Many countries are facing the aging problem caused by the growth of the elderly population. Nursing home (NH) is a common solution to long-term care for the elderly. This paper develops a simulator to model elder behavior in an NH, which considers public areas where elders interact and imitates their general, group, and special activities. Elders have their preferences to decide activities taken by them. The simulator takes account of the movement of elders and abnormal events. Based on the simulator, two seeking methods are proposed for caregivers to search lost elders efficiently, which helps them fast find out elders who may incur accidents.

This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.

Mobility as a Service (MaaS) is expected to spread globally and in Japan as a solution for social issues related to transportation. Researchers have conducted MaaS trials in several cities. However, only a few trials have reached full-scale practical use. Therefore, it is essential to clarify issues such as the business model and user acceptability and seek solutions to social problems rather than simply conducting trials. This paper describes the introduction of a MaaS project supported by the Japanese government known as the “Smart Mobility Challenge” project, conducted in 2020 and 2021. We employed five themes necessary for social implementation from the first trial of this MaaS project. As a consortium, we also promoted regional demonstrations by soliciting regional applications based on these five themes. In addition, we conducted fundamental research using data from the MaaS projects to clarify local transportation issues in detail, collect residents' mobile behavior data, and assess the project's effects on the participant's happiness. We employed the life-space assessment method to investigate the spread of the residents' behavioral life-space resulting from using mobility services. The spread of the life-space mobility before and after using mobility services confirmed an expansion of the life-space because of specific services. Moreover, we conducted questionnaire surveys and clarified the relationships between life-space assessment, human characteristics, and subjective happiness using path analysis. We also conducted a persona-based approach in addition to objective data collection using GPS and wearable monitors and a web-based questionnaire. We found differences between the actual participants and participants assumed by local governments. We conducted interviews and developed tips for improving mobility service. We propose that qualitative data help clarify the image of mobility services that meet the residents' needs.

To simultaneously enhance data rate and physical layer security (PLS) for low-luminance smartphone screen to camera uplink communication, space division multiplexing using high-luminance cell-size reduction arrangement is numerically analyzed and experimentally verified. The uplink consists of a low-luminance smartphone screen and an indoor telephoto camera at a long distance of 3.5 meters. The high-luminance cell-size reduction arrangement avoids the influence of spatial inter-symbol interference (ISI) and ambient light to obtain a stable low-luminance screen. To reduce the screen luminance without decreasing the screen pixel value, the arrangement reduces only the high-luminance cell area while keeping the cell spacing. In this study, two technical issues related to high-luminance cell-size reduction arrangement are solved. First, a numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more effective in reducing the spatial ISI at low luminance than the conventional low-luminance cell arrangement. Second, in view point of PLS enhancement at wide angles, symbol error rate should be low in front of the screen and high at wide angles. A numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more suitable for enhancing PLS at wide angles than the conventional low-luminance cell arrangement.

In quasi-synchronous FH multiple-access (QS-FHMA) systems, no-hit-zone frequency-hopping sequences (NHZ-FHSs) can offer interference-free FHMA performance. But, outside the no-hit-zone (NHZ), the Hamming correlation of traditional NHZ-FHZs maybe so large that the performance becomes not good. And in high-speed mobile environment, Doppler shift phenomenon will appear. In order to ensure the performance of FHMA, it is necessary to study the NHZ-FHSs in the presence of transmission delay and frequency offset. In this paper, We derive a lower bound on the maximum time-frequency two-dimensional Hamming correlation outside of the NHZ of NHZ-FHSs. The Zeng-Zhou-Liu-Liu bound is a particular situation of the new bound for frequency shift is zero.

This paper summarizes the modulation configurations of phase locked loops (PLLs) and their integration in semiconductor circuits, e.g., the input modulation for cellular phones, direct-modulation for low power wireless sensor networks, feedback-loop modulation for high-speed transmission, and two-point modulation for short-range radio transceivers. In this survey, basic configuration examples of integrated circuits for wired and wireless applications which are using the PLL modulation configurations are explained. It is important to select the method for simply and effectively determining the characteristics corresponding to the specific application. The paper also surveys technologies for future PLL design for digitizing of an entire PLL to reduce the phase noise due to a modulation by using a feedback loop with a precise digital phase comparison and a numerically controlled oscillator with high linearity.

In this paper, some properties of Boolean functions via the unitary transform and c-correlation functions are presented. Based on the unitary transform, we present two classes of secondary constructions for c-bent4 functions. Also, by using the c-correlation functions, a direct link between c-autocorrelation function and the unitary transform of Boolean functions is provided, and the relationship among c-crosscorrelation functions of arbitrary four Boolean functions can be obtained.

The problem caused by fake news continues to worsen in today's online social networks. Intuitively, it seems effective to issue corrections as a countermeasure. However, corrections can, ironically, strengthen attention to fake news, which worsens the situation. This paper proposes a model for describing the interaction between fake news and the corrections as a reaction-diffusion system; this yields the mechanism by which corrections increase attention to fake news. In this model, the emergence of groups of users who believe in fake news is understood as a Turing pattern that appears in the activator-inhibitor model. Numerical calculations show that even if the network structure has no spatial bias, the interaction between fake news and the corrections creates groups that are strongly interested in discussing fake news. Also, we propose and evaluate a basic strategy to counter fake news.

Reconfigurable intelligent surface (RIS) has the capability of boosting system performance by manipulating the wireless propagation environment. This paper investigates a downlink RIS-aided non-orthogonal multiple access (NOMA) system, where a RIS is deployed to enhance physical-layer security (PLS) in the presence of an eavesdropper. In order to improve the main link's security, the RIS is deployed between the source and the users, in which a reflecting element separation scheme is developed to aid data transmission of both the cell-center and the cell-edge users. Additionally, the closed-form expressions of secrecy outage probability (SOP) are derived for the proposed RIS-aided NOMA scheme. To obtain more deep insights on the derived results, the asymptotic performance of the derived SOP is analyzed. Moreover, the secrecy diversity order is derived according to the asymptotic approximation in the high signal-to-noise ratio (SNR) and main-to-eavesdropper ratio (MER) regime. Furthermore, based on the derived results, the power allocation coefficient and number of elements are optimized to minimize the system SOP. Simulations demonstrate that the theoretical results match well with the simulation results and the SOP of the proposed scheme is clearly less than that of the conventional orthogonal multiple access (OMA) scheme obviously.

Multi-band transmission technologies promise to cost-effectively expand the capacity of optical networks by exploiting low-loss spectrum windows beyond the conventional band used in already-deployed fibers. While such technologies offer a high potential for capacity upgrades, available capacity is seriously restricted not only by the wavelength-continuity constraint but also by the signal-to-noise ratio (SNR) constraint. In fact, exploiting more bands can cause higher SNR imbalance over multiple bands, which is mainly due to stimulated Raman scattering. To relax these constraints, we propose wavelength-selective band switching-enabled networks (BSNs), where each wavelength channel can be freely switched to any band and in any direction at any optical node on the route. We also present two typical optical node configurations utilizing all-optical wavelength converters, which can realize the switching proposal. Moreover, numerical analyses clarify that our BSN can reduce the fiber resource requirements by more than 20% compared to a conventional multi-band network under realistic conditions. We also discuss the impact of physical-layer performance of band switching operations on available benefits to investigate the feasibility of BSNs. In addition, we report on a proof-of-concept demonstration of a BSN with a prototype node, where C+L-band wavelength-division-multiplexed 112-Gb/s dual-polarization quadrature phase-shift keying signals are successfully transmitted while the bands of individual channels are switched node-by-node for up to 4 cascaded nodes.

In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.

The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.

Car navigation systems provide traffic jam information. In this study, we attempt to provide more detailed traffic jam information that considers the lane in which a traffic jam is in. This makes it possible for users to avoid long waits in queued traffic going toward an unintended destination. Lane-specific traffic jam detection utilizes image processing, which incurs long processing time and high cost. To reduce these, we propose a “suddenness index (SI)” to categorize candidate areas as sudden or periodic. Sudden traffic jams are prioritized as they may lead to accidents. This technology aggregates the number of connected cars for each mesh on a map and quantifies the degree of deviation from the ordinary state. In this paper, we evaluate the proposed method using actual global positioning system (GPS) data and found that the proposed index can cover 100% of sudden lane-specific traffic jams while excluding 82.2% of traffic jam candidates. We also demonstrate the effectiveness of time savings by integrating the proposed method into a demonstration framework. In addition, we improved the proposed method's ability to automatically determine the SI threshold to select the appropriate traffic jam candidates to avoid manual parameter settings.

Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.

Recent studies in protein structure prediction such as AlphaFold have enabled deep learning to achieve great attention on the Drug-Target Affinity (DTA) task. Most works are dedicated to embed single molecular property and homogeneous information, ignoring the diverse heterogeneous information gains that are contained in the molecules and interactions. Motivated by this, we propose an end-to-end deep learning framework to perform Molecular Heterogeneous features Fusion (MolHF) for DTA prediction on heterogeneity. To address the challenges that biochemical attributes locates in different heterogeneous spaces, we design a Molecular Heterogeneous Information Learning module with multi-strategy learning. Especially, Molecular Heterogeneous Attention Fusion module is present to obtain the gains of molecular heterogeneous features. With these, the diversity of molecular structure information for drugs can be extracted. Extensive experiments on two benchmark datasets show that our method outperforms the baselines in all four metrics. Ablation studies validate the effect of attentive fusion and multi-group of drug heterogeneous features. Visual presentations demonstrate the impact of protein embedding level and the model ability of fitting data. In summary, the diverse gains brought by heterogeneous information contribute to drug-target affinity prediction.

The data augmentation method is known as a helpful technique to generate a dataset with a large number of images from one with a small number of images for supervised training in deep learning. However, a low validity augmentation method for image recognition was reported in a recent study on artificial intelligence (AI). This study aimed to clarify the optimal data augmentation method in deep learning model generation for the recognition of white blood cells (WBCs). Study Design: We conducted three different data augmentation methods (rotation, scaling, and distortion) on original WBC images, with each AI model for WBC recognition generated by supervised training. The subjects of the clinical assessment were 51 healthy persons. Thin-layer blood smears were prepared from peripheral blood and subjected to May-Grünwald-Giemsa staining. Results: The only significantly effective technique among the AI models for WBC recognition was data augmentation with rotation. By contrast, the effectiveness of both image distortion and image scaling was poor, and improved accuracy was limited to a specific WBC subcategory. Conclusion: Although data augmentation methods are often used for achieving high accuracy in AI generation with supervised training, we consider that it is necessary to select the optimal data augmentation method for medical AI generation based on the characteristics of medical images.

In recent years, driver's visual attention has been actively studied for driving automation technology. However, the number of models is few to perceive an insight understanding of driver's attention in various moments. All attention models process multi-level image representations by a two-stream/multi-stream network, increasing the computational cost due to an increment of model parameters. However, multi-level image representation such as optical flow plays a vital role in tasks involving videos. Therefore, to reduce the computational cost of a two-stream network and use multi-level image representation, this work proposes a single stream driver's visual attention model for a critical situation. The experiment was conducted using a publicly available critical driving dataset named BDD-A. Qualitative results confirm the effectiveness of the proposed model. Moreover, quantitative results highlight that the proposed model outperforms state-of-the-art visual attention models according to CC and SIM. Extensive ablation studies verify the presence of optical flow in the model, the position of optical flow in the spatial network, the convolution layers to process optical flow, and the computational cost compared to a two-stream model.

Machine vision-based automatic anti-bird thorn failure inspection, instead of manual identification, remains a great challenge. In this paper, we proposed a novel Object Position Embedding Network (OPENnet), which can improve the precision of anti-bird thorn localization. OPENnet can simultaneously predict the location boxes of the support device and anti-bird thorn by using the proposed double-head network. And then, OPENnet is optimized using the proposed symbiotic loss function (SymLoss), which embeds the object position into the network. The comprehensive experiments are conducted on the real railway video dataset. OPENnet yields competitive performance on anti-bird thorn localization. Specifically, the localization performance gains +3.65 AP, +2.10 AP50, and +1.22 AP75.

To achieve object recognition, it is necessary to find the unique features of the objects to be recognized. Results in prior research suggest that methods that use multiple modalities information are effective to find the unique features. In this paper, the overview of the system that can extract the features of the objects to be recognized by integrating visual, tactile, and auditory information as multimodal sensor information with VRAE is shown. Furthermore, a discussion about changing the combination of modalities information is also shown.

In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in the past decades. Especially, perturbation methods based on Geo-Indistinguishability (GeoI), which randomly perturb a true location to a pseudolocation, are getting attention due to its strong privacy guarantee inherited from differential privacy. However, GeoI is based on the Euclidean plane even though many LBSs are based on road networks (e.g. ride-sharing services). This causes unnecessary noise and thus an insufficient tradeoff between utility and privacy for LBSs on road networks. To address this issue, we propose a new privacy notion, Geo-Graph-Indistinguishability (GeoGI), for locations on a road network to achieve a better tradeoff. We propose Graph-Exponential Mechanism (GEM), which satisfies GeoGI. Moreover, we formalize the optimization problem to find the optimal GEM in terms of the tradeoff. However, the computational complexity of a naive method to find the optimal solution is prohibitive, so we propose a greedy algorithm to find an approximate solution in an acceptable amount of time. Finally, our experiments show that our proposed mechanism outperforms GeoI mechanisms, including optimal GeoI mechanism, with respect to the tradeoff.