基于内容的自适应小波域数字音频水印算法研究

以整型提升小波变换、静态图像压缩编码、人类听觉系统（HAS）为基础，提出了一种将灰度图像（即二维数字水印）嵌入到数字音频信号的新水印算法．该算法具有以下特点：（1）应用静态图像压缩编码技术，实现了以灰度图像作为水印信号的数字音频水印算法；（2）充分利用人类听觉系统（HAS），实现了二维数字水印的自适应嵌入，增强了算法的透明性和鲁棒性，（3）二维数字水印的提取不需要原始音频信号．仿真实验表明：该自适应数字音频水印算法不仅具有较好的透明性，而且对诸如叠加噪声、有损压缩、低通滤波、重新采样、重新量化等攻击均具有较好的鲁棒性．     

连续波背景下多信号处理方法

对接收到的信号进行短时傅里叶变换，通过分析短时傅里叶变换中每一个时间片内的信号频谱，检测出连续波背景下的脉冲信号。滤除线性调频脉冲信号成分，保留连续波信号成分，得到调频连续波信号具有周期性的时频变化曲线。根据连续波信号时频变化曲线的频谱特征，估计出其主要参数，然后滤除脉冲信号出现时间内信号中的连续波成分，并采用相应的方法估计出其参数。仿真结果表明，本文算法可以准确估计出线性调频连续波（LFMCW）信号和线性调频（LFM）脉冲信号的参数，当LFMCW信号的信噪比高于-8dB，并且其功率比脉冲信号功率小6dB以上时，算法具有良好的估计精度和稳定性。     

基于小波的数字调制信号特征分析

介绍了一种新的Leago小渡，分析了其基本特性，并利用Leago小波对幅度键控（ASK）、频移键控（FSK）、相移键控（PSK）信号的突变特征进行了分析，得到了一些不同于经典小波分析的特征规律。实验结果表明，Leago小波确实具有独特的性能特点．能够显著地表征数字调制信号的突变特征。     

ARC-FSK中程多载波水声通信新技术

海洋水声信道是一种极其复杂多变的时-空-频变参信道。信道通带窄、多途干扰强、衰落严重足水声通信高速可靠传输信息的主要障碍。该文针对中程（10～30km）多载波水声通信系统提出一种非对称升余弦频移键控调制（ARC—FSK）信号。该信号在频率调制的基础上，结合了非对称信号的稳健抗多途特性与相位调制（CPM）信号的高频谱效率，实现了最佳系统性能。理论分析及仿真结果表明：该ARC—FSK信号多释抑制有效，性能稳健，更适合于恶劣水声信道，在中程（10-30km）水下多载波（MC）高速通信中具有良好的应用前景。     

水电机组PLC转速信号装置

主要阐述了基于西门子S7-200PLC的水电机组转速信号装置，分析了其硬件构成及基本原理。该装置采用残压和齿盘双路机组频率（转速）信号，通过PLC内部的高速计数器直接测频，任何一路频率（转速）信号故障均不会影响装置的正确动作，该装置具有结构简单，可靠性高的特点。采用的转速信号装置在现场运行情况良好，工作十分可靠。     

基于估计熵的模糊滤波

介绍了近年来出现的一种度量信号复杂程度的统计量——估计熵（Ａｐｐｒｏｘｉｍａｔｅｅｎｔｒｏｐｙ），并将其用于信号的模糊滤波。这种滤波器具有能随信号的特征自动调节滤波器结构的优点，特别是当信号为非平稳时有较好的效果。这种滤波方法具有一定的自适性，适合于工程应用。     

EMD+AR模型在运动想象脑电处理中的应用

为了在处理运动想象脑电信号时得到理想的特征向量，提取脑电波（Electroencephalogram,EEG）信号特征之前使用经验模态分解（Empirical Mode Decomposition,EMD）信号，再用自回归（Auto Regressive,AR）模型提取特征。与单独使用AR模型相比，EMD+AR算法模型得到的频谱图特征更为明显，表明EMD+AR算法模型提取的特征具有较强的鉴别力。     

AFE4300低成本、集成模拟前端

AFE4300是一款低成本模拟前端，此模拟前端组装有两个独立的信号链：一个信号链用于体重计（WS）测量，而另外一个信号链用于体成分测量（BCM）分析。个16位，860SPS模数转换器（ADC）在两个信号链间复用。     

基于ARM7的多路韦根信号采集与处理

在门禁、安防、楼宇自动化等领域中，各类身份识别卡（如磁卡、射频IC卡）包含的ID信息经读卡器（Reader）读取后转化为二进制数据，一般采用标准韦根（Wiegand）接口或串行通信方式与各类控制设备相连，以实现相应的管理和控制功能。与串行通信相比，韦根信号具有响应速度快、数据保密性及兼容性好的优点，应用较为普遍。本文介绍了一种基于ARM7的多路韦根信号采集与处理的方法，具有实现方便、功能强的特点。     

高线性度620～1100MHz正交调制器

凌力尔特公司（Linear Technology Corporation）推出的新型高性能正交调制器LT5571，针对850～965MHz GSM、CDMA2000、ISM和RFID调制器应用进行了优化。该器件接受I（同相）和Q（正交相位）基带信号并将其直接调制至射频信号。其零或低中频（IF）发射器架构使基站设计师能够实现高性能，同时降低功耗并减少器件数，因此可以缩小系统尺寸并降低系统成本。该器件具有卓越的线性度，     

Implementing projection pursuit learning

This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations are done for heuristics to choose good initial projection directions. A comparison of a projection pursuit learning network with a single hidden-layer sigmoidal neural network shows why grouping hidden units in a projection pursuit learning network is useful. Learning robot arm inverse dynamics is used as an example problem.     

基于PPR的煤矿瓦斯监测数据相似搜索方法

研究基于时间序列相似搜索技术的煤矿瓦斯涌出分析新途径,提出基于PPR的煤矿瓦斯监测数据相似搜索方法。实验采用玉华煤矿的真实煤矿瓦斯监测数据,评价指标为信息损失量及相似查询效率。与基于离散傅立叶变换(DFT)和离散小波变换(DWT)的时间序列相似搜索算法的对比实验显示：在相同压缩比下,3种方法的信息损失相近;但是基于PPR的相似搜索算法的平均查询效率分别比基于DFT和基于DWT方法高32%和34%。因此PPR算法适合用于瓦斯监测数据相似搜索。     

大规模时间序列数据库降维及相似搜索

提出一种基于分段多项式表示（PPR）的时间序列数据库相似查询的系统化方法．PPR是一类基于线性多项式回归的正交变换．用PPR变换索引时间序列数据在理论上具备非漏报性质．文中分析了PPR的计算复杂性以及查询阈值的下界,并提出了一种衡量时间序列相似查询算法之查询效率的定量指标．与基于离散傅立叶变换（DFT）和离散小波变换（DWT）的时间序列相似查询算法所作的对比实验表明,所提算法可以用低的索引结构维数获得高的查询效率．     

Polynomial Preserving Recovery for High Frequency Wave Propagation

Polynomial preserving recovery (PPR) was first proposed and analyzed in Zhang and Naga in SIAM J Sci Comput 26(4):1192–1213, (2005), with intensive following applications on elliptic problems. In this paper, we generalize the study of PPR to high-frequency wave propagation. Specifically, we establish the supercloseness between finite element solution and its interpolation with explicit dependence on the frequency of wavefield, and then prove the superconvergence of PPR for high-frequency solutions to wave equation based on the supercloseness. We also present several numerical examples of PPR for both low-frequency and high-frequency wave propagation in order to confirm the theoretical results of superconvergence analysis.     

Comparison of fault detection techniques

Haar Wave-Net (HWN) and Projection Pursuit Regression (PPR) are two useful modeling tools for pattern classification. In this study, the two methodologies are compared with respect to the problem of misclassification close to class boundaries with sparse training data. A variety of examples were specifically tailored to elucidate their respective properties. It is observed that PPR locates the class boundaries at the midline of two classes of training data, which is a logical choice for the class boundary location, in the absence of sufficient information. For HWN, both the initial positioning of receptive fields and the density of training data near the class boundary may have great impact on the definition of the class boundary. Additionally, PPR and HWN are also compared to the Backpropagation Network (BPN), a standard technique for fault detection, with respect to their sensitivity to noise. The orthonormal and localized properties of the Haar basis functions enable a HWN to limit the noise effect within its local receptive fields. BPN propagates the noise effect throughout the input space. PPR provides a good tradeoff between reasonable generalization and noise localization. The fault diagnosis problem is investigated in a CSTR process, at both steady state and dynamic conditions. It is found that, for the dynamic case, the misclassification close to the class boundary is often due to lack of system observability.     

Pattern recognition of manufacturing process signals using Gaussian mixture models-based recognition systems

Unnatural patterns exhibited in manufacturing processes can be associated with certain assignable causes for process variation. Hence, accurate identification of various process patterns (PPs) can significantly narrow down the scope of possible causes that must be investigated, and speed up the troubleshooting process. This paper proposes a Gaussian mixture models (GMM)-based PP recognition (PPR) model, which employs a collection of several GMMs trained for PPR. By using statistical features and wavelet energy features as the input features, the proposed PPR model provides more simple training procedure and better generalization performance than using single recognizer, and hence is easier to be used by quality engineers and operators. Furthermore, the proposed model is capable of adapting novel PPs through using a dynamic modeling scheme. The simulation results indicate that the GMM-based PPR model shows good detection and recognition of current PPs and adapts further novel PPs effectively. Analysis from this study provides guidelines in developing GMM – based SPC recognition systems.     

Use of bias term in projection pursuit learning improvesapproximation and convergence properties

In a regression problem, one is given a multidimensional random vector X, the components of which are called predictor variables, and a random variable, Y, called response. A regression surface describes a general relationship between X and Y. A nonparametric regression technique that has been successfully applied to high-dimensional data is projection pursuit regression (PPR). The regression surface is approximated by a sum of empirically determined univariate functions of linear combinations of the predictors. Projection pursuit learning (PPL) formulates PPR using a 2-layer feedforward neural network. The smoothers in PPR are nonparametric, whereas those in PPL are based on Hermite functions of some predefined highest order R. We demonstrate that PPL networks in the original form do not have the universal approximation property for any finite R, and thus cannot converge to the desired function even with an arbitrarily large number of hidden units. But, by including a bias term in each linear projection of the predictor variables, PPL networks can regain these capabilities, independent of the exact choice of R. Experimentally, it is shown in this paper that this modification increases the rate of convergence with respect to the number of hidden units, improves the generalization performance, and makes it less sensitive to the setting of R. Finally, we apply PPL to chaotic time series prediction, and obtain superior results compared with the cascade-correlation architecture.     

The needs and benefits of Text Mining applications on Post-Project Reviews

Post-Project Reviews (PPRs) are a rich source of knowledge and data for organisations – if organisations have the time and resources to analyse them. Too often these reports are stored, unread by many who could benefit from them. PPR reports attempt to document the project experience – both good and bad. If these reports were analysed collectively, they may expose important detail, e.g. recurring problems or examples of good practice, perhaps repeated across a number of projects. However, because most companies do not have the resources to thoroughly examine PPR reports, either individually or collectively, important insights and opportunities to learn from previous projects, are missed. This research explores the application of knowledge discovery techniques and Text Mining to uncover patterns, associations, and trends from PPR reports. The results might then be used to address problem areas, enhance processes, and improve customer relationships. A case study related to two construction companies is presented in this paper and knowledge discovery techniques are used to analyse 50 PPR reports collected during the last three years. The case study has been examined in six contexts and the results show that Text Mining has a good potential to improve overall knowledge reuse and exploitation.     

Improving the approximation and convergence capabilities of projection pursuit learning

One nonparametric regression technique that has been successfully applied to high-dimensional data is projection pursuit regression (PPR). In this method, the regression surface is approximated by a sum of empirically determined univariate functions of linear combinations of the predictors. Projection pursuit learning (PPL) proposed by Hwanget al. formulates PPR using a two-layer feedforward neural network. One of the main differences between PPR and PPL is that the smoothers in PPR are nonparametric, whereas those in PPL are based on Hermite functions of some predefined highest orderR. While the convergence property of PPR is already known, that for PPL has not been thoroughly studied. In this paper, we demonstrate that PPL networks do not have the universal approximation and strong convergence properties for any finiteR. But, by including a bias term in each linear combination of the predictor variables, PPL networks can regain these capabilities, independent of the exact choice ofR. It is also shown experimentally that this modification improves the generalization performance in regression problems, and creates smoother decision surfaces for classification problems.     

一种适用于无线多媒体传感器网络的节点不相交多路径路由协议

设计了一种适用于无线多媒体传感器网络的高效节点不相交多路径路由协议.以源路由的方式建立多路径,中间节点通过有选择的转发RREQs,降低路由建立开销.将链路质量量化为数据接收率,利用数据接收率、节点能量及路径跳数等,定义了路径效率模型,将路径效率模型引入到路由建立过程中.仿真结果表明,与SMR及TinyONDMR协议相比,新协议建立路由的开销更低,所建路径上节点能量分布更理想,进行数据传输时的能量效率更高,同时不会显著增加路径的传输时延.