A multilevel data transmission system by using multiple Rotating Polarization Waves is proposed. Multilevel data are transmitted on plural RPWs which have different initial rotating phases at the same frequency. The receiver receives arriving waves on the optimum polarization at the optimum timing within the rotating period where the best communication quality was provided. The RPW system generates three different polarization channels when radio devices are surrounded by many electromagnetic scatters which disable to use line-of-sight paths. The selections of an optimum receiving polarization with and without a time block code achieve the best and better diversity effects by using independent three and two polarization channels, respectively.

The method to express electromagnetic scatting processes, which are reflection, penetration, and diffraction, by using only radiating rays that are generated when an original ray collides into an obstacle is proposed. Every electromagnetic phenomenon is obtained through the straight-forward recurring process of collision and radiation of a ray. Simultaneous parallel execution of such a process enables to accelerate calculation of the electromagnetic field using ray-tracing method. A prototype of calculation engine by using the graphic card with GPU that has more than 0.3 million numeric cores achieves field calculations including these scatting processes in the area equal to one floor of usual office building in less than a minute.

To realize connected and/or self-driving vehicles for a mobility environment, it requires an authentication mechanism to prevent spoofing of on-board units of the vehicle and a mechanism to prevent eavesdropping and tampering of communication data between an on-board unit and servers in the cloud. Considering the huge number of vehicles in the system, an authentication method is required to mitigate the complexity of operation and management for the number of vehicles. In this paper, we propose a data utilization system that securely stores in-vehicle LAN data, which consist of control data flowing inside a vehicle, in the cloud in real time, and provides this information securely to third parties such as vehicle users, car dealers, and non-life insurance companies. The system uses ID-based cryptography as a mutual authentication method between on-board units and the data collection servers, and uses a key generated from the ID of an on-board unit to ease the key management. We integrate the management information of the vehicle and the authentication key of the vehicle in the system, and thus reduces the complexity of the operation and the key management.

A social issue that leads to the safety and security of people is the network that monitors the safety of people living alone in remote areas. Various research and developments of indoor human activity recognition using IoT sensor nodes have been conducted in recent years. In this research, we focus on turning on and off indoor lighting and propose an activity recognition system at home using an illuminance sensor. An IoT sensor node with a built-in illuminance sensor is attached to the wall near the indoor lighting equipment. The living activities of people in the room are estimated from changes in the illuminance. We assume a significant change in the illuminance of the indoor lighting is observed at least once a day at the sampling interval of the IoT sensor node. In that case, it is possible to estimate the operation of the indoor lighting due to daily activities and to confirm the safety of people living alone in remote areas. The activity recognition system was evaluated for a total of 380 days, excluding the missing period. The estimation of indoor lighting manipulation by daily activities was accurate for 376 days. Precision, Recall, and F-Measure score values, which are evaluation indices for activity estimation, were 94.9%, 98.2%, and 96.5%, respectively.

In recent years, many electronic control units called ECUs have been installed in automobiles to perform various functions such as automatic driving technology and automatic safety devices. ECUs send and receive data using a communication protocol called CAN. It has been reported that it is possible to disable the brakes and hijack the gas pedal by sending unauthorized messages. Therefore, it is essential to take countermeasures against attacks such as eavesdropping, analysis, and spoofing attacks. This paper proposes low power consumption and secure CAN communication system. The proposed system adopted Midori-OTR which combines the lightweight block cipher Midori with authenticated encryption OTR. To improve security, countermeasures against DoS attacks will be implemented. Evaluation experiments using microcontroller boards verify the validity of the proposed system.

Radio frequency identification (RFID) systems enable us to achieve highly efficient inventory management and inspection work. However, if an RFID reader increases its transmission power to read densely arranged RFID tags in a cardboard box, stray reads of the RFID tags occur. These unwanted reads degrade the overall inventory performance. This letter proposes a reliable reading method for densely arranged RFID tags while suppressing stray reads.

This paper proposed a process of river-water-level monitoring using a 24 GHz FMCW (Frequency modulated continuous wave) radar. To improve its monitoring accuracy, the use of detected river velocities and the quartile-range limitation method excludes the outliers of calculated distances to water. The experiments at an actual river validated the effectiveness of the proposed process.

According to the report of investigation consideration committee of ground penetrating radar technic, almost applications of the ground penetrating radar (GPR) are investigation of cavity or public infrastructure. Although the considerations on other fields are advanced gradually, the GPR is not so become used light-heartedly due to the expensive cost. With this in mind, a cost-effective impulse radar has been considered for sensing of the bamboo-shoot, which grows under the ground at depth of about 10cm. Main emphasis was placed on the estimation of the impulse waveform for transmitting and received waves. In the primarily theoretical and experimental considerations, detection of a metal plate embedded into underground at a depth of 10cm was successfully preformed.

Previously, we proposed a simple flooding scheme to transmit a route request (RREQ) message based on the remaining power of its own node without control packets and complex calculations. This letter presents that the proposed scheme is more effective in improving energy consumption and lifetime with full-duplex than half-duplex in static and mobile multi-hop communications scenarios via the network simulator, ns-3. Moreover, we show that the lifetime of the proposed scheme with full-duplex is longer than that of half-duplex in all scenarios.

The number of landslides in Japan has been increasing in recent years. Because the main cause of landslides is to increase the water content in soil, the complex dielectric content of the hydrous decomposed granite was measured at 1MHz frequency to estimate the water content of the decomposed granite by receiving the AM broadcasting radio waves. The relative dielectric constant was changed from 3 to 20 versus the water content from 0 % to 20 %, and the loss tangent was also changed from 0.1 to 50 in the same range of the water content. It was obvious that the water content in soil may be estimated by investigating the propagation characteristics of the AM broadcasting radio waves.

It is effective to utilize the spectrum resources currently in use as well as to develop new spectrum resources in order to realize more advanced mobile communication systems. One of the goals of 5G is efficient connection to a large number of IoT devices. It is needed to automate a spectrum sharing for many IoT devices rather than the conventional manual centralized management. On the other hand, blockchain is a high availability autonomous decentralized service platform. This paper first provides an overview of the major spectrum sharing technologies that use blockchain. Next, we propose the autonomous decentralized dynamic spectrum sharing for IoT. This method uses blockchain to coordinate the automatic transfer of spectrum rights from PUs to SUs. It issues more crypto assets to IoT devices that share a large amount of sensing data. Since this crypto asset can be used for spectrum auctions, IoT devices that share a lot of data can get more spectrum sharing opportunities. We also propose the use of carrier aggregation to use shared spectrum and show the implementation method using OSS in O-RAN environment. Finally, we discuss processing time, use cases, changes in spectrum usage data, and changes in the number of bidders.

In recent years, digital transformation (DX) has been promoted. In addition to the integration of databases and systems, the creation of new value (increase in profit) through cooperation among multiple departments is also expected. In previous research, maintenance considering dynamic pricing is studied, which corresponds to cooperation between the sales department and the manufacturing department. In order to maximize profit, it is necessary to optimize the production quantity, but the production quantity is fixed in the previous research. In this research, maintenance considering dynamic pricing and production quantity selection is studied. A new maintenance method is proposed, which maximizes the expected profit based on statistical decision theory. Markov decision process is used in order to represent the maintenance problem. The effectiveness of the proposed method is shown by some computational examples. The expected profit of the proposed method is greater than that of a comparison target. In this research, the expected profit is maximized under the condition that all states and probabilities are known. But some of those are unknown in real cases. An expansion of this research to research with unknown information is one of further works.

The purpose of this study is to propose the method for constructing a model to evaluate the quality of the integrated report, called IRQ. The model, which is constructed by the proposed method, is a logistic regression model, that is trained on the descriptions of integrated reports selected and not selected by “Excellent Integrated Reports” of GPIF (Government Pension Investment Fund in Japan). As a result, first, it is confirmed from both quantitative and qualitative perspectives that the IRQ, which is calculated from the model, represents the quality of the integrated report. Second, two of three explanatory variables in the model are statistically significant, indicating that the model captures the descriptive characteristics of the selected integrated reports. Lastly, the model is shown to have a precision of 0.85, which is approximately 0.16 better than precision of a model, that is developed by the method introduced in the previous study. In conclusion, by using the model developed by the proposed method, it is possible to quantitatively clarify the difference in descriptive characteristics between the selected and non-selected integrated reports.

The method for designing tourism experiences has changed from summing up the evaluation of each Point of Interest to evaluating the whole of tourism. Individual excitement level (IEL) curves, which show the time-series changes in the degree of excitement of individual tourists, attract attention as a method for designing tourism experiences by evaluating entire tourism. The tourism experience needs to be evaluated using data collected during and after tourism to implement the IEL curve generation method and tourism design in tourist destinations. However, the previous evaluation of the IEL curve generation method is limited to using the data collected after tourism. Digital transformation in tourism enables data collection during tourism. Thus, this study aims to evaluate the IEL curve generation method from the viewpoints of the data collected not only after tourism but also during sightseeing, focusing on the electro dermal activity data. Besides, our article considers a tourism design based on IEL curves through an experiment. As a result, this article reveals that the generated IEL curve is suitable for reproducing subtle changes in the excitement in tourism and partially expresses the general shape of the changes. The interview survey we conducted complements the validity of these findings.

The purpose of this study is to propose a method for detecting ambiguous expressions from requirement specifications using text mining in order to improve the efficiency of checking the contents of the documents. It is assumed that questions and opinions about requirement specifications include details about ambiguities in the descriptions. Therefore, by text mining these questions and opinions, we construct a dictionary. We were able to detect ambiguous expressions in the requirement specification by using the dictionary. We believe that using our proposed method to detect ambiguous expressions with a high degree of accuracy will improve the efficiency of checking the contents of requirement specifications, reduce the difficulty of specification clarification work, delay delivery dates, and increase costs.

Aiming at the control of a powered prosthetic hand, this paper compares methods for the classification of intended hand motions using muscle bulging patterns caused by muscle contraction. Two sheets of Polyvinylidene Difluoride (PVDF) film were used as sensors to detect the muscle bulging on the forearm caused by intended hand motions. A neural network had been successfully trained for the classification of 6 types of hand motions using the muscle bulging pattern detected by the two PVDF sensors. In this paper, we further studied the motion classification methods of back propagation neural network (BPNN), k-nearest neighbor algorithm (k-NN), and support vector machine (SVM) to compare their classification performance. We found that all three methods had a similar classification rate of about 95 % for 6 types of hand motions. Moreover, a regressive analysis comparison of the time for each classification method to converge to 95 % of the total classification rate showed that SVM converged significantly earlier than BPNN and k-NN. The time it takes for SVM to converge the classification rate to 95 % is less than 0.1 s, suggesting that real-time motion classification is possible by using SVM. In like manner, we found that SVM requires the least training data of the three methods at only 9 trials for a type of motion. Furthermore, SVM had the highest classification rate at about 90 % in practical experimental conditions. In conclusion, SVM was found to be the most practical of the three methods.

In this paper, we made improvements and evaluated our proposed model for non-reference speech intelligibility estimation on reverberant speech, attempting to improve the estimation accuracy significantly. Our proposed method consists of a DNN for speech enhancement and a separate DNN for intelligibility estimation. The latter uses features obtained from enhanced and degraded speech to estimate intelligibility. Although previous studies have effectively estimated intelligibility for speech degraded by additive noise using similar models, they did not consider the degradation of distortion caused by reverberation. They also did not quantify the effect of various speech enhancement DNN models, the structure of the intelligibility prediction DNN, and the selection of parameters during feature calculation on estimation accuracy. Accordingly, we compared two top-of-the-line speech enhancement DNN models and used their output to train intelligibility prediction DNNs for reverberant speech while also varying the parameters used in the feature calculation. Consequently, the linear correlation coefficient between subjective and estimated intelligibility came to 0.801 with the best combination.

There is a lot of previous research on sales promotion strategy using digital signage. Maximization of expected sales is one of important topics on sales promotion strategy using digital signage. But in the previous research, maximization of expected sales considering changes in purchasing intentions has not been studied. In this research, a new sales promotion strategy which maximizes the expected sales based on statistical decision theory is proposed. In the proposed method, the expected sales is maximized considering changes in purchasing intentions with respect to a Bayes criterion. The effectiveness of the proposed method is shown by some computational examples. The expected sales of the proposed method is greater than that of a comparison target. In this research, the expected sales is maximized under the condition that all probabilities are known. But the probabilities are unknown in real cases. An expansion of this research with unknown probabilities is one of further works.

In this letter, a synthesis method for semi-strongly stabilizable compensators based on disturbance observers (DOB) for second-order lag systems is proposed. The basic idea is to synthesize the gain of the DOB such that some of the compensator poles are fixed on the imaginary axis, while another can be assigned to the open left half-plane. Usefulness of the proposed method is illustrated by an experimental example showing semi-strong stabilization and disturbance attenuation performance.