ID 原文 译文
19445 为了改善负载跳变对低压差线性稳压器(LDO)的影响，该文提出一种用于无片外电容LDO(CL-LDO)的新型快速响应技术。 A novel technique for increasing the load response speed of Capacitor-Less Low-DropOut linearregulator (CL-LDO) is proposed to improve the transient response of CL-LDO when its load current changes.
19446 通过增加一条额外的快速通路，实现CL-LDO的快速瞬态响应，并且能够减小LDO输出过冲和下冲的幅度。 With an additional fast signal feedback path, the CL-LDO can achieve fast transient response so that the overshoot and undershoot of its output voltage can be dramatically reduced.
19447 该文电路基于0.18 μm CMOS工艺设计实现，面积为0.00529 mm2。 A CL-LDO with fast response isrealized in 0.18 μm CMOS and occupies an active area of 0.00529 mm2.
19448 流片测试结果表明，当输入电压范围为1.5～2.5 V时，输出电压为1.194 V； The CL-LDO has an output voltage of1.194 V when the input supply voltage ranges from 1.5 V to 2.5 V.
19449 当负载电流以 1 μs的上升时间和下降时间在 100 μA～10 mA之间变化时，CL-LDO的过冲恢复时间为489.537 ns，下冲恢复为960.918 ns； When the load current changes from 100 μAto 10 mA with the rise and fall time of 1 μs, the output of LDO can be recovered from its overshoot andundershoot to a stable voltage within 489.537 ns and 960.918 ns, respectively.
19450 相比未采用该技术的传统CL-LDO，响应速度能够提高7.41倍，输出过冲和下冲的电压幅值能够分别下降35.3%和78.1%。 Compared with a traditional CL-LDO without this proposed technique, the transient response speed of this CL-LDO is increased by 7.41 times.The overshoot and undershoot of the output voltage is decreased by 35.3% and 78.1%, respectively.
19451 针对视频监控中运动小目标难以检测的问题，该文提出一种基于航迹的检测算法。 To solve the problem that small moving object is difficult to be detected in video surveillance, atrack-based detection algorithm is proposed.
19452 首先，为了降低检测漏警率，提出区域纹理特征与差值概率融合的自适应前景提取方法； Firstly, in order to reduce missing alarm, an adaptive foreground extraction method combining regional texture features and difference probability is presented.
19453 其次，为了降低检测虚警率，设计航迹关联的概率计算模型以建立疑似目标在视频帧间的关联，并设置双门限以区分疑似目标中的真实目标与虚假目标。 Then, for reducing false alarm, the probability computing model of track correlation is designed to establish the correlation of suspected objects between frames, and double-threshold are set to distinguish between true and false positive.
19454 实验结果表明，与多种经典算法相比，该算法能对定量范围内的运动小目标以更低的漏警率和虚警率实施准确检测。 Experimental results show that compared with many classical algorithms, this algorithm can accurately detect small moving object within the quantitative range with lower missing and false alarm.