The experimental evidence of the decreased heterodyne-detection efficiency caused by the lag-angle is shown using a long-range wind sensing coherent Doppler lidar (CDL) with fast beam scanning. The recovery of the efficiency with the lag-angle compensation is also demonstrated. The receiving beam alignment method synchronized with the beam scanner is used. The measurable range of 12 km with the fast beam scanning of 20 deg/s in the case of 8 Hz line-of-sight (LOS) update rate was demonstrated with the compensation. This demonstration shows the potential for the wind sensing CDL to satisfy the all requirements of long-range, real-time, and fast beam scanning measurement.

Edge computing, which locates edge servers with limited computing and storage resources at the edge of networks, is expected as a novel architecture for low-latency applications. In edge computing, users offload a task to edge servers due to poor computing resources and batteries of mobile devices so that the edge servers execute the offloaded task and return the result of it to users. Users can thus enjoy various applications without depending on the limitations of mobile devices. However, when the edge server's load is too heavy, a large number of tasks will be offloaded to distant cloud servers. The long distance between users and cloud servers significantly degrades the quality of mobile applications. To prevent this problem, we propose flow splitting and aggregation schemes to improve offload delay to cloud servers in edge computing. This scheme splits TCP connections between users and cloud servers at the edge server, and then aggregates TCP connections between the edge server and cloud servers. We show the effectiveness of our scheme through simulation evaluations.

Information-centric networking (ICN) is expected as a novel network architecture for content delivery services. In ICN, content routers (CRs) with a cache function store a chunk received from upstream CRs or servers. Therefore, clients can retrieve distributed chunks from multiple nodes simultaneously. However, it will cause heavy congestion due to competition of multiple flows in a network as well as long time to acquire content. In this study, we propose an efficient congestion control scheme to retrieve distributed chunks from multiple nodes in ICN. This scheme aggregates competing flows and then allocates an adequate transmission rate to the aggregated flows. We show the effictiveness of the proposed scheme through simulation evaluations.

A content-centric networking (CCN) is expected for high quality streaming services with in-network caching. In mobile environment, users cannot frequently receive requested segments due to handovers. This will cause retransmissions of missing segments from a server as well as playback delays and interruptions. In this study, we propose a more effective scheme to utilize caches near users for high quality streaming services in mobile CCN, and demonstrate the effectiveness of our scheme through simulation evaluations.

In conceptual design for an attractive product, the visualization of Kansei value as requirements and the realization of its value for creating product ideas are desired. Therefore, the Idea Creation Support System (ICSS)-for an idea creation with Kando understanding process through Word Of Mouth (WOM) effectiveness—has been developed. ICSS has consisted of two understanding processes, i.e., problem and Kando understanding processes. However, ICSS does not include shape and layout understanding process—which is grasping the imagined topology of mind as requirement—which is most important process as for mechanical products. In this study, we propose the shape and layout understanding method which consisted of SIMP method as a topology optimization and Kansei information understanding of topology using Electroencephalography (EEG) through Brain Machine Interface (BMI), and also discuss the system configuration of ICSS with shape and layout understanding method.

In this paper, we describe technologies of the robot Accelite that is the winner of the RoboCup2015 Humanoid KidSize League. In 2015, the rules of the humanoid league were changed dramatically. The ball was changed from a monochromatic orange ball to a real soccer ball. Furthermore, the goal color was also changed from yellow to white. Therefore, the soccer robots in the humanoid league were not able to detect the ball based on just colors. We developed some detection methods and verified their effectiveness. Consequently, our robots detected the ball robustly and we succeeded in the technical challenge “Goal-Kick from Moving Ball”. In RoboCup 2015, the field of play was also changed from a punch carpet to an artificial grass. Because the pile length of the artificial grass in RoboCup2015 was long, our robots were not able to walk robustly when we started to use the field. We also explain how we solved the problem. We also discuss how we succeeded in the technical challenges.