Method for quickly acquiring camera parameters in wide-range vision coordinate measurement

本发明涉及一种大范围视觉坐标测量中像机参数的快速获取方法，包括以下步骤：以特定参照物建立世界坐标系；将两个光学靶标作为两个已知的坐标点；通过两个CCD摄像机分别采集两个光学靶标图片数据，获取相应的像面坐标；通过全站仪或GPS测量两个光学靶标的位置，即两光学靶标的世界坐标；通过全站仪或GPS测量两个CCD摄像机的平移矢量；将两个光学靶标的世界坐标、两个光学靶标的像面坐标以及两个CCD摄像机的平移矢量代入测量方程进行解算，得到两个CCD摄像机的焦距及光轴水平偏角。本发明方法速度快、可操作性强、实用和算法性能分析容易，只需放置两个光学靶，便可完成像机参数的一次性线性求解；还具有精度较高、适用于远距离等特点

Research on Underwater Target Recognition and Location Technique Based on Deep Learning

为实现自主水下潜器(Autonomous Underwater Vehicle,简称AUV)的自主目标探测识别与定位任务,以侧扫声呐数据为依据,考虑到扫描式声呐成像的特点,针对金属球类目标,基于Darknet框架设计了一种轻量化深度学习目标识别模型,并结合人工特征进行目标特性分析。同时对声呐图像设计了有效的图像增强方法。实验表明:上述目标识别方法在保证目标识别准确率的同时,具有较高的目标识别速率,适于低功耗嵌入式平台部署。</p

Image Gaussian filtering method with dynamically configured mode and realization device thereof

本发明涉及一种模式可动态配置的图像高斯滤波方法及实现装置，利用可编程逻辑门阵列根据不同需要动态配置滤波模式；在相应滤波模式下，利用高斯卷积核的可分离性将二维图像高斯滤波过程分解为两个一维图像高斯滤波过程，先进行其中一个维度方向进行高斯滤波，并将此高斯滤波数据缓存在FPGA内部的存储器中，再进行另一个维度方向的高斯滤波，对该维度方向的高斯滤波进行移位处理，得到该滤波模式下最终的高斯滤波图像。本发明使用方式灵活减小了开发的难度，缩短了开发周期，使得较大尺寸的高斯滤波得以在工程中有效地实现，增强了高斯滤波在工程中的实用性

Image Gaussian filtering method with dynamically configured mode and realization device thereof

本发明涉及一种模式可动态配置的图像高斯滤波方法及实现装置，利用可编程逻辑门阵列根据不同需要动态配置滤波模式；在相应滤波模式下，利用高斯卷积核的可分离性将二维图像高斯滤波过程分解为两个一维图像高斯滤波过程，先进行其中一个维度方向进行高斯滤波，并将此高斯滤波数据缓存在FPGA内部的存储器中，再进行另一个维度方向的高斯滤波，对该维度方向的高斯滤波进行移位处理，得到该滤波模式下最终的高斯滤波图像。本发明使用方式灵活减小了开发的难度，缩短了开发周期，使得较大尺寸的高斯滤波得以在工程中有效地实现，增强了高斯滤波在工程中的实用性

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies