飞机遇险呼救还是干扰信号?

121．5MHz是国际电联规定的飞机遇险呼救专用频率，其他任何部门和个人不得占用。一旦出现呼救信号，必须立即找到信号源．否则后果不堪设想。     

双通道矢量信号分析仪测试系统延迟特性

使用双通道分析仪表测试两路相位相参的调制信号间的时间关系．一方面验证使用互相关技术方法测试信号间时间关系的可行性．另一方面通过对被测电路器件的输出和输入信号的相关测试．得到电路在复杂激励状态下的延迟特性。[编者按]     

Le traitement du signal vocal voice signal processing

The speech processing studies have advanced rapidly in recent years spurred on by great progresses in thevlsi technologies and in the digitalization of the networks. This paper offers an overview of the most attractive techniques which have focused the recent researchs and developments in speech coding, recognition and synthesis areas. For speech compression, the emphasis is put on a family of techniques named code-excited linear prediction (celp) which dominates current studies for rates in the range of 4 to 16 kbit/s. In terms of speech recognition, particular emphasis is placed on the following three elements which are essential in order to increase the robustness of the systems : telephone line adaptation, rejection of parasite noise and out-of-vocabulary words, and keyword spotting. In terms of text-to-speech synthesis, thepsola (pitch synchronous overlap and add) technique is outlined herein. This technique gives rise to a new generation of synthesis systems which produce speech with very natural timbre. The analysis of current tendencies for each area allows to suggest attractive directions for future research.     

基于矢量信号发生器的微波着陆信号模拟

针对当前微波着陆装备测试的迫切需要,提出一种基于矢量信号发生器硬件架构的微波着陆模拟器的实现方式,并提出了一种基于FPGA的微波着陆系统基带信号设计方法。对微波着陆信号的格式和信号时序进行了仿真,并对各种角功能信号和数据字结构进行了实际测试,证明了该模拟器性能达到装备测试要求。     

集成语音信号与表面肌电信号的情感自动识别模型

为了提高情感识别的正确率,针对单一语音信号特征和表面肌电信号特征存在的局限性,提出了一种集成语音信号特征和表面肌电信号特征的情感自动识别模型.首先对语音信号和表面肌电信号进行预处理,并分别提取相关的语音信号和表面肌电信号特征,然后采用支持向量机对语音信号和表面肌电信号特征进行学习,分别建立相应的情感分类器,得到相应的识别结果,最后将识别结果分别输入到支持向量机确定两种特征的权重系数,从而得到最终的情感识别结果.两个标准语情感数据库的仿真结果表明,相对于其它情感识别模型,本文模型大幅提高了情感识别的正确率,人机交互情感识别系统提供了一种新的研究工具.     

Multiple digital signal processor environment for intelligent signal processing

We describe a novel, expandable, multiple digital signal processor (DSP) architecture with a symbolic processing host. A multiprocessor board, called Odyssey, based on this architecture has been developed to combine symbolic and real-time digital signal processing in a single computing environment. Some of the key features of the board are: 20 million multiply/accumulates per second, 512K bytes of data space, and expandability to 16 boards on a NuBus host. The DSPs used are the TMS32020 signal processing chips developed by Texas Instruments, and the host is Texas Instruments' Explorer, a LISP machine workstation. This provides environment to perform many intelligent signal processing tasks by associating meaningful relationships between quantitative (signal processing) and qualitative (symbolic processing) entities to develop inferences using expert system technology. Applications such as grammar-driven connected speech recognition, neural network simulation, EEG analysis, and generation of speech from general English text with natural language processing are some of the tasks that can utilize the computational power of the multiple DSP and/or the associated symbolic processing capabilities. Software development tools to implement applications include the device driver to facilitate communication between the host processor and the Odyssey board, a unique window-based debugger resident on the Explorer that allows for simultaneous state display of all the processors on the board, a FORTH interpreter for high-level language programming, and a cross-assembler/linker for assembly level programming.     

Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal

Multidimensional Systems and Signal Processing - In this research paper we present designing and evaluating the electrocardiography (ECG) and Myoelectric signal (EMG) pattern recognition methods...     

Resonance-based signal decomposition: A new sparsity-enabled signal analysis method

Numerous signals arising from physiological and physical processes, in addition to being non-stationary, are moreover a mixture of sustained oscillations and non-oscillatory transients that are difficult to disentangle by linear methods. Examples of such signals include speech, biomedical, and geophysical signals. Therefore, this paper describes a new nonlinear signal analysis method based on signal resonance, rather than on frequency or scale, as provided by the Fourier and wavelet transforms. This method expresses a signal as the sum of a ‘high-resonance’ and a ‘low-resonance’ component—a high-resonance component being a signal consisting of multiple simultaneous sustained oscillations; a low-resonance component being a signal consisting of non-oscillatory transients of unspecified shape and duration. The resonance-based signal decomposition algorithm presented in this paper utilizes sparse signal representations, morphological component analysis, and constant-Q (wavelet) transforms with adjustable Q-factor.     

基于信号重构和最小二乘的线性调频信号估计

通过多延时离散多项相位变换重构出具有较高信噪比的信号,随后解出瞬时相位曲线,利用最小二乘法估计出线性调频信号的一阶、二阶相位参数.在低信噪比时,估计量均方误差仍达到CRB.另外,该方法信噪比门限低且可调,运算量小,易于实现.仿真验证了其有效性.     

数字信号处理及DSP芯片的发展前景

简要介绍了DSP的设计流程及其实现方法,着重介绍了DSPs芯片结构特点、运算速度、应用与市场,并展望了DSPs芯片的发展前景。