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There are two kinds of tutorial articles: those that provide a primer on an established topic and those that let us in on the ground floor of something of emerging importance. The first type of tutorial can have a noted expert who has been gracious (and brave) enough to write a field guide about a particular topic. The other sort of tutorial typically involves researchers who have each been laboring on a topic for some years. Both sorts of tutorial articles are very much desired. But we, as an editorial board for both Systems and Transactions, know that there has been no logical place for them in the AESS until this series was started several years ago. With these tutorials, we hope to continue to give them a home, a welcome, and provide a service to our membership. We do not intend to publish tutorials on a regular basis, but we hope to deliver them once or twice per year. We need and welcome good, useful tutorial articles (both kinds) in relevant AESS areas. If you, the reader, can offer a topic of interest and an author to write about it, please contact us. Self-nominations are welcome, and even more ideal is a suggestion of an article that the editor(s) can solicit. All articles will be reviewed in detail. Criteria on which they will be judged include their clarity of presentation, relevance, and likely audience, and, of course, their correctness and scientific merit. As to the mathematical level, the articles in this issue are a good guide: in each case the author has striven to explain complicated topics in simple-well, tutorial-terms. There should be no (or very little) novel material: the home for archival science is the Transactions Magazine, and submissions that need to be properly peer reviewed would be rerouted there. Likewise, articles that are interesting and descriptive, but lack significant tutorial content, ought more properly be submitted to the Systems Magazine.

What is a tutorial

Infrared and visible image fusion methods and applications: A survey

Multiwavelets are a new addition to the body of wavelet theory. Realizable as matrix-valued filterbanks leading to wavelet bases, multiwavelets offer simultaneous orthogonality, symmetry, and short support, which is not possible with scalar two-channel wavelet systems. After reviewing this theory, we examine the use of multiwavelets in a filterbank setting for discrete-time signal and image processing. Multiwavelets differ from scalar wavelet systems in requiring two or more input streams to the multiwavelet filterbank. We describe two methods (repeated row and approximation/deapproximation) for obtaining such a vector input stream from a one-dimensional (1-D) signal. Algorithms for symmetric extension of signals at boundaries are then developed, and naturally integrated with approximation-based preprocessing. We describe an additional algorithm for multiwavelet processing of two-dimensional (2-D) signals, two rows at a time, and develop a new family of multiwavelets (the constrained pairs) that is well-suited to this approach. This suite of novel techniques is then applied to two basic signal processing problems, denoising via wavelet-shrinkage, and data compression. After developing the approach via model problems in one dimension, we apply multiwavelet processing to images, frequently obtaining performance superior to the comparable scalar wavelet transform.

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The application of multiwavelet filterbanks to image processing

We consider the numerical calculation of several matrix eigenvalue problems which require some manipulation before the standard algorithms may be used. This includes finding the stationary values of a quadratic form subject to linear constraints and determining the eigenvalues of a matrix which is modified by a matrix of rank one. We also consider several inverse eigenvalue problems. This includes the problem of determining the coefficients for the Gauss–Radau and Gauss–Lobatto quadrature rules. In addition, we study several eigenvalue problems which arise in least squares.

Some modified matrix eigenvalue problems.

The fractional Fourier transform (FrFT) can be thought of as a generalization of the Fourier transform to rotate a signal representation by an arbitrary angle /spl alpha/ in the time-frequency plane. A lower bound on the uncertainty product of signal representations in two FrFT domains for real signals is obtained, and it is shown that a Gaussian signal achieves the lower bound. The effect of shifting and scaling the signal on the uncertainty relation is discussed. An example is given in which the uncertainty relation for a real signal is obtained, and it is shown that this relation matches with that given by the uncertainty relation derived.

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An uncertainty principle for real signals in the fractional Fourier transform domain

Modeling of surface roughness in precision machining of metal matrix composites using ANN

Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification

Image visibility is affected by the presence of haze, fog, smoke, aerosol, etc. Image dehazing using either single visible image or visible and near-infrared (NIR) image pair is often considered as a solution to improve the visual quality of such scenes. In this paper, we address this problem from a visible–NIR image fusion perspective, instead of the conventional haze imaging model. The proposed algorithm uses a Laplacian–Gaussian pyramid based multi-resolution fusion process, guided by weight maps generated using local entropy, local contrast and visibility as metrics that control the fusion result. The proposed algorithm is free from any human intervention, and produces results that outperform the existing image-dehazing algorithms both visually as well as quantitatively. The algorithm proves to be efficient not only for the outdoor scenes with or without haze, but also for the indoor scenes in improving scene visibility.

/pdf/visible-and-nir-image-fusion-using-weight-map-guided-5fogv6lce7.pdf

Visible and NIR image fusion using weight-map-guided Laplacian–Gaussian pyramid for improving scene visibility

List of Symbols. List of Acronyms. List of Figures. List of Tables. Acknowledgments. Preface. -1: Introduction. 1. Complexity of Broadband Network Traffic. 2. Teletraffic Modeling: Historical Perspective. 3. Motivation for the Problem. 4. Contributions of the Monograph. 5. Organization of the Monograph. -2: Mathematical Preliminaries. 1. Random Processes. 2. Bernoulli (Counting) Process. 3. Poisson Process. 4. Markov Process. 5. Independent Increment Processes. 6. Self Similar Processes. 7. Fractional Brownian Motion. 8. Heavy Tailed Processes. 9. Analysis and Estimation Techniques for Self Similar Processes. 10. Wavelet Based Analysis of Self Similar Process. 11. The Need for Multifractal Processes. 12. Salient points from the chapter. -3: Broadband Network Traffic Modeling. 1. Broadband Network Traffic Characteristics. 2. Network Traffic Modeling Methodology. 3. State-of-the-Art in Teletraffic Modeling. 4. Video Traffic Modeling. 5. Summary of Broadband Traffic Models. - 4: Multiplicative Cascades. 1. Binomial Multiplicative Cascade. 2. Characterizing Multifractals through Multifractal Spectrum. 3. Methods to Estimate Multifractal Spectrum. 4. Interpreting the Multifractal Spectrum. 5. Generalized Dimensions. 6. Summary of the Chapter. - 5: V.V.G.M Multifractal Model. 1. Development of V.V.G.M Multifractal Model. 2. Statistical Comparison Tests. 3. Test for Robustness of the Parametric Model. 4. Performance and Queuing Tests. 5. Inter Departure Process. 6. V.V.G.M and MWM: A Comparison. 7. Salient Points of the V.V.G.M Model. - 6: Analysis of the Multiplexing of Traffic. 1. Analysis of Multiplexing using V.V.G.M Model. 2. Analysis of Multiplexing using Multifractal Spectrum. 3. Analysis of the Multiplexing using Entropy. 4. How Complex is Inter Arrival Data? 5. Salient points from the chapter. - 7: Modeling of VBR Video Traces. 1. VBR Video Modeling Using Multifractals. 2. Statistical Tests for Model Evaluation. 3. Results of Queuing Simulations. 4. Complexity of VBR Video Traces. 5. Salient points from the Chapter. - 8: QOS Issues and Control of Broadband Traffic. 1. Statistics of Multiplicative Cascade Processes. 2. Queuing Theory for Cascade Processes. 3. Effective Bandwidth Estimation for QoS. 4. Estimation and Prediction of Burstiness. 5. Salient points from the chapter. 9: Conclusions. 1. V.V.G.M Cascade Process for Broadband Network Traffic. 2. Future Work. Appendices: A. Wavelet Transform. B. Legendre Transform. C. Large Deviation Theory. D. Norros's Queuing Model. D.1. Parameterizing QoS Requirements. D.1.1. Parameterizing Buffer Length. D.1.2. Parameterizing Service Rate. D.1.3. Parameterizing Queue Length Distribution. E. Effective Bandwidth. E.1. Properties. E.2. Application in Queuing Systems. F. Kalman Filter.

Vikram M. Gadre

Papers

An uncertainty principle for real signals in the fractional Fourier transform domain

Modeling of surface roughness in precision machining of metal matrix composites using ANN

Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification

Visible and NIR image fusion using weight-map-guided Laplacian–Gaussian pyramid for improving scene visibility

Multifractal Based Network Traffic Modeling