The isolation characteristics between the antennas of transmission and reception are important for signal quality. In this paper, we propose a small dual-polarized antenna with dual vertical split-ring resonators and differential feeding. This novel antenna is based on artificial magnetic conductors (AMCs) modified to radiate directly. A high isolation characteristic between two ports of over 70 dB was achieved.

In advanced driver assistance systems (ADAS) and autonomous driving, multiple-input multiple-output (MIMO) frequency-modulated continuous wave (FMCW) millimeter-wave radars are essential for detecting distant, high-velocity targets. However, these radars inevitably suffer from degraded range resolution, which leads to inferior target size identification performance of targets, as demonstrated by the experimental results in this paper. Accurate size identification has recently garnered significant attention for achieving high-performance point cloud-based imaging radars. To address this challenge, an appropriate interpolation technique is needed. This paper proposes a simple resampling technique to address this issue. Experimental results validate that the proposed technique provides accurate target size identification.

In-network caching enables us to efficiently retrieve contents by storing the contents in the cache storage of routers. In order to utilize in-network caching environments, an appropriate routing method for content requests is required. This paper proposes a reinforcement learning-based routing method using hierarchical content names for in-network caching. The proposed method applies the multi-armed bandit (MAB) algorithm, which is one of reinforcement learning, to routing decision at routers. The routers have tree-shaped routing tables managing hierarchical names of contents. Content requests arriving at the routers are forwarded to directions determined by the MAB algorithm referring to the hierarchical content names. The proposed method dynamically changes the tree-shaped routing tables according to arrival content requests, so that it can efficiently reduce the size of routing tables. Through simulation experiments, we show that the proposed method keeps high cache hit ratio and low hop counts to retrieve contents, while reducing the size of routing tables.

In recent years, large-scale wireless multi-hop networks have been established for smart cities. These network systems collect various data from hundreds of nodes. The IPv6 Routing Protocol for Low power and Lossy Networks (RPL) is widely used for building large-scale wireless multi-hop networks. However, previous studies have pointed out that the routing overhead of RPL is significantly high. With an increase in nodes, there is a tendency for the overhead of control messages to rise, which may lead to a decrease in data collection rates due to radio interference between nodes. Moreover, since the radio propagation characteristics in real environments are not uniform, so there are concerns that in poor radio conditions, the retransmission of control messages could locally increase traffic load. To address this issue and allow for flexible adjustment of RPL’s routing overhead, we propose to run RPL over Time Division Multiple Access (TDMA) based access control scheme. To evaluate the effectiveness of this approach, we conducted field experiments of a large-scale wireless multi-hop network, and report the results in this paper.

This paper presents a design process of a bandpass filter (BPF) with a dielectric-tube supported metal rod transmission line (DTM line). First, the equivalent circuit was shown, and the outline of the design was presented. Next, each dimension was designed using HFSS as electromagnetic field simulator, and an ideal characteristic with the BPF was confirmed. Finally, the prototype and the measured result were presented. The prototype BPF achieved an insertion loss of 1.7dB at 81GHz with a fractional bandwidth of 2.4%. In addition, the result was compared with those of BPFs with other transmission lines, and it was showed that the prototype BPF had higher performance than BPFs with the lines that used printed substrates at the same frequency bands.

In this paper, we evaluate an effectiveness of parameter optimization of thermal-camera-based sensor using frame difference method to improve an energy saving effect of an occupancy lighting control. Our experiment, which employed data collected in a private office for 8 days, showed that the thermal-camera-based sensor increased the energy saving effect of the occupancy control by 1.90%-2.86% compared with a PIR sensor. The optimization of frame interval parameter, which has not been discussed in the literature, increased the energy saving effect of the thermal-camera-based sensor by 0.96% at the most, thereby accounting for 33.57% of the entire improvement owing to the thermal-camera-based sensor. Further findings and important analysis are included in this paper to achieve improvement of the thermal-camera-based sensor.

Pitch estimation plays an important role in speech information systems. In this paper, we propose the use of a bone conduction filter, implemented as an infinite impulse response (IIR) filter, for pitch estimation of voiced speech in noisy environments. Among pitch estimation methods, classified into signal processing-based and data-driven approaches, we focus on the signal processing-based autocorrelation function method. To evaluate the bone conduction filter, we concentrate solely on the core pitch estimation process, excluding pre-processing and post-processing, and avoid data-driven methods requiring extensive training datasets. Air-conducted speech is transformed into bone-conducted speech using the filter, and pitch estimation is performed using a normalized autocorrelation function. Experimental results demonstrate the bone conduction filter's effectiveness in diverse noise environments, particularly at signal-to-noise ratios (SNRs) above 10dB. Furthermore, the filter significantly improves pitch estimation accuracy in challenging conditions, such as white noise and HF channel noise, even at extremely low SNRs like -5dB.

Currently, infrastructure structures are aging and there is a demand to extend the service life of infrastructure structures. Infrastructure monitoring systems are attracting attention as extending their service life. In this paper, we propose a system to measure ground moisture content at multiple points in real time in order to realize robust embankments.

5G mobile network system has been practically used in recent years and many security issues have been pointed out. Meanwhile, 5G open-source implementations are progressing as 5G networks are becoming more open. As a result, it has become relatively easy to test the functions of base stations and other devices. Various security issues are becoming more realistic. The threat posed by fake base stations has been pointed out and countermeasures are needed. Many countermeasures have been proposed so far, but most of them involve modification of communication protocols. Therefore, a great deal of work such as standardization is required. This study proposes a base station identification method necessary for detecting fake base stations by third parties that does not assume modification of the communication protocol. This method achieves high identification performance by adopting the eigenvalues of CSI used in OFDM and MIMO antennas. We compared and evaluated the proposed method with an improved version of existing research and confirmed its high identification performance.

One of the crucial problems with urban railways is the decrease in punctuality due to chronic train delays. By using analytical or simulation models, previous studies have tried to clarify the delay phenomenon caused by multiple factors, such as train running/dwell time and weather. However, few studies have reflected on the actual phenomenon and described the mechanism of train delay, and few have considered the weather. Therefore, in this study, we constructed a Bayesian network showing the spatiotemporal relationship between stations and trains using detailed operation record data of the Tokyo Metro Tozai Line for an extended period and data from the Japan Metro-logical Agency. In addition, the mechanism of the delay phenomenon and the influence of the weather were quantitatively analyzed using the network. As a result, it was possible to analyze stations and trains explicitly, and it was clarified that the delay probability becomes large on rainy days and that the influence of rainfall becomes significant in the ground section and the section where congestion is concentrated.

This study analyzes the factors influencing travel satisfaction based on a survey on travel satisfaction, focusing on conditions that contribute to higher satisfaction. In addition to demographic factors such as gender and age, the analysis also considers variables such as the number of previous trips, travel expenses, and whether a travel companion is present. First, we conducted a statistical test to examine whether there are gender differences in attitudes toward travel. Next, a Bayesian network analysis was performed to identify combinations of factors that lead to higher satisfaction. Additionally, decision tree analysis was used to explore the specific conditions under which satisfaction levels are likely to be higher. Based on these findings, the paper proposes recommendations for how to encourage higher travel satisfaction for different genders by considering the key factors that influence satisfaction in travel planning. The results suggest that both individual preferences and travel conditions should be carefully considered when designing travel experiences. Furthermore, the analysis results aim to enable travel service providers to develop services that take new attributes into account, rather than relying on conventional rules of thumb in service development.

Designing a cooperative problem-solving process requires utilizing the experience and knowledge of each member and selecting problem-solving techniques that fit the target problem-solving process. To achieve these requirements, we model the problem-solving process as a sequence of sensorimotor action patterns (called a schema sequence) and develop a method to use this model to select problem-solving techniques.

In this paper, we first explain the trends and challenges of problem-solving techniques, then propose a schema sequence and a method for selecting problem-solving techniques that fit the target problem-solving process based on the schema sequence. Finally, we evaluate the proposed method through a case study.

As the social issues surrounding government become increasingly complex and diverse, and digital technologies capable of addressing these issues advance rapidly, local government employees are expected to proactively acquire knowledge and skills tailored to the issues they encounter. This study presents a method to extract and identify the digital competencies required by local government employees who are working to promote digital transformation (DX) using DX case information and text generative AI. Utilizing text generative AI enables the abstraction of prompt meanings by incorporating information and context not explicitly included in the prompt, thereby generating responses grounded in broader contexts and related knowledge. By evaluating the reliability, usefulness and consistency of the output produced by this method, the study clarifies the potential for issue-based estimation of the digital competencies required by local government employees to address the social issues they encounter.

The study proposes the methods to assess whether the integrated reports issued from Japanese companies can be used for making investment decisions. In this study, IRQ score is calculated for each integrated reports as a proxy variable for the quality of the reports. The proposed method reveals the relationship between IRQ score and corporate value, and between IRQ score and stock price. As a result, a negative relationship is shown between IRQ score and firm value, while a positive relationship is revealed for stock price. This result implies that, because of the costs involved in preparing an integrated report, it will have a negative impact on corporate value in short term. On the other hand, since stock prices are formed by incorporating long-term business growth, a positive relationship is generated between the integrated reports with high IRQ score and stock value. Because the result is consistent, the integrated reports can be used as a tool for investment decisions.

In recent years, the rapid advancement of IT has significantly transformed the business environment, requiring companies to deliver services and products faster to meet market demands. Open source software (OSS) has become a key tool for achieving both speed and quality in development. Companies use OSS not only to reduce costs and development time but also to ensure stability, transparency, and flexibility while avoiding vendor lock-in. However, since OSS relies on community support, its maintenance and long-term quality can be uncertain. Studies indicate that around 82% of OSS projects become inactive, making it essential for businesses to assess the support quality of OSS before adopting it. In this paper, we focus on the issue resolution rate as a key indicator of OSS support quality, analyzing trends to help companies make informed decisions about which OSS projects to trust, ensuring they select software with active community support and sustainable development. Additionally, this paper seeks to provide insights into predicting OSS support quality by examining the early response patterns observed in OSS projects after their initiation.

In today’s business landscape, shareholders are increasingly focused on corporate initiatives, particularly those related to Environmental, Social, and Governance (ESG) factors. As such, integrating ESG considerations into corporate management practices has become essential. A critical element for the effective promotion of ESG management lies in understanding and leveraging the real feedback from end users to shape corporate policies. On the other hand, companies that confine their activities to internal operations may prioritize profit over broader responsibilities, rendering them susceptible to unforeseen challenges related to environmental, social, and governance issues. These vulnerabilities can pose significant risks to their overall management strategies. This study highlights that the growth of Japanese industrial companies including those in the automotive, construction, energy, and manufacturing sectors cannot be fully captured through financial metrics alone. To address this, the paper proposes a new approach employing Plutchik’s emotion wheel to quantitatively analyze public sentiment regarding ESG issues. By mapping the emotions expressed in user comments, this method aims to facilitate a deeper understanding of stakeholder perspectives, ultimately guiding corporate decision-making toward more responsible and impactful outcomes.