Multiresolution Markov models for signal and image processing

Reviews a significant component of the rich field of statistical multiresolution (MR) modeling and processing. These MR methods have found application and permeated the literature of a widely scattered set of disciplines, and one of our principal objectives is to present a single, coherent picture of this framework. A second goal is to describe how this topic fits into the even larger field of MR methods and concepts-in particular, making ties to topics such as wavelets and multigrid methods. A third goal is to provide several alternate viewpoints for this body of work, as the methods and concepts we describe intersect with a number of other fields. The principle focus of our presentation is the class of MR Markov processes defined on pyramidally organized trees. The attractiveness of these models stems from both the very efficient algorithms they admit and their expressive power and broad applicability. We show how a variety of methods and models relate to this framework including models for self-similar and 1/f processes. We also illustrate how these methods have been used in practice.     

A wreath product group approach to signal and image processing .I.Multiresolution analysis

We propose the use of spectral analysis on certain noncommutative finite groups in digital signal processing and, in particular, image processing. We pay significant attention to groups constructed as wreath products of cyclic groups. Within this large class of groups, our approach recovers the discrete Fourier transform (DFT), the Haar wavelet transform, various multichannel pyramid filter banks, and other aspects of multiresolution analysis as special cases of a more general phenomenon. In addition, the group structure provides a rich algebraic structure that can be exploited for the analysis and manipulation of signals. Our approach relies on a synthesis of ideas found in the early work of Holmes (1987, 1990), Karpovsky and Trachtenberg (1985), and others on noncommutative filtering, as well as Diaconis's (1989) spectral analysis approach to understanding data     

Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal

Multidimensional Systems and Signal Processing - In this research paper we present designing and evaluating the electrocardiography (ECG) and Myoelectric signal (EMG) pattern recognition methods...     

Automotive signal diagnostics using wavelets and machine learning

In this paper, we describe an intelligent signal analysis system employing the wavelet transformation in the solution of vehicle engine diagnosis problems. Vehicle engine diagnosis often involves multiple signal analysis. The developed system first partitions a leading signal into small segments representing physical events or states based on wavelet multi-resolution analysis. Second, by applying the segmentation result of the leading signal to the other signals, the detailed properties of each segment, including inter-signal relationships, are extracted to form a feature vector. Finally, a fuzzy intelligent system is used to learn diagnostic features from a training set containing feature vectors extracted from signal segments at various vehicle states. The fuzzy system applies its diagnostic knowledge to classify signals as abnormal or normal. The implementation of the system is described and experiment results are presented     

Active and cooperative learning in signal processing courses

This work describes positive effects of using active and cooperative learning (ACL) methods to improve signal processing instruction. It provides examples, references, and assessment data that encourage other instructors to consider this approach. Conclusions are based on impressions gathered through conversations with students during office hours as well as on responses from anonymous student opinion surveys. In addition to these subjective assessments, preliminary quantitative data measured with the signals and systems concept inventory (SSCI) support the benefits of ACL techniques in signal processing courses.     

样条多分辨分析

本文根据平移不变子空间二尺度分解理论，系统，简结地构造了各类样条多分辨分析，其中涉及的运算只有序列的卷积，求逆，开平方和抽取以及信号的内积。     

Multiresolution ESPRIT algorithm

Multiresolution ESPRIT is an extension of the ESPRIT direction finding algorithm to antenna arrays with multiple baselines. A short (half wavelength) baseline is necessary to avoid aliasing, and a long baseline is preferred for accuracy. The MR-ESPRIT algorithm allows the combination of both estimates. The ratio of the longest baseline to the shortest one is a measure of the gain in accuracy. Because of various factors, including noise, signal bandwidth, and measurement error, the achievable gain in accuracy is bounded     

Multiresolution image modelling

A novel approach to the statistical modelling of images based on widely used multiresolution image representations, such as pyramids and wavelets, is presented. It is shown how models which are causal in the scale dimension provide an effective way of expressing the concept of successive approximation, or coarse-fine refinement, by the addition of details to the coarse structure of images. The ideas are illustrated using applications including image enhancement, data compression and segmentation     

Machine learning for multimodality genomic signal processing

This paper discusses how machine learning can be applied to genomic signal processing, particularly via fusion of multiple biological or algorithmic modalities, to improve prediction performance.     

A methodology for microcontroller signal frequency stress prediction

This paper proposes a methodology for analysis and prediction of microcontroller failures due to signal frequency stress. Microcontrollers are constantly under signal frequency stress when controlling external devices. However, signal frequency stress has received relatively little attention in comparison with other sources of stress such as temperature, humidity, and voltage. This study involved the following steps: (1) identifying the highest stress point before failure; (2) dividing signal frequency into different stress levels; (3) characterizing the impact of signal frequency stress on IC functionality; (4) constructing a thermal profile of a microcontroller under signal frequency stress over time; (5) predicting stress levels using regression and neural network methods; and (6) comparing and contrasting performance differences for each method. Results indicated that the average prediction error is about 7.9% for the neural network approach and about 23.8% for the statistical regression approach. This may be due to the neural network modeling approach’s inherent ability to tolerate noise in the data due to factors such as variation in quality due to variations in the manufacturing process. This general methodology has also been utilized with low error rates in failure analysis and stress prediction in operational/power amplifiers (8% error rate), timer oscillator chips (25%) and resistors (30%).     

Multiresolution approximation scale and time-shift subspaces

Multiresolution Approximation subspaces are -subspaces defined for each scale over all time shifts, i.e., “scale subspaces”, while with respect to a given wavelet, the signal space not only admits orthogonal scale subspaces basis, but orthogonal “time shift subspaces” basis as well. It is therefore natural to expect both scale subspaces and time shift subspaces to play a role in Wavelet Theory and, in particular, in Multiresolution Approximation as well. This is what will be discussed in the paper.     

基于机器学习的大学体育成绩预测与分析

为了科学评价大学生身体素质,提出基于机器学习算法的体育成绩预测模型。对当前大学生体育成绩预测的研究现状进行分析,指出导致当前模型预测精度低的原因,采用机器学习算法——支持向量机建立大学生体育成绩预测模型,并采用粒子群算法选择模型参数,最后将该模型应用于某大学的体育成绩建模和预测中。应用实例结果表明,机器学习算法可以克服传统模型的不足,使得大学体育成绩预测效果得到改善,预测结果可以指导大学体育学科的改革。     

Nonlinear blind signal separation with intelligent controlled learning

The paper proposes a new nonlinear blind source separation algorithm with hybridisation of fuzzy logic based learning rate control and simulated annealing to improve the global solution search. Benefits of fuzzy systems and simulated annealing are incorporated into a multilayer perceptron network. Fuzzy logic control allows adjustments of learning rate to enhance the rate of convergence of the algorithm. Simulated annealing is implemented to avoid the algorithm becoming trapped in local minima. A simple and computationally efficient method for controlling learning rate and ensuring a global solution is proposed. The performance of the proposed algorithm in terms of convergence of entropy, is studied alongside other techniques of learning rate adaptation. Simulations show that the proposed nonlinear algorithm outperforms other existing nonlinear algorithms based on fixed learning rates.     

战场电磁信号环境最复杂原理预测

战场电磁环境分布状态预测是有针对性地构建电子对抗训练复杂电磁环境的前提。目前的战场电磁环境分析方法不能对电磁环境分布状态进行解析预测,使得电磁环境的构建、控制和评估缺少理论依据和具体指标,针对这一问题,将熵的概念应用于战场电磁环境频谱分布预测。介绍了基于熵的战场电磁环境复杂度研究现状,组成论对战场电磁信号环境的组成、复杂性进行了分析,给出了战场电磁信号环境时间、空间、频谱占有差异的复杂程度计算公式,以战场电磁兼容为约束条件,使用最复杂原理,预测了战场电磁信号环境的频谱宽度的分布函数,最后,进行了仿真计算,仿真结果与理论预测吻合。     

Detection of non-Gaussian signals: a paradigm for modernstatistical signal processing [and prolog]

Non-Gaussian signals arise in a wide variety of applications, including sonar, digital communications, seismology, and radio astronomy. In this tutorial overview, a hierarchical approach to signal modeling and detector design for non-Gaussian signals is described. In addition to being of interest in applications, this problem serves as a paradigm within which most of the areas of active research in statistical signal processing arise. In particular, the methodologies of nonlinear signal processing, higher order statistical analysis, signal representations, and learning algorithms, all can be juxtaposed quite naturally in this framework     

Unsupervised learning for signal versus noise (Corresp.)

The Bayes solution to the unsupervised sequential learning problem induced by a mixture model for the two-class signal versus noise decision problem generates a computational and storage explosion. A quasi-Bayes approximate learning procedure is proposed that avoids the computational explosion while retaining the flavor of the Bayes solution. Convergence is established and efficiency is investigated.     

基于深度学习的ADS-B辐射源个体识别

辐射源个体识别技术在电子对抗领域起着关键性作用.针对传统人工提取特征主要依赖于专家经验,文中提出了一种基于深度学习的ADS-B辐射源个体识别方法.该方法采用卷积神经网络结合center loss损失函数来训练和测试相位数据.并加入假冒飞机序列号,查看中心损失值来判断是否异常.实验表明,文中提出的方法识别精度能达到99％...