Distance-Based Decision Fusion in a Distributed Wireless Sensor Network

Target classification fusion problem in a distributed, wireless sensor network is investigated. We propose a distance-based decision fusion scheme exploiting the relationship between sensor to target distance, signal to noise ratio and classification rate, which requires less communication while achieving higher region classification rate when compared to conventional majority-vote-based fusion schemes. Several different methods are tested, and very encouraging simulation results using real world experimental data samples are also observed.     

基于小波变换/小波包变换的多载波调制技术

小波/小波包在通信系统中的应用是近年来一个新的研究领域,而基于小波/小波包变换的多载波调制技术是其中一大研究热点。由于小波/小波包基函数具有良好的正交性与时频局域性等特点,基于小波/小波包变换的多载波调制技术,能够有效地提高通信系统性能。本文介绍了当前几种主要的基于小波/小波包变换的多载波调制方案,分析了这些方案的性能特点及发展趋势,并与其他方案进行了比较。     

Two-dimensional orthogonal wavelets with vanishing moments

We investigate a very general subset of 2-D, orthogonal, compactly supported wavelets. This subset includes all the wavelets with a corresponding wavelet (polyphase) matrix that can be factored as a product of factors of degree-1 in one variable. In this paper, we consider, in particular, wavelets with vanishing moments. The number of vanishing moments that can be achieved increases with the increase in the McMillan degrees of the wavelet matrix. We design wavelets with the maximal number of vanishing moments for given McMillan degrees by solving a set of nonlinear constraints on the free parameters defining the wavelet matrix and discuss their relation to regular, smooth wavelets. Design examples are given for two fundamental sampling schemes: the quincunx and the four-band separable sampling. The relation of the wavelets to the well-known 1-D Daubechies wavelets with vanishing moments is discussed     

A wavelet-like filter based on neuron action potentials for analysis of human scalp electroencephalographs

This paper describes the development and testing of a wavelet-like filter, named the SNAP, created from a neural activity simulation and used, in place of a wavelet, in a wavelet transform for improving EEG wavelet analysis, intended for brain-computer interfaces. The hypothesis is that an optimal wavelet can be approximated by deriving it from underlying components of the EEG. The SNAP was compared to standard wavelets by measuring Support Vector Machine-based EEG classification accuracy when using different wavelets/filters for EEG analysis. When classifying P300 evoked potentials, the error, as a function of the wavelet/filter used, ranged from 6.92% to 11.99%, almost twofold. Classification using the SNAP was more accurate than that with any of the six standard wavelets tested. Similarly, when differentiating between preparation for left- or right-hand movements, classification using the SNAP was more accurate (10.03% error) than for four out of five of the standard wavelets (9.54% to 12.00% error) and internationally competitive (7% error) on the 2001 NIPS competition test set. Phenomena shown only in maps of discriminatory EEG activity may explain why the SNAP appears to have promise for improving EEG wavelet analysis. It represents the initial exploration of a potential family of EEG-specific wavelets.     

CBS: Contourlet-Based Steganalysis Method

An ideal steganographic technique embeds secret information into a carrier cover object with virtually imperceptible modification of the cover object. Steganalysis is a technique to discover the presence of hidden embedded information in a given object. Each steganalysis method is composed of feature extraction and feature classification components. Using features that are more sensitive to information hiding yields higher success in steganalysis. So far, several steganalysis methods have been presented which extract some features from DCT or wavelet coefficients of images. Multi-scale and time-frequency localization of an image is offered by wavelets. However, wavelets are not effective in representing the images in different directions. Contourlet transform addresses this problem by providing two additional properties, directionality and anisotropy. The present paper offers an universal approach to steganalysis called CBS, which uses statistical moments of contourlet coefficients as features for analysis. After feature extraction, a non-linear SVM classifier is applied to classify cover and stego images. The efficiency of the proposed method is demonstrated by experimental investigations. The proposed steganalysis method is compared with two well-known steganalyzers against typical steganography methods. The results showed the superior performance of our method.     

Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using Complex Daubechies wavelets

Breast cancer is the most frequent cause of cancer induced death among women in the world. Diagnosis of this cancer can be done through radiological, surgical, and pathological assessments of breast tissue samples. A common test for detection of this cancer involves visual microscopic inspection of Fine Needle Aspiration Cytology (FNAC) samples of breast tissue. The result of analysis on this sample by a cyto-pathologist is crucial for the breast cancer patient. For the assessment of malignancy, the chromatin texture patterns of the cell nuclei are essential. Wavelet transforms have been shown to be good tools for extracting information about texture. In this paper, it has been investigated whether complex wavelets can provide better performance than the more common real valued wavelet transform. The features extracted through the wavelets are used as input to a k-nn classifier. The correct classification results are obtained as 93.9% for the complex wavelets and 70.3% for the real wavelets.     

Gradient-driven update lifting for adaptive wavelets

Over the past few years, wavelets have become extremely popular in signal and image processing applications. The classical linear wavelet transform, however, performs a homogeneous smoothing of the signal contents which, in some cases, is not desirable. This has led to a growing interest in (nonlinear) wavelet representations that can preserve discontinuities, such as transitions and edges.

In this paper, we present the construction of adaptive wavelets by means of an extension of the lifting scheme. The basic idea is to choose the update filters according to some decision criterion which depends on the local characteristics of the input signal. We show that these adaptive schemes yield lower entropies than schemes with fixed update filters, a property that is highly relevant in the context of compression. Moreover, we analyze the effect of a scalar uniform quantization and the stability in such adaptive wavelet decompositions.     

利用近距离目标红外图像的机动检测问题研究

机动检测问题是目标跟踪(尤其是对近距离目标跟踪)过程中的一类关键性问题.传统的机动检测方法一般将目标看做点目标来处理,而在能得到目标红外图像的情况下,依据红外图像跟踪可以得到更快、更准确的机动检测性能,本文提出了一种针对近距离目标红外图像的依据类散度的小波包变换、初始聚类后叠代逼近以及能量最强直线段提取的图像处理方法,该方法在较低信噪比情况下依然能够保持较好的目标中心的跟踪精度,并进一步改善机动检测的性能.     

Intelligent Decision Making System for Digital Modulation Scheme Classification in Software Radio Using Wavelet Transform and Higher Order Statistical Moments

This paper proposes a neural network (NN) based intelligent decision making system for digital modulation classification using wavelet transform, histogram peak and higher order statistical moments. The decision making system is developed to classify the modulation schemes buried in additive white Gaussian noise and channel interference utilizing NN classifier. The performance is verified and validated for M-ary PSK, M-ary FSK, M-ary QAM and GMSK modulation schemes by examining the receiver operating characteristics, confusion matrix and probability of correct identification for various signal-to-noise ratios (SNR) and also for various decision parameters. The performance of the proposed system also has been compared with existing methods and found that this method can be considered as reliable classification method for Digital Modulation Scheme with lower SNR upto  − 5 dB.

基于正交小波的判决反馈均衡算法

本文提出了一种基于正交小波的线性均衡算法，用一组规范正交小波及其对应的系数来表示均衡器，在此基础上，提出基于正交小波的判决反馈均衡算法，文中给出了自适应算法，并对算法性能做了分析，采用这种结构的均衡算法，比传统的基于ＬＭＳ算法的线性均衡器和判决反馈均衡器收敛速度快，误码性能相同，而计算量增加不多，计算机模拟也证实了上述结论。     

Properties determining choice of mother wavelet

Properties of wavelets with finite as well as infinite support are summarised to facilitate mother wavelet selection in a chosen application. The quantitative guidelines reduce dependence on trial-and-error schemes resorted to for selection and underscore the importance of such selection in any application of interest. In wavelet-based image sequence superresolution, studied during the last four years, the use of a B-spline mother wavelet is justified.     

Robust fabric defect detection and classification using multiple adaptive wavelets

The wavelet transform has been widely used for defect detection and classification in fabric images. The detection and classification performance of the wavelet transform approach is closely related to the selection of the wavelet. Instead of predetermining a wavelet, a method of designing a wavelet to adapt to the detection or classification of fabric defects has been developed. For further improvement of the performance, the paper extends the adaptive wavelet-based methodology from the use of a single adaptive wavelet to multiple adaptive wavelets. For each class of fabric defect, a defect-specific adaptive wavelet was designed to enhance the defect region at one channel of the wavelet transform, where the defect region can be detected by using a simple threshold classifier. Corresponding to the multiple defect-specific adaptive wavelets, the multiscale edge responses to defect regions have been shown to be more efficient in characterising defects, which leads to a new approach to the classification of defects. In comparison with the single adaptive wavelet approach, the use of multiple adaptive wavelets yields better performance on defect detection and classification, especially for defects that are poorly detected by the single adaptive wavelet approach. The proposed method has been evaluated on the inspection of 56 images containing eight classes of fabric defects, and 64 images without defects. In defect detection, 98.2% detection rate and 1.5% false alarm rate were achieved, and in defect classification, 97.5% accuracy was achieved.     

Consensual and Hierarchical Classification of Remotely Sensed Multispectral Images

Consensual and hierarchical approaches are developed for the classification of remotely sensed multispectral images. The proposed method consists of preprocessing of input patterns, generating multiple classification results by hierarchical neural networks, and a combining scheme to generate a consensus of multiple classification results. Transformations of input patterns by random matrices and nonlinear filtering are used for preprocessing. By varying the input patterns, the multiple classification results are generated with sufficiently independent errors by using a single type of classifier. This helps to improve classification performance when the multiple classification results are combined. Hierarchical neural networks involve the use of successive classifiers which are tuned to reduce the remaining errors to increase the classification performance. This structure includes detection schemes to decide whether successive classifiers are utilized for each input. Consensual and hierarchical approaches generate more reliable and accurate results based on group decision.     

An improved naive Bayesian classifier technique coupled with anovel input solution method [rainfall prediction]

Data mining is the study of how to determine underlying patterns in the data to help make optimal decisions on computers when the database involved is voluminous, hard to characterize accurately and constantly changing. It deploys techniques based on machine learning alongside more conventional methods. These techniques can generate decision or prediction models based on actual historical data. Therefore, they represent true evidence-based decision support. Rainfall prediction is a good problem to solve by data mining techniques. This paper proposes an improved naive Bayes classifier (INCB) technique and explores the use of genetic algorithms (GAs) for the selection of a subset of input features in classification problems. It then carries out a comparison with several other techniques. It compares the following algorithms on real meteorological data in Hong Kong: (1) genetic algorithms with average classification or general classification (GA-AC and GA-C), (2) C4.5 with pruning, and (3) INBC with relative frequency or initial probability density (INBC-RF and INBC-IPD). Two simple schemes are proposed to construct a suitable data set for improving their performance. Scheme I uses all the basic input parameters for rainfall prediction. Scheme II uses the optimal subset of input variables which are selected by a GA. The results show that, among the methods we compared, INBC achieved about a 90% accuracy rate on the rain/no-rain classification problems. This method also attained reasonable performance on rainfall prediction with three-level depth and five-level depth, which are around 65%-70%     

Activity classification using realistic data from wearable sensors

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.     

Circuit Theoretic Classification of Parallel Connected DC–DC Converters

This paper describes a classification of paralleling schemes for dc-dc converters from a circuit theoretic viewpoint. The purpose is to provide a systematic classification of the types of parallel converters that can clearly identify all possible structures and control configurations, allowing simple and direct comparison of the characteristics and limitations of different paralleling schemes. In the proposed classification, converters are modeled as current sources or voltage sources, and their connection possibilities, as constrained by Kirchhoff's laws, are categorized systematically into three basic types. Moreover, control arrangements are classified according to the presence of current sharing and voltage-regulation loops. Computer simulations are presented to illustrate the characteristics of the various paralleling schemes     

插值子波变换的优化设计

Ｄｅｓｌａｕｒｉｅｒｓ－ｄｕｂｕｃ子波是一类被广泛采用的插值子波,其不足之处在于系统冗余较大,一般插值子波是Ｄｅｓｌａｕｒｉｅｒｓ－ｄｕｂｕｃ子波的推广,设计上具有更大发为活性,本文研究了一般插值子波系统的性质、优化设计准则以及有效的优化设计方法。最后,通过对实测信号的压缩说明了本文方法的有效性。     

Shearlet-Based Total Variation Diffusion for Denoising

We propose a shearlet formulation of the total variation (TV) method for denoising images. Shearlets have been mathematically proven to represent distributed discontinuities such as edges better than traditional wavelets and are a suitable tool for edge characterization. Common approaches in combining wavelet-like representations such as curvelets with TV or diffusion methods aim at reducing Gibbs-type artifacts after obtaining a nearly optimal estimate. We show that it is possible to obtain much better estimates from a shearlet representation by constraining the residual coefficients using a projected adaptive total variation scheme in the shearlet domain. We also analyze the performance of a shearlet-based diffusion method. Numerical examples demonstrate that these schemes are highly effective at denoising complex images and outperform a related method based on the use of the curvelet transform. Furthermore, the shearlet-TV scheme requires far fewer iterations than similar competitors.     

基于优化的决策树算法在热轧工艺中的应用

决策树分类方法是一种非常有效的机器学习方法,具有分类精度高、对噪声数据有很好的健壮性以及形成树状模式等优点,对决策树算法的优化也主要是从分支属性的选择标准,对决策树的修剪,以及引入模糊理论、粗糙集理论、遗传算法和神经网络算法等几个方面进行优化。引入粗糙集理论中的属性重要性原理来对决策树进行优化,首先计算出每个条件属性对分类的重要度,然后根据重要度大小来对样本集进行一个筛选,在不损害分类准确率的同时减小决策树的规模。整个算法在Visual C++6.0环境下编程实现,并应用于热轧工艺模型中,通过对热轧数据的处理,验证了算法的有效性。     

Distributed classification of Gaussian space-time sources in wireless sensor networks

Distributed signal processing techniques for classification of objects are studied assuming knowledge of sensor measurement statistics. The spatio-temporal signal field generated by an object is modeled as a bandlimited stationary ergodic Gaussian field. The model suggests a simple abstraction of correlation between node measurements: it partitions the network into disjoint spatial coherence regions over which the signal remains strongly correlated, whereas the signal in distinct coherence regions is approximately uncorrelated. The size of coherence regions is determined by spatial signal bandwidths. It is shown that this partitioning imposes a structure on optimal distributed classification algorithms that is naturally suited to the communication constraints of the network: local high-bandwidth exchange of feature vectors within each coherence region to improve the measurement signal-to-noise ratio (SNR), and global low-bandwidth exchange of local decisions across coherence regions to stabilize the inherent variability in the signal. Classifier performance is analyzed for both soft and hard decision fusion across coherence regions assuming noise-free, as well as noisy communication links between nodes. Under mild conditions, the probability of error of all classification schemes (soft, hard, noisy) decays exponentially to zero with the number of independent node measurements-the error exponent depends on both the measurement and communication SNRs and decreases from soft to hard to noisy fusion. Numerical results based on real data illustrate the remarkable advantage of multiple sensor measurements in distributed decision making.