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This study develops a training system for a multimodal comprehensive care methodology for dementia patients called Humanitude. Humanitude has attracted much attention as a gentle and effective care technique. It consists of four main techniques, namely, eye contact, verbal communication, touch, and standing up, and more than 150 care elements. Learning Humanitude thus requires much time. To provide an effective training system for Humanitude, we develop a training system that realizes sensing and interaction simultaneously by combining a real entity and augmented reality technology. To imitate the interaction between a patient and a caregiver, we superimpose a three-dimensional CG model of a patient’s face onto the head of a soft doll using augmented reality technology. Touch information such as position and force is sensed using the whole body wearable tactile sensor developed to quantify touch skills. This training system enables the evaluation of eye contact and touch skills simultaneously. We build a prototype of the proposed training system and evaluate the usefulness of the system in public lectures.

This study proposes two kinds of systems for building intelligent service robots, namely a sensor terminal named ”Portable” for making various personal mobility robots intelligent and a distributed sensor system for constructing an informationally structured environment consisting of laser range finders and active beacons. The sensor terminal Portable is equipped with a laser range finder and a gyro. Two types of personal mobility robot, namely standing type and wheelchair type, are made intelligent by installing Portable to provide navigation functions such as localization, obstacle detection, and path planning. The sensor system is mainly used to acquire position information about the personal mobility robots, obstacles, and moving objects (e.g., people); this information is used by Portable for navigation. The obtained information is transmitted to the robot, allowing it to operate in a complicated environment. The ability of the proposed system to navigate personal mobility robots is verified in two real environments.

A porous spinel body was fabricated from porous alumina using a template method. First, alumina with 72.5% porosity was fabricated by heating a compact from platelets at 1400 °C for 1 h. The resultant porous alumina was then immersed in an 8 mol/L magnesium nitrate solution, dried, and reheated at 1400 °C for 2 h to convert it to spinel. If necessary, this solution immersion and reheating treatment was performed up to five times. The influence of the number of treatments on the porosity, conversion ratio to spinel, compressive strength, and thermal conductivity was investigated. After five solution treatments, the conversion ratio to spinel was calculated to be approximately 30% in molar ratio, while the remaining majority maintained the morphology of the original alumina platelets. The porosity, compressive strength, and thermal conductivity of the final sample at room temperature were 61.0%, 13.0 MPa, and 1.8 Wm−1K−1, respectively.

This paper presents a new method to finish flexible plates with high accuracy and high productivity. Precision steel plates are finished conventionally by face milling with electro-magnetic chucks. It is difficult to improve flatness of the flexible plates, because they deform to fit the chuck surfaces while clamped. The authors have applied simultaneous double-sided milling to solve this problem, but it causes forced chatter vibration which is not allowed in precision finishing. Thus, the new method is proposed and verified to suppress the forced chatter vibration, in which the both surfaces are finished simultaneously with single tooth milling cutters with synchronization so that the thrust forces are cancelled out on the both sides.

In this paper, a Kalman filter-based method for restoration of video images acquired by an in-vehicle camera in foggy conditions is proposed. In order to realize Kalman filter-based restoration, the proposed method clips local blocks from the target frame by using a sliding window and regards the intensities in each block as elements of the state variable of the Kalman filter. Furthermore, the proposed method designs the following two models for restoration of foggy images. The first one is an observation model, which represents a fog deterioration model. The proposed method automatically determines all parameters of the fog deterioration model from only the foggy images to design the observation model. The second one is a non-linear state transition model, which represents the target frame in the original video image from its previous frame based on motion vectors. By utilizing the observation and state transition models, the correlation between successive frames can be effectively utilized for restoration, and accurate restoration of images obtained in foggy conditions can be achieved. Experimental results show that the proposed method has better performance than that of the traditional method based on the fog deterioration model.

In this paper, an ER (Error-Reduction) algorithm-based method for removal of adherent water drops from images obtained by a rear view camera mounted on a vehicle in rainy conditions is proposed. Since Fourier-domain and object-domain constraints are needed for any ER algorithm-based method, the proposed method introduces the following two novel constraints for the removal of adherent water drops. The first one is the Fourier-domain constraint that utilizes the Fourier transform magnitude of the previous frame in the obtained images as that of the target frame. Noting that images obtained by the rear view camera have the unique characteristics of objects moving like ripples because the rear view camera is generally composed of a fish-eye lens for a wide view angle, the proposed method assumes that the Fourier transform magnitudes of the target frame and the previous frame are the same in the polar coordinate system. The second constraint is the object-domain constraint that utilizes intensities in an area of the target frame to which water drops have adhered. Specifically, the proposed method models a deterioration process of intensities that are corrupted by the water drop adhering to the rear view camera lens. By utilizing these novel constraints, the proposed ER algorithm can remove adherent water drops from images obtained by the rear view camera. Experimental results that verify the performance of the proposed method are represented.

We study the effect of indium–gallium–zinc oxide (IGZO) crystallinity on oxygen vacancies that play an important role in the characteristics of IGZO-based devices. Optical and electrical measurements revealed that deep defect levels due to oxygen vacancies are largely eliminated in c-axis-aligned crystal IGZO (CAAC-IGZO), which has increased crystallinity without clear grain boundaries. In this study, the correlation between crystallinity and oxygen vacancy formation has been examined by first-principles calculations to investigate the effect of oxygen vacancies in IGZO. Furthermore, the likelihood of oxygen vacancy formation at an edge portion of single-crystal IGZO has been verified by observations of oxygen atoms at the edge region of the IGZO film by annular bright-field scanning transmission electron microscopy (ABF-STEM). Experimental and calculation results show that the high crystallinity of IGZO is important for the inhibition of oxygen vacancies.

This paper proposes a Kalman filter based restoration method for images obtained by in-vehicle camera in foggy conditions. The proposed method introduces two novel approaches into the Kalman filter based restoration. The first one is an automatic determination of a fog deterioration model. A vanishing point in the foggy image is estimated by using cross ratio of lane marking, and automatic determination of all parameters of the fog deterioration model is realized. Furthermore, the obtained model is introduced into the Kalman filter. Specifically, our method regards each frame as a state variable and its observation model is defined by the fog deterioration model. Then, since the correlation between successive frame can be effectively utilized by the Kalman filter, the accurate restoration of foggy images is achieved. Experimental results show that the proposed method achieves higher performance than the traditional method based on the fog deterioration model.

In this paper, a Kalman filter-based approach for adaptive restoration of video images acquired by an in-vehicle camera in foggy conditions is proposed. In order to realize Kalman filter-based restoration, the proposed method regards the intensities in each frame as elements of the state variable of the Kalman filter and designs the following two models for restoration of foggy images. The first one is an observation model, which represents a fog deterioration model. The second one is a non-linear state transition model, which represents the target frame in the original video image from its previous frame based on motion vectors. By utilizing the observation and state transition models, the correlation between successive frames can be effectively utilized for restoration. Further, the proposed method introduces a new estimation scheme of the parameter, which determines the deterioration characteristic in foggy conditions, into the Kalman filter algorithm. Consequently, since automatic determination of the fog deterioration model, which specifies the observation model, from only the foggy images is realized, the accurate restoration can be achieved. Experimental results show that the proposed method has better performance than that of the traditional method based on the fog deterioration model.