一种基于自相关的激光脉冲编码信号解码方法

激光脉冲编码是半主动激光制导武器采用的一种抗干扰措施,为满足半主动激光制导武器光电对抗的需要,以激光信号的脉冲到达时间(TOA)为参数,依据激光脉冲编码信号的时间相关性,提出了一种通过首先对由脉冲到达时间组成的脉冲到达时间序列作自相关处理,计算出信号的重复周期,然后再在一个信号周期内,通过确定脉冲重复间隔的时间值、个数和位序来鉴别信号码型,从而实现信号解码的方法。阐述了该解码方法的原理及实现。原理实验和数字仿真实验证明,对于当前半主动激光制导武器采用的大多数激光脉冲编码信号,采用该解码方法,一般在4个信号周期时间内就能实现解码。     

采用单工循环编码解码的分布式光纤喇曼温度传感器

针对连续脉冲编码解码技术在分布式光纤喇曼温度传感器应用中出现被测光纤非线性峰值阀值光功率变低、编码过程较复杂等问题,提出了一种将光脉冲码均匀分布在整条被测光纤的单工循环编码解码技术和应用方案,并在27km单模系统中与传统单脉冲技术系统做了对比实验.结果表明:相比连续编码解码技术,单工循环编码解码技术不仅保持了编码解码技术相对于传统单脉冲技术的信噪比改善能力,而且有效地提高了光纤的非线性峰值阀值光功率.在编码时只需循环发送一组码,就可使系统的信噪比和测量距离获得极大的提高.采用211位循环编码解码技术的多模系统经16万次重复测量后,可在25km处获得±1.5℃的温度不确定度,空间分辨率为3m.     

雾对大气激光通信系统的影响及克服方法

分析了雾产生的原因、相关数学模型及其对大气激光通信系统的影响.为了克服雾对无线光通信链路的影响,将LDPC码作为信道编码应用到大气激光通信系统中,并结合置信传播迭代译码算法和副载波PSK强度调制方案,在有雾的大气信道中进行了仿真.仿真结果表明:LDPC码具有优越的纠错能力,并获得了较大的编码增益,该方案可以满足大气激光通信系统的需要.     

雾对大气激光通信系统的影响及克服方法

分析了雾产生的原因、相关数学模型及其对大气激光通信系统的影响.为了克服雾对无线光通信链路的影响,将LDPC码作为信道编码应用到大气激光通信系统中,并结合置信传播迭代译码算法和副载波PSK强度调制方案,在有雾的大气信道中进行了仿真.仿真结果表明：LDPC码具有优越的纠错能力,并获得了较大的编码增益,该方案可以满足大气激光通信系统的需要.     

一种激光制导信号分选识别算法的研究

为了对制导武器实现有效的干扰, 必须进行码型的识别及预测。针对现有可查的激光制导武器, 通过对其编码技术的分析, 深入讨论了激光脉冲信号的分选技术和码型识别技术, 结合雷达信号的重频分选算法(PRI算法), 提出了一种新的制导脉冲分选识别算法, 该算法融合了脉冲的分选及编码的识别流程, 并通过Matlab进行了仿真验证, 达到了预期解码识别的目的。基于FPGA, 在硬件上对该干扰流程进行了实现, 提供了简单的可行性方案。     

量子信令的偏振态差分相位编码方案及仿真

为了研究量子通信信令系统,实现多用户之间进行通信,提出了一种量子信令的偏振态差分相位编码方案,阐述了编码过程.发送方使用光子的不同偏振态来表示不同的信令,然后对光子进行相位调制,通过前后两个光脉冲的相位差进行量子信令编码.接收方根据两个光脉冲在光合束器处发生干涉,由于相位差不同可以被不同的检测器检测到而得到0或1信号完成解码.根据高斯脉冲分布,导出了光子传输过程中由于脉冲延迟引起的差分相位编码系统的量子误码率与脉冲宽度以及延迟时间分布参量之间的定量关系,推出高斯分布的单光子脉冲在确定的时间抖动分布情况下系统的量子误码率.对量子信令编码效率和误码率进行分析并仿真,结果表明本文提出的编码方案可以达到75％编码效率,能够满足量子移动通信的需要.     

基于低密度奇偶校验码的MPPM比特符号映射及系统性能研究

建立了基于低密度奇偶校验(LDPC)码和多脉冲位置调制(MPPM)的无线光通信系统模型,给出了相应系统模型下的解码算法,研究了(m,2)MPPM的比特符号映射方式,提出了用改进的遗传算法(GA)搜索的优化映射方法,最后在大气湍流信道下进行了仿真。仿真结果表明基于LDPC码和MPPM的无线光通信系统性能明显优于未编码的MPPM系统,结合LDPC码和多光束发射技术可以提高系统抗强大气湍流影响的能力;通过GA优化的映射方法在不增加系统复杂性的情况下能进一步提高系统的纠错性能,可以获得更大的编码增益。     

傅里叶望远镜部分周期信号解调及对成像的影响

为了解决傅里叶望远镜高功率短脉宽激光只能包含部分周期信号的难题,本文提出多脉冲拼接技术并对其基本原理和应用范围进行深入研究.首先介绍高功率短脉宽激光与声光移频器移频带宽之间的矛盾,进而引出利用傅里叶望远镜传统时间解调公式进行部分周期信号解调存在的困难.然后给出多脉冲拼接技术的基本理论,推导出理想情况下移频频率、脉冲重复率和脉宽满足的拼接条件.进而详细分析移频频率稳定性和脉冲重复率稳定性对多脉冲拼接成像结果的影响,并通过计算机仿真研究一般情况下脉宽变化对多脉冲拼接成像结果的影响.最后得出移频频率和脉冲重复率对成像无影响,激光脉宽变化小于25%仍可识别目标轮廓的结论.     

傅里叶望远镜部分周期信号解调及对成像的影响

为了解决傅里叶望远镜高功率短脉宽激光只能包含部分周期信号的难题,本文提出多脉冲拼接技术并对其基本原理和应用范围进行深入研究.首先介绍高功率短脉宽激光与声光移频器移频带宽之间的矛盾,进而引出利用傅里叶望远镜传统时间解调公式进行部分周期信号解调存在的困难.然后给出多脉冲拼接技术的基本理论,推导出理想情况下移频频率、脉冲重复率和脉宽满足的拼接条件.进而详细分析移频频率稳定性和脉冲重复率稳定性对多脉冲拼接成像结果的影响,并通过计算机仿真研究一般情况下脉宽变化对多脉冲拼接成像结果的影响.最后得出移频频率和脉冲重复率对成像无影响,激光脉宽变化小于25％仍可识别目标轮廓的结论.     

基于RS编码及网格编码调制的光PPM通信纠错技术

分析了RS(Reed-Solomon)纠错码和网格编码调制（Trellis Coded Modulation,TCM）技术在光脉冲位置调制通信中的应用.在此基础上,提出了以RS码作为外码,以网格编码调制技术作为内码,用于光脉冲位置调制通信的新编码方案,能以几乎不减少通信速率的优势,提高传统RS码系统在时变带限光信道中的通信性能.模拟研究了在不同空间光信道条件下,传统RS码的符号正确传输率和误码率,并对网格编码调制的编码增益、RS码与网格编码调制级联时的编码增益进行了仿真研究,证实了本方案的有效性.     

An efficient adaptive arithmetic coding image compression technology

This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology.     

概率神经网络在胃镜样品红外光谱检测中的应用

将人工神经网络方法应用于人体胃镜样品红外光谱检测,以克服常规线性判别分析方法的局限性,从而提高了胃镜样品判别的准确率。概率神经网络是一种适用模式分类的径向基神经网络,采用样本的先验概率和最优判定原则对新的样本进行分类,具有识别率高、训练速度快、不会陷入局部极值等优点。文章采用概率神经网络进行胃镜样品红外光谱模式识别,将预处理后的胃镜样品光谱进行主成分分析,将得分值作为输入,建立概率神经网络判别模型。文中选取118例胃镜离体样品进行红外光谱判别分析,其中正常胃组织19例,胃炎组织64例,胃癌35例,选取其中59例样品建立概率神经网络校正模型,其余样品作为预测集来检验模型。实验结果表明,正常、炎症及癌症胃镜样品检测的总体准确率达到81.4%,对胃镜样品的判别取得了较好的结果。     

Detection of micro solder balls using active thermography and probabilistic neural network

Micro solder ball/bump has been widely used in electronic packaging. It has been challenging to inspect these structures as the solder balls/bumps are often embedded between the component and substrates, especially in flip-chip packaging. In this paper, a detection method for micro solder ball/bump based on the active thermography and the probabilistic neural network is investigated. A VH680 infrared imager is used to capture the thermal image of the test vehicle, SFA10 packages. The temperature curves are processed using moving average technique to remove the peak noise. And the principal component analysis (PCA) is adopted to reconstruct the thermal images. The missed solder balls can be recognized explicitly in the second principal component image. Probabilistic neural network (PNN) is then established to identify the defective bump intelligently. The hot spots corresponding to the solder balls are segmented from the PCA reconstructed image, and statistic parameters are calculated. To characterize the thermal properties of solder bump quantitatively, three representative features are selected and used as the input vector in PNN clustering. The results show that the actual outputs and the expected outputs are consistent in identification of the missed solder balls, and all the bumps were recognized accurately, which demonstrates the viability of the PNN in effective defect inspection in high-density microelectronic packaging.     

Neural-based pile-up correction and ballistic deficit correction of X-ray semiconductor detectors using the Monte Carlo simulation and the Ramo theorem

Pile-up distortion is a common problem in many nuclear radiation detection systems, especially in high count rates. It can be solved by hardware-based pile-up rejections, but there is no complete pile-up elimination in this way. Additionally, the methods can lead to poor quantitative results. Generally, time characteristics of semiconductor detector pulses are different from Scintillator detector pulses due to ballistic deficit. Hence, pulse processing-based pile-up correction in the detectors should consider this specification. In this paper, the artificial neural network pile-up correction method is applied for silicon detector piled-up pulses. For this purpose, the interaction of photons with a silicon detector is simulated by the MCNP4c code and the pulse current is calculated by Ramo's theorem. In this approach, we use a sub-Nyquist frequency sampling. The results show that the proposed method is reliable for pile-up correction and ballistic deficit in semiconductor detectors. The technique is remarkable for commercial considerations and high-speed, real-time calculations.     

Communications using multi—mode laser system based on chaotic synchronization

The communication using chaotic synchronization between two multi-mode Nd:YAG lasers has been investigated numerically.The digital communication using multi-mode chatoic lasers is quite different from that using single mode lasers.By introducing a short break between two adjacent bits in the communication shceme,the digital signals can besuccessfully encoded into the spiky pulses and decoded through the subtraction of receiver output from the transmitted.Even when the two multi-mode lasers are not synchronized perfectly with certain deviations,the encoded digits can still be decoded correctly.     

基于PNN网络和Freeman链码的抽油机井工况诊断

利用概率神经网络(PNN)对抽油机井工况进行诊断,建立了抽油机井工况诊断的概率神经网络模型。对示功图提取特征值的质量好坏直接影响识别效率和可靠性,提出了用Freeman链码对等效的电流示功图提取特征参数,进行预处理,建立抽油机典型工况的链码特征样本库。将Freeman链码作为特征向量,利用MATLAB对网络进行训练。结果表明,Freeman链码能够有效的识别各种典型工况示功图,并且该概率神经网络学习速度快、诊断准确率高,可用于抽油机井工况的实时监测和诊断。     

FS水声遥控系统

本文介绍一种可实用的ＦＳ式编码的水声遥控系统，该系统采用９个频率点进行移频编码，可有效抗多途对译码的影响，编码设计能使遥控指令的码间距做得较大，通过自适应滤波器有效译码，可将误码率控制在希望值以下。理论分析和试验结果均表明，本系统误码率接近０，解码率１５００ｍ以内大于０．９，近距离大于０．９６本系统已应用于水下物理场测量系统中。     

二值时空编码照明三维测量中的冗余编码方法

在采用二值时空编码结构照明的三维测量中,由于遮挡、阴影及光学系统的低通滤波特性等原因导致被测物体部分区域解码错误或无法解码.为减轻这些因素对测量结果的影响,提出了一种采用冗余编码的二值时空编码照明三维测量方法.该方法在空间相邻区域设置相同编码,以时间坐标进行区别.解码时,根据编码的冗余性对结果进行校验、残缺编码恢复.与原有编码相比,在不增加投影模式数量的情况下,获得了更多的有效点数,提高了解码的可靠性,并为进一步的纠错提供了更多信息.     

HL-2A tokamak disruption forecasting based on an artificial neural network

Artificial neural networks are trained to forecast the plasma disruption in HL-2A tokamak. Optimized network architecture is obtained. Saliency analysis is made to assess the relative importance of different diagnostic signals as network input. The trained networks can successfully detect the disruptive pulses of HL-2A tokamak. The results obtained show the possibility of developing a neural network predictor that intervenes well in advance for avoiding plasma disruption or mitigating its effects.