语音识别的非线性方法

语音信号是一个复杂的非线性过程，这使得基于线性系统理论发展起来的传统语音识别技术性能难以进一步提高。近年来人们开始逐渐重视非线性在语音识别技术中的应用，本文概括地介绍了非线性理论在语音识别技术中的所取得的成果和发展方向，除了涉及较为流行的隐马尔柯夫过程和人工神经网络在语音识别中的应用外，文中着重论述了近年来发展迅猛的混沌，分形理论在语音识别中的应用，本文最后还提到不可忽视的分形理论在语音编码中的应     

Tutorial on higher-order statistics (spectra) in signal processingand system theory: theoretical results and some applications

A compendium of recent theoretical results associated with using higher-order statistics in signal processing and system theory is provided, and the utility of applying higher-order statistics to practical problems is demonstrated. Most of the results are given for one-dimensional processes, but some extensions to vector processes and multichannel systems are discussed. The topics covered include cumulant-polyspectra formulas; impulse response formulas; autoregressive (AR) coefficients; relationships between second-order and higher-order statistics for linear systems; double C(q,k) formulas for extracting autoregressive moving average (ARMA) coefficients; bicepstral formulas; multichannel formulas; harmonic processes; estimates of cumulants; and applications to identification of various systems, including the identification of systems from just output measurements, identification of AR systems, identification of moving-average systems, and identification of ARMA systems     

A new initial-value method for on-line filtering and estimation (Corresp.)

Utilizing the theory of invariant imbedding, a Cauchy system suitable for real-time calculation is presented for the solution of the basic Fredholm integral equation of linear least squares filtering theory when the signal process is stationary. Extensions are also mentioned for more general nonstationary processes.     

时频分析综述及其应用

本文主要介绍了非平稳信号处理理论中信号时频表示的各种方法。并以线性调频信号和两个单频信号为例，给出各种时频表示的仿真结果，分析了各种时频表示特性、时频聚集性、交叉项抑制等，为以后的应用打下良好的基础。同时，本文还简要介绍了时频分布在信号处理中的应用。     

CS与GPR联合反演目标成像

压缩传感(CS)理论是在已知信号具有稀疏性或可压缩性的条件下对信号数据进行采集、编解码的新理论。压缩传感采用非自适应线性投影来保持信号的原始结构,能通过数值最优化问题准确重构原始信号。压缩传感以远低于奈奎斯特频率进行采样,在高分辨压缩成像系统、视频图像采集系统、雷达成像以及MRI医疗成像等领域有着广阔的应用前景。阐述了压缩传感理论框架以及信号稀疏表示、CS编解码模型,并进行了压缩传感与探地雷达联合反演目标成像。反演结果表明,随机孔径压缩传感成像算法比递归反向投影算法和最小二乘法所需数据量少,成像效果好,目标旁瓣小,对噪声的鲁棒性更好。     

Signal processing with higher-order spectra

The strengths and limitations of correlation-based signal processing methods are discussed. The definitions, properties, and computation of higher-order statistics and spectra, with emphasis on the bispectrum and trispectrum are presented. Parametric and nonparametric expressions for polyspectra of linear and nonlinear processes are described. The applications of higher-order spectra in signal processing are discussed     

Decoupling and iterative approaches to the control of discrete linear repetitive processes

This paper reports new results on the analysis and control of discrete linear repetitive processes which are a distinct class of 2D discrete linear systems of both systems theoretic and applications interest. In particular, we first propose an extension to the basic state-space model to include a coupling term previously neglected but which arises in some applications and then proceed to show how computationally efficient control laws can be designed for this new model.     

Recent developments in the core of digital signal processing

In this article members of the Digital Signal Processing (DSP) Technical Committee (TC) report recent breakthroughs in signal processing fundamentals that have happened in the last two decades. These breakthroughs include various advances and extensions from old techniques to new techniques. For example, signal processing techniques have moved from single-rate to multirate processing, from time-invariant to adaptive processing, from frequency-domain (the traditional Fourier transform, as we know it) to time-frequency analysis, and from linear to non-linear signal processing. Recent developments in these areas have not only renovated the theory of digital signal processing, they have also resulted in new tools that find applications in various domains. For instance, multirate signal processing has triggered recent advances in modem technology and speech/audio coding; adaptive filtering has made echo cancellation and noise suppression possible; time-frequency analysis has found its way into various applications in radar and medical signal processing; and non-linear processing has made engineers rethink various problems in speech recognition and image analysis. This article provides an extensive list of highlights from these recent developments.     

Maximum likelihood estimation of signals in autoregressive noise

Time series modeling as the sum of a deterministic signal and an autoregressive (AR) process is studied. Maximum likelihood estimation of the signal amplitudes and AR parameters is seen to result in a nonlinear estimation problem. However, it is shown that for a given class of signals, the use of a parameter transformation can reduce the problem to a linear least squares one. For unknown signal parameters, in addition to the signal amplitudes, the maximization can be reduced to one over the additional signal parameters. The general class of signals for which such parameter transformations are applicable, thereby reducing estimator complexity drastically, is derived. This class includes sinusoids as well as polynomials and polynomial-times-exponential signals. The ideas are based on the theory of invariant subspaces for linear operators. The results form a powerful modeling tool in signal plus noise problems and therefore find application in a large variety of statistical signal processing problems. The authors briefly discuss some applications such as spectral analysis, broadband/transient detection using line array data, and fundamental frequency estimation for periodic signals     

Determination of the linear output signal of a process containing single-valued nonlinearities

Previously it has been shown how the crosscorrelation of the linear channel of a single-valued nonlinear open-loop process can be obtained. Here it is shown that the same ideas can be applied to obtain the output signal of the linear channel of the nonlinear process when the input test signal is deterministic. The applications of this to step, sinusoidal and pseudorandom identification techniques are discussed.     

数字线性调频源设计

简要介绍了线性调频信号产生技术的现状,详细介绍了直接数字频率合成(DDS)的基本原理。给出了高性能DDS芯片AD9858的主要特点和配置方法,同时以图形方式给出了基于AD9858芯片硬件结构及互连方法,以及控制DDS芯片的VHDL语言工作波形图,描述了输出线性调频信号的控制流程。     

基于倒谱分析的雷达信号脉内调制识别

提出对雷达信号进行时域倒谱分析,能够将卷积性或乘法性信号分量分离,进而分析出雷达信号的调制信息。计算机仿真表明,在低信噪比条件下,倒谱分析对线性调频信号和相位编码信号的调制参数都能够进行有效估计。     

Improved single-trial signal extraction of low SNR events

Initial investigations of Birch's outlier processing method (OPM) have demonstrated an ability to extract a special class of finite-duration signals from colored noise processes. This special class of signals are low SNR signals whose shapes and latencies can vary dramatically from trial to trial. Such signals, termed highly variable events (HVE) in the present paper, are commonly found in physiological signal analysis applications. The present paper identifies that the OPM produces suboptimal HVE estimates due to its use of time-invariant influence functions and demonstrates that the addition of time-varying influence functions (TVIFs) produce improved estimates. Simulation experiments with signals in white and colored noise processes were used to demonstrate the modified OPM algorithm's superior performance compared to the performance of the original algorithm and to the performance of a time-invariant minimum mean-square-error (MMSE) filter for linear and stationary processes. The simulation results indicate that the OPM algorithm with TVIFs can extract HVEs from a linear and stationary process for SNR levels above -2.5 dB and can work effectively as low as -10.0 dB in certain situations     

Atomic,WA-systems,and R-functions applied in modern radio physics problems: Part I

The main physical applications of atomic, WA-systems, and R-functions are presented. The Whittaker-Kotelnikov-Shannon sampling theorem is generalized on the basis of atomic functions. Applications of atomic functions in the theory of probability and random processes, interpolation of stationary random processes with atomic functions, and a new class of probabilistic weighting functions used in digital signal and image processing are considered.     

Rasterization theory,architectures, and implementations for a class of two-dimensional problems

This paper first presents a theory for rasterizing the class of two-dimensional problems which include signal/image processing, computer vision, and linear algebra. The rasterization theory is steered by an isomorphism relationship between the two-dimensional shuffle exchange network (2DSE) and the two-dimensional butterfly network (2DBN). Since in real-time applications, data are often acquired in a raster scan format, it is important to develop architectures to support the raster data structure. Algorithms are developed first by using 2DSE network, then transformed into 2DBN format. Rasterization architectures can be derived for the algorithms described by 2DBN format. In the PEACE project, we have been able to show that a single, fixed communication topology, namely 2DSE, provides solution times on a 2DSE parallel computing system that for many problems approach known theoretical lower bounds. Secondly, this paper presents the generic architectures and VLSI implementation examples for the rasterization structures.     

The mean field theory in EM procedures for Markov random fields

In many signal processing and pattern recognition applications, the hidden data are modeled as Markov processes, and the main difficulty of using the maximisation (EM) algorithm for these applications is the calculation of the conditional expectations of the hidden Markov processes. It is shown how the mean field theory from statistical mechanics can be used to calculate the conditional expectations for these problems efficiently. The efficacy of the mean field theory approach is demonstrated on parameter estimation for one-dimensional mixture data and two-dimensional unsupervised stochastic model-based image segmentation. Experimental results indicate that in the 1-D case, the mean field theory approach provides results comparable to those obtained by Baum's (1987) algorithm, which is known to be optimal. In the 2-D case, where Baum's algorithm can no longer be used, the mean field theory provides good parameter estimates and image segmentation for both synthetic and real-world images     

Strong practical stability and stabilization of discrete linear repetitive processes

This paper considers two-dimensional (2D) discrete linear systems recursive over the upper right quadrant described by well known state-space models. Included are discrete linear repetitive processes that evolve over subset of this quadrant. A stability theory exists for these processes based on a bounded-input bounded-output approach and there has also been work on the design of stabilizing control laws, elements of which have led to the assertion that this stability theory is too strong in many cases of applications interest. This paper develops so-called strong practical stability as an alternative in such cases. The analysis includes computationally efficient tests that lead directly to the design of stabilizing control laws, including the case when there is uncertainty associated with the process model. The results are illustrated by application to a linear model approximation of the dynamics of a metal rolling process.

Wavelet-based identification of delamination defect in CMP (Cu-low k) using nonstationary acoustic emission signal

Wavelet-based multiscale analysis approaches have revolutionized the tasks of signal processing, such as image and data compression. However, the scope of wavelet-based methods in the fields of statistical applications, such as process monitoring, density estimation, and defect identification, are still in their early stages of evolution. Recent literature contains some applications of wavelet-based methods in monitoring, such as tool-life monitoring, bearing defect monitoring, and monitoring of ultra-precision processes. This paper presents a novel application of a wavelet-based multiscale method in a nanomachining process [chemical mechanical planarization (CMP)] of wafer fabrication. The application involves identification of delamination defect of low-k dielectric layers by analyzing the nonstationary acoustic emission (AE) signal and coefficient of friction (CoF) signal collected during copper damascene (Cu-low k) CMP process. An offline strategy and a moving window-based strategy for online implementation of the wavelet monitoring approach are developed. Both offline and moving window-based strategies are implemented on the data collected from two different sources. The results show that the wavelet-based approach using the AE signal offers an efficient means for real-time detection of delamination defects in CMP processes. Such an online strategy, in contrast to the existing offline approaches, offers a viable tool for CMP process control. The results also indicate that the CoF signal is insensitive to delamination defect.     

Sparse signal transmission under lossy wireless links based on double process of compressive sensing

In resource-limited wireless sensor networks,links with poor quality hinder its large-scale applications seriously.Thanks to the inherent sparse property of signals in WSN,the framework of sparse signal transmission based on double process of compressive sensing was proposed,providing an insight into a new way of real-time,accurate and energy-efficient sparse signal transmission.Firstly,the random packet loss during transmission under lossy wireless links was modeled as a linear dimension-reduced measurement process of CS (a passive process of CS).Then,considering that a large packet was often adopted in WSN for higher transmission efficiency,a random linear dimension-reduced projection (a simple source coding operation) was employed at the sender node (an active process of CS) to prevent block data loss.Now,the raw signal could be recovered from the lossy data at the receiver node using CS reconstruction algorithms.Furtherly,according to the theory of CS reconstruction and the formula of packet reception rate in wireless communication,the minimum compression ratio and the maximum packet length allowed were obtained.Extensive simulations demonstrate that the reliability of data transmission and its accuracy,the data transmission volume,the transmission delay and energy consumption could be greatly optimized by means of proposed method.