提出了一种双层脉冲耦合神经网络与数学形态学相结合的区域标识算法。
This paper brings forward a new approach for region labeling by using double Pulse Coupled Neural Networks (PCNN) and morphology.
该算法首先利用噪声的拓扑连通性实现对图像的脉冲噪声点的标识，然后利用噪声点周围非噪声点的信息，对其进行修复。
Firstly the noise points are identified through the topology connectivity. Then they are repaired by using the non-noise points information.
电子支援措施目标识别包括利用基本电波参数、脉冲波形特征和通过对己方电子支援措施电波的接收分析进行识别。
Electronic support measure target recognition utilizes basic electric wave parameter, the characteristic of pulse wave shape and the analysis on electric wave received to perform target recognition.
并利用细导线散射场数据仿真了理论E脉冲和综合E脉冲目标识别性能；验证了本文提出的方法的可行性。
The target identification performance simulations of synthesis E-pulse and theoretical E-pulse based three finite thin wires scattering data show that the proposed method in this paper is effective.
该方法在中值滤波之前进行一次脉冲噪声检测，确定受到噪声污染的像素点，并进行记录标识。
Impulse noise checking was made before median filtering to confirm the pixels polluted by noise, which were recorded and marked.
给出了参数设计原则以及综合E脉冲波形的系统组成框图；并利用细导线散射场数据仿真了理论E脉冲和综合E脉冲目标识别性能；
Then an E-pulse waveform synthesis method using Fourier series expression is proposed, as well as the principles for its parameters choice and system block diagram for E-pulse waveform synthesis.
给出了参数设计原则以及综合E脉冲波形的系统组成框图；并利用细导线散射场数据仿真了理论E脉冲和综合E脉冲目标识别性能；
Then an E-pulse waveform synthesis method using Fourier series expression is proposed, as well as the principles for its parameters choice and system block diagram for E-pulse waveform synthesis.
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