Improving energy efficiency: a highly efficient coaxial design for a laser ranging system with a splicing lens.

Light detection and ranging (LiDAR) systems have made significant contributions in different applications. The laser ranging (LR) system is one of the core components of LiDARs. However, existing coaxial LR systems suffer from low energy efficiency due to obstruction of the reflection mirror. In this study, we carefully design a laser transmitter and receiver subsystem and consequently propose a highly energy-efficient coaxial design for a time of light-based LR system, where a perforated mirror and splicing lens account for the promotion of energy efficiency. The small hole in the perforated mirror is located on the object focus of the focusing lens to ensure the laser beam will pass through the perforated mirror without obstructions. The ring-shape splicing lens, consisting of two parts, is used for laser collimation and laser reception simultaneously. Laboratory experiments proved that the proposed design eliminates the complex calibration process for noncoaxial LR systems while reaching a comparable energy efficiency, which is higher than 98%. We believe it is an economical yet efficient way to promote the energy efficiency of coaxial LR systems.

Image-registration-based solar meridian detection for accurate and robust polarization navigation.

Skylight polarization, inspired by the foraging behavior of insects, has been widely used for navigation for various platforms, such as robots, unmanned aerial vehicles, and others, owing to its stability and non-error-accumulation. Among the characteristics of skylight-polarized patterns, the angle of polarization (AOP) and the degree of polarization (DOP) are two of the most significant characteristics that provide abundant information regarding the position of the sun. In this study, we propose an accurate method for detecting the solar meridian for real-time bioinspired navigation through image registration. This method uses the AOP pattern to detect the solar meridian and eliminates the ambiguity between anti-solar meridian and solar meridian using the DOP pattern, resulting in an accurate heading of the observer. Simulation experiments demonstrated the superior performance of the proposed method compared to the alternative approaches. Field experiments demonstrate that the proposed method achieves real-time, robust, and accurate performance under different weather conditions with a root mean square error of 0.1° under a clear sky, 0.18° under an overcast sky with a thin layer of clouds, and 0.32° under an isolated thick cloud cover. Our findings suggest that the proposed method can be potentially used in skylight polarization for real-time and accurate navigation in GPS-denied environments.

Cryptanalysis of random-phase-encoding-based optical cryptosystem via deep learning.

Random Phase Encoding (RPE) techniques for image encryption have drawn increasing attention during the past decades. We demonstrate in this contribution that the RPE-based optical cryptosystems are vulnerable to the chosen-plaintext attack (CPA) with deep learning strategy. A deep neural network (DNN) model is employed and trained to learn the working mechanism of optical cryptosystems, and finally obtaining a certain optimized DNN that acts as a decryption system. Numerical simulations were carried out to verify its feasibility and reliability of not only the classical Double RPE (DRPE) scheme but also the security-enhanced Tripe RPE (TRPE) scheme. The results further indicate the possibility of reconstructing images (plaintexts) outside the original data set.