For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digital...For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digitally modulated signal (i.e. MPSK/QAM) is proposed. Firstly a section from the received signal is cut as the template, and then the signal is matched sectionwise by making use of the signal selfsimilarity. So a signal con- taining the information of symbol jumping is got, and the symbol-rate can be estimated by DFT (Discrete Fou- rier Transformation). The validity of the new method has been verified by experiments.展开更多
在尺度、遮挡等因素的影响下,人脸检测算法速度和精度不匹配进而表现出检测性能差等系列缺点,采用融入模板匹配思想的神经网络检测器算法解决这一不足,提出了二分支神经网络匹配的人脸检测算法。以此得到检测速度与精度相匹配的改进方...在尺度、遮挡等因素的影响下,人脸检测算法速度和精度不匹配进而表现出检测性能差等系列缺点,采用融入模板匹配思想的神经网络检测器算法解决这一不足,提出了二分支神经网络匹配的人脸检测算法。以此得到检测速度与精度相匹配的改进方法。首先根据五官位置关系构建人脸模板框架并设置一定的阈值;其次依据构建的模板和锚策略对不同尺度人脸的鲁棒性,在DSFD人脸检测器的特征增强模块层中实现对样本数据中人脸区域的提取及标记;最后依据提取、标记的人脸区域与构建的模板进行相关性匹配。改进算法在Face Detection Data Set and Benchmark、FDDB、WIDER FACE人脸数据集的实验结果表明,在提升检测速度的同时保证算法的精度,相比几类相近算法,该算法的优势更加明显。展开更多
In this paper, a real-time collision-free path planning of the rust removal robot in a ship environment is proposed, which is based on an improved biologically inspired neural network algorithm. This improved algorith...In this paper, a real-time collision-free path planning of the rust removal robot in a ship environment is proposed, which is based on an improved biologically inspired neural network algorithm. This improved algorithm is based on the biologically inspired neural network and modified with obstacle detection sensors and kinematic state templates, and is implemented in a ship rust removal robot planning system for dynamic trajectory generation. The real-time optimal trajectory is generated by the biologically inspired neural network, and the moving obstacle detection process of a ship robot working on the wall is simulated with the obstacle detection sensors models. The local real-time trajectory can be re-planned by the updated local map information, where the obstacle detection sensors are used to inspect partial environment information and update the robot nearby information in real time in the original neural network algorithm. At the same time, the method of the kinematic state templates matching and searching is used to solve the pipes' influence of the rust removal robot climbing on the wall, which can not only provide a smooth path, but also can judge the motion direction and turning angle of the robot. Comparison of the proposed approach with the simulation shows that the improved algorithm is capable of planning a real-time collision-free path with achieving the local environmental information and judging the rust removal robot's motion direction and turning angle. This proposed algorithm can be good used in the ship rust removal robot.展开更多
文摘For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digitally modulated signal (i.e. MPSK/QAM) is proposed. Firstly a section from the received signal is cut as the template, and then the signal is matched sectionwise by making use of the signal selfsimilarity. So a signal con- taining the information of symbol jumping is got, and the symbol-rate can be estimated by DFT (Discrete Fou- rier Transformation). The validity of the new method has been verified by experiments.
文摘在尺度、遮挡等因素的影响下,人脸检测算法速度和精度不匹配进而表现出检测性能差等系列缺点,采用融入模板匹配思想的神经网络检测器算法解决这一不足,提出了二分支神经网络匹配的人脸检测算法。以此得到检测速度与精度相匹配的改进方法。首先根据五官位置关系构建人脸模板框架并设置一定的阈值;其次依据构建的模板和锚策略对不同尺度人脸的鲁棒性,在DSFD人脸检测器的特征增强模块层中实现对样本数据中人脸区域的提取及标记;最后依据提取、标记的人脸区域与构建的模板进行相关性匹配。改进算法在Face Detection Data Set and Benchmark、FDDB、WIDER FACE人脸数据集的实验结果表明,在提升检测速度的同时保证算法的精度,相比几类相近算法,该算法的优势更加明显。
文摘In this paper, a real-time collision-free path planning of the rust removal robot in a ship environment is proposed, which is based on an improved biologically inspired neural network algorithm. This improved algorithm is based on the biologically inspired neural network and modified with obstacle detection sensors and kinematic state templates, and is implemented in a ship rust removal robot planning system for dynamic trajectory generation. The real-time optimal trajectory is generated by the biologically inspired neural network, and the moving obstacle detection process of a ship robot working on the wall is simulated with the obstacle detection sensors models. The local real-time trajectory can be re-planned by the updated local map information, where the obstacle detection sensors are used to inspect partial environment information and update the robot nearby information in real time in the original neural network algorithm. At the same time, the method of the kinematic state templates matching and searching is used to solve the pipes' influence of the rust removal robot climbing on the wall, which can not only provide a smooth path, but also can judge the motion direction and turning angle of the robot. Comparison of the proposed approach with the simulation shows that the improved algorithm is capable of planning a real-time collision-free path with achieving the local environmental information and judging the rust removal robot's motion direction and turning angle. This proposed algorithm can be good used in the ship rust removal robot.
