[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 success of sparse representation in face recognition has motivated the development of sparse representation-based appearance models for visual tracking. These sparse representation-based trackers show state-of-the-art performance, but at the cost of computationally expensive l 1 -norm minimization. As the computational cost prevents the tracker from being used in real-time systems such as real-time surveillance and military operations, it has become a very important issue. With the aim of reducing the computational complexity of l 1 -norm minimization, a structural local DCT sparse appearance model is proposed in a particle filter framework. Application of DCT on local patches helps to reduce the dimensions of the dictionary as well as candidate samples by using low-pass filtered DCT coefficients. This in turn helps to remove the information relating to occlusion and background clutter thereby reducing the ambiguity created while computing the confidences of the target samples. The proposed method is evaluated on the challenging image sequences available in the literature and its performance compared with three recent state-of-the-art methods. It is shown that the proposed method provides superior/similar performance for most of the sequences with reduced computational complexity in l 1 -norm minimization.

Structural local DCT sparse appearance model for visual tracking

Digital watermarking has been widely used in the copyright protected images in multimedia. This paper addresses the blind watermark detection problem in contourlet domain. It is known that the contourlet coefficients of images have non- Gaussian property and can be well modelled by non-Gaussian distributions such as the normal inverse Gaussian (NIG). In view of this, we exploit this model to derive closed-form expressions for the test statistics and design an optimum blind watermark detector in the contourlet domain. Through conducting several experiments, the performance of the proposed detector is evaluated in terms of the probabilities of detection and false alarm and compared to that of the other existing detectors. It is shown that the proposed detector using the NIG distribution is superior to other detectors in terms of providing higher rate of detection. It is also shown that the proposed NIG-based detector is more robust than other detectors against attacks, such as JPEG compression and Gaussian noise.

Optimum Multiplicative Watermark Detector in Contourlet Domain Using the Normal Inverse

New radix-(2×2×2)/(4×4×4) and radix-(2×2×2)/(8×8×8) DIF FFT algorithms for 3-D DFT.

A fully programmable switched-current IIR filter using switched-current delay-multiplies units is described. The characteristics of the filter are fully programmable by simply changing the ratios of the coefficient transistors. To reduce the effect of non-ideal characteristics of MOS transistors, a high-performance differential switched-current memory cell is used as a basic building block. To reduce the chip area and maintain the required accuracy of the coefficients, an array consisting of three different sizes of transistors is designed instead of using a unit transistor array as coefficient transistors. A prototype second-order switched-current IIR filter array which consists of six second-order switched-current IIR filters has been fabricated with the standard 1.2 /spl mu/m CMOS process technology. Hard wiring technique is used for programming the filters. The test results show that the characteristics of the filters satisfy the design requirements.

M. Omair Ahmad

Papers

Structural local DCT sparse appearance model for visual tracking

Optimum Multiplicative Watermark Detector in Contourlet Domain Using the Normal Inverse

New radix-(2×2×2)/(4×4×4) and radix-(2×2×2)/(8×8×8) DIF FFT algorithms for 3-D DFT.

A novel fully programmable switched-current IIR filter

All digital skew tolerant synchronous interfacing methods for high-performance point-to-point communications in deep sub-micron SoCs