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This paper presents a feasibility study on monitoring earthquake-caused furniture vibrations using radiofrequency identification (RFID) sensor tags. Finding unstable objects by exploiting the vibrations caused by weaker earthquakes is effective as one of the potential countermeasures for large-scale earthquakes in earthquake-prone areas. For this purpose, a previously proposed ultrahigh-frequency (UHF)-band RFID-based batteryless vibration/physical shock sensing system enabled long-term monitoring. This RFID sensor system introduced standby and active modes for long-term monitoring. This system enabled lower-cost wireless vibration measurements without affecting the vibration of furniture because the RFID-based sensor tags provide lightweight, low-cost, and battery-free operations. This RFID sensor system observed earthquake-cased furniture vibrations in a room on the fourth floor of a building eight stories high at Ibaraki University, Hitachi, Ibaraki, Japan. The observation results revealed that the RFID sensor tags identified the vibrations of furniture caused by earthquakes. The RFID sensor system also observed the vibration duration times of the objects in a room and specified the most unstable reference object. Hence, the proposed vibration sensing system helped achieve safe living in indoor environments. [ABSTRACT FROM AUTHOR]

With a substantial rise in life expectancy throughout the last century, society faces the imperative of seeking inventive approaches to foster active aging and provide adequate aging care. The e-VITA initiative, jointly funded by the European Union and Japan, centers on an advanced virtual coaching methodology designed to target essential aspects of promoting active and healthy aging. This paper describes the technical framework underlying the e-VITA virtual coaching system platform and presents preliminary feedback on its use. At its core is the e-VITA Manager, a pivotal component responsible for harmonizing the seamless integration of various specialized devices and modules. These modules include the Dialogue Manager, Data Fusion, and Emotional Detection, each making distinct contributions to enhance the platform's functionalities. The platform's design incorporates a multitude of devices and software components from Europe and Japan, each built upon diverse technologies and standards. This versatile platform facilitates communication and seamless integration among smart devices such as sensors and robots while efficiently managing data to provide comprehensive coaching functionalities. [ABSTRACT FROM AUTHOR]

Wireless Mesh Networks (WMNs) can build a communications infrastructure using only routers (called mesh routers), making it possible to form networks over a wide area at low cost. The mesh routers cover clients (called mesh clients), allowing mesh clients to communicate with different nodes. Since the communication performance of WMNs is affected by the position of mesh routers, the communication performance can be improved by optimizing the mesh router placement. In this paper, we present a Coverage Construction Method (CCM) that optimizes mesh router placement. In addition, we propose an integrated optimization approach that combine Simulated Annealing (SA) and Delaunay Edges (DE) in CCM to improve the performance of mesh router placement optimization. The proposed approach can build and provide a communication infrastructure by WMNs in disaster environments. We consider a real scenario for the placement of mesh clients in an evacuation area of Kurashiki City, Japan. From the simulation results, we found that the proposed approach can optimize the placement of mesh routers in order to cover all mesh clients in the evacuation area. Additionally, the DECCM-based SA approach covers more mesh clients than the CCM-based SA approach on average and can improve network connectivity of WMNs. [ABSTRACT FROM AUTHOR]

Centimeter level augmentation system (CLAS) of the quasi-zenith satellite system (QZSS) is the first precise point positioning-real time kinematic (PPP-RTK) augmentation system of the global navigation satellite system (GNSS), which is currently providing services for Japan. CLAS broadcasts the state-space representation of correction messages along with integrity messages regarding satellite faults and the quality index of each correction. In other GNSS augmentation systems, such as the space-based augmentation system (SBAS) of GNSS, the quality indices of correction messages are used to generate fault-free protection levels that represent a position bound containing a true user position with a probability of missed detections. Although the protection level equations are well defined for the SBAS, a protection level equation for the CLAS PPP-RTK service has not been rigorously discussed in the literature. This paper proposes a fault-free protection level equation for the PPP-RTK methods that considers the probability of correct integer ambiguity fixes in the GNSS carrier phase measurements as well as the CLAS correction quality messages. The computed protection levels with position errors were experimentally compared by processing the GNSS measurements from the GNSS Earth Observation Network (GEONET) stations in Japan and the L6 messages from the CLAS broadcast using the virtual reference station-real time kinematic (VRS-RTK) techniques. Our results, based on the GEONET dataset spanning 7 days, showed that the computed protection levels using the proposed equations were larger than the position errors for all epochs. In the dataset, the RMS errors of the CLAS VRS-RTK position were 4.6 and 14 cm in the horizontal and vertical directions, respectively, whereas the horizontal protection levels ranged from 25 cm to 2.3 m and the vertical protection levels ranged from 50 cm to 5.2 m based on fault-free integrity risk of 10 − 7 . [ABSTRACT FROM AUTHOR]

The gross charge distribution in an electrified cloud has already been estimated by polarity distribution of the electrostatic field on the ground surface. While either a dipole or a tripole charge structure is commonly accepted, the increase–decrease and motion of each point charge in those models are both still unclear. This paper presents a new network of electric field mills for multipoint electrostatic measurement to evaluate the temporal variations of a simple cloud charge model with second-scale resolution. Details of our newly developed equipment are described, with an emphasis on its advantages. This network was deployed in the north Kanto area of Japan and operated during the summer season in 2020. In order to simplify the relationship between cloud charge positions and the horizontal distribution of the measured electrostatic field, an isolated thundercloud is focused on. As an initial analysis, a negative point charge model is applied to an isolated cloud observed on 27 August 2020. The quantity and height of the point charge were estimated as being approximately −20 C and 7 km, respectively. The calculated charge location is generally coincident with the C-band radar echo regions. Significant correspondence is demonstrated between the intensity distribution of the electrostatic fields measured at seven sites and that calculated with estimated point charge. This result indicates the possibility to determine the amounts and positions of cloud charges inside the dipole charge structure based on multipoint measurement of the electrostatic field. [ABSTRACT FROM AUTHOR]

This paper discusses the origins of torsion and its effect on the response of structures with a focus on the contribution of experimental data. The fact that torsion increases the stresses in structures, augmenting strain and damage during earthquakes, was confirmed in the 1960s. Over the years, the torsional response of structures has mainly been analysed through numerical studies, because few buildings are equipped with translational sensors, and even fewer are equipped with rotational sensors. This is likely to change as building instrumentation becomes more widespread and new generations of rotational sensors are developed. Therefore, this paper focusses on a number of scientific questions concerning the rotational response of structures during earthquakes and the contribution of experimental data to the understanding of this phenomenon. [ABSTRACT FROM AUTHOR]

Mechanical weed management is a drudging task that requires manpower and has risks when conducted within rows of orchards. However, intrarow weeding must still be conducted by manual labor due to the restricted movements of riding mowers within the rows of orchards due to their confined structures with nets and poles. However, autonomous robotic weeders still face challenges identifying uncut weeds due to the obstruction of Global Navigation Satellite System (GNSS) signals caused by poles and tree canopies. A properly designed intelligent vision system would have the potential to achieve the desired outcome by utilizing an autonomous weeder to perform operations in uncut sections. Therefore, the objective of this study is to develop a vision module using a custom-trained dataset on YOLO instance segmentation algorithms to support autonomous robotic weeders in recognizing uncut weeds and obstacles (i.e., fruit tree trunks, fixed poles) within rows. The training dataset was acquired from a pear orchard located at the Tsukuba Plant Innovation Research Center (T-PIRC) at the University of Tsukuba, Japan. In total, 5000 images were preprocessed and labeled for training and testing using YOLO models. Four versions of edge-device-dedicated YOLO instance segmentation were utilized in this research—YOLOv5n-seg, YOLOv5s-seg, YOLOv8n-seg, and YOLOv8s-seg—for real-time application with an autonomous weeder. A comparison study was conducted to evaluate all YOLO models in terms of detection accuracy, model complexity, and inference speed. The smaller YOLOv5-based and YOLOv8-based models were found to be more efficient than the larger models, and YOLOv8n-seg was selected as the vision module for the autonomous weeder. In the evaluation process, YOLOv8n-seg had better segmentation accuracy than YOLOv5n-seg, while the latter had the fastest inference time. The performance of YOLOv8n-seg was also acceptable when it was deployed on a resource-constrained device that is appropriate for robotic weeders. The results indicated that the proposed deep learning-based detection accuracy and inference speed can be used for object recognition via edge devices for robotic operation during intrarow weeding operations in orchards. [ABSTRACT FROM AUTHOR]

Japanese Sign Language (JSL) is vital for communication in Japan's deaf and hard-of-hearing community. But probably because of the large number of patterns, 46 types, there is a mixture of static and dynamic, and the dynamic ones have been excluded in most studies. Few researchers have been working to develop a dynamic JSL alphabet, and their performance accuracy is unsatisfactory. We proposed a dynamic JSL recognition system using effective feature extraction and feature selection approaches to overcome the challenges. In the procedure, we follow the hand pose estimation, effective feature extraction, and machine learning techniques. We collected a video dataset capturing JSL gestures through standard RGB cameras and employed MediaPipe for hand pose estimation. Four types of features were proposed. The significance of these features is that the same feature generation method can be used regardless of the number of frames or whether the features are dynamic or static. We employed a Random forest (RF) based feature selection approach to select the potential feature. Finally, we fed the reduced features into the kernels-based Support Vector Machine (SVM) algorithm classification. Evaluations conducted on our proprietary newly created dynamic Japanese sign language alphabet dataset and LSA64 dynamic dataset yielded recognition accuracies of 97.20% and 98.40%, respectively. This innovative approach not only addresses the complexities of JSL but also holds the potential to bridge communication gaps, offering effective communication for the deaf and hard-of-hearing, and has broader implications for sign language recognition systems globally. [ABSTRACT FROM AUTHOR]

Water utilities in Japan face a number of challenges, including declining water demand due to a shrinking population, shrinking workforce, and aging water supply facilities. Widespread use of smart water meters is crucial for solving these problems. The widespread use of smart water meters is expected to bring many benefits such as reduced labor by automating meter reading, early identification of leaks, and visualization of pipeline data to strengthen the infrastructure of water services, business continuity, and customer service, as detailed data can be obtained using wireless communication. Demonstration tests are actively conducted in Japan; however, many problems have been reported with cast iron meter boxes blocking radio waves. To address the issue, a low-cost slit structure for cast iron meter boxes is investigated in this study. The results confirm that the L-shaped tapered slit array structure with a cavity, which can be fabricated in a cast iron integral structure, satisfies the design loads required for road installation. The proposed slit structure achieved gain characteristics from −3.32 to more than 9.54 dBi in the 800 to 920 MHz band. The gain characteristics of conventional cast iron meter boxes range from −15 to −20 dBi, and the gain has been significantly improved. Antennas with a gain of −2.0 to +1.5 dB (0.8 to 2.5 GHz) were used for the transmitter antenna, which was found to have a higher gain than the transmit antenna in the 800 to 880 MHz frequency band. In the 1.5 to 2.0 GHz band, a high peak gain of 4.25 dBi was achieved at 1660 MHz, with no null and the lowest gain confirmed that this is an improvement of more than 10 dBi over conventional products. [ABSTRACT FROM AUTHOR]

Traffic sign recognition is a complex and challenging yet popular problem that can assist drivers on the road and reduce traffic accidents. Most existing methods for traffic sign recognition use convolutional neural networks (CNNs) and can achieve high recognition accuracy. However, these methods first require a large number of carefully crafted traffic sign datasets for the training process. Moreover, since traffic signs differ in each country and there is a variety of traffic signs, these methods need to be fine-tuned when recognizing new traffic sign categories. To address these issues, we propose a traffic sign matching method for zero-shot recognition. Our proposed method can perform traffic sign recognition without training data by directly matching the similarity of target and template traffic sign images. Our method uses the midlevel features of CNNs to obtain robust feature representations of traffic signs without additional training or fine-tuning. We discovered that midlevel features improve the accuracy of zero-shot traffic sign recognition. The proposed method achieves promising recognition results on the German Traffic Sign Recognition Benchmark open dataset and a real-world dataset taken from Sapporo City, Japan. [ABSTRACT FROM AUTHOR]

This paper presents the details of our research and the activities involved. Japan is one of the most advanced countries in medicine worldwide. However, in terms of technology, knowledge sharing, and successor development, Japanese medicine lags behind other developed countries, and these matters require addressing. The country is also facing a shortage of doctors, among other things, and this medical problem will surely become critical in the near future. In this study, we aim to help solve such problems from the medical engineering viewpoint, analyze and create systems based on the experience of doctors from the engineering viewpoint, and make it easy for patients to understand orthodox and general statistical analysis methods. We perform a visualization and quantitative medical data analysis and examine diagnostic support. We consider sleep apnea syndrome (SAS), and orthostatic dysregulation (OD) in children in this study. This research aims to detect SAS early, identify people with pre-SAS who are likely to become SAS in the near future, and identify OD. We analyze and identify these diseases through statistics and a multivariate analysis and create a dedicated analysis system for them. Our research and system development will allow specialists to make informed diagnoses, reproduce empirical rules, improve work efficiency, and improve patients' health awareness. This research has only looks at two diseases, but these methods can be expected to be applied to other diseases. [ABSTRACT FROM AUTHOR]

Assistive technology (AT) is any item, device, software, or product system used to enhance, maintain, or improve the functional abilities of people with disabilities. There are many people with disabilities in the world, including the visually impaired, the hearing impaired, and the physically impaired. We established the National KOSEN Support Equipment Development Network (KOSEN-AT) with technical college faculty members 10 years ago to assist these disabled and elderly people. However, Japan is facing the challenge of a rapidly aging society, and the digital transformation of assistive device development for people with disabilities has not been adequately addressed. A major reason for this is the lack of engineers in Japan who can develop products with an understanding of the needs of people with disabilities and the elderly. In this paper, we describe a new initiative of the GEAR 5.0 program, a practical engineer education program that will enable the development of assistive devices for the physically challenged and the elderly, which started in 2020 at the National Institute of Technology in Japan. We believe that it is necessary to educate technicians not only with conventional specialized skills, but also with a full understanding of the concept of disability and basic skills in assistive technology. Next, we developed "Touch Talker", a digital text reading system for the visually impaired. As a part of the GEAR 5.0 program, we conducted an evaluation experiment in which students from a technical college experienced visual impairment in the same blindfolded environment as visually impaired people to evaluate the developed assistive device. To verify its importance, we developed a digital text-to-speech system for the visually impaired, "Touch Talker", as part of the GEAR 5.0 program. We thought that by conducting evaluation experiments in the same blindfolded environment as visually impaired people, we could make technical college students aware of the difficulties of operating digital devices due to visual impairment. The results of the experiment showed that the developed "Touch Talker" was effective for both the visually impaired group and the blindfolded technical college student group. The evaluation results also showed a similar trend, confirming that the evaluation by blindfolded technical college students is effective for the development of assistive devices for the visually impaired. In addition, the technical college students who participated in the evaluation experiment were able to understand the difficulty of operating digital devices by experiencing visual impairment. It was suggested that the perspective of the people involved is important in the development of assistive devices. [ABSTRACT FROM AUTHOR]

Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT's ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD. [ABSTRACT FROM AUTHOR]

For this study, an online survey was conducted to discover the preferences of older adults when they used sensors in their households, rather than the preferences of the researchers who developed them. The sample size was 400 Japanese community-dwelling people aged 65 years and older. The numbers of samples for men and women, household composition (single-person/couple-only household), and younger senior (younger than 74 years old) and older senior (older than 75 years old) were equally assigned. The survey results showed that "informational security" and "constancy of life" were considered more important than other factors when installing sensors. Furthermore, looking at the results regarding the type of sensors that face resistance, we found that both cameras and microphones were evaluated as facing slightly strong resistance, while doors/windows, temperature/humidity, CO2/gas/smoke, and water flow were evaluated as not facing such strong resistance. The elderly who are likely to need sensors in the future also have various attributes, and the introduction of ambient sensors in elderly households may be further advanced by recommending applications that are easy to introduce based on the attributes of the target population, rather than discussing all of them in general. [ABSTRACT FROM AUTHOR]

There are a large number of excellent research cases in Global Navigation Satellite System (GNSS) positioning and disaster prediction in Japan region, where the simulation and prediction of total electron content (TEC) is a powerful research method. In this study, we used the data of the GNSS Earth Observation Network (GEONET) established by the Geographical Survey Institute of Japan (GSI) to compare the performance of two regional ionospheric models in Japan, in which the spherical cap harmonic (SCH) model has the best performance. In this paper, we investigated the spatial and temporal variations of ionospheric TEC in Japan and their relationship with latitude, longitude, seasons, and solar activity. The results show that the TEC in Japan increases as the latitude decreases, with the highest average TEC in spring and summer and the lowest in winter, and has a strong correlation with solar activity. In addition, the observation and analysis of ionospheric disturbances over Japan before the 2016 Kumamoto earthquake and geomagnetic storms showed that GNSS observing of ionospheric TEC seems to be very effective in forecasting natural disasters and monitoring space weather. [ABSTRACT FROM AUTHOR]

Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow's position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application's user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness. [ABSTRACT FROM AUTHOR]

Very low frequency (VLF) signals are considered as an important tool to study ionosphere disturbances. We have studied variations in signal amplitude of the Japanese JJI transmitter received by a network of eight Japan stations. The distinctions between characteristics of daytime and nighttime disturbances are considered. Signal processing based on spectral analysis is used to evaluate typical periodicities in the VLF signals in the time range from minutes to hours. In particular, we have retrieved quasi-wave oscillations of the received signal with periods of 4–10 and 20–25 min, which can be associated with atmospheric gravity waves excited by the solar terminator, earthquakes or other reasons. In addition, oscillations at periods of 3–4 h are observed, probably, caused by long-period gravity waves. We also calculate the information entropy to identify main details in daily VLF variations and influence of solar flares. It is shown that the information entropy increases near sunrise and sunset with seasonal variation, and that solar flares also lead to the growth in information entropy. A theoretical interpretation is given to the typical features of ultra-low frequency modulation of VLF electronagnetic wave spectra in Waveguide Earth-Ionosphere, found by processing the experimental data. [ABSTRACT FROM AUTHOR]

Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events. [ABSTRACT FROM AUTHOR]

We investigated the distribution of air temperature (Ta) and the factors affecting it in low-rise areas surrounding an isolated high-rise building during the Japanese winter. The study site was the central part of a regional city in Japan (36°5 0 N, 140°120 E), lying north-east of the Tokyo metropolitan area. The daytime surface temperature (Ts) in the shade is generally considered to be comparable to (Ta); however, according to airborne remote sensing conducted in December 2009 where a multi-spectral scanner was installed on a fixed-wing aircraft, (Ts) for pavements in the shade of a high-rise building was significantly lower than (Ta) of sub-urban areas, indicating an influence of cold storage on (Ts). Then, we conducted mobile observations using instruments (thermocouple, four component radiometer, and so on) installed on a bicycle in January 2016 to investigate the detailed distribution of (Ta) and the factors affecting it. The results showed the (Ta) over the pavements in the shade of the high-rise building was lower than the (Ta) of sunlit areas in the same urban area by −2°C and lower than the (Ta) of sub-urban areas by −1–1.5°C, although the advection effect was large due to strong winds around the building. In conclusion, a locally lower (Ta) compared to the surrounding areas can develop during the day in winter, even in spaces that are open to areas beyond the canopy. [ABSTRACT FROM AUTHOR]

In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12–2 PM), low-light (5–6 PM), and no-light (7–8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions. [ABSTRACT FROM AUTHOR]

Owing to the aging of the rural population in the hilly and mountainous areas of Japan, mowing on narrow ridges and steep slopes is done manually by the elderly—individuals over 65 years of age. Studies have shown that many accidents that occurred during mowing were caused by workers' unstable posture, especially when mowing on steep surfaces where there is a high risk of falling. It is necessary to analyze the body movements of mowing workers to elucidate the elements related to the risk of falls. Therefore, in this study, based on a high-precision motion-capture device and a series of experiments with elderly, skilled mowing workers, we focused on the movements of mowing. We sought to identify effective and safe mowing patterns and the factors that lead to the risk of falls. In various mowing styles, compared to the stride (S) and downward (D) mowing patterns, the basic (B) and moving (M) patterns were the most efficient; however, the risk of falls was also the highest among these patterns. While mowing, workers need to pay more attention to their arm strength and take appropriate measures to reduce the risk of falls according to their age and physique. The results can be used as data for the development of fall-detection systems and offer useful insights for the training of new mowing workers. [ABSTRACT FROM AUTHOR]

In Japan, the aging population is expected to increase the number of elderly people in the future. The purpose of this study was to develop a hip lift transfer assistive system to improve the QOL of elderly living and to prevent back pain for caregivers. We extracted the impediment factor and the necessary scene for the assistance, decided on the transfer process from the wheelchair in the toilet, and considered the reduction method of the burden based on the quantitative evaluation of the caregiver's lumbar burden and developed the device. Then we proposed the algorithm of the system by grasping the behavior and lumbar burden characteristics at the time of using the hip lift transfer assistive system by the developed device in which the proposed support algorithm of standing seating assistance operation is implemented in the actual use environment. Through the assistive movement evaluation experiment and the actual operation in the toilet, we have verified that the use of this device can reduce the caregivers' lumbar burden below the standard value (3400 N) and have proved the effectiveness of the proposed transfer assistive system. [ABSTRACT FROM AUTHOR]

Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy. [ABSTRACT FROM AUTHOR]

The Body Condition Score (BCS) for cows indicates their energy reserves, the scoring for which ranges from very thin to overweight. These measurements are especially useful during calving, as well as early lactation. Achieving a correct BCS helps avoid calving difficulties, losses and other health problems. Although BCS can be rated by experts, it is time-consuming and often inconsistent when performed by different experts. Therefore, the aim of our system is to develop a computerized system to reduce inconsistencies and to provide a time-saving solution. In our proposed system, the automatic body condition scoring system is introduced by using a 3D camera, image processing techniques and regression models. The experimental data were collected on a rotary parlor milking station on a large-scale dairy farm in Japan. The system includes an application platform for automatic image selection as a primary step, which was developed for smart monitoring of individual cows on large-scale farms. Moreover, two analytical models are proposed in two regions of interest (ROI) by extracting 3D surface roughness parameters. By applying the extracted parameters in mathematical equations, the BCS is automatically evaluated based on measurements of model accuracy, with one of the two models achieving a mean absolute percentage error (MAPE) of 3.9%, and a mean absolute error (MAE) of 0.13. [ABSTRACT FROM AUTHOR]

Tropospheric delay is a major error source that affects the initialization and re-initialization speed of the Global Navigation Satellite System's (GNSS) medium-/long-range baseline in Network Real-Time Kinematic (NRTK) positioning. Fusing the meteorological data from the Numerical Weather Prediction (NWP) model to estimate the zenith tropospheric delay (ZTD) is one of the current research hotspots. However, research has shown that the ZTD derived from NWP models is still not accurate enough for high-precision GNSS positioning applications without the estimation of the residual tropospheric delay. To date, General Regression Neural Network (GRNN) has been applied in many fields. It has a high learning speed and simple structure, and can approximate any function with arbitrary precision. In this study, we developed a regional NWP tropospheric delay inversion method based on a GRNN model to improve the accuracy of the tropospheric delay derived from the NWP model. The accuracy of the tropospheric delays derived from reanalysis data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the US National Centers for Environmental Prediction (NCEP) was assessed through comparisons with the results of the International GPS Service (IGS). The variation characteristics of the residual of the ZTD inverted by NWP data were analyzed considering the factors of temperature, humidity, latitude, and season. To evaluate the performance of this new method, the National Center Atmospheric Research (NCAR) troposphere data of 650 stations in Japan in 2005 were collected as a reference to compare the accuracy of the ZTD before and after using the new method. The experimental results showed that the GRNN model has obvious advantages in fitting the NWP ZTD residual. The mean residual and the root mean square deviation (RMSD) of the ZTD inverted using the method of this study were 9.5 mm and 12.7 mm, respectively, showing reductions of 20.8% and 19.1%, respectively, as compared to the standard NWP model. For long-range baseline (155 km and 207 km), the corrected NWP-constrained RTK showed a reduction of over 43% in the initialization time compared with the standard RTK, and showed a reduction of over 24% in the initialization time compared with the standard NWP-constrained RTK. [ABSTRACT FROM AUTHOR]

Civil infrastructure is expanding around the world. The ever-growing trend toward urbanization drives the demand for new investments. However, the new constructions and gradual deterioration of those already existing, especially bridges, give rise to concerns about their proper maintenance. To improve safety and drive down maintenance costs of civil structures, there is a need for inexpensive sensing systems capable of reliable and automated monitoring. In this study, we present a new concept of thin-film strain sensors arranged in an array with a concentric layout that is incorporated into a flexible substrate sheet. The designed sensor array is intended to analyze strains in the proximity of round holes made at the crack tips, found in the investigated construction elements of civil structures. In this study, the performance of the sensor array was demonstrated using measurements taken on a highway bridge in one of the largest cities in Japan. We show that it can measure local strain distribution and indicate a region with risk for crack formation. The demonstrated results show new area of potential applications for the printed strain sensors in monitoring civil structures. [ABSTRACT FROM AUTHOR]

The enormous advances in sensing and data processing technologies in combination with recent developments in nuclear radiation detection and imaging enable unprecedented and "smarter" ways to detect, map, and visualize nuclear radiation. The recently developed concept of three-dimensional (3-D) Scene-data fusion allows us now to "see" nuclear radiation in three dimensions, in real time, and specific to radionuclides. It is based on a multi-sensor instrument that is able to map a local scene and to fuse the scene data with nuclear radiation data in 3-D while the instrument is freely moving through the scene. This new concept is agnostic of the deployment platform and the specific radiation detection or imaging modality. We have demonstrated this 3-D Scene-data fusion concept in a range of configurations in locations, such as the Fukushima Prefecture in Japan or Chernobyl in Ukraine on unmanned and manned aerial and ground-based platforms. It provides new means in the detection, mapping, and visualization of radiological and nuclear materials relevant for the safe and secure operation of nuclear and radiological facilities or in the response to accidental or intentional releases of radioactive materials where a timely, accurate, and effective assessment is critical. In addition, the ability to visualize nuclear radiation in 3-D and in real time provides new means in the communication with public and facilitates to overcome one of the major public concerns of not being able to "see" nuclear radiation. [ABSTRACT FROM AUTHOR]