Polarization imaging and sensing techniques have shown great potential for biomedical and clinical applications. As a novel optical biosensing technology, Mueller matrix polarimetry can provide abundant microstructural information of tissue samples. However, polarimetric aberrations, which lead to inaccurate characterization of polarization properties, can be induced by uneven biomedical sample surfaces while measuring Mueller matrices with complex spatial illuminations. In this study, we analyze the detailed features of complex spatial illumination-induced aberrations by measuring the backscattering Mueller matrices of experimental phantom and tissue samples. We obtain the aberrations under different spatial illumination schemes in Mueller matrix imaging. Furthermore, we give the corresponding suggestions for selecting appropriate illumination schemes to extract specific polarization properties, and then provide strategies to alleviate polarimetric aberrations by adjusting the incident and detection angles in Mueller matrix imaging. The optimized scheme gives critical criteria for the spatial illumination scheme selection of non-collinear backscattering Mueller matrix measurements, which can be helpful for the further development of quantitative tissue polarimetric imaging and biosensing.

Mueller matrix polarimetry has great potential for tissue detection and clinical diagnosis due to its ability to provide rich microstructural information accurately. However, in practical in vivo tissue imaging based on backscattering Mueller matrix polarimetry, specular reflection is often inevitable, leading to overexposed regions and the following inaccurate polarization information acquisition of tissues. In this study, we probe the influence of specular reflection and overexposure on backscattering Mueller matrix polarimetry for tissue imaging and sensing. We investigate in detail the differentiation of polarization behaviors between the specular reflection and non-specular reflection tissue regions using a 3 × 3 backscattering Mueller matrix measurement. Then, we obtain the vertical projection profiles to further quantify the Mueller matrix elements of porcine liver tissue in different specular reflection regions. Finally, we calculate the polarization feature parameters derived from a 3 × 3 Mueller matrix and analyze their behavior in overexposed regions. Based on the quantitative analysis and comparisons, we obtain a group of polarization feature parameters with strong immunity to the specular reflection process. This study offers a strategy for selecting the polarization parameters during in vivo polarimetric imaging applications, provides valuable references for further eliminating the characterization errors induced by specular reflection, and may contribute to the advancement of quantitative tissue polarimetric imaging and sensing.

Cooperative coordination at unsignalized road intersections, which aims to improve the driving safety and traffic throughput for connected and automated vehicles, has attracted increasing interests in recent years. However, most existing investigations either suffer from computational complexity or cannot harness the full potential of the road infrastructure. To this end, we first present a dedicated intersection coordination framework, where the involved vehicles hand over their control authorities and follow instructions from a centralized coordinator. Then a unified cooperative trajectory optimization problem will be formulated to maximize the traffic throughput while ensuring the driving safety and long-term stability of the coordination system. To address the key computational challenges in the real-world deployment, we reformulate this non-convex sequential decision problem into a model-free Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement learning (DRL) framework. Simulation and practical experiments show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve the traffic throughput in the realistic continuous traffic flow. The most remarkable advantage is that our strategy could reduce the time complexity of computation to milliseconds, and is shown scalable when the road lanes increase.