With the advancement of artificial intelligence, deep learning technology is applied in many fields. The autonomous car system is one of the most important application areas of artificial intelligence. LiDAR (Light Detection and Ranging) is one of the most critical components of self-driving cars. LiDAR can quickly scan the environment to obtain a large amount of high-precision three-dimensional depth information. Self-driving cars use LiDAR to reconstruct the three-dimensional environment. The autonomous car system can identify various situations in the vicinity through the information provided by LiDAR and choose a safer route. This paper is based on Velodyne HDL-64 LiDAR to decode data packets of LiDAR. The decoder we designed converts the information of the original data packet into X, Y, and Z point cloud data so that the autonomous vehicle can use the decoded information to reconstruct the three-dimensional environment and perform object detection and object classification. In order to prove the performance of the proposed LiDAR decoder, we use the standard original packets used for the comparison of experimental data, which are all taken from the Map GMU (George Mason University). The average decoding time of a frame is 7.678 milliseconds. Compared to other methods, the proposed LiDAR decoder has higher decoding speed and efficiency.

A new amplification strategy of electrochemical signaling from antigen-antibody interactions was proposed via back-filling immobilization of horseradish peroxidase (HRP), immunoglobulin G antibodies (anti-IgG) and gold nanoparticles onto a three-dimensional sol-gel (3DSG)-functionalized biorecognition interface. The 3DSG sol-gel network was employed not only as a building block for the surface modification but also as a matrix for ligand functionalization. The signal-amplification was based on the bioelectrocatalytic reaction of the back-filling immobilization of HRP to H(2)O(2). With the non-competitive format, the formation of the antigen-antibody complex by a simple one-step immunoreaction between the immobilized anti-IgG and IgG in sample solution inhibited partly the active center of HRP, and decreased the immobilized HRP towards H(2)O(2) reduction. Under optimal conditions, the proposed immunosensor exhibited a good electrochemical behavior to IgG in a dynamic range of 1.12-162 ng/mL with a detection limit of 0.56 ng/mL (at 3delta). Moreover, the precision, reproducibility and stability of the as-prepared immunosensor were acceptable. Importantly, the proposed methodology would be valuable for diagnosis and monitoring of biomarkers and its metastasis.

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