Mathematical and computational biology is playing an increasingly important role in the biological sciences. This science brings forward unique challenges, many of which are, at the moment, beyond the theoretical techniques available. Developmental biology, due to its complexity, has lagged somewhat behind its sister disciplines (such as...
This book includes a collection of chapters on the state of art in the area of intelligent machines. This research would provide a sound basis to make autonomous systems human-like.
The contributions include: an introduction to intelligent machines; supervisory control of multiple UAVs; intelligent autonomous UAV task allocation;...
This book concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). The main objective of the book is to show that inference-based NLU has the potential for practical large scale applications. First, an introduction to research areas relevant for NLU is given. We review approaches to linguistic...
Logic networks and automata are facets of digital systems. The change of the design of logic networks from skills and art into a scientific discipline was possible by the development of the underlying mathematical theory called the Switching Theory. The fundamentals of this theory come from the attempts towards an algebraic...
Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional programmers. This is simply not the case. With just a few dozen simple algorithms, easily implemented with open source programming languages, you can fully utilize the medical information contained in...
Unsupervised Signal Processing: Channel Equalization and Source Separation provides a unified, systematic, and synthetic presentation of the theory of unsupervised signal processing. Always maintaining the focus on a signal processing-oriented approach, this book describes how the subject has evolved and assumed a wider scope...
For this book, the editors invited contributions from indispensable research areas relevant to "chance discovery", which has been defined as the discovery of events significant for making a decision, and studied since 2000. The chapters contain contributions to identifying rare or hidden events and explaining their significance. The...
Approaches to building machines that can learn from experience abound - from connectionist learning algorithms and genetic algorithms to statistical mechanics and a learning system based on Piaget's theories of early childhood development. This monograph describes results derived from the mathematically oriented framework of computational...
Authors Jim Jeffers and James Reinders spent two years helping educate customers about the prototype and pre-production hardware before Intel introduced the first Intel Xeon Phi coprocessor. They have distilled their own experiences coupled with insights from many expert customers, Intel Field Engineers, Application Engineers and Technical...
The first textbook of its kind, Quantitative Corpus Linguistics with R demonstrates how to use the open source programming language R for corpus linguistic analyses. Computational and corpus linguists doing corpus work will find that R provides an enormous range of functions that currently require several programs to...
The abstract branch of theoretical computer science known as Computation Theory typically appears in undergraduate academic curricula in a form that obscures both the mathematical concepts that are central to the various components of the theory and the relevance of the theory to the typical student. This regrettable situation is due largely...
Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational...
