The identification of the radio access technology (RAT) of the primary user by secondary users is important to avoid interference for spectrum-sharing techniques using cognitive radio (CR). RATs have become more diversified with the introduction of various services that use wireless communication. Therefore, it is desirable that a RAT identification system, which can easily cope with the diversification of RATs, is applied to CR. The purpose of this study is to use online learning to identify multiple RATs in the same frequency band. To improve the accuracy of identifying RATs with similar features, we evaluate a normalization of radio feature method proposed in our previous research for the features extracted from the signal's spectrogram. We evaluate the RAT classifier created using the proposed method by calculating the curve of receiver operating characteristics (ROC) and the identification accuracy. The results for the ROC curve show that the proposed method is effective for several supervised learning methods. Moreover, the results for the identification accuracy show that the proposed method improves the identification performance compared to the identification accuracy of conventional methods.
With the spread of the Internet, e-learning which is a way of learning using information technology has become popular. Although lecture videos in e-learning are designed so that many learners can understand, they do not take into consideration individual differences of learners. To address this problem, we designed a system that can grasp the concentration state of learners using electroencephalogram and dynamically provide contents according to the state. Moreover, we defined a metric using alpha waves in brain waves acquired from the occipital region as an index of changing learning materials used in the system. It enables providing suitable contents to each learner. In this research, we investigated the usefulness of the index and compared it with other brain waves such as theta waves and beta waves. As a result, it was confirmed that the proposed index was useful to extract learners who are relatively concentrating and not relatively concentrating. Moreover, it was suggested that alpha waves were more useful as the index for concentrating state than other waves.
This paper deals with map-construction problems for visual localization. Basically, the map is an aggregation of visual landmarks, and it is desirable that such landmarks exist densely and permanently for long-term localization. However, there are no landmarks with these attributes at the same time. In order to solve this problem, we propose a hybrid map with permanent landmarks and temporal landmarks. As a permanent landmark, we employ 3D wireframe which can be easily obtained from architectural CAD. For a temporal landmark, we use line segments which are visually detected in images captured by a camera. To handle these two types of landmarks on the same map, we develop two algorithms. One is to extract temporal line segments from images containing two mixed landmarks, and the other is to reconstruct them into the 3D wireframe map. We experimentally demonstrated that the proposed hybrid map outperformed the 3D wireframe map in terms of localization accuracy.
We analyzed a mechanism to alleviate aggressions on computer mediated communications. A title that supposedly reflects the user's behavior or status, such as calm, expert, optimist, or aggressive is displayed on the screen. Two types of title generation, title reflecting and not reflecting exact aggression level, are compared to the case without title using laboratory experiments, where participants select comments to be posted after reading the topics of discussion and other participants' comments. The results indicate that displaying a title that is unrelated to the aggressiveness of comments posted by the user is equivalent to displaying a title based on accurately calculated aggression level of comments. Therefore, simply displaying their titles to users is effective to reduce aggressiveness of their comments in pseudonym type computer mediated communications.
This paper presents flow configuration software enhancement on the industrial internet of things (IIoT) environment using field device tool (FDT) ver. 2 (FDT2) technology. The focus of this paper is to exhibit how FDT2 business logic components are preserved for IIoT enhancement and how easily IIoT environment is realized. We show the value of this development by explaining the effectiveness of IIoT use cases. The FDT IIoT server (FITS) and the reference architecture model Industrie 4.0 (RAMI 4.0) relation analysis is done to prove its effectiveness. We confirmed a dramatical change of field device management work by each use case analysis which covers a variety of end-user requirement. Decoupling the DTM user interface and DTM business logic means that digital data produced in DTM business logic is transformed appropriately according to each end-user scenario. We decoupled flow measurement application extended functionality, and it means that achievement in developing key technologies for an asset management tool that makes full use of IIoT to measure mass flow.