基于小波变换的多聚焦图像融合评述

概述了数据融合的基本概念和应用,简明分析了多聚焦图像的成像机理,从像素级层面上描述了基于小波多尺度变换的图像融合思想和过程.由于在基于小波变换的图像融合过程中,融合规则起很重要的作用,因此本文重点综述了适于多聚焦图像融合的三种融合规则：基于像素的融合规则、基于窗口的融合规则和基于区域的融合规则,并对这些规则进行了比较,最后给出了图像融合效果的客观评测方法.     

基于希尔伯特-黄与小波变换的压气机失速信号分析方法

针对多级跨声速压气机失速过程壁面静压测量数据,分别采用希尔伯特-黄变换(HHT)和小波分析方法进行时频分析。相比于小波分析方法,HHT时频图中失速过程的频率特征变化更为明显,且利用HHT图中失速发生后的特征频率可判断出失速团的旋转速度。结合方差分析,进一步探索了获取失速先兆信号的可行性。结果表明,两种方法均可用于压气机失速先兆预测,其中HHT方法能够提供更高层次的分解信号,得到的失速先兆更为提前。     

Analysis of chaotic vibrations for the distributed systems in the form of the Bernoulli-Euler beams using the wavelet transform

A problem on chaotic vibrations of the Bernoulli-Euler beams is formulated. The wavelet transform was first applied to investigate the complex beam vibrations. The validity of results was provided by using two methods of solution, namely, the finite element method and finite difference method.     

基于改进小波变换和包络分析的滚动轴承故障诊断

采用改进的小波分解和重构算法与包络分析相结合的方法,提取滚动轴承振动信号的故障特征频率。改进的小波分解和重构方法避免了 Mallat 算法频率混淆的缺陷,通过对重构信号特定频带进行包络分析,更加准确地提取了滚动轴承的故障特征频率。通过对无故障滚动轴承和内圈、外圈有故障的滚动轴承振动信号的分析,说明这种方法能够有效诊断滚动轴承的故障,并将该方法成功应用于某型航空发动机主轴承故障诊断。     

利用小波分析实现对HRTEM图像信息的提取

应用小波变换技术尝试去除高分辨电子显微图像中的噪音成分，并提取高分辨电子显微图像细节部分的有用信息以及提高图像的分辨率。可以预见小波分析技术在HRTEM图像处理中应该有较大的应用空间。并针对HRTEM图像处理给出了适用的母小波选择。     

基于小波分析的航空发动机故障诊断方法研究

小波变换是一种多分辨率的时频分析方法,应用在振动信号处理、故障诊断方面较传统方法优越。在阐述小波分析理论基础上,对某型航空发动机的振动信号进行分析,采用小波分解和信号重构的方法,提取了噪声掩盖下振动信号中的故障信息,根据航空发动机整机振动的典型故障特征频率,判断该发动机是否发生故障。     

Spatio-temporal pattern recognition using hidden Markov models

A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7%, are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single-look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared with single-frame techniques     

Detection of polyphase pulse compression waveforms using the radon-ambiguity transform

The recently developed Radon-ambiguity transform (RAT) detects unknown linear frequency modulated (LFM) signals by computing line integrals through the origin of the signal's ambiguity function (AF) magnitude. It is shown that this method also detects the step LFM and frequency-derived polyphase pulse compression waveforms with varying performance degradation. Simulations are provided to estimate the detection loss relative to the LFM.     

Efficient wideband signal parameter estimation using a radon-ambiguity transform slice

A novel efficient technique based on a single slice Radon-ambiguity transform (RAT) for time-delay and time-scale estimation is proposed. The proposed approach combines the narrowband cross-ambiguity function (NBCAF), the wideband cross-ambiguity function (WBCAF), and a single slice RAT to estimate multiple target parameters in noisy environments. The square modulus of Gaussian-enveloped linear frequency modulated (GLFM) signals has high-energy centrality in the ambiguity plane. Its peaks in the NBCAF fall along nearly straight lines whose slopes depend on the Doppler rates of the moving targets. These lines could be effectively detected by computing the entire Radon transform of the NBCAF for all possible angles; however, it is a computationally intensive procedure. It is shown that without calculating the entire RAT, it is possible to estimate target parameters using only a single slice of the RAT, i.e., using an appropriate projection of the NBCAF. It is demonstrated that the proposed method can successfully separate overlapping targets efficiently. The efficiency is achieved due to fast Fourier transform (FFT)-bascd processing, use of a single slice of RAT, and the use of only one-dimensional (1-D) searches.     

On the vulnerability of biometric security systems

This paper presents an overview of the weakness of biometric security systems and possible solutions to improve it. Different levels of attack are described, and the strengths and weaknesses of the main biometric system are emphasized. Solutions are provided with special emphasis on cryptography and watermarking techniques.     

Automatic target recognition using enhanced resolution SAR data

Using advanced technology, a new automatic target recognition (ATR) system has been developed that provides significantly improved target recognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in target recognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing target recognition. A computationally efficient two-level implementation of a template-based classifier is used to perform target recognition. The improvement in target recognition performance achieved using superresolution image processing in this new ATR system is quantified     

基于小波分析的涡扇发动机起动过程压力脉动研究

利用小波分析所具有的时频特性特点,针对涡扇发动机起动过程存在的压力脉动异常现象,获取信号随转速变化的频谱图。重点介绍了发动机脉动频率分析方法,并结合试验测点布局,根据不同脉动的频率特点,对其脉动成分进行分解,分析出脉动频率产生的原因,为发动机起动问题的解决提供支持。研究结果表明:利用小波分析进行发动机起动过程的脉动异常研究,形象、直观,具有一定的工程应用价值。     

Changes to vascular biometric system security & performance

When considering biometrics for a Personal Identification System, different modalities can be considered. The final selection will depend on specific application requirements. From the several modalities existing nowadays, vascular systems have appeared on the scene lately. Vein-based identification is claimed to be as reliable as fingerprint or iris identification, but with the usability of hand geometry or even the face. As with any other biometric modalities, vascular solutions have to be deeply analyzed for all relevant factors that could affect their performance or the security level achieved. This will show the analysis of the performance and security achieved by a commercial vascular biometric system, when being applied in several real-world scenarios. These scenarios will cover typical office environments to the more extreme environments, such as extreme light and temperatures, as those suffered in a banking ATM, or with high humidity such as in a gym. The results will show the strengths of this modality, as well as those points where further improvements are needed.     

Automatic target recognition using sequences of high resolutionradar range-profiles

In this paper, we address the problem of joint tracking and recognition of a target using a sequence of high resolution radar (HRR) range-profiles. The likelihood function for the scene configuration combines a dynamics-based prior on the sequence of target orientations with a likelihood for range-profiles given the target orientation. A deterministic model and a conditionally Gaussian model for range-profiles are introduced, and the likelihood functions under each model are compared. Simulations are presented demonstrating recognition of mobile aircraft and ground targets, and results showing performance of the algorithm are given in terms of the expected angular estimation error and the rate of correct recognition     

Automatic target recognition for hyperspectral imagery using high-order statistics

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective     

Quantitative SNR analysis for ISAR imaging using jointtime-frequency analysis-Short time Fourier transform

V.C. Chen recently presented an inverse synthetic aperture radar (ISAR) imaging technique using the joint time-frequency analysis (JTFA), which has been shown having a better performance for maneuvering targets over the conventional Fourier transform method. The main reason is because the frequencies of the radar returns of the maneuvering targets are time varying and a JTFA is a technique that is suitable for such signals, in particular a JTFA may concentrate a wideband signal, such as a chirp, while it spreads noise. We quantitatively study the signal-to-noise ratio (SNR) in the ISAR imaging using one of the typical JTFA techniques, namely the short time Fourier transform (STFT). We show that the SNR increases in the joint time-frequency (TF) domain over the one in the time or the frequency domain alone both theoretically and numerically. This quantitatively shows the advantage of the JTFA technique for the ISAR imaging     

基于小波神经网络的PCBA检测

为了实现对PCBA的快速准确检测,提出了基于小波神经网络的检测模型,分析了网络的拓扑结构.仿真显示,该检测模型的误差小,精确度高,检测速度快,提高了SMT生产线上的一次通过率的可靠性.     

Complexity analysis of wavelet signal decomposition andreconstruction

We give certain sequential and parallel algorithms and their computational analysis for signal decomposition and reconstruction based on wavelets. The signal decomposition (respectively, reconstruction) process is separated into two stages: the first is the preprocessing stage where certain constants are computed for implementation to prepare for the second stage in which signal decomposition (respectively, reconstruction) is performed. In the decomposition (respectively, reconstruction) stage, the input signal is transformed via different methods to compute the output signal without changing the setup initialized in the preprocessing stage. We describe certain sequential algorithms for both the preprocessing and the decomposition (respectively, reconstruction) stages, and parallel algorithms for the latter. The algorithms are finally illustrated for compactly supported spline-wavelets and are analyzed in detail in terms of the required arithmetic operations     

Iris recognition technology

IriScan Inc. has been developing an identification/verification system capable of positively identifying and verifying the identity of individuals without physical contact or human intervention. A new technology, using the unique patterns of the human iris, shows promise of overcoming previous shortcomings and providing positive identification of an individual without contact or invasion, at extremely high confidence levels. The video-based system locates the eye and iris; evaluates the degree of occlusion by eyelid and spectral reflection; determines the quality of image focus; and determines the center and boundary of the pupil and the limbus (outer edge of the iris) for processing. The iris is zoned, and the features therein measured and encoded into a 256-byte (2048 bit) IrisCode for enrollment or identification. The presented biometric is compared to an extensive database for identification, or to a referenced IrisCode for verification. Computations and decisions are accomplished at extremely high rates of speed, resulting in processing times of less than two seconds. The process is based on the unique nature and extreme richness of the human iris. The multiple features produce a non-duplicable organ with more than 400 degrees of freedom, or measurable variables. The IriScan process typically uses about 200 of these to create a code which can be compared to an entire database in milliseconds, producing a positive identification with “imposter odds” as high as 1 in 10³⁴     

Signature recognition state-of-the-art

A summarization of one of the most successful behavioral biometric recognition methods: signature recognition. Probably this is one of the oldest biometric recognition methods, with high legal acceptance. Technological advances have made possible new perspectives for signature recognition, by means of capturing devices which provide more than the simple signature image: pressure, acceleration, etc., making it even more difficult to forge a signature.