[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.

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Principal component analysis

Photoplethysmography and its application in clinical physiological measurement

The authors describe a fast block matching algorithm based on a feature-bit-plane matching scheme in which two types of transformed planes characterising the spatial distribution of pixel intensities of a frame are employed. Simulation results show that this method provides a prediction accuracy similar to that of the full-search method, while requiring only /spl sim/10% of the computation needed by the latter.

Feature-bit-plane matching technique for estimation of motion vectors

It has been shown by some researchers that in a problem of weighted least-square (WLS) design of an FIR filter, most of the design computation pertains to the evaluation of the inverse of a matrix in order to solve a system of equations. A new iterative procedure is developed for the inversion of the matrices involved in the design. By expanding the inverse of a matrix as a convergent series, an updating formula for evaluating the inverse for each iteration is obtained, so that the proposed algorithm requires an inverse for only a few initial iterations but does not need any numerical operations for matrix inversion in succeeding iterations. It is also shown that the proposed iterative procedure is applicable for a wide range of weighting functions used for least-square designs.

An iterative procedure for matrix inversion in weighted least-square design of FIR filters

In synthetic aperture radar (SAR) images, speckle noise reduction is a crucial pre-processing step for their successful interpretation and thus has drawn a great deal of attention of researchers in the image processing community. The Bayesian estimation is a powerful signal estimation technique and has been widely used for speckle noise removal in images. In this work, a low complexity wavelet-based Bayesian estimation technique for despeckling of images is developed. The main idea of the proposed technique is in establishing suitable statistical models for the wavelet coefficients and then in using these models to develop a shrinkage function with a low-complexity realization for the estimation of the wavelet coefficients of the noise-free images. The experimental results demonstrate the effectiveness of the proposed despeckling scheme in providing a significant reduction in the speckle noise at a very low computational cost and simultaneously preserving the image details.

A low-complexity MMSE Bayesian estimator for suppression of speckle in SAR images

Deep neural networks use a nonlinear end-to-end mapping in order to transform a low resolution image to the high resolution one. Residual blocks facilitate the flow of the information in deep neural networks and enhance the network performance. In this paper, a new residual block that enhances the representational capability of a super resolution network is proposed. The proposed residual block combines the features generated in various receptive fields using different hierarchical levels of convolution operations or convolution operations in conjunction with the space-to-depth and depth-to-space operations in order to provide a rich set of residual features. The experimental results demonstrate the superiority of the super resolution network using the proposed residual block over the state-of-the-art light-weight super resolution networks in terms of objective and subjective metrics.

Srnmfrb: A Deep Light-Weight Super Resolution Network Using Multi-Receptive Field Feature Generation Residual Blocks

In this paper, a phase spectrum based acoustic parameter is presented for the detection of nasalized vowels from the mixture of oral and nasalized vowels of normal speakers. Acoustic analysis shows that during the event of nasalization, although additional formants (resonances) at various frequency locations are introduced, the introduction of a new formant in low frequency region around 250 Hz is found to remain consistent irrespective of female or male speakers in the modified group delay derived from the phase spectrum. By exploiting and verifying this fact on the band-limited modified group delay spectrum capable of resolving two closely spaced formants, an acoustic parameter RMGD is derived. Utilizing RMGD, the problem of detecting nasalized vowels is solved based on a threshold based scheme or a Euclidean distance based classifier. Simulation Results on TIMIT database show that the proposed method even with a simple classifier is superior in performance in comparison to that of the methods using Mel-frequency cepstral coefficients as a feature and Hidden Markov Modeling or Support Vector Machine as a classifier.

M. Omair Ahmad

Papers

Feature-bit-plane matching technique for estimation of motion vectors

An iterative procedure for matrix inversion in weighted least-square design of FIR filters

A low-complexity MMSE Bayesian estimator for suppression of speckle in SAR images

Srnmfrb: A Deep Light-Weight Super Resolution Network Using Multi-Receptive Field Feature Generation Residual Blocks

A detection method of nasalised vowels based on an acoustic parameter derived from phase spectrum