Shift-invariance of short-time Fourier transform in fractional Fourier domains

Fractional Fourier domains form a continuum of domains making arbitrary angles with the time or frequency domains on the time-frequency plane. Signal representations in these domains are related to the fractional Fourier transform (FrFT). In this paper, a new proof on the shift-invariance of linear time-frequency distributions on fractional Fourier domains is given. We show that short-time Fourier transform (STFT) is the unique linear distribution satisfying magnitude-wise shift-invariance in the fractional Fourier domains. The magnitude-wise shift-invariance property in arbitrary fractional Fourier domains distinguishes STFT among all linear time-frequency distributions and simplifies the interpretation of the resultant distribution as shown by numerical examples.     

人脸特征检测的最佳Gabor滤波器设计方法

Gabor小波滤波器的应用可以转化为在不同的问题中如何合理选取Gabor滤波器参数的问题,不同的应用场合参数选取的依据应有所不同。本文通过实验给出了Gabor滤波器在人脸特征检测方法中参数的选择依据并给出具体的参数。     

用于计算离散分数阶傅立叶变换的MA-CDFRFT算法改进

基于中心类型DFT矩阵特征分解的MA-CDFRFT(Multiangle Centered Discrete Fractional Fourier Transform)算法在计算一组离散分数阶傅立叶变换DFRFT(Discrete Fractional Fourier Transform)时充分利用FFT运算来减小运算量。结合偶数点离散傅立叶变换DFT(Discrete Fourier Transform)运算的对称性原理，通过数学推导将MA-CDFRFT算法中的一维对称性扩展到频率和变换阶数的二维平面上。利用这个二维对称性原理，改进算法将原算法的主要计算量减小了一半左右。仿真测试结果证明了改进算法的正确性。     

雷达信号的时——频分析和应用

针对常见的雷达脉冲信号（常规信号（NS）,线性调频信号（LFM）,双线性调频信号（DLFM）运用时频工具对调制方式的识别进行研究。运用两种时频分析方法1.短时傅里叶变换2.相位展开算法.通过这两种算法对雷达信号的调制方式进行分析,并通过仿真得到雷达信号的时--频分布以及时—频,时—相曲线,对仿真结果进行研究。通过仿真实验表明,雷达信号调制方式识别算法是可行的,有效的,而且具有良好的性能。     

Optimized search over the Gabor dictionary for note decomposition and recognition

Performing real-time note detection of multiple audio sources, such as guitar signals, is a difficult task due to the complex nature of these signals. For instance, guitar notes are close in pitch and the harmonic overtones of each note are strongly interlaced, thus preventing standard filtering techniques. In order to accomplish note detection, a harmonic matching pursuit algorithm has been used to decompose an audio signal in terms of elementary waveforms called harmonic atoms. These atoms are derived from the standard matching pursuit algorithm, and are part of an extended and overcomplete Gabor dictionary. In this paper, the search over Gabor dictionary is optimized by using signal modeling of the guitar signal in order to pre-calculate a parameter set; therefore avoiding a costly search over the extended Gabor dictionary. The parameter set defined for the proposed algorithm includes time-location, decay rate, frequency, scale and phase, which are calculated at the onset of each note played. This optimized algorithm is demonstrated through synthesized and real guitar signal examples. Considerable computational savings of this proposed algorithm over the harmonic matching pursuit algorithm are achieved.     

Chaos in the fractional order unified system and its synchronization

The chaotic behaviors in the fractional order unified system are numerically investigated. By utilizing the fractional calculus techniques, we found that chaos exists in the fractional order unified system with order less than 3. The lowest order we found to have chaos in this system is 2.76. Chaos synchronization of the fractional order unified system is theoretically and numerically studied using the one-way coupling method. The suitable conditions for achieving synchronization of the fractional order differential system are derived by using the Laplace transform theory. It is noticed that the time required for achieving synchronization of the drive system and the response system and the synchronization effect sensitively depend on the coupling strength. Numerical simulations are performed to verify the theoretical analysis.     

Gabor传递函数相似性测度图像质量评价算法

数字图像在压缩和融合等处理中会产生退化效应,当前对图像退化中质量评价指标如均方误差(MSE),峰值信噪比(PSNR)等不能有效反映图像在退化过程中传递性能,而只是单纯计算图像之间灰度值差异,对图像质量评价不准。提出一种利用Gabor传递函数相似性测度为评价指标的图像质量评价算法,采用图像的亮度、对比度和结构相似度为场景结构基准特征,设计Gabor传递函数。通过双正交滤波器得到Gabor传递函数相似性测度,该指标能有效反映图像退化演变过程中的传递性能,合理评价参考图像和失真图像质量。仿真实验表明,利用Gabor传递函数相似性测度指标评价图像质量,能准确反映图像失真等级,在几乎所有失真类型上都能有较满意的结果,评价结果比较稳定。     

Combining fields for query expansion and adaptive query expansion

In this paper, we aim to improve query expansion for ad-hoc retrieval, by proposing a more fine-grained term reweighting process. This fine-grained process uses statistics from the representation of documents in various fields, such as their titles, the anchor text of their incoming links, and their body content. The contribution of this paper is twofold: First, we propose a novel query expansion mechanism on fields by combining field evidence available in a corpora. Second, we propose an adaptive query expansion mechanism that selects an appropriate collection resource, either the local collection, or a high-quality external resource, for query expansion on a per-query basis. The two proposed query expansion approaches are thoroughly evaluated using two standard Text Retrieval Conference (TREC) Web collections, namely the WT10G collection and the large-scale .GOV2 collection. From the experimental results, we observe a statistically significant improvement compared with the baselines. Moreover, we conclude that the adaptive query expansion mechanism is very effective when the external collection used is much larger than the local collection.     

基于2D Gabor滤波器和LBP的车牌识别研究

本文提出一种基于2DGabor滤波器和LBP的车牌识别方法,与基于2DGabor滤波器的方法相比,该方法降低了图像维数,识别时间短,识别精度高。首先利用2DGabor滤波器对归一化后的图像进行低通滤波,然后使用LBP提取车牌纹理特征,最后用支持向量机作为分类器。在课题组自建的车牌库上进行实验,取得了94.3%的识别率和每张图像0.291s的识别时间,说明该方法基本满足实际需要。     

A correlation-based computational model for synthesizing long-range dependent data

The authors present a computation model to generate long-range dependent (LRD) data according to a given correlation structure by filtering white noise. The simulation procedure of LRD data according to a given correlation structure is explained. A case study is illustrated with real LRD data series in the Internet.     

A review of ontology based query expansion

This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described.     

一种扩展功能可选取的文本检索系统

文章采用了查询扩展的一些不同规则及方法,设计出一种扩展功能可选取的文本检索系统。在该检索系统中,用户可以根据自己对所要找的信息想进行哪方面的扩展检索而选择对应的扩展功能,这样可以更好、更灵活的根据用户的需求进行检索,避免了大量无关信息的返回,从而更好的提高了检索的性能。     

A Prospect-Guided global query expansion strategy using word embeddings

The effectiveness of query expansion methods depends essentially on identifying good candidates, or prospects, semantically related to query terms. Word embeddings have been used recently in an attempt to address this problem. Nevertheless query disambiguation is still necessary as the semantic relatedness of each word in the corpus is modeled, but choosing the right terms for expansion from the standpoint of the un-modeled query semantics remains an open issue. In this paper we propose a novel query expansion method using word embeddings that models the global query semantics from the standpoint of prospect vocabulary terms. The proposed method allows to explore query-vocabulary semantic closeness in such a way that new terms, semantically related to more relevant topics, are elicited and added in function of the query as a whole. The method includes candidates pooling strategies that address disambiguation issues without using exogenous resources. We tested our method with three topic sets over CLEF corpora and compared it across different Information Retrieval models and against another expansion technique using word embeddings as well. Our experiments indicate that our method achieves significant results that outperform the baselines, improving both recall and precision metrics without relevance feedback.     

A knowledge-based semantic framework for query expansion

Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.     

基于分数阶Fourier变换的水声通信多普勒系数估计技术研究

本文基于传统多普勒系数估计方法,提出一种基于分数阶Fourier变换的多普勒系数估计方法,该方法利用分数阶Fourier变换估计得到经过信道后LFM信号调频斜率的变化量值,进而得到多普勒系数的估计。通过计算机仿真研究验证了该方法的有效性与稳健性。     

High performance query expansion using adaptive co-training

The quality of feedback documents is crucial to the effectiveness of query expansion (QE) in ad hoc retrieval. Recently, machine learning methods have been adopted to tackle this issue by training classifiers from feedback documents. However, the lack of proper training data has prevented these methods from selecting good feedback documents. In this paper, we propose a new method, called AdapCOT, which applies co-training in an adaptive manner to select feedback documents for boosting QE’s effectiveness. Co-training is an effective technique for classification over limited training data, which is particularly suitable for selecting feedback documents. The proposed AdapCOT method makes use of a small set of training documents, and labels the feedback documents according to their quality through an iterative process. Two exclusive sets of term-based features are selected to train the classifiers. Finally, QE is performed on the labeled positive documents. Our extensive experiments show that the proposed method improves QE’s effectiveness, and outperforms strong baselines on various standard TREC collections.     

岩滩水电站扩建工程建设安全管理

介绍岩滩水电站扩建工程的施工特点及安全管理,对其中的关键措施进行详细论述。     

基于小波变换的电网谐波检测

电网的谐波检测，一直是电力工作者研究的一项重要课题。本文介绍了电力系统中目前已有的几种主要的谐波检测方法，探讨了小波变换理论在谐波检测中的应用。