Multirate analog-digital systems for signal processing andconversion

Systems containing both analog and digital functions have been investigated with the purpose of realizing mixed-signal integrated circuits with higher levels of functionality and integration. In most cases, such mixed-signal systems are inherently multirate because of the different sampling rates employed at various stages of the system. The multirate concepts have been used for traditional applications, such as subband coding and narrowband filter design. Some unconventional applications of multirate signal processing are also emerging, both for converting between analog samples and digital words, and for realizing processing functions in an easier and more economical way than would be possible using purely digital or analog techniques. This paper reviews fundamental multirate concepts, discusses some developments in this area of integrated multirate analog-digital systems, and outlines some possible future directions for research and application     

Adaptive Filter Processing in Microwave Remote Heart Monitors

This commmunication describes some current applications tions of adaptive filtering to the processing of microwave Doppler signals for heart rate monitoring. The problem has been approached in the past using signal processing techniques such as peak detection or autocorrelation. These methods either require large amounts of data or tend to be unreliable. This communication utilizes some recent techniques used in speech processing and applies them to heartbeat detection, thus allowing on-line processing of sampled microwave heart signals. The presentation includes a model for the signal, brief discussions of the algorithms evaluated, and qualitative analysis of performance compared to EKG measurements.     

Algorithm-based low-power and high-performance multimedia signalprocessing

Low power and high performance are the two most important criteria for many signal-processing system designs, particularly in real-time multimedia applications. There have been many approaches to achieve these two design goals at many different implementation levels ranging from very-large-scale-integration fabrication technology to system design. We review the works that have been done at various levels and focus on the algorithm-based approaches for low-power and high-performance design of signal processing systems. We present the concept of multirate computing that originates from filterbank design, then show how to employ it along with the other algorithmic methods to develop low-power and high-performance signal processing systems. The proposed multirate design methodology is systematic and applicable to many problems. We demonstrate that multirate computing is a powerful tool at the algorithmic level that enables designers to achieve either significant power reduction or high throughput depending on their choice. Design examples on basic multimedia processing blocks such as filtering, source coding, and channel coding are given. A digital signal-processing engine that is an adaptive reconfigurable architecture is also derived from the common features of our approach. Such an architecture forms a new generation of high-performance embedded signal processor based on the adaptive computing model. The goal of this paper is to demonstrate the flexibility and effectiveness of algorithm-based approaches and to show that the multirate approach is an effective and systematic design methodology to achieve low-power and high throughput signal processing at the algorithmic and architectural level     

Toward multimodal human-computer interface

Recent advances in various signal processing technologies, coupled with an explosion in the available computing power, have given rise to a number of novel human-computer interaction (HCI) modalities: speech, vision-based gesture recognition, eye tracking, electroencephalograph, etc. Successful embodiment of these modalities into an interface has the potential of easing the HCI bottleneck that has become noticeable with the advances in computing and communication. It has also become increasingly evident that the difficulties encountered in the analysis and interpretation of individual sensing modalities may be overcome by integrating them into a multimodal human-computer interface. We examine several promising directions toward achieving multimodal HCI. We consider some of the emerging novel input modalities for HCI and the fundamental issues in integrating them at various levels, from early signal level to intermediate feature level to late decision level. We discuss the different computational approaches that may be applied at the different levels of modality integration. We also briefly review several demonstrated multimodal HCI systems and applications. Despite all the recent developments, it is clear that further research is needed for interpreting and fitting multiple sensing modalities in the context of HCI. This research can benefit from many disparate fields of study that increase our understanding of the different human communication modalities and their potential role in HCI     

Multirate signal processing techniques for high-speed communication over power lines

With the widespread use of Internet and multimedia applications, there is renewed interest in utilizing the electric power lines for transmission of high data rate communication signals. Power lines offer a convenient and pervasive communication medium that does not require new cable installation. However, power lines are classified as a noisy and unpredictable communication channel. Advanced signal processing techniques, such as wavelet filter banks and multirate systems, can be utilized to improve the quality of communication over power lines. In this article an overview of the diverse applications of wavelet filter banks in power line communications is presented with particular reference to wavelet-based multicarrier modulation techniques. Moreover, some suggestions are given regarding the future applications of multirate signal processing techniques in PLC.     

Synthesis of folded pipelined architectures for multirate DSPalgorithms

In this paper we formalize a novel multirate folding transformation which is a tool used to systematically synthesize control circuits for pipelined VLSI architectures which implement multirate algorithms. Although multirate algorithms contain decimators and expanders which change the effective sample rate of a discrete-time signal, multirate folding time-multiplexes the multirate algorithm to hardware in such a manner that the resulting synchronous architecture requires only a single-clock signal. Multirate folding equations are derived and these equations are used to address two related issues. The first issue is memory requirements in folded architectures. We derive expressions for the minimum number of registers required by a folded architecture which implements a multirate algorithm. The second issue is retiming. Based on the noble identities of multirate signal processing, we derive retiming for folding constraints which indicate how a multirate data-flow graph must be retimed for a given schedule to be feasible. The techniques introduced in this paper can be used to synthesize architectures for a wide variety of digital signal processing applications which are based on multirate algorithms, such as signal analysis and coding based on subband decompositions and wavelet transforms     

The past, present, and future of neural networks for signalprocessing

The article provides a review of the fundamental of neural networks and reports recent progress. Topics covered include dynamic modeling, model-based neural networks, statistical learning, eigenstructure-based processing, active learning, and generalization capability. Current and potential applications of neural networks are also described in detail. Those applications include optical character recognition, speech recognition and synthesis, automobile and aircraft control, image analysis and neural vision, and several medical applications. Essentially, neural networks have become a very effective tool in signal processing, particularly in various recognition tasks     

分数阶傅里叶域两通道滤波器组

 基于两通道滤波器组构建的子带信号处理方法已在图像、语音信号处理中得到广泛的应用.本文从分数阶傅里叶域多抽样率信号处理基本理论和分数阶卷积定理出发,推导了分数阶傅里叶域两通道滤波器组准确重建的基本条件,并基于传统傅里叶域有限长标准正交镜像滤波器组和共轭正交镜像滤波器组的原型滤波器设计了分数阶傅里叶域标准正交镜像滤波器组和共轭正交镜像滤波器组.本文所提出的结论为分数阶傅里叶域滤波器组理论的建立提供了基本依据,同时也为分数阶傅里叶变换在图像、语音信号处理等工程实践中的应用奠定了理论基础.最后,仿真实验验证了所提分数阶傅里叶域滤波器设计方法的有效性.     

光折变晶体中的相位共轭

近几年来,光折变相位共轭器及其在非线性光学中的应用取得了有意义的进步。本文主要叙述光折变晶体中的四波混频以及各种光学相位共轭器的结构,简单地介绍了相位共轭器在光信息处理中的一些应用。     

Genetic algorithms and their applications

This article introduces the genetic algorithm (GA) as an emerging optimization algorithm for signal processing. After a discussion of traditional optimization techniques, it reviews the fundamental operations of a simple GA and discusses procedures to improve its functionality. The properties of the GA that relate to signal processing are summarized, and a number of applications, such as IIR adaptive filtering, time delay estimation, active noise control, and speech processing, that are being successfully implemented are described     

多采样率信号处理的发展

数字通信系统中,为适应传输、降低资源消耗、适于处理操作,常需要变换信号的采样率。多采样率信号处理理论从语音信号处理中发展起来,在应用中不断丰富。随着软件无线电的应用,多采样率变换在数字信号领域占据越来越重要的地位。多采样率信号处理技术与小波分析、分数阶傅里叶变换等其他信号处理技术相结合是未来发展的方向。     

基于MATLAB的语音增强系统的设计

语音增强是信号处理领域中的一个重要的组成部分。在许多语音处理的应用中,例如移动通信,语音识别和助听器,语音信号的处理不得不在具有噪声的环境下进行。在过去的几十年里,人们提出了许多方法去消除噪声和减少语音失真,例如谱减法,基于小波的方法,隐式马尔科夫模型法和信号子空间法等。小波分析由于能同时在时域和频域中对信号进行分析,所以它能有效地实现对信号的去噪。介绍了一种语音增强系统的设计方法,采用Least Mean Square（LMS）算法和小波变换相结合的方法对带噪语音进行去噪,并在MATLAB的Simulink环境下建立了该系统的模型。通过对该模型的仿真表明：该方法去噪效果明显,为该系统在硬件上的实现打下了理论基础。     

Multirate digital filters, filter banks, polyphase networks, andapplications: a tutorial

The basic concepts and building blocks in multirate digital signal processing (DSP), including the digital polyphase representation, are reviewed. Recent progress, as reported by several authors in this area, is discussed. Several applications are described, including subband coding of waveforms, voice privacy systems, integral and fractional sampling rate conversion (such as in digital audio), digital crossover networks, and multirate coding of narrowband filter coefficients. The M-band quadrature mirror filter (QMF) bank is discussed in considerable detail, including an analysis of various errors and imperfections. Recent techniques for perfect signal reconstruction in such systems are reviewed. The connection between QMF banks and other related topics, such as block digital filtering and periodically time-varying systems, is examined in a pseudo-circulant-matrix framework. Unconventional applications of the polyphase concept are discussed     

Implementation of adaptive and synthetic-aperture processingschemes in integrated active-passive sonar systems

Progress in the implementation of state-of-the-art signal processing schemes in sonar systems is limited mainly by the moderate advance made in sonar computing architectures and the lack of operational evaluation of the advanced processing schemes. Until recently, matrix-based processing techniques, such as adaptive and synthetic-aperture processing, could not be efficiently implemented in the current type of sonar systems, even though it is widely believed that they have advantages that can address the requirements associated with the difficult operational problems that next-generation sonars will have to solve. Interestingly, adaptive and synthetic-aperture techniques may be viewed by other disciplines as conventional schemes. For the sonar technology discipline, however, they are considered as advanced schemes because of the very limited progress that has been made in their implementation in sonar systems. This paper is intended to address issues of implementation of advanced processing schemes in sonar systems and also to serve as a brief overview to the principles and applications of advanced sonar signal processing. The main development reported in this paper deals with the definition of a generic beam-forming structure that allows the implementation of nonconventional signal-processing techniques in integrated active-passive sonar systems. These schemes are adaptive and synthetic-aperture beam formers that have been shown experimentally to provide improvements in array gain for signals embedded in partially correlated noise fields. Using target tracking and localization results as performance criteria, the impact and merits of these techniques are contrasted with those obtained using the conventional beam former     

Gait recognition: a challenging signal processing technology for biometric identification

This article provides an overview of the basic research directions in the field of gait analysis and recognition. The recent developments in gait research indicate that gait technologies still need to mature and that limited practical applications should be expected in the immediate future. At present, there is a potential for initial deployment of gait for recognition in conjunction with other biometrics. However, future advances in gait analysis and recognition - an open, challenging research area - are expected to result in wide deployment of gait technologies not only in surveillance, but in many other applications as well. This article exposes the gait analysis and recognition problem to the signal processing community and it will stimulates the involvement of more researchers in gait research in the future.     

Speech, audio, and acoustic processing for multimedia

The primary advances in speech and audio signal processing that contributed to the maturing of multimedia applications are discussed in the areas of speech and audio signal compression, speech synthesis, acoustic processing and echo control, and network echo cancellation     

全数字接收机中的多速率信号处理技术

全数字接收机和软件无线电是无线电接收机的发展方向，本文介绍了多速率数字信号处理技术以及在全数字接收机中的应用。     

Multirate signal processing on finite fields

The paper addresses the problem of multirate signal processing over arbitrary fields. Studies of multirate systems and filter banks have proceeded in parallel, and a wealth of results are available in literature. The authors concentrate their attention on cyclic systems. These structures are ideally suited to generalising the concepts to finite fields. The perfect reconstruction property for quadrature mirror filter banks is obtained. It is shown how the cyclic wavelet transform (CWT) can be derived from such systems; the relationships between cyclic filter banks and CWTs are explored in detail. The results obtained are potentially very well suited for speech and image encoding, as well as for fast algorithms in signal processing     

Short-range surveillance radar systems

Small, light-weight, coherent radar systems have been successfully exploited in the military domain for many years. Recent advances in both technology and signal processing techniques are enabling the production of more versatile systems aimed at a wider range of applications spanning both the military and civil markets. In this paper the fundamental design concepts underpinning this class of radar are briefly introduced to place in context current and emerging technical developments. The relationships between these developments, an increasing range of future potential applications and the areas of most significant technical challenge are discussed