of approximately 700 m in radius.

We propose to extend the concept of difference lists to general data structures and call them difference data structures. Difference lists are well-known in logic programming as a data structure that realizes constant-time concatenation by retaining a reference to the tail as well as the head of the list. In recent years, they are also called list segments and appear as a typical example of separation logic, a logic for handling heaps and pointers. Difference data structures can be utilized to discuss various important concepts in programming languages including functions, evaluation contexts, and continuations, in a unified setting. To handle such structures succinctly and safely, we base this work on a graph rewriting language. We propose a typing method for graph rewriting languages based on graph grammar, which covers structures beyond algebraic data types, to allow users to define difference data types for two different major applications, i.e., (i) users can describe powerful pattern matching with user-defined shapes and (ii) the implementation can statically type-check operations on difference data structures efficiently. Our method introduces the concept of difference at the meta-level, i.e., difference data types are automatically derived from the base data type defined by graph grammar so that we can treat the base structure and difference structure uniformly without type conversion.

We propose to apply graph convolutional networks to a novel domain, mobile app usage. User state estimation from smartphone usage has attracted attention thanks to the prevalence of smartphones. Basic statistics such as the sum of the number of used apps and usage duration have been used for estimating the user state. However, the accuracy of user state estimation can be improved by considering the sequence of apps used and the relationship between each app based on graph convolutional networks. In this paper, we proposed a representation learning method for mobile app usage using graph convolutional networks. We evaluate the proposed method through comparison to another deep learning approach, i. e., long short-term memory, for a classification problem.

Billions of people are using smartphones everyday and they often face problems and troubles with both the hardware as well as the software. Such problems lead to frustrated users and low customer satisfaction. Developing an automatic machine learning-based solution that would detect that the user has a problem and would engage in troubleshooting has the potential to significantly improve customer satisfaction and retention. Here, we design and implement a system that based on the user's smartphone activity detects that the user has a problem and requires help. Our system automatically detects a user has a problem and then helps with the troubleshooting by recommending possible solutions to the identified problem. We train our system based on large-scale customer support center data and show that it can both detect that a user has a problem as well as predict the category of the problem (89.7% accuracy) and quickly provide a solution (in 10.4ms). Our system has been deployed in commercial service since January, 2019. Online evaluation result showed that machine learning based approach outperforms the existing method by approximately 30% regarding the user problem solving rate.

This paper demonstrates a new ID card based momentum measurement system for office workers' health managements in companies. Our system can measure office workers' momentum with a new ID card that implements a microprocessor, sensors and a RFID tag because an amount of momentum has a relation with a health condition and it is an important indicator to prevent chronic diseases. RFID based ID cards are widely used in office buildings to manage a security control and to record working hours. Therefore, companies can install the proposed system by updating software for a RFID reader/writer in entrance gates, doors, etc. The demonstration shows a prototype system for health management system with ID card based measurement devices of momentum.

The anxiety of merging tiny materials and resin materials in the manufacturing process has become one of the serious issues because it may become cause of overheat such as lithium-ion batteries, flat panel displays, and so on. Therefore, a study on detecting method of the tiny metals is a significant issue. A combination of high-frequency induction heating and thermal detection method is one of the useful approaches. In this paper, MHz-frequency GaN inverter based IH system is built and tested to verify heating size limitation of the tiny metals. Furthermore, the validity of the proposed method to detect the tiny metals by proposed IH thermal technique is evaluated and discussed in detail.

We are furthering research on a remote sports system in which persons at different locations compete by communicating physical information such as positions and forces. In general, various techniques for a bilateral master-slave type of remote control seem applicable to a remote sports system. This is basically true; but it is also true that there are special issues to be solved inherent in a remote sports system. For instance, players at different locations are masters and also slaves, so the relationship between them must be completely symmetrical. It is important to clarify what information players must communicate and how the environment of each side should be identified. As a part of this approach, we built a remote "arm-wrestling" system in our laboratory. In the present paper, we explain the prototype system and describe some experimental results for verifying whether or how techniques for many types of master-slave based remote control systems proposed so far are effective. We also suggest some future subjects toward a remote arm-wrestling system with a better and more realistic presence.