Basics of qualitative research: Grounded theory procedures and techniques

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing on the correspondence between the graph Laplacian, the Laplace Beltrami operator on the manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for representing the high-dimensional data. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality-preserving properties and a natural connection to clustering. Some potential applications and illustrative examples are discussed.

/pdf/laplacian-eigenmaps-for-dimensionality-reduction-and-data-4k0gjfvthz.pdf

Laplacian Eigenmaps for dimensionality reduction and data representation

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

/pdf/ensemble-empirical-mode-decomposition-a-noise-assisted-data-4htvj7tcuy.pdf

Ensemble empirical mode decomposition: a noise-assisted data analysis method

Brushlets: A Tool for Directional Image Analysis and Image Compression

This work investigates a new approach to the tracking of regions in an image sequence. The approach relies on two successive operations: detection and discrimination of moving targets and then pursuit of the targets. A motion-based segmentation algorithm, previously developed in the laboratory, provides the detection and discrimination stage. This paper emphasizes the pursuit stage. A pursuit algorithm has been designed that directly tracks the region representing the projection of a moving object in the image, rather than relying on the set of trajectories of individual points or segments. The region tracking is based on the dense estimation of an affine model of the motion field within each region, which makes it possible to predict the position of the target in the next frame. A multiresolution scheme provides reliable estimates of the motion parameters, even in the case of large displacements. Two interacting linear dynamic systems describe the temporal evolution of the geometry and the motion of the tracked regions. Experiments conducted on real images demonstrate that the approach is robust against occlusion and can handle large interframe displacements and complex motions.

Region-based tracking using affine motion models in long image sequences

Wavelets are ill-suited to represent oscillatory patterns: rapid variations of intensity can only be described by the small scale wavelet coefficients, which are often quantized to zero, even at high bit rates. Our goal is to provide a fast numerical implementation of the best wavelet packet algorithm in order to demonstrate that an advantage can be gained by constructing a basis adapted to a target image. Emphasis is placed on developing algorithms that are computationally efficient. We developed a new fast two-dimensional (2-D) convolution decimation algorithm with factorized nonseparable 2-D filters. The algorithm is four times faster than a standard convolution-decimation. An extensive evaluation of the algorithm was performed on a large class of textured images. Because of its ability to reproduce textures so well, the wavelet packet coder significantly out performs one of the best wavelet coder on images such as Barbara and fingerprints, both visually and in term of PSNR.

Fast adaptive wavelet packet image compression

Chapter 1. Fundamental Concepts.Chapter 2. Fourier Analysis.Chapter 3. Z-Transform and Digital Filters.Chapter 4. Filter Design and Implementation.Chapter 5. Multivariate Signal Processing.Chapter 6. Finite-Wordlength Effects.Chapter 7. Adaptive Signal Processing.Chapter 8. Least-Squares Adaptive Algorithms.Chapter 9. Linear Prediction.Chapter 10. Image Processing Fundamentals.Chapter 11. Image Compression and Coding.Appendix. Concepts of Linear Algebra.Index.

/pdf/digital-signal-and-image-processing-3kp5fved8z.pdf

François G. Meyer

Papers

Brushlets: A Tool for Directional Image Analysis and Image Compression

Region-based tracking using affine motion models in long image sequences

Fast adaptive wavelet packet image compression

Digital Signal and Image Processing

Comparison of detrending methods for optimal fMRI preprocessing.