In recent years, various tactile displays having the ability to change their surface friction have been proposed. These displays can express many types of textures and shapes that the materials used for them do not possess. In our study, we found that the ultrasound converged on the surface of polystyrene foam reduces the surface friction. This method has potential applications in disposable and three-dimensional tactile displays. In this study, physical and psychophysical experiments were conducted to verify the effectiveness of the proposed method and to examine the basic conditions under which it is perceived. As a result, we confirmed that the surface friction was reduced on the polystyrene foam, which may be due to the squeeze film effect caused by the external ultrasound excitation of the surface.

We propose a method for estimating the frictional force between a contacted surface and the human touch using thermal video images captured using an infrared thermographic camera. Because this method can estimate force remotely, its application to various situations, in which the measurement is difficult to obtain using conventional contact-based methods, is expected. Furthermore, thermal images have the advantage of measuring physical quantities directly related to frictional force. As a result of machine learning using the measured data from multiple subjects and materials, we succeeded in estimating the frictional force with a high accuracy from the information of the temperature change on the surface. In addition, we account for both the frictional and direct heat transferred between the finger and object affecting the temperature change; therefore, we attempted to improve the accuracy by extracting only frictional heat. Consequently, our method succeeded in improving the accuracy.

High-bandwidth irregular oscillations caused by optical time-delayed feedback subjected to the laser cavity, known as laser chaos, have been investigated for various engineering applications. Recently, a fast decision-making algorithm for a multi-arm bandit problem by utilizing laser chaos time series has been demonstrated. Furthermore, the arms order recognition of the reward expectation for each arm has been successfully developed by incorporating the notion of the confidence interval regarding the reward estimate. However, in previous studies, the verification was limited to numerical experiments; real-world demonstrations were not conducted. This study experimentally demonstrated that the arm-order recognition algorithm is successfully operated in channel order recognition in wireless communications while revising the original strategy to take into account the wireless application requirements. Such accurate arm rank recognition involving non-best arms would be useful for various real-world applications such as channel bonding, among others.