An integral imaging-based 2D/3D convertible display system is proposed by using a lens-array holographic optical element (LAHOE), a polymer dispersed liquid crystal (PDLC) film, and a projector. The LAHOE is closely attached to the PDLC film to constitute a projection screen. The LAHOE is used to realize integral imaging 3D display. When the PDLC film with an applied voltage is in the transparent state, the projector projects a Bragg matched 3D image, and the display system works in 3D mode. When the PDLC film without an applied voltage is in the scattering state, the projector projects a 2D image, and the display system works in 2D mode. A prototype of the integral imaging-based 2D/3D convertible display is developed, and it provides 2D/3D convertible images properly.

The combination of deep learning and intrusion detection has become a hot topic in today’s network security. In the face of massive, high-dimensional network traffic with uneven sample distribution, how to be able to accurately detect anomalous traffic is the primary task of intrusion detection. Most research on intrusion detection systems based on network anomalous traffic detection has focused on supervised learning; however, the process of obtaining labeled data often requires a lot of time and effort, as well as the support of network experts. Therefore, it is worthwhile investigating the development of label-free self-supervised learning-based approaches called BYOL which is a simple and elegant framework with sufficiently powerful feature extraction capabilities for intrusion detection systems. In this paper, we propose a new data augmentation strategy for intrusion detection data and an intrusion detection model based on label-free self-supervised learning, using a new data augmentation strategy to introduce a perturbation enhancement model to learn invariant feature representation capability and an improved BYOL self-supervised learning method to train the UNSW-NB15 intrusion detection dataset without labels to extract network traffic feature representations. Linear evaluation on UNSW-NB15 and transfer learning on NSK-KDD, KDD CUP99, CIC IDS2017, and CIDDS_001 achieve excellent performance in all metrics.

Pneumatic soft robots show great potential application value in the field of hand rehabilitation. This paper presents a novel pneumatic soft robotic glove for hand rehabilitation with a portable pneumatic box. The glove uses positive-negative pneumatic actuator made of bellows, which is very light (only 149g), easy to wear, comfortable, customizable, safe, easy to make, and low-cost. The matching portable pneumatic box is compact in size and lightweight, which not only has six outputting gas paths, but also adjust pressure and flow. Within the allowable range of motion, the glove can assist affected hand to achieve flexion and extension of five fingers, and adduction and abduction of the thumb. The output force of the actuators is experimentally measured. For the flexion/extension actuator, the maximum force is 4.6N when extending and 1.9N when flexing. For the adduction/abduction actuator, the maximum force is 8.1N when adducting and 5.7N when abducting. In addition, demonstration with complete system is performed to do a functional evaluation, which verified the practical availability of the glove in rehabilitation training.

We propose an optical method to eliminate pseudoscopic issue in the integral imaging three-dimensional (3D) display by using a transmissive mirror device (TMD) and a light filter. Object light rays passing through the TMD can form an undistorted and depth inverted real image. Therefore, the TMD can eliminate the pseudoscopic issue existing in the traditional integral imaging. However, two ghost images appeared in integral imaging using the TMD. After studying the causes of the ghost images, a light filter is designed and fabricated to eliminate the ghost images. Integral imaging using TMD is developed, and it presents a high quality 3D image without a pseudoscopic issue.

In quantitative deflectometry, a test optical component is generally divided into numerous sample regions by the pixels on a camera’s CCD detector, and the adjacent intervals of sample regions are unequal in off-axis configurations. In this case, errors will be introduced in the reconstruction result if the traditional Southwell zonal integration method is arbitrarily used. Thus, an improved zonal method is proposed in this paper. Both simulations and experiments are conducted to demonstrate the validity and accuracy of the improved zonal method. In the simulations, compared with the traditional zonal method, the reconstruction accuracy for three different figures of sphere, hyperbolic, and flat surfaces using our proposed method is obviously improved, especially when the aperture of the test optics is not rectangular, but circular. Experimental results also show that when we integrate the slopes measured at unequal-spaced sampling points, the proposed zonal method is superior to the traditional zonal method in accuracy; meanwhile, it has advantages over the modal methods in reconstructing local detail information, such as a slight surface scratch, on the test optical component.

Ghost imaging plays an important role in the field of optical imaging. To realize color ghost imaging through the scattering media, we propose a deep learning method with high generation ability. Through our method, we can efficiently reconstruct color images with rich details, in line with human perception and close to the target color pictures. Experimental results show that our method can image through the scattering media with different scattering intensities and achieve good results even at a sampling rate of 0.1.