Electro-holography equipped with a spatial light modulator (SLM) is considered to be an important basic technique for 3D display system. However, the resolving power of ordinary SLM seems to be very low, and so it looks very difficult to satisfy the space-bandwidth product (SBP) condition. It is unfavorable since the values of the size and the viewing angle of the reproduced images are known to be restricted. In these processes, since we have jetted out the scatterer into the space directly, fluctuation reducing process of the screen has come to be an important problem. In this paper, we shall report that, to improve the stability of the mist screen, a spatial screen employed a bladeless fan (without blades) is introduced. As this result, we confirmed that a performance of highly bright projected images is carried out by this new system, and the effective display area is exactly enlarged.

Various studies on sign language recognition are conducted around the world. In particular, RGB image based methods are often used. This approach, however, includes the potential of its degrading accuracy, because it also learns the features included in the background. In addition, methods that use whole image as input cannot represent local features such as hand and arm movements. Therefore, in this study, we aim to improve the accuracy of sign language recognition by using a skeleton-based deep learning model with integration of multiple transformer encoders that utilizes the skeletal coordinate change and represents both global and local features. The skeletal coordinates obtained by Mediapipe are divided into four parts and four trained models are created individually. As the result of the experiments with the American sign language dataset WLASL (Word-Level American Sign Language) as the training data, the recognition accuracy of the proposed method improved more than that of color based methods, and we have confirmed the effectiveness.