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。     

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.     

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.     

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...     

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 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.     

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     

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.     

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     

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     

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.     

基于某型机的二次雷达信息处理技术的研究

从某型机的实际工程背景出发,对其协同敌我识别系统的工作原理和实现方法进行了研究,特别对询问、应答信号编码和解码的实现方法进行了探索。多个设计实例验证和背景工程实验的验证都取得了令人满意的结果,表明了该方法的有效性和较好的可靠性。     

Discriminative temporal feature extraction for robust speechrecognition

A discriminative temporal feature processing method for robust speech recognition is presented by combining the knowledge and the statistical methods. The cepstral features are first filtered by a RASTA method based on human hearing perception and then processed using the minimum classification error algorithm. Improved recognition performance can be achieved in both quiet and noisy environments     

Wavelet packet feature extraction for vibration monitoring

Condition monitoring of dynamic systems based on vibration signatures has generally relied upon Fourier-based analysis as a means of translating vibration signals in the time domain into the frequency domain. However, Fourier analysis provided a poor representation of signals well localized in time. In this case, it is difficult to detect and identify the signal pattern from the expansion coefficients because the information is diluted across the whole basis. The wavelet packet transform (WPT) is introduced as an alternative means of extracting time-frequency information from vibration signatures. The resulting WPT coefficients provide one with arbitrary time-frequency resolution of a signal. With the aid of statistical-based feature selection criteria, many of the feature components containing little discriminant information could be discarded, resulting in a feature subset having a reduced number of parameters without compromising the classification performance. The extracted reduced dimensional feature vector is then used as input to a neural network classifier. This significantly reduces the long training time that is often associated with the neural network classifier and improves its generalization capability