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Dynamics And The Problem Of Recognition In Biological Macromolecules
From within complex structures of organisms and cells down to the molecular level, biological processes all involve movement. Muscular fibers slide on each other to activate the muscle, as polymerases do along nucleic acids for replicating and transcribing the genetic material. Cells move and organize themselves into organs by recognizing each other through macromolecular surface-specific interactions. These recognition processes involve the mu- tual adaptation of structures that rely on their flexibility. All sorts of conformational changes occur in proteins involved in through-membrane signal transmission, showing another aspect of the flexibility of these macromolecules. The movement and flexibility are inscribed in the polymeric nature of essential biological macromolecules such as proteins and nucleic acids. For instance, the well-defined structures formed by the long protein chain are held together by weak noncovalent interac- tions that design a complex potential well in which the protein floats, permanently fluctuating between several micro- or macroconformations in a wide range of frequencies and ampli- tudes.
The inherent mobility of biomolecular edifices may be crucial to the adaptation of their structures to particular functions. Progress in methods for investigating macromolecular structures and dynamics make this hypothesis not only attractive but more and more testable.
Feature Recognition System
A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals
This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers.
Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability.
There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems.
Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book
- Offers both foundations and advances on emotion recognition in a single volume
- Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains
- Inspires young researchers to prepare themselves for their own research
- Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
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