Signal processing techniques in genomic engineering

Now that the human genome has been sequenced, the measurement, processing, and analysis of specific genomic information in real time are gaining considerable interest because of their importance to better the understanding of the inherent genomic function, the early diagnosis of disease, and the discovery of new drugs. Traditional methods to process and analyze deoxyribonucleic acid (DNA) or ribonucleic acid data, based on the statistical or Fourier theories, are not robust enough and are time-consuming, and thus not well suited for future routine and rapid medical applications, particularly for emergency cases. In this paper, we present an overview of some recent applications of signal processing techniques for DNA structure prediction, detection, feature extraction, and classification of differentially expressed genes. Our emphasis is placed on the application of wavelet transform in DNA sequence analysis and on cellular neural networks in microarray image analysis, which can have a potentially large effect on the real-time realization of DNA analysis. Finally, some interesting areas for possible future research are summarized, which include a biomodel-based signal processing technique for genomic feature extraction and hybrid multidimensional approaches to process the dynamic genomic information in real time.     

通信辐射源个体特征提取技术

个体特征选择和提取是辐射源个体识别的关键,直接决定分类识别性能的好坏。由于在实际工程应用中,利用暂态特征进行通信辐射源个体识别难以实现,本文从稳态特征出发,对通信辐射源个体特征提取技术进行了综述,对特征的产生机理、在信号传播过程中所受到的污染以及在实际工程应用中的可行性做了归纳与分析。最后,指出了目前通信辐射源个体特征提取技术存在的问题,展望了个体识别技术未来可能的研究方面。     

A computer-aided diagnosis system for digital mammograms based onfuzzy-neural and feature extraction techniques

An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcification patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-neural and feature extraction techniques for detecting and diagnosing microcalcifications' patterns in digital mammograms. We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features (such as entropy, standard deviation and number of pixels) is the best combination to distinguish a benign microcalcification pattern from one that is malignant. A fuzzy technique in conjunction with three features was used to detect a microcalcification pattern and a neural network was used to classify it into benign/malignant. The system was developed on a Microsoft Windows platform. It is an easy-to-use intelligent system that gives the user options to diagnose, detect, enlarge, zoom and measure distances of areas in digital mammograms     

Application of state variable techniques to optimal feature extraction

Optimal continuous linear feature extraction for the binary Gaussian pattern recognition or detection problem necessitates finding the double orthogonal expansion of the observable random process under hypothesis Hi, i = 1, 2. State variable techniques are utilized here to yield efficient computer-implementable procedures for obtaining the double orthogonal expansion.     

逆合成孔径(ISAR)雷达目标检测方法

本文研究ISAR雷达目标回波的信号检测方法.由于ISAR雷达目标检测实质上是延伸目标检测(Extended target detection),其目标散射模型不是低分辨雷达(LRR)中的等效点目标.在距离维上呈现出多个强散射点 .本文提出的目标回波的检测方法是对同一目标的距离维上各散射点进行相关积累,以提高雷达目标信噪比.分析和实际结果表明:利用本文所提出的雷达目标检测方法比低距离分辨(LRR)雷达的性能有很大的改进.     

Signal processing advances for 3G WCDMA: From rake receivers to blind techniques

As already deployed and proven technology, code-division multiple access has evolved to support competitive high-data-rate low-latency multimedia services over wireless cellular networks. In this article we introduce advanced signal processing techniques to enhance CDMA receiver performance further. In particular, we consider the possibility of optimal joint multiuser detection for long code WCDMA using fast inversion based on a state-space approach with reasonable complexity, and semi-blind channel estimation techniques to realize rate-efficient transmission for the latest 3G standards.     

A new feature extraction and classification mechanisms For EEG signal processing

Multidimensional Systems and Signal Processing - The Electroencephalogram (EEG) signal processing is one of the extensively used research field in recent days, in which the epileptic seizure...     

基于MEEMD-HHT的分布式光纤振动传感系统信号特征提取方法

实际应用中,分布式光纤振动传感系统所测信号多为非平稳随机信号,对其进行模式识别的关键是准确获取信号的幅值-时间-频率瞬时特征。现有的相关研究表明,经验模态分解EMD方法结合希尔伯特变换可获得所测信号中固有模态分量的瞬时能量和瞬时频率,但存在模态混叠问题,后续改进的总体经验模态分解EEMD方法存在伪分量,重构误差大,互补经验模态分解CEEMD方法减小了重构误差的同时增加了运算量,无法保证特征提取与分类的效率与准确性。文中基于改进型经验模态分解方法结合希尔伯特变换MEEMD-HHT方法实现分布式光纤振动传感系统的特征提取,引入的排列熵的评价机制优化了分解过程中随机噪声迭代次数,通过仿真分析与实验对比,验证了该方法可有效解决上述方法中存在的问题,使系统在处理时间、特征准确度等性能皆有提高。实验结果表明,所提出的方法对于单频振动信号平均特征提取准确率达99.2%;对于混频振动信号平均特征提取准确率达98.1%,相对于EMD和CEEMD分别提高15.6%和7%,算法平均耗时最短,为3.8259 s,为分布式光纤振动传感系统的信号特征提取提供了一种可靠、高效的方法。     

A new signal processing technique for ISAR ranging: the Vernierranging method

A new technique for inversed synthetic aperture radar (ISAR) ranging, which resembles the principle of the Vernier measuring system, is presented. In this technique, the transmitted ISAR pulse comprises a train of chirp subpulses with uniformly stepped up center frequencies. The return ISAR echo is first processed, using hardware, to determine a coarse estimate of the target range. Further refinements of the range estimate are achieved through software processing, consisting of two stages of discrete Fourier transform operation. The ranging accuracy can be increased without the need for increased bandwidth, but at the expense of a slight increase in computational complexity. Numerical evaluation shows that a noiseless system is capable of achieving high-ranging accuracy, of the order of millimeters, even in the presence of dispersion and target motion. From computer simulations, the proposed system is also found to be robust against additive system noise and frequency jitter under practical conditions     

Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs

Wireless positioning has attracted much research attention and has become increasingly important in recent years. Wireless positioning has been found very useful for other applications besides E911 service, ranging from vehicle navigation and network optimization to resource management and automated billing. Although many positioning devices and services are currently available, it is necessary to develop an integrated and seamless positioning platform to provide a uniform solution for different network configurations. This article surveys the state-of-the-art positioning designs, focusing specifically on signal processing techniques in network-aided positioning. It serves as a tutorial for researchers and engineers interested in this rapidly growing field. It also provides new directions for future research for those who have been working in this field for many years.     

Target rotation parameter estimation for ISAR imaging via frame processing

Frame processing method offers a model-based approach to Inverse Synthetic Aperture Radar (ISAR) imaging. It also provides a way to estimate the rotation rate of a non-cooperative target from radar returns via the frame operator properties. In this paper, the relationship between the best achievable ISAR image and the reconstructed image from radar returns was derived in the framework of Finite Frame Processing theory. We show that image defocusing caused by the use of an incorrect target rotation rate is interpreted under the FP method as a frame operator mismatch problem which causes energy dispersion. The unknown target rotation rate may be computed by optimizing the frame operator via a prominent point. Consequently, a prominent intensity maximization method in FP framework was proposed to estimate the underlying target rotation rate from radar returns. In addition, an image filtering technique was implemented to assist searching for a prominent point in practice. The proposed method is justified via a simulation analysis on the performance of FP imaging versus target rotation rate error. Effectiveness of the proposed method is also confirmed from real ISAR data experiments.     

Generalized feature extraction for time-varying autoregressivemodels

In this paper, a feature extraction scheme for a general type of nonstationary time series is described. A non-stationary time series is one in which the statistics of the process are a function of time; this time dependency makes it impossible to utilize standard globally derived statistical attributes such as autocorrelations, partial correlations, and higher order moments as features. In order to overcome this difficulty, the time series vectors are considered within a finite-time interval and are modeled as time-varying autoregressive (AR) processes. The AR coefficients that characterize the process are functions of time that may be represented by a family of basis vectors. A novel Bayesian formulation is developed that allows the model order of a time-varying AR process as well as the form of the family of basis vectors used in the representation of each of the AR coefficients to be determined. The corresponding basis coefficients are then invariant over the time window and, since they directly relate to the time-varying AR coefficients, are suitable features for discrimination. Results illustrate the effectiveness of the method     

Nonparametric weighted feature extraction for classification

In this paper, a new nonparametric feature extraction method is proposed for high-dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L-1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for nonnormal datasets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy.     

Optimizing feature extraction for multiclass problems

Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature extraction methods are performed using a criterion function defined between two classes or a global function. Although these methods work relatively well in most cases, it is generally not optimal in any sense for multiclass problems. In order to address this problem, the authors propose a method to optimize feature extraction for multiclass problems. The authors first investigate the distribution of classification accuracies of multiclass problems in the feature space and find that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then the authors propose an algorithm that finds such features. Experiments with remotely sensed data show that the proposed algorithm consistently provides better performances compared with the conventional feature extraction algorithms     

Signal processing for networked multimedia

Real-time transmission of multimedia data over packet networks poses several interesting problems for signal processing research. Although the range of these problems covers a large variety of topics, currently two groups appear to attract the most attention. The first group concerns adapting the signal compression techniques to address the special requirements imposed by the packet networks, including accommodating for packet losses, delays, and jitter; providing capability for multipoint; and coping with the heterogeneous nature of today's networks. The second group of problems is related to protecting the intellectual property rights associated with the transmitted multimedia data. The increasing availability of high-bandwidth networking makes it extremely easy to illegally duplicate and disseminate digital information. Unless a mechanism can be established to protect the rights of the content providers, commercial use of networked multimedia will remain extremely limited     

用于空间转换的巴克ISAR信号形成与图像重构

讨论了基于巴克相位编码调制发射信号和三维逆合成孔径雷达（ISAR）结构的ISAR概念。ISAR信号形成可理解为3D图像功能向2D信号功能的非对称空间转换,而图像重构被认为是2D信号功能向2D图像功能（重构的图像）的空间逆转换。经证实,这种图像重构算法由距离压缩互相关、方位压缩傅里叶变换（被认为是第一级运动补偿）和更高级相位校正组成（被认为是更高级运动补偿,通称自聚焦程序）。引入熵作为图像成本功能,以评估相位校正功能的多项式系数。通过数值实验以验证ISAR的几何结构,信号形成模式和图像重构算法。     

Signal processing for optical communication

Optical communication plays a significant and increasing role in our society. The public demand for higher network speed requires an optical backbone network with larger capacity. Accompanying high transmission-rate optical communications system are severe technical specifications for optical devices and systems. Many popular optical devices could be represented with a digital filter model as described in this article. Use of well-developed signal processing techniques and algorithms to design these optical devices is a wise use of existing technology. The wavelength division multiplexing (WDM) system, which is the dominating optical communication system, is introduced in this article. Three signal processing application examples for optical communications are presented: optical wavelength interleaver, an all-pass filter for chromatic dispersion compensation, and an electronic equalizer. As demonstrated in this article, signal processing could play an important role in the development of advanced optical communication systems. However, as demonstrated in the case of an electronic equalizer, some optical system characteristics may require special attention if signal processing techniques are to be applied successfully. Therefore, interdisciplinary cooperation between researchers in optics and signal processing will be crucial for optical communications to fully benefit from signal processing.