ID 原文 译文
1083 针对状态时滞随机系统，应用事件驱动近似二次性能指标和随机均方有界理论， This paper is concerned with the event-triggered controller and the corresponding event transmission rule for a class of stochastic system subject to state delay. The basic theories are approximate quadratic performance index and mean-square boundedness theory.
1084 同时考虑控制输入和事件决策，设计了反馈控制器和相应的事件驱动控制策略。 First, the event-triggered state feedback control scheme constrained by the approximate quadratic performance index is proposed which used the current state and time-delay state simultaneously.
1085 基于状态反馈的事件驱动策略同时使用当前状态和时滞状态进行事件的触发，并利用近似二次性能指标进行约束;基于输出反馈的事件驱动策略使用当前状态进行事件的触发。 Subsequently, an event-triggered output feedback control scheme is established which eliminates the influence of the state-delay terms accord-ing to the mean-square boundedness theory.
1086 最后，通过实验进行了仿真，对事件驱动性能指标进行量化，并与相关文献进行对比，验证了所提出方案可以在保证系统性能的前提下有效减少通信传输，延长无线传感网络的使用寿命。 Finally, a numerical example is given to illustrate the effectiveness of the pro-posed strategies.
1087 非合作接收条件下，连续相位调制( CPM，Continuous Phase Modulation) 信号多变未知的信号体制使其符号速率盲估计一直是分析该类信号的难点之一。 In the case of non-cooperative reception, blind estimation for the symbol rate estimation of continuous phase modulation ( CPM) signals is usually one of the difficulties in signal analysis due to the varied signal specification.
1088 现有的算法大多直接基于信号的瞬时频率或循环平稳性，存在抗噪性能差，不适用于多指数 CPM 信号等问题。 Most of the existing algorithms are based directly on instantaneous frequency or cyclostationarity, which have poor anti-noiseperformance and are not suitable for multi-h CPM signals.
1089 针对该问题，本文通过分析小波变换在信号分解和时频分析中的优势，提出一种综合利用离散小波( DWT，Discrete Wavelet Transform) 分解和频率脊线提取的 CPM 信号符号速率估计的新算法。 By analyzing the advantages of wavelet transform in signal de-composition and time-frequency, a new algorithm based on discrete wavelet transform ( DWT) and frequency ridge extrac-tion is proposed to estimate the symbol rate of CPM signal.
1090 算法对比分析表明，所提算法具有更好的抗噪性能且在小数据量时也能达到较好的估计性能。 Comparative analysis of the algorithm shows that the proposed method has better performance in condition of the low signal to noise ratio ( SNR) and small data volume.
1091 目标检测在基于传统手工特征及深度学习算法上已经取得较大发展， Object detection has been well studied based on the traditional manual features and deep learning algo-rithm.
1092 然而针对小目标检测的研究近几年才开始出现，研究成果较少， However, the research on small object detection has just begun in recent years and there are little research outcome a-vailable.