基于TMS320C54X的DTMF信号检测

ＤＴＭＦ信号是音频电话中的拨号信号,广泛应用于新一代电话机。本文介绍一种基于ＴＭＳ３２０Ｃ５４Ｘ系列ＤＳＰ芯片的ＤＴＭＦ信号的检测,解决了以往所用方法的不足之处,使得ＤＴＭＦ信号检测变得准确、迅速和灵活,从而可能实现多种的电信增值的服务。     

基于DSP芯片ADSP21××的DTMF检测

ＤＴＭＦ是音频电话中的拨号信号，广泛运用于电话信息查询系统、电子邮件系统等交互式的控制应用中。介绍了基于ＡＤＳＰ２１ｘｘ系列ＤＳＰ芯片的ＤＴＭＦ软件检测方法，解决了采用专用芯片所带来的不足之处，使得ＤＴＭＦ检测变为迅速、灵活和准确。     

基于DSP的信号实时采集与处理系统

介绍了一种以数字信号处理器（ＤＳＰ）为ＣＰＵ的分布式高速,高精度多通道模拟及频信号实时采集与处理系统,可广泛应用于实时的多通道声、光信号检测与处理。该系统主、从板均采用ＤＳＰ作为ＣＰＵ,可实现处理;主、从板间的信息交换既可利用适于少量数据灵活传送的双向ＦＩＦＯ,又可利用适于大数据量快速传送的ＤＭＡ;用户程序可灵活方便从ＰＣ机上下载避免了写入ＥＰＲＯＭ的麻烦。     

DWMT系统在TMS320C80上的仿真实现

基于小波变换的离散多音传输系统（ＤＷＭＴ系统）是一种抗干扰能力强的高效数据传输系统,其主要在两种实现方案：（１）基于通用可编程ＤＳＰ芯片的软件实现方案;（２）基于通用ＤＳＰ芯片和专用ＶＬＳＩ芯片的软硬件结合方案。本文在分析了ＤＷＭＴ系统的实实现结构、运算量以及滤波器优化方法等基础上,利用ＴＭＳ３２０Ｃ８０软件开发系统对基于通用ＤＳＰ的软件实现方案进行了仿真,并且详细地介绍了该方案的软件流程以及一些     

伪Newton－δ族算法对一般目标函数的收敛性

伪Ｎｅｗｔｏｎ－δ族算法对一般目标函数的收敛性赵云彬（中国科学院应用数学研究所）段虞荣（重庆大学系统科学与工程研究所）ＣＯＮＶＥＲＧＥＮＣＥＯＦＴＨＥＰＳＥＵＤＯ－ＮＥＷＴＯＮ－δＣＬＡＳＳＭＥＴＨＯＤＳＦＯＲＧＥＮＥＲＡＬＯＢＪＥＣＴＩＶＥＦＵＮＣ...     

旋转变压器—数字转换器AD2S83在伺服系统中的应用

介绍了旋转变压器－数字转换器 ＡＤ ２ Ｓ ８３在伺服系统中的应用,重点介绍了该器件与主控芯片ＤＳＰ（ＴＭＳ ３２０Ｆ２４０）的接口电路设计。     

一种提高二维离散小波变换的重构性的方法

分析了导致ＤＷＴ不能完全重构原始信号的原因,提出了一种提高二维ＤＷＴ信号重构性的方法。实验结果表明这种新方法比著名的Ｍａｌｌａｔ二维算法具有更为优良的信号重构性,特别适用于需要进行二维ＤＷＴ级数较多的场合。     

多功能数字信号调制器的研究

本文介绍以８０３１单片机为核心的数字信号调制器的实现原理，由于采用了倍频技术和复合波形合成技术，因而这种数字信号调制器的传输速率在１００－９６００ｂｉｔ／ｓ范围内可调，并且在ＡＳＫ、ＦＳＫ、ＰＳＫ、ＤＰＳＫ、ＱＡＭ等信号调制方式可供选择，以便适用于各种特性的信道。     

双DSP实现气象雷达信号处理

ＤＳＰ和ＭＣ比较,ＤＳＰ具有高速、高精度、极为适合信号处理和控制的特点,构成的系统成本与ＭＣ相当或更低,因此ＤＳＰ越来越受到人们的重视。本文讨论了利用ＴＩ的定点ＤＳＰ双ＴＭＤ３２０Ｆ２０６实现气象雷达信号处理器的方法。     

一种基于DSP的新型交流调速系统

介绍了一种运用新型ＡＳＩＣ芯片ＡＤＭＣ３３１设计和实现的 ＳＰＷＭ交流调速系统,系统主要由基于 ＤＳＰ的电机调速专用集成电路ＡＤＭＣ３３１和功率ＭＯＳＦＥＴ及其驱动电路构成。实践证明该系统具有良好的运行性能。     

Fractional wavelet transform for the quantitative spectral resolution of the composite signals of the active compounds in a two-component mixture

Fractional calculus is a powerful tool that has been applied successfully for the analysis of the complex systems. One interesting example of a complex mixture is given by the multicomponent pharmaceutical samples having constant matrix content. The main aim of this study is to develop a new approach based on the combined use of the fractional wavelet transform (FWT) and the continuous wavelet transform (CWT) in order to quantify atorvastatin (ATO) and amlodipine (AML) in their mixtures without requiring a chemical pretreatment. In the first step, the absorption spectra of the compounds and their samples were processed by the FWT method. In the next step, the CWT approach was applied to the fractional wavelet spectra obtained in the above step. The aim of the application of FWT is data reduction corresponding to the spectra of compounds and their commercial samples. In the following step, the CWT was used for the quantitative resolution of the composite signals of the analyzed compounds. After method validation, the proposed signal processing methods based on the combined use of the FWT and the CWT were successfully applied to the resolution of the composite spectra for the quantitation of atorvastatin (ATO) and amlodipine (AML) in tablets.     

EMGdi signal enhancement based on ICA decomposition and wavelet transform

Diaphragmatic electromyogram (EMGdi) signal plays an important role in the diagnosis and analysis of respiratory diseases. However, EMGdi recordings are often contaminated by electrocardiographic (ECG) interference, which posing serious obstacle to traditional denoising approaches due to overlapped spectra of these signals. In this paper, a novel method based on wavelet transform and independent component analysis (ICA) is proposed to remove the ECG interference from noisy EMGdi signals. With the proposed method, the original independent components of contaminated EMGdi signal were first obtained with ICA. Then the ECG components contained were removed by a specially designed wavelet domain filter. After that, the purified independent components were reconstructed back to the original signal space by ICA to obtain clean EMGdi signals. Experimental results achieved on practical clinical data show that the proposed approach is better than several traditional methods include wavelet transform (WT), ICA, digital filter and adaptive filter in ECG interference removing.     

Fourier变换和小波变换一般形式 —线调频小波变换和多普勒小波变换

Fourier变换、窗口Fourier变换与小波变换在许多领域得到广泛的应用。该文回顾了Fourier变换和小波变换的发展;介绍了两种新的处理非平稳信号的方法,即线调频小波变换和多普勒小波变换;分析了线调频小波变换是短时Fourier变换和小波变换的时频分析的统一时频表示形式,Fourier变换、小波变换以及线调频小波变换都是多普勒小波变换的特殊情况。线调频小波变换和多普勒小波变换比Fourier变换和小波变换更具灵活性,为图像、信号处理提供了新的方法和工具。     

A deep wavelet sparse autoencoder method for online and automatic electrooculographical artifact removal

Electrooculographical (EOG) artifacts are problematic to electroencephalographical (EEG) signal analysis and degrade performance of brain–computer interfaces. A novel, robust deep wavelet sparse autoencoder (DWSAE) method is presented and validated for fully automated EOG artifact removal. DWSAE takes advantage of wavelet transform and sparse autoencoder to become a universal EOG artifact corrector. After being trained without supervision, the sparse autoencoder performs EOG correction on time–frequency coefficients collected after brain wave signal wavelet decomposition. Corrected coefficients are then used for wavelet reconstruction of uncontaminated EEG signals. DWSAE is compared with five other methods: second-order blind identification, information maximization, joint approximation diagonalization of eigen-matrices, wavelet neural network (WNN) and wavelet thresholding (WT). Experimental results on a visual attention task dataset, a mental state recognition dataset and a semi-simulated contaminated EEG dataset show that DWSAE is capable of suppressing EOG artifacts effectively, while preserving the nature of background EEG signals. The mean square error of signals before and after correction by DWSAE on a semi-simulated contaminated EEG segment of 30 s is the lowest (65.62) when compared to the results produced by WNN and WT. DWSAE addresses limitations posed by these methods in three ways. First, DWSAE can be performed automatically and online in a single channel of EEG data; this has advantages over independent component analysis-based methods. Second, its results are robust and stable in comparison with those of other wavelet-based methods. Third, as an unsupervised learning scheme, DWSAE does not require the off-line training that is necessary for WNN and other supervised learning machine learning-based methods.

     

Realtime DSP-based system for spectral analysis

The spectral analysis of the signals generated by a physical system is a powerful way to analyse that system's behaviour. In the past the inability to achieve realtime analysis of signals and the high cost of spectrum analysers limited the use of this method of study in the area of physical system control. Today the availability of cheap and powerful devices — digital signal processors, overcomes the earlier problems. The paper presents a system for realtime spectral analysis based on a TMS32010 DSP chip. This system consists of two main components: a DSP board where data are processed, and a PC which acts as a terminal for the operator, as a display for the analysis results and as a temporary database. The system's hardware and software architectures and an application in seismic signal monitoring are described.     

小波分析在水下回波奇异性检测中的应用

本文研究了信号的奇异性检测问题,给出了小波变换和信号奇异性的关系,实现了小波分析对信号各类奇异间断点的有效检测,最后进行了实例分析。此理论与方法适用于对边缘信号与突变信号的处理和提取,为水下目标的特征提取及识别提供理论依据。     

基于DSP的并行软件系统的设计与实现

随着超大规模集成电路技术的发展,数字信号处理器的处理能力不断提高.介绍系统软件运行的硬件平台及功能,描述系统硬件的软件接口技术及其实现方法,设计并实现了一个基于VME总线技术.采用Link口互联的DSP 21160N并行软件系统,对系统性能进行测试与分析,结果表明,该并行软件系统能够满足设计要求.最后对系统的研究和实际工作做出总结.     

多抽样率小波信号分解

在信号分析过程中,用二进小波对信号进行分解以便观察信号在不同子带内的时频特征是一种非常有效的时频分析方法。但由于受信号采样频率的限制,往往使所感兴趣的信号分量不能完整地出现在某一子带内。既使是提高小波分解层数或者用小波包技术也不能很好地解决这一问题。文章提出多抽样率小波信号分解方法,该方法根据目标信号的频带宽度,通过对原始信号的插值和抽取改变抽样频率,以实现不同子带分量的分离。     

小波与时间序列在数字信号质量监测中的应用

描述了小波和时间序列分析在数字信号质量监测中的应用。与传统监测信号方法不同,这种方法从被测信号的组成成分入手,通过小波变换对原始信号依尺度分解成不同层次,并从其中提取特征信号,同时对其建立自回归滑动平均(ARMA)模型,通过分析ARMA模型的参数得出信号的质量状况。另外还描述了如何根据被分析信号的特征、小波重构信号的能力及计算速度选择合理的小波基的方法。     

Implementing a DSP-based Petri-net simulation tool

An efficient simulator of Petri nets, suitable for both loosely and tightly coupled parallel systems and featuring high-execution speed, is proposed. This speed is obtained through an optimized software structure on an original hardware architecture that is based on digital signal processors of the TMS320 family. The Petri net class outlined compactly represents a wide range of processes by restricting the model size and the relevant simulation times. The simulation algorithm and simulator implementation are described