基于差分Pattern时延差编码和海豚whistles信号的仿生水声通信技术研究

为解决传统的隐蔽水声通信方法带来的通信性能降低问题, 提出了一种将差分 Pattern 时延差编码通信体制与海豚whistles信号相结合的仿生水声通信技术. 海豚whistles信号频带较窄且各信息码元间隔不等、码元之间互相关性较弱, 选取whistles信号作同步码和Pattern 码, 并以相邻whistles信号之间的时延差值携带信息. 这种仿生的水声通信信号不易被敌方探测、截获, 且差分Pattern时延差特殊的编码方式也不易使信息被破译, 因此该水声通信技术具有较强的隐蔽性和保密性, 且在抗码间干扰以及抗多普勒效应方面具有优异性能. 本文对系统进行了水池实验, 在信噪比为0 dB、存在相对运动时实现了通信速率为67 bit/s的低误码数据传输, 验证了系统的有效性、稳健性和隐蔽性. 关键词： 仿生水声通信 差分Pattern时延差编码 海豚whistles信号 隐蔽性     

编码光系统的颜色耦合与颜色失衡校正

为了减小编码光系统中的颜色耦合与颜色失衡的干扰,提高形状与颜色重建的准确度,建立了彩色编码光系统,并对其所采用的编解码方法、颜色重建的两个相关环节及颜色校正方法进行了研究。首先,给出了系统的彩色梯形相移强度比编解码法,分析了编解码过程中存在的RGB基色相互耦合现象,设计了应用Caspi模型硬件标定的颜色耦合校正方案;然后,给出了系统的逐点颜色重建法,分析了颜色重建过程中由表面曲率导致的颜色失衡现象,设计了利用表面几何信息校正颜色失衡的方案。实验结果表明：校正后单一绿色图像中的红色分量值约为校正前的1/10;单色表面色差约为0.1,趋近于人眼颜色分辨率,远低于校正前的0.4;重建的单色复杂被测表面颜色均匀、视觉效果良好,符合实际情况。提出的方法符合编码光系统抗干扰能力强的要求,有助于提高形状与颜色重建的准确度。     

扭曲光束可提高光纤信息承载能力

美国的一个科研小组发表在近期《科学》杂志上的最新研究成果显示,通过不同形状的扭曲光束来编码信息,可以提高互联网“信息高速公路”的承载能力,从而有效地缓解网络拥堵。互联网流量正在呈几何级数增长,研究人员也一直在设法提升光缆的通信容量。过去20年来使用的一个成功方法,基本上是依靠增加更多的“车道”,也就是用不同的颜色或波长来传输不同的信号。但就像在真正的高速公路上一样,“车道”虽然多了,每一条的宽度却变得更窄,因而数据流也只能混杂在一起。从前几年开始,就有多个研究团队在尝试通过光束的形状来为信息编码,以此缓解网络交通堵塞,该技术利用到了被称为轨道角动量的光属性。     

光源编码的白光散斑剪切干涉计量术

本文提出了一种用非相干光源照明进行剪切干涉计量的方法—光源编码的白光散斑剪切干涉计量术.它采用光栅对光源进行编码以提高空间相干性,使物体的两个剪切像的波面部分相干叠加,以实现剪切干涉.     

多路音频信号的数字化光纤传输系统

在信息传输的各个领域中，光纤作为传输信号媒介的应用得以迅速发展。光纤的音频多路数字化传输也是光纤领域目前开发的热点课题之一，它充分利用光纤本身所具有的各种特点，使音频信号的传输更有效、更可靠。本文利用串行信号的时分复用技术，完成了多路音频数字化信号的光纤传输。提出信号传输中,数据信号和控制信号的复用及解复用解决方法。     

用偏振缠绕的光子对进行量子通讯和密集编码

研究如何利用两粒子形成的ＥＰＲ－Ｂｅｌｌ态传输光子的量子态，并提出依赖于两光子缠绕的密集编码实验设想。     

A realizable quantum encryption algorithm for qubits

A realizable quantum encryption algorithm for qubits is presented by employing bit-wise quantum computation. System extension and bit-swapping are introduced into the encryption process, which makes the ciphertext space expanded greatly. The security of the proposed algorithm is analysed in detail and the schematic physical implementation is also provided. It is shown that the algorithm, which can prevent quantum attack strategy as well as classical attack strategy, is effective to protect qubits. Finally, we extend our algorithm to encrypt classical binary bits and quantum entanglements.     

Speech recognition based on a combination of acousticfeatures with articulatory information

The contributions of the static and dynamic articulatory information to speech recognition were evaluated, and the recognition approaches by combining the articulatory information with acoustic features were discussed. Articulatory movements were observed by the Electromagnetic Articulographic System for reading speech, and the speech signals were recorded simultaneously. First, we conducted several speech recognition experiments by using articulatory features alone, consisting of a number of specific articulatory channels, to evaluate the contribution of each observation point on articulators. Then, the displacement information of articulatory data were combined with acoustic features directly and adopted in speech recognition. The results show that articulatory information provides with additional information for speech recognition which is not encoded in acoustic features. Furthermore, the contribution of the dynamic information of the articulatory data was evaluated by combining them in speech recognition. It is found that the second derivative of articulatory information provided quite larger contribution to speech recognition comparing with the second derivative of acoustical information. At last, the combination methods of articulatory features and acoustic ones were investigated for speech recognition. The basic approach is that the Bayesian Network (BN) is added to each state of HMM, where the articulatory information is represented by the BN as a factor of observed signals during training the model and is marginalized as a hidden variable in recognition stage. Results based on this HMM/BN framework show a better performance than the traditional method.     

基于状态驻留时间下由转移弧产生输出的HMM模型中的参数估计

本讨论了用状态驻留时间来模型化传统的HMM模型。HMM的一个基本假设是它认为语音信号是准平稳的。然而由状态输出yt的HMM模型，并不能很好地表征语音信号中平稳段或平稳段之间的具体特征；由转移弧产生输出的自左向右HMM系统,则对语音特征作更为细致的描述。本主要讨论在[2]的基础上,对新建模型进行参数估计。     

短时非线性预测方法对汉语语音特性的研究

本文应用Short[8-10]改进了的短时非线性预测方法对正常语速的汉语音节和短语进行了分析!究，揭示了汉语语音中浊音和清音的短时非线性预测能力的差异，并且发现这种差异即使在强背景噪声下仍能用短时非线性预测方法加以辨别。这些为浊音和清音的切分提供了一种可能性手段。