High-precision 3D topography measurement is essential to ensure quality and performance of shaft parts. Generally, the main difficulty of measuring rotary objects on a turntable is the low accuracy of axis calibration. To solve this problem, this study introduces two methods for calibrating the rotation axis of the turntable and proposes a method for reconstructing the 3D shape of the object by converting the rotation axis parameters of the binocular vision. A calibration camera is used to shoot a 2D checkerboard calibration board against the rotating table, and the calibration process of a high-precision space rotation axis is developed and implemented. This laser vision measurement system consists of a calibration camera, a measurement camera, and a rotation system. This system can realize reconstruction of the 3D shape of the object in accordance with the rotation axis parameters in any position. The experiment on measuring standard parts verifies the effectiveness of the algorithm. It provides a new idea for the measurement of rotary object shapes.

A series of 11 1,1-dimethyl-3-aryl-thioureas were synthesised in good yields (70–92%) by reacting arylamines with S-aryl- N,N-dimethylthiocarbamate in DMSO in the presence of NaH at 90 °C. It is noteworthy that this method can also be used for arylamines containing a halogen atom at the ortho position.

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The complex electromagnetic environment will limit the efficacy of communication equipment. It is critical to construct a complex electromagnetic environment to test communication equipment in order to maximize its capability. One of the most important methods for constructing a complex electromagnetic environment is signal reconstruction. This paper proposes a VAE-GAN-based method for reconstructing direct sequence spread spectrum (DSSS) signals. In this method, the deep residual shrinkage network (DRSN) and self-attention mechanism are added to the encoder and discriminator of VAE-GAN. In feature learning, the DRSNs can reduce the redundant information caused by noise in the collected signal. The self-attention mechanism can establish the long-distance dependence between the input sequences, making it easier for the network to learn the samples’ pseudonoise (PN) sequence features. In addition, feature loss is applied to the encoder and generator to improve network stability during training. The results of the experiments indicate that this method can reconstruct DSSS signals with the characteristics of the target signal.