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This paper proposes a simple self-calibration method for the internal time synchronization of MEMS(Micro-electromechanical systems) LiDAR during research and development. Firstly, we introduced the problem of internal time misalignment in MEMS lidar. Then, a robust Minimum Vertical Gradient(MVG) prior is proposed to calibrate the time difference between the laser and MEMS mirror, which can be calculated automatically without any artificial participation or specially designed cooperation target. Finally, actual experiments on MEMS LiDARs are implemented to demonstrate the effectiveness of the proposed method. It should be noted that the calibration can be implemented in a simple laboratory environment without any ranging equipment and artificial participation, which greatly accelerate the progress of research and development in practical applications., Comment: 9 pages, 8 figures

A \textit{linear $3$-graph}, $H = (V, E)$, is a set, $V$, of vertices together with a set, $E$, of $3$-element subsets of $V$, called edges, so that any two distinct edges intersect in at most one vertex. The linear Tur\'an number, ${\rm ex}(n,F)$, is the maximum number of edges in a linear $3$-graph $H$ with $n$ vertices containing no copy of $F$. We focus here on the \textit{crown}, $C$, which consists of three pairwise disjoint edges (jewels) and a fourth edge (base) which intersects all of the jewels. Our main result is that every linear $3$-graph with minimum degree at least $4$ contains a crown. This is not true if $4$ is replaced by $3$. In fact the known bounds of the Tur\'an number are \[ 6 \left\lfloor{\frac{n - 3}{4}}\right\rfloor \leq {\rm ex}(n, C) \leq 2n, \] and in the construction providing the lower bound all but three vertices have degree $3$. We conjecture that ${\rm ex}(n, C) \sim \frac{3n}{2}$ but even if this were known it would not imply our main result. Our second result is a step towards a possible proof of ${\rm ex}(n,C) \leq \frac{3n}{2}$ (i.e., determining it within a constant error). We show that a minimal counterexample to this statement must contain certain configurations with $9$ edges and we conjecture that all of them lead to contradiction., Comment: 10 pages, 5 figures, comments welcome

This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising. The method contains two deep sub-networks, an Image Contrast Enhancement Network (ICE-Net) and a Re-Enhancement and Denoising Network (RED-Net). The ICE-Net takes the low light image as input and produces a contrast enhanced image. The RED-Net takes the result of ICE-Net and the low light image as input, and can re-enhance the low light image and denoise at the same time. Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed. In the ME-Retinex model, a new constraint on the reflectance image is introduced that the maximum channel of the reflectance image conforms to the maximum channel of the low light image and its entropy should be the largest, which converts the decomposition of reflectance and illumination in Retinex model to a non-ill-conditioned problem and allows the ICE-Net to be trained with a self-supervised way. The loss functions of RED-Net are carefully formulated to separate the noises and details during training, and they are based on the idea that, if noises are independently distributed, after the processing of smoothing filters (\eg mean filter), the gradient of the noise part should be smaller than the gradient of the detail part. It can be proved qualitatively and quantitatively through experiments that the proposed method is efficient., Comment: 10 pages, 7 figures