多媒体信号处理的数学理论前沿进展

深度学习模型广泛应用于多媒体信号处理领域,通过引入非线性能够极大地提升性能,但是其黑箱结构无法解析地给出最优点和优化条件。因此如何利用传统信号处理理论,基于变换/基映射模型逼近深度学习模型,解析优化问题,成为当前研究的前沿问题。本文从信号处理的基础理论出发,分析了当前针对高维非线性非规则结构方法的数学模型和理论边界,主要包括：结构化稀疏表示模型、基于框架理论的深度网络模型、多层卷积稀疏编码模型以及图信号处理理论。详细描述了基于组稀疏性和层次化稀疏性的表示模型和优化方法,分析基于半离散框架和卷积稀疏编码构建深度/多层网络模型,进一步在非欧氏空间上扩展形成图信号处理模型,并对国内外关于记忆网络的研究进展进行了比较。最后,展望了多媒体信号处理的理论模型发展,认为图信号处理通过解析谱图模型的数学性质,解释其中的关联性,为建立广义的大规模非规则多媒体信号处理模型提供理论基础,是未来研究的重要领域之一。     

The use of PCA and signal processing techniques for processing time-based construction settlement data of road embankments

Instrumentation is beneficial in civil engineering for monitoring structures during their construction and operation. The data collected can be used to observe real-time response and develop data-driven models for predicting future behaviour. However, a limited number of sensors are usually used for on-site civil engineering construction due to cost restrictions and practicalities. This results in relatively small raw datasets, which often contain errors and anomalies. Interpreting and making judicious use of the available dataset for developing reliable predictive model represents a significant challenge. Therefore, it is essential to pre-process and clean the data for improving their quality. To date, little investigation has been performed in the application of such data cleaning methods to geotechnical engineering datasets collected from full-scale sites. The purpose of this study is to apply simple and effective data pre-processing techniques to site-data collected from a highway embankment constructed on a sequence of soil layers of different physical make-up and non-linear consolidation characteristics. Various cleaning methods were applied to magnetic extensometer data collected for monitoring settlement within foundation soils beneath the embankment. PCA was used to explore raw data, identify and remove outliers. Numerous filtering and smoothing methods were used to clean noise in the data and their results were further compared using RMSE and NMSE. The methods adopted for data pre-processing and cleaning proved very effective for capturing the raw settlement behaviour on site. The findings from this study would be useful to site engineers regarding complex decision-making relating to ground response due to embankment construction. This also has positive prospects for developing dynamic prediction models for embankment settlement.     

量子信号处理

对量子系统的基本原理以及建立在对这一理论体系进行仿真基础上的量子信号处理算法的理论框架进行了分析和研究,提出了量子信号处理在信号处理和图像处理领域的应用方法,并根据量子信号处理的理论框架得到了更为通用的量子算法设计模型,指出了量子算法所具有的并行化特性,为算法并行化提供了一种新的思路。     

Parallel processing for biomedical signal processing

The authors describe how systematic mapping methodologies can be used to derive special-purpose processor arrays for the estimation of the bispectrum via the third-order moments. A novel design that is optimal in terms of total execution time for multiple pipelined data blocks is proposed, and it is shown how formal verification of the design can be achieved analytically. The discussion focuses on mapping regular algorithms to parallel architectures, bispectrum estimation from the third-order moments, and systolic array design for the third-order moments     

Configurable analog signal processing

We present a viewpoint showing that analog signal processing approaches are becoming configurable and programmable like their digital counterparts, while retaining a huge computational efficiency, for a given power budget, compared to their digital counterparts. We present recent results in programmable and configurable analog signal processing describing the widespread potential of these approaches. We discuss issues with configurable systems, including size, power, and computational tradeoffs, as well as address the computational efficiency of these approaches. Analog circuits and systems research and education can significantly benefit from the computational flexibility provided by large-scale FPAAs. The component density of these devices is sufficient to synthesize large systems in a short period of time. However, this level of reconfigurable and programmable complexity requires a development platform and CAD tools to demonstrate the capabilities of large-scale FPAAs before they will be widely accepted. To address this need, a self-contained FPAA setup has been developed along with an integrated software design flow. With only an Ethernet connection and an AC power outlet, a researcher or student can explore the numerous analog circuit possibilities provided by large-scale FPAAs.     

盲信号处理中的次序不确定性问题研究

深入研究了盲信号处理中解决次序不确定的互参数法的理论基础,通过公式推导和高斯白噪声下的仿真实验,揭示了互参数法有效的原因之一是离散傅立叶的计算方法提供了互参数的相关性,从而得出了互参数法的应用可以拓展至语音信号以外的信号的结论;通过对源信号计算互参数来反映相关系数Corr和KL距离在语音信号卷积混合频域解法中的性能,发现KL距离的性能优于相关系数,但是在某些情况下,由于离散化带来的误差和信号长度太短而引起的独立性下降等因素,使得KL距离仍然存在着不能够进行有效进行频率对准的可能。     

实时阵列信号处理系统设计

根据实际项目需求设计了一种实时阵列信号处理系统,采用多路高速光纤作为数据输入、输出接口;4片处理器组成互联的拓扑结构,任何两片处理器之间都可以互相传送数据,多片处理器可同时对数据进行高速处理;系统配备了大容量存储器,可满足大量数据的存储要求;采用两片现场可编程门阵列器件对系统接口、时序进行控制;为每片处理器分配了处理任务,并对信号处理算法运行时间及数据传输时间进行评估。该系统已在实际项目中运行,充分满足项目要求,运行稳定,验证了本系统设计的可行性。     

反垃圾电子邮件方法研究进展

随着垃圾电子邮件对互联网技术的威胁日益严峻,反垃圾电子邮件研究已成为当今的研究热点.综述了反垃圾电子邮件研究的历史、现状和最新进展.首先介绍并分析了3种类型的邮件特征提取方法——基于文本、图片和行为的特征提取方法.然后,在此基础上,详细论述了当前的反垃圾邮件技术——法律手段、简单方法和智能型处理技术.接着,介绍了反垃圾邮件系统性能评估准则和标准数据集.最后,对反垃圾电子邮件现状进行了分析和总结,并指明了未来反垃圾电子邮件研究的发展方向,包括改进邮件特征提取技术、完善相关法案和引入新的智能反垃圾邮件方法.     

基于FPGA的语音信号实时处理

介绍一种在语音识别系统中运用FPGA技术对语音信号进行前期实时处理的方法.利用DSP Builder设计信号处理算法的图形化电路模块,运用硬件环(HIL Hardware in the Loop)技术对模块进行软硬件协同仿真.满足设计要求后,再用Signal Compiler将模块转换成VHDL语言和Quartus Ⅱ工程文件下载至目标芯片.结果表明此方法可以快速灵活地设计出语音处理模块,语音数据能在要求的时间范围内处理完毕,达到了实时处理的目的.     

Tektronix混合信号存储示波器在信号处理中的应用研究

介绍矩形窗函数的实验及TektrolqiXMS02000型混合信号永波器为数据处理和分析提供了大量真实的原始实验数据,及与仿真软件结合进行信号处理,克服信号采集和数字化硬件的瓶颈制约。     

光纤陀螺信号的数字处理方法

光纤陀螺（FOG）有着广泛地应用,如何有效地提高精度具有重要意义。根据FOG信号漂移的数学模型,分别采用了经典傅立叶分析的FIR低通滤波器和小波变换的阈值去噪对某型FOG的输出信号进行了消噪处理。实验证明了小波阈值在消除陀螺漂移和基座动态干扰应用中的优越性。     

Advances and applications on microfluidic velocimetry techniques

The development and analysis of the performance of microfluidic components for lab-on-a-chip devices are becoming increasingly important because microfluidic applications are continuing to expand in the fields of biology, nanotechnology, and manufacturing. Therefore, the characterization of fluid behavior at the scales of micro- and nanometer levels is essential. A variety of microfluidic velocimetry techniques like micron-resolution Particle Image Velocimetry (μPIV), particle-tracking velocimetry (PTV), and others have been developed to characterize such microfluidic systems with spatial resolutions on the order of micrometers or less. This article discusses the fundamentals of established velocimetry techniques as well as the technical applications found in literature.     

FAD-flexibility in digital signal processing

A digital filter and detection chip (FAD) has been developed by British Telecom in the UK for application as a tone detector in digital telephone exchanges. The chip has a flexible structure and has since found application in many other communication systems. This paper describes the chip and indicates how it can be used to perform a variety of different algorithms for these purposes.     

Microelectromechanical filters for signal processing

Microelectromechanical filters based on coupled lateral microresonators are demonstrated. This new class of microelectromechanical systems (MEMS) has potential signal-processing applications for filters which require narrow bandwidth (high Q), good signal-to-noise ratio, and stable temperature and aging characteristics. Microfilters presented in this paper are made by surface-micromachining technologies and tested by using an off-chip modulation technique. The frequency range of these filters is from approximately 5 kHz to on the order of 1 MHz for polysilicon microstructures with suspension beams having a 2-μm-square cross section. A series-coupled resonator pair, designed for operation at atmospheric pressure, has a measured center frequency of 18.7 kHz and a pass bandwidth of 1.2 kHz. A planar hermetic sealing process has been developed to enable high quality factors for these mechanical filters and make possible wafer-level vacuum encapsulations. This process uses a low-stress silicon nitride shell for vacuum sealing, and experimental results show that a measured quality factor of 2200 for comb-shape microresonators can be achieved     

Immunocomputing for intelligent signal processing

Based on immunocomputing (IC), this paper describes an approach to intelligent signal processing. The approach includes both low-level feature extraction and high-level (intelligent) pattern recognition. The key model is the formal immune network (FIN), which includes apoptosis (programmed cell death) and immunization and controls them by cytokines (messenger proteins). Such FIN can be formed from the signal or combination of signals using discrete tree transform, singular value decomposition and the proposed index of inseparability as a measure of quality of FIN. Several comparisons with neural computing and support vector machines suggest that IC may outperform state of the art approaches to intelligent signal processing.     

基于自学习算法的BCG信号处理方法

针对心冲击描记(BCG)信号中心跳波形缺乏确定模型且受多种因素影响的特点,提出了基于自学习的心跳识别算法.使用聚类分析的方法,有效提取了BCG信号中具有高度相关性的一组曲线,并将其作为心跳模型.将心跳模型与BCG信号进行匹配,捕捉到心跳信号进而得到心跳周期.经实验验证:算法得出的心跳周期误差在±2%以内,并在Android平台上进一步验证了其准确性和实用性.     

基于小波包分析的数字信号处理

小波包分析具有优良的时频特性,可以有效地用于数字信号的消噪处理和对信号进行特征提取。小波包分析可以对电机振动故障进行诊断,以便随时排除故障。同时,给出并分析了试验结果。     

电力智能传感器中的信号处理研究

探讨了误差最小均方(LMS)法在电力智能传感器的电压分量中的运用。提出并分析和比较了获取电力智能传感器中电压有效值和初始相位角的几种方法,指出误差LMS法因降低了对电路性能的要求而能有效地简化系统、降低成本。仿真实验表明:直接根据定义分析电力参数,需要很高的采样频率与信噪比,而利用误差LMS法在获取电压有效值时采样频率可降低3倍,在求解初始相位角时对信号中干扰幅值的要求从小于信号幅值的5%可放宽到信号幅值的15%。     

光寻址电位传感器阵列信号处理仿真研究

基于微透镜和频率调制盘的设计,提出了光寻址电位传感器阵列(LAPS)中光脉冲阵列的频率调制方法和基本的测量系统.采用Matlab对LAPS阵列可能检测到的光电流信号进行快速傅立叶变换(FFT)和小波分析的仿真.从仿真得到的频谱图,各频率的脉冲光所对应的光电流能够分辨出来,进一步证明多频率脉冲光源LAPS传感器阵列具有很好的频域分辨率,从而得到LAPS阵列对应各个传感元的信号,为实际LAPS阵列器件的制作和信号处理提供了理论依据.