Jukna, S., University of Trier, Germany
With Applications in Computer Science
2000. XI, 360 pp.
3-540-66313-4
The book is a concise, self-contained and up-to-date introduction
to extremal combinatorics for non-specialists.
Strong emphasis is made on theorems with particularly elegant and
informative proofs which may be called gems of
the theory. A wide spectrum of most powerful combinatorial tools
is presented: methods of extremal set theory,
the linear algebra method, the probabilistic method and fragments
of Ramsey theory. A throughout discussion of
some recent applications to computer science motivates the
liveliness and inherent usefulness of these methods
to approach problems outside combinatorics. No special
combinatorial or algebraic background is assumed. All
necessary elements of linear algebra and discrete probability are
introduced before their combinatorial
applications. Aimed primarily as an introductory text for
graduates, it provides also a compact source of modern
extremal combinatorics for researchers in computer science and
other fields of discrete mathematics.
Keywords: combinatorics, extremal combinatorics, computational
complexity, extremal set theory, discrete
mathematics
Contents: Introduction.- I. The Classis: Counting.- The
Pigeon-Hole Principle.- Principle of Inclusion and
Exclusion.- Systems of Distinct Representatives.- Colorings.-
Chains and Antichains.- Intersecting Families.-
Covers and Transversals.- Sunflowers.- Density and Universality.-
Designs.- Witness Sets.- Isolation Lemmas.-
II. The Linear Algebra Method: Basic Method.- The Polynomial
Technique.- Monotone Span Programs.- III. The
Probabilistic Method: Basic Tools.- Counting Sieve.- Lov?sz
Sieve.- Linearity of Expectation.- The Deletion
Method.- Second Moment Method.- Bounding of Large Deviations.-
Randomized Algorithms.- Derandomization.-
The Entropy Function.- Random Walks and Search Problems.- IV.
Fragments of Ramsey Theory: Ramsey's
Theorem.- The Hales-Jewett Theorem.- Epilogue: What Next?-
Bibliography.- Index.
Series: Texts in Theoretical Computer Science. An EATCS Series.
1999. XIV, 297 pp. 1 fig.
3-540-66455-6
This book gives a systematic presentation of the main results on
the transformation of measure induced by shift
transformations on Wiener space. This topic has its origins in
the work of Cameron and Martin (anticipative shifts,
1940's) and that of Girsanov (non-anticipative shifts, 1960's).
It played an important role in the development of
non-anticipative stochastic calculus and itself developed under
the impulse of the stochastic calculus of
variations. Basic probability theory and the Ito calculus are
assumed known; the necessary results from the
Malliavin calculus are presented in the appendix. Aimed at
graduate students and researchers, it can be used as a
text for a course or a se- minar.
Keywords: Probability, Brownian motion, stochastic analysis,
Wiener space
Series: Springer Monographs in Mathematics.
Niederreiter, H., Vienna, Austria
Spanier, J., Claremont Graduate University, Claremont, CA, USA
(Eds.)
Proceedings of a Conference at the Claremont
Graduate University, Claremont,
California, USA, June 22-26, 1998
1999. XVI, 472 pp.
3-540-66176-X
This book represents the refereed proceedings of the Third
International Conference on Monte Carlo and
Quasi-Monte Carlo Methods in Scientific Computing which was held
at Claremont Graduate University in 1998.
An important feature are invited surveys of the state of the art
in key areas such as multidimensional numerical
integration, low-discrepancy point sets, random number
generation, and applications of Monte Carlo and
quasi-Monte Carlo methods. These proceedings include also
carefully selected contributed papers on all aspects
of Monte Carlo and quasi-Monte Carlo methods. The reader will be
informed about current research in this very
active area.
Keywords: Monte Carlo methods, quasi - Monte Carlo methods,
simulation methods, random number generation,
numerical integration
Chen, G., University of Houston,
TX, USA
(Ed.)
1999. Approx. 690 pp.
0-8493-0579-9
Over the last two decades, chaos in engineering systems has moved
from being simply a curious phenomenon to
one with real, practical significance. Controlling Chaos and
Bifurcations in Engineering Systems provides a
state-of-the-art survey of the control and anti-control of chaos
in dynamical systems. Internationally known
experts in the field have joined forces to form this
tutorial-style combination of overview and technical report on
the latest advances in the theory and applications of chaos
control.
Keywords: chaos control
Contents: Reconstructing Input-Output Dynamics from Time Series.-
Black and Grey-Box Modeling of
Nonlinear Systems: Identification and Analysis from Time Series.-
Design and Implementation of Chaos Control
Systems.- Chaos in Mechanical Systems and Its Control.- Utilizing
Chaos in Control System Design.- Control and
Synchronization of Spatiotemporal Chaos.- Chaotic Vibration of
the Wave Equation by Nonlinear Feedback
Boundary Control.- Sensitivity to Initial Conditions of Chaos in
Electronics.- Frequency Domain Methods for
Chaos Control.- Controlling Limit Cycles and Bifurcations.-
Theory and Experiments on Nonlinear Time-Delayed
Feedback Systems with Application to Chaos Control.- Time Delayed
Feedback Control of Chaos.- Impulsive
Control and Synchronization of Chaos.- Control and Anticontrol of
Bifurcations with Application to Active Control
of Rayleigh-Bnard Convection.- Delay Feedback Control of Cardiac
Activity Models.- Bifurcation Stabilization
with Applications in Jet Engine Control.- Bifurcations of Control
Systems in Normal Form.- Controlling
Bifurcations in Nonsmooth Dynamical Systems.- Adaptive
Observer-Based Synchronization.- Discrete-Time
Observers and Synchronization.- Separating a Chaotic Signal from
Noise and Applications.- Digital
Communications Using Chaos.- Synchronization in Arrays of Coupled
Chaotic Circuits and Systems: Theory and
Applications.- Chaos in Phase Systems: Generation and
Synchronization, Chaos and Bifurcations in Feedback
Control Systems, Chaos and Bifurcations in Coupled Networks and
Their Control.- Return Map Modulation in
Nonautonomous Relaxation Oscillator.- Controlling Chaos in
Discrete-Time Computational Ecosystems.
Konar, A., Jadavpur University, Calcutta, India
Behavioral and Cognitive Modeling of the Human
Brain
2000. Approx. 750 pp.
0-8493-1385-6
Artificial Intelligence and Soft Computing presents both the
traditional and the modern aspects of AI and
soft computing in a clear, insightful, and highly comprehensive
style. It provides an in-depth analysis of
mathematical models and algorithms and demonstrates their
applications in real world problems. Beginning with the
behavioral perspective of "human cognition," the text
covers the tools and techniques required for its intelligent
realization on machines. The author addresses the classical
aspects search, symbolic logic, planning, and machine
learning in detail and includes the latest research in these
areas. He introduces the modern aspects of soft
computing from first principles and discusses them in a manner
that enables a beginner to grasp the subject. He
also covers a number of other leading aspects of AI research,
including nonmonotonic and spatio-temporal
reasoning, knowledge acquisition, and much more. This book is
unique for its diverse content, clear presentation,
and overall completeness. It provides a practical, detailed
introduction that will prove valuable to computer
science practitioners and students as well as to researchers
migrating to the subject from other disciplines.
Keywords: fuzzy logic, fuzzy systems, GA, AI, fuzzy petri nets
Contents: Introduction to AI and Soft Computing.- The
Psychological Perspective of Cognition.- Production
Systems.- Search and Problem Solving.- The Logic of Propositions
and Predicates.- Elements of Logic
Programming.- Default and Non-Monotonic Logics.- Structured
Approach to Knowledge Representation and
Reasoning.- Dealing with Imprecision and Uncertainty.- Structured
Approach to Fuzzy Reasoning.- Reasoning
with Space and Time.- Intelligent Planning.- Machine Learning.-
Machine Learning Using Neural Nets.- Genetic
Algorithms.- Realising Cognition with Fuzzy Neural Nets.- Visual
Perception.- Linguistic Perception.- Problem
Solving By Constraint Satisfaction.- Acquisition of Knowledge.-
Verification, Validation and Maintenance Issues.-
Parallel and Distributed Architecture for Intelligent Systems.-
Case Study I : Building a System for Criminal
Investigation.- Case Study II : Building a System for
Navigational Planning of Robots.- Appendix: How to use the
diskette? Subject index.- Author index
Jain, L.C., University of South
Australia, Mawson Lakes, SA, Australia
(Ed.)
1999. Approx. 300 pp.
0-8493-1965-X
Computer scientists and applications engineers must have a
working knowledge of new techniques, stay abreast of
recent advances, and have the opportunity to incorporate them
into their own systems and designs. This volume
fills this need by providing an overview of the field and
offering state-of-the-art reviews of the most important
techniques and applications of evolutionary computing. The top
experts from around the world discuss
developments in genetic algorithms, genetic programming, and
evolutionary strategies and applications including
VLSI CAD, robot sensors, neural networks, and fuzzy
classification systems.
Keywords: neural networks, robot sensors, VLSI CAD, fuzzy
classification systems
Contents: An Introduction to Evolutionary Computing.- Exploring
the Design Space of Artificial Self-Replicating
Structures.- Evolution of Concurrent Systems.- Evolutionary
Approaches to the Learning of Fuzzy Rule-Based
Classification Systems.- Some Experiments in the Evolution of
Robot Sensors.- Evolution of Neural Networks
using a Two-Dimensional Approach.- Evolution of Neural Network
Modules: Artificial Brain Project.- VLSI CAD
and the Integration of Evolutionary Techniques.
Series: International Series on Computational Intelligence.
Gill, D.W., New Jersey, NJ, USA
2000. Approx 290 pp.
0-8493-2048-8
With the advent of Visual J++ 6.0, the Java programming
environment has changed substantially and makes most
Java references and manuals obsolete. Programmers, information
technologists, and computer scientists need a
text that addresses the visual development capability and Windows
orientation in addition to in-depth coverage of
the major concepts and techniques of programming. Java with
Visual J++ is just such a text. It offers a thorough
treatment of Java, event and object oriented programming, network
programming, and Java applications. Each
chapter contains clearly stated objectives, hands-on exercises,
review questions, a list of key words and
concepts, and a summary.
Keywords: java, visual basic
Contents: Introduction to Visual J++ Application Development.-
Java Language Preliminaries.- Event-Oriented
Programs.- Object- Oriented Programming.- Expressions and
Operators.- Control Structures.- Network
Programming.- Java Applications.
Lisboa, P.J.G., Liverpool John
Moores University, Liverpool, UK
Ifeachor, E.C., University of Plymouth, UK
Szczepaniak, P.S., Technical University of Lodz, Poland
(Eds.)
1999. Approx. 305 pp.
1-85233-005-8
This volume provides a state-of-the-art survey of artificial
neural network applications in biomedical diagnosis,
laboratory data analysis and related practical areas. It looks at
biomedical applications which involve customising
neural network technology to resolve specific difficulties with
data processing, and deals with applications relating
to particular aspects of clinical practice and laboratory or
medically-related analysis. Each chapter is
self-contained with regard to the technology used, covering
important technical points and implementation issues
like the design of user interfaces and hardware/software
platforms. Artificial Neural Networks in
Biomedicine will be of interest to computer scientists and neural
network practitioners who want to extend their
knowledge of issues relevant to biomedical applications,
developers of clinical computer systems, and medical
researchers looking for new methods and computational tools.
Contents: Introduction.- Tutorials: The Bayesian Paradigm: Second
Generation Neural Computing.- The Role
of the Artificial Neural Network in the Characterisation of
Complex System and the Prediction of Disease.-
Genetic Evolution of Neural Network Architectures.-
Computer-Aided Diagnosis: The Application of
PAPNET to Diagnostic Cytology.- ProstAsure Index - a Serum-Based
Composite Diagnostic Index for Early
Detection of Prostate Cancer.- Neurometric Assessment of Adequacy
of Intraoperative Anaesthetic.- Tumour
Identification from Nuclear Magnetic Resonance Spectra.-
Classifying Spinal Measurements Using a Radial Basis
Function Network.- GEORGIA: An Overview of a Neural Network
System for the Diagnosis of Lung Conditions.-
Patient Monitoring Using an Artificial Neural Network.- Benchmark
of Approaches to Sequential Diagnosis.-
Signal Processing: Independent Components Analysis.- Rest EEG
Hidden Dynamic as a Discriminant for Brain
Tumour Classification.- Artificial Neural Network Control on
Functional Electrical Stimulation (FES).- Assisted
Gait for Persons with Spinal Cord Injury.- The Application of
Neural Networks to Interpret Evoked Potential
Waveforms. Image Processing: Intelligent Decision Support Systems
for the Cytodiagnosis of Breast
Carcinoma.- A Neural-Based System for the Automatic
Classification and Follow-Up of Diabetic Retinopathies.-
Review of Neural Networks for Automated Image Analysis in
Cytogenics.- The Relevance of Features and
Primitives and Representation Structures for Multichannel Image
Processing.- Application of Neural Networks in
the Diagnosis of Pathological Speech.- Conclusion.
Series: Perspectives in Neural Computing.