ID 原文 译文
22425 医学电子计算机断层扫描(CT)序列图像中肝脏的准确分割是实现计算机辅助肝手术的重要前提，然而图像中存在的组织病变、边界模糊或缺失、不同组织间的粘连给肝脏分割带来极大挑战。 The accurate segmentation of liver in medical Computed Tomography (CT) sequence images is important prerequisite for computer-assisted liver surgery. However, the presence of tissue lesions, the blurred or missing boundary and the adhesion between different organs/tissues poses great challenges to liver segmentation.
22426 针对这些问题，该文提出一种基于图像序列间先验约束的半自动分割方法，并进一步采取了多视角信息融合的方式实现肝脏的准确分割。 To address these problems, this paper presents a semi-automatic segmentation method based on the sequential constraints of image sequences, and introduces further a multi-view information fusion method to achieve the accurate segmentation of the liver.
22427 该方法的优势在于无需大量数据的收集和复杂的先验训练。 One advantage of this approach is that it does not need extensive data collection and complicated prior training.
22428 在 Sliver07 公开数据集合的验证结果显示，和领域内主要方法相比，该方法具有较高的分割准确度，特别是当肝脏区域存在病灶、边界模糊或缺失的情况下具有明显提升。 The validation and comparison results on the Sliver07 public data show that the proposed method shows competitive performance, especially when there is liver tumor, blurred or missing liver boundary.
22429 针对低截获概率(LPI)雷达信号识别率低且特征提取困难的问题，该文提出一种基于 Choi-Williams 分布(CWD)和栈式稀疏自编码器(s SAE)的自动分类识别系统。 In order to solve the problem that the correct recognition rate of Low Probability of Intercept (LPI) radar signal is low and the feature extraction is difficult, an automatic classification and recognition system based on Choi-Williams Distribution (CWD) and stacked Sparse Auto-Encoder (sSAE) is proposed.
22430 该系统从反映信号本质特征的时频图像入手，首先对 LPI雷达信号进行 CWD 时频分析，获取 2 维时频图像； The system starts from the time-frequency image which reflects the essential characteristics of the signal. Firstly, the CWD is performed on the LPI radar signal to obtain the two-dimensional time-frequency image.
22431 然后对得到的时频原始图像进行预处理，并把预处理后的图像送入多层稀疏自编码器(SAE)进行离线训练； Then, the obtained time-frequency original image is preprocessed and the preprocessed image is sent into the multilayer SAE for off-line training.
22432 最后把 SAE 自动提取的特征输入 softmax 分类器，实现雷达信号的在线分类识别。 Finally, the feature automatically extracted from the SAE is sent to the softmax classifier, to achieve on-line classification and identification of the radar signal.
22433 仿真结果表明，信噪比为 −6 dB 时，该系统对 8 种 LPI 雷达信号(LFM,  BPSK,  Costas,  Frank和 T1~T4)的整体平均识别率达到 96.4%，在低信噪比条件下明显优于人工设计提取信号特征的识别方法。 Simulation results show that the classification system achieves overall correct recognition rate of 96.4% at SNR of −6 dB for the eight LPI radar signals (LFM, BPSK, Costas, Frank and T1~T4), which is better than the method of manually designing the extract signal characteristics under low SNR conditions.
22434 压制式干扰是广播式自动相关监视(ADS-B)系统面临的最常见且最有威胁的干扰之一。 Jamming is the one of most common and serious threats for Automatic Dependent Surveillance- Broadcast (ADS-B) system.