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

世界経済・社会統計 = World development indicators

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

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

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Handbook of Fingerprint Recognition

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

The paper describes an enhanced fingerprint recognition system consisting of image preprocessing, feature extraction and matching that runs accurately and effectively on a personal computer platform. The image preprocessing includes histogram equalisation, modification of directional codes, dynamic thresholding and ridgeline thinning which are sufficient to enhance the image to a state ready for feature extraction. Only features extracted are stored in a file for fingerprint matching. The matching algorithm presented is a modification and improvement of the structural approach. Experimental results acquired for matching are accurate, reliable and fast for implementation using a PC and a fingerprint scanner. The proposed fingerprint recognition scheme can provide an efficient way of automated identification and can be extended to numerous other security or administration applications.

Novel approach to automated fingerprint recognition

This paper presents an EEG-based emotion recognition system using self-organizing map for boundary detection Features from EEG signals are classified by considering the subjects’ emotional responses using scores from SAM questionnaire The selection of appropriate threshold levels for arousal and valence is critical to the performance of the recognition system Therefore, this paper investigates the performance of a proposed EEG-based emotion recognition system that employed self-organizing map to identify the boundaries between separable regions A study was performed to collect 8 channels of EEG data from 26 healthy right-handed subjects in experiencing 4 emotional states while exposed to audio-visual emotional stimuli EEG features were extracted using the magnitude squared coherence of the EEG signals The boundaries of the EEG features were then extracted using SOM 5-fold cross-validation was then performed using the k-nn classifier The results showed that proposed method improved the accuracies to 845%

EEG-based Emotion Recognition Using Self-Organizing Map for Boundary Detection

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

Driving Profile Modeling and Recognition Based on Soft Computing Approach

The aim of this chapter is to consider two challenging problems in photo-acoustic imaging. We consider extended optical sources in an attenuating acoustic background. We provide algorithms to correct the effects of imposed boundary conditions and that of attenuation as well. By testing our measurements against an appropriate family of functions, we show that we can access the Radon transform of the initial condition in the acoustic wave equation, and thus recover quantitatively the absorbing energy density. We also show how to compensate the effect of acoustic attenuation on image quality by using the stationary phase theorem.

Photoacoustic Imaging for Attenuating Acoustic Media

This book is the first to comprehensively explore elasticity imaging and examines recent, important developments in asymptotic imaging, modeling, and analysis of deterministic and stochastic elastic wave propagation phenomena. It derives the best possible functional images for small inclusions and cracks within the context of stability and resolution, and introduces a topological derivative-based imaging framework for detecting elastic inclusions in the time-harmonic regime. For imaging extended elastic inclusions, accurate optimal control methodologies are designed and the effects of uncertainties of the geometric or physical parameters on stability and resolution properties are evaluated. In particular, the book shows how localized damage to a mechanical structure affects its dynamic characteristics, and how measured eigenparameters are linked to elastic inclusion or crack location, orientation, and size. Demonstrating a novel method for identifying, locating, and estimating inclusions and cracks in elastic structures, the book opens possibilities for a mathematical and numerical framework for elasticity imaging of nanoparticles and cellular structures.

Abdul Wahab

Papers

Novel approach to automated fingerprint recognition

EEG-based Emotion Recognition Using Self-Organizing Map for Boundary Detection

Driving Profile Modeling and Recognition Based on Soft Computing Approach

Photoacoustic Imaging for Attenuating Acoustic Media

Mathematical Methods in Elasticity Imaging