基于视频数字水印的保密通信系统

传统的加密通信不具备伪装性,容易被攻击者截取.为了有效地将信息隐藏实现保密通信,设计了一种基于视频数字水印技术的保密通信系统.该系统将文本信息经海明编码后嵌入到离散余弦变换的系数中来实现信息隐藏,采用直流系数的相对变化关系来实现同步和实时随机检测,能有效克服传统加密通信的缺点.实验表明该系统对视频质量影响较小,能实现隐藏信息的可靠传输.     

无线通信中同步兼备信息传输的波形一体化设计

文章以波形一体化及信息同步传输技术为基础,分别对数据帧解码器及电路硬件进行设计,构建一种简单的波形一体化同步兼备信息传输系统,并进行仿真实验。研究结果表明,该系统能够降低信息传递错误率,提高无线通信数据信息同步效率。     

基于入侵特征选择的网络异常数据检测模型

为了提高网络运动可靠性和安全性,针对传统的防火墙检测方法对网络异常数据检测准确性不高的问题,提出一种基于入侵特征选择的网络异常数据检测模型。对网络传输信道中的数据采用关联维求解方法进行特征挖掘提取,并对提取的关联维信息特征进行优选实现入侵信息识别和分类,结合模糊C均值聚类算法实现对网络异常数据的有效挖掘和检测。仿真结果表明,该检测模型能提高对网络异常数据和入侵信息的有效识别和检测能力。     

基于时滞不确定Lorenz混沌同步的保密通信

针对一类含保密信息的时延不确定Lorenz混沌系统,提出了基于径向基神经网络的同结构同步控制方案,在混沌系统同步的基础上,有效地恢复出隐藏的多路明文信号。利用神经网络的良好逼近能力对时延Lorenz混沌系统设计鲁棒同步控制器,实现了同步误差的收敛。当同步误差收敛时,混沌系统所传输的隐藏信号则可以正确恢复。仿真结果表明,文章所给出的同步控制器可以在5s内实现时延不确定Lorenz混沌系统同步,并能恢复出多路明文信号。     

语音通信中传输隐藏数据的技术研究与系统设计

为了在不影响语音通信效果的前提下,在语音通信的同时实现传输有用的隐藏数据,提出了一种在发射端采用改进的回声隐藏算法嵌入隐藏数据,而在接收端采用倒谱算法检测原始语音信号和隐藏数据的方法,能够解决一般传输中可靠性差、误码率高的问题.最后构建了以Sun Spot为核心的硬件平台系统,借助Spot的无线传感器、ARM920T、CC2420无线通讯器件和创新的Squawk JAVA虚拟机,通过JAVA语言编程和嵌入技术实现语音信号的采集、隐藏数据的嵌入、混合信号的同步传输和信号的检测还原等功能.实验证明了改进回声算法的有效性和系统的可行性,该系统为工业应用提供参考.     

基于卷积神经网络的双极化气象雷达冰雹检测方法

针对现有冰雹检测算法过分依赖专家经验的问题。本文提出一种基于卷积神经网络的双极化气象雷达冰雹检测方法。首先对单个雷达分辨单元分割并利用相邻分辨单元的极化信息进行填充，将极化信息转化为数据矩阵。接着搭建卷积神经网络模型进行特征提取和分类，然后用样本数据对网络进行训练调整模型参数。最后使用训练好的模型进行冰雹检测。通过仿真数据和实测数据的实验结果表明，该方法能够在提取数据特征和自适应调节网络参数基础上有效进行冰雹检测。     

基于PSWF的非正弦通信系统同步方法

基于椭圆球面波函数(Prolate Spheroidal Wave Function,PSWF)的非正弦时域正交调制信号的自相关值呈现明显的起伏震荡特性,这给同步检测带来了较大的困难。针对这一问题,提出了一种采用m序列调制基带PSWF脉冲作为同步头的同步提取方法,该同步头具有尖锐的自相关峰,有效解决了同步困难的问题。同时,给出了该同步方法的实现方式,可有效降低同步捕获过程运算量,缩短同步捕获时间,提高同步捕获精度。     

基于USRP X310和LabVIEW的双基地短距目标检测

基于软件无线电平台USRP X310及LabVIEW实现一种双基地短距雷达系统,用于近距离微弱慢动目标的探测。与传统双基地雷达需要独立参考信号完成同步不同,本系统利用收发天线直达波能量较高的特性及Chirp信号较强的自相关性,通过USRP X310的FPGA实现高精度时钟同步,降低了系统复杂度及上位机处理负荷。针对静态杂波及直达波干扰,给出一种基于子空间投影及相参积累的目标检测方法,该方法基于回波信号构建杂波子空间,将接收信号投影至正交于杂波子空间的信号子空间,并结合相参积累抑制干扰,提高目标检测的性能。系统实测数据处理结果证明该方法能有效提取微弱慢动目标信息,性能明显优于动目标指示(MTI)算法。     

基于RBF神经网络的混沌保密通信的研究

提出一种径向基函数神绎网络(RBFNN)参数调制混沌保密通信方法。在通信系统的发射端设计一个RBF神经网络跟踪器RBFNN1，将信息信号隐藏在混沌系统状态量参数中，再进行混沌掩盖。在接收端，相应的设计一个RBF神经网络跟踪器RBFNN2，则RBFNN2的输出就是解调出的原信息信号。该方法结构简单、易于实现、同步快。通过Chua混沌系统仿真，实验结果表明是可行的。     

基于SAE-LSTM的工艺数据异常检测方法

为解决工业网络安全防护中工艺数据异常检测误报率较高的问题,本文提出一种基于时间序列的异常检测方法.该方法对工艺数据进行相关性分析、向量映射等处理,再采用堆叠自编码神经网络(SAE)对工艺数据特征进行降维,根据工艺数据在传输序列间的相互关联性,设计基于长短期记忆神经网络(LSTM)的异常检测模型,最后进行工艺数据异常检测仿真实验验证分析.实验结果表明,基于时间序列的异常检测模型能有效提高工艺数据异常检测准确率,并且误报率要低于传统隐马尔可夫异常检测模型,同时获得较好的异常检测实时性.     

基于深度神经网络的多传感器联合信号检测方法

针对多传感器分布式接收中的信号检测问题,提出了一种最小错误概率准则下的联合检测方法。所提方法采用分布式软信息融合处理策略,将多传感器信号检测视为二元假设检验,借助深度神经网络优异的函数逼近能力,在对神经网络结构、目标函数和网络输入输出进行分析基础上,给出了基于深度神经网络的假设检验后验概率求解方法。各独立接收单元利用深度神经网络估计信号有无两种假设的后验概率,然后送入融合中心,计算联合后验概率分布,并做出判决。与传统处理过程依赖严密的数学推导不同,所提方法参数解析和特征提取无需人工解算。最后,通过仿真实验对所提方法有效性进行了验证,并与现有方法进行了对比。结果表明,所提方法能够实现多个传感器信号有效融合,随着接收单元数目增加,能够显著提升信号检测概率,并降低虚警概率;与当前典型的S/K融合方法相比,所提方法在低信噪比下具有明显优势。     

基于前馈神经网络的非合作PCMA信号盲分离算法

针对非合作接收PCMA混合信号盲分离中高复杂度束缚,提出一种基于前馈神经网络的分离算法,通过搭建神经网络分离平台,规避传统的发送符号遍历思想,实现PCMA混合信号低复杂度高性能盲分离.仿真实验表明,神经网络能够极大挖掘信号内在信息,针对QPSK调制PCMA混合信号,在信噪比7dB时误比特率达到10^-3数量级,并伴随着较PSP分离算法算术平方根级别的复杂度降低.     

基于循环神经网络的卫星幅相信号调制识别与解调算法

针对卫星通信中常用的幅相调制信号,提出了一种基于循环神经网络的信号识别与解调模型.通过循环神经单元直接对信号时序进行深层特征提取,结合全连接神经网络对特征进行维度映射,最终完成目标信号的调制识别与解调.该方法不需要预估目标信号载噪比,克服了人为确定阈值的缺陷,对信号频偏误差、定时误差容忍能力强;且在开发维护和更新拓展方面,克服了传统算法需重新部署判决规则的缺点,符合实际工程需求.仿真实验表明,当网络训练达到稳态时,在信噪比为6dB的条件下,目标信号识别率接近98%,解调误码率接近理论门限.本文所建立的理论形式为当今智能化信号处理提供了新思路,其思想同样可应用于其他通信信号处理领域.     

噪声背景下雷达低速小目标检测的一种新方法

该文在研究Duffing振子特性之后,基于Duffing振子与小波变换和神经网络相结合,提出了一种适用于低速小目标的Duffing振子雷达检测新方法。仿真实验表明,该检测方法能在低信噪比环境中,以低虚警率对低速目标进行有效检测。     

基于深度神经网络的光纤传感识别算法

为解决光纤传感过程中不同类型事件信号混叠造成识别概率降低的问题,搭建了一种采用差分相关计算的双光纤传感结构,并在此基础上提出了基于深度神经网络的信号识别算法。首先利用双光纤回波信号计算相关系数,再通过不同事件类型信号特征设置阈值范围,从而通过相关计算与阈值滤波提高信噪比。设计了包含三个隐藏层的深度神经网络模型,以分离输入层与相关运算层的形式完成低频噪声抑制与信号混叠解调的目的。实验分别对三种常见入侵事件进行测试,并在此基础上分析了不同算法对组合事件的识别概率。结果显示三种事件的回波谱形具有显著特征。三种算法对单一触发事件的识别概率均在95%以上,该算法的识别均值为98.5%。当两个事件同时触发时,三种算法的平均识别概率分别为73.4%、 84.5%和96.4%。当三个事件同时触发时,三种算法的平均识别概率分别为65.2%、78.3%和93.5%。可见,该算法在光纤传感中信号存在干扰及混叠时具有更好的识别效果。     

Noncoherent Quantized Digital Delay-Lock Tracking of Binary Signals

The delay-lock discriminator is a nonlinear feedback system which employs a form of cross correlation to estimate the relative delay between a reference signal and a delayed version of that signal which is perturbed with additive noise. Previous treatments of delaylock tracking have assumed that the binary maximal-length code sequences used as the signal are detected in a coherent manner. The present paper is concerned with delay-lock discriminator tracking behavior when noncoherent detection of the received signal is required and when two-bit quantization is employed to reduce the cost of implementation of the cross-correlation network. It is found that the error signal generated by the cross-correlation network is characterized by "clustering" in the absence of noise, however, this mode of error signal behavior gradually vanishes as the noise level is increased and is replaced by an error signal generation characteristic that more closely resembles the behavior that obtains without quantization. The selection of an appropriate threshold value to achieve minimum steady-state errors for the delay-lock discriminator is reduced to the problem of determining the probability density function of the error signal under the condition of exact alignment.     

Performance analysis of a DS/CDMA system with noncoherent M-aryorthogonal modulation in Nakagami fading

The probability of error performance of a direct sequence code-division multiple-access (DS/CDMA) system employing noncoherent M-ary orthogonal signaling in a Nakagami multipath fading channel is analyzed. A RAKE receiver structure with square-law demodulation is used at the receiver. The multiple-access interference are modeled as Gaussian and expressions derived for the exact probability of error. The performance is also evaluated in terms of the number of users that can be supported by the system at a given probability of error. The effect of correlated fading on system performance is also investigated by considering two correlation models, which can be characterized by a single correlation coefficient ρ. In the first model, the correlation coefficient between any two diversity branches is constant. In the second model, it is assumed that the correlation coefficient between any two diversity branches decreases exponentially as the separation between them increases. For both models, it is found that the presence of correlation deteriorates system performance. The use of larger signal alphabets than binary modulation in conjunction with diversity reception provides a considerable performance improvement even in the presence of correlated fading     

Quantitative analysis of 2-f.s.k. traffic detection

Two different methods of detecting signal flow in a noncoherent 2-f.s.k. channel are analysed with respect to the necessary signal/noise ratio for a certain error probability. It is found that separate detection of each frequency requires s.n.r. ?18 dB compared to about 19.5 dB for sum detection when the error probability is 10?7.     

海面短波地波通信中基于DNN神经网络的单样本极化滤波器预测研究

针对海面通信受大气噪声干扰严重的问题,该文提出一种基于DNN（Deep Neural Network）神经网络的单样本极化滤波器预测模型,研究其对海面短波地波通信链路中的大气噪声的抑制作用.与传统算法不同,DNN神经网络直接从大量输入数据获取信息间的非线性特性,并以此更新网络参数,通过对模型参数调整使得模型达到最优状态.选择三种脉冲成分比例不同的大气噪声进行仿真,结果表明传统算法与DNN网络模型在低信噪比约0~15dB时对信号的误码率影响基本一致,在高信噪比约超过15dB,误码率达到10^-4时,深度学习模型比传统算法所需信噪比显著提高约5dB.实验结果验证了神经网络在单样本极化滤波器预测方向的可行性与准确性,具有很好的实用参考价值.     

Implementing Efficient Audio and Image Watermarking Using Multi-resolution Dual Wavelet and Fireflies Approach in Wireless Network

Watermarking is the process of embedding the particular information into the audio signal for managing the ownership copyrights through wireless network. During the watermarking process, the audio signal consumes high energy, conflicting problem of robustness and imperceptibility, signal to noise ratio, bit error rate and normalized correlation. To overcome these issues present in the audio watermarking process, novel wavelet decomposition and evolutionary algorithm is utilized. Initially the input information or message has been split into two and the spillted message is watermarked using the audio and the image in wireless network. The first half of the message is watermarked with the help of the image and the next half of the image is watermarked by audio. Initially the watermarked image is transferred into YIQ image, from the transferred image the scrambled image is generated with the help of the Hidden Markov tree counter let wavelet transform method for generating the watermarking image. Then the next part of information is watermarked by audio signal which is decomposed into various sub bands using the Multi-resolution complex dual tree wavelet method. From the decomposed audio signal, the message authentication code based watermarks have been embedded in the lower frequency coefficients. Then the embedded process is performed by using the Dead zone quantization process which is optimized with the help of the Fireflies algorithm for enhancing the quality of the watermarking process. Finally both watermarking process is embedded for improving the security to the information with efficient manner through wireless network. In addition the efficient watermarking process through wireless network reduces the various attacks while extracting the water marker. Thus the optimized wavelet and quantization process improves the image and audio watermarking through wireless network and efficiency of the proposed system is evaluated using the experimental results in terms of the Signal to noise ratio, normalized correlation and bit error rate.