Edited by: C. Y. Chang
Simon M. Sze, (Both of National Chiao Tung Univ., Taiwan)
ULSI Devices
One of the hottest topics in the area of semiconductor devices, ultra large scale integrated circuits (ULSI) are the subject of intense research in both academia and industry. This collection of expert contributions is compiled and edited by two of the foremost authorities on semiconductor device physics. They present cutting-edge research in the field, explaining the advantages of ULSI technology in providing low-power, low-voltage, high-speed systems on a single microchip.
Contents
Bipolar Transistor Fundamentals (E. Kaspter).
MOSFET Fundamentals (P. Wong).
Device Miniaturization and Simulation (B. Streetman & S. Banerjee).
SOI and 3D Structure (J. Colinge).
Device Reliability: The Hot Carrier Effect (B. Doyle).
DRAM and SRAM (S. Shichijo).
Nonvolatile Memory (J. Caywood).
CMOS Digital and Analog Building Block Circuits for Mixed-Signal Application (D. Pehlke & F. Chang).
Low-Power/Low-Voltageigh-Speed Operation (I. Chen & W. Liu).
System-on-Chip Concepts (M. Pelgrom).
Appendices.
Index.
ISBN: 0-471-24067-2
Hardcover
Projected Pub Date: Apr 2000
Copyright: 2000
Samprit Chatterjee (New York Univ.)
Ali S. Hadi (Cornell Univ.)
Bertram Price (Price Associates, Inc.)
Regression Analysis by Example, 3rd Ed.
Regression analysis provides a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Third Edition explains the principles underlying exploratory data analysis, emphasizing data analysis rather than statistical theory. This is not just another edition of the book; it is a major rewriting and reorganization of the previous edition. The new edition is expanded and updated to reflect recent advances in the field, offering in-depth treatment of diagnostic plots, time series regression, multicollinearity, and logistic regression.
Suitable for anyone with an understanding of elementary statistics, Regression Analysis by Example, Third Edition illustrates methods of regression analysis, with examples containing the types of irregularities commonly encountered in the real world. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. Each of the methods described can be carried out with most currently available statistical software packages.
Contents
imple Linear Regression.
Multiple Linear Regression.
Regression Diagnostics: Detection of Model Violations.
Qualitative Variables as Predictors.
Transformation of Variables.
Weighted Least Squares.
The Problem of Correlated Errors.
Analysis of Collinear Data.
Biased Estimation of Regression Coefficients.
Variable Selection Procedures.
Logistic Regression.
Appendix.
References.
Index.
Subject: Statistics / Regression /
Series Title: Wiley Series in Probability and Statistics: Texts and References Section
ISBN: 0-471-31946-5
Hardcover
Pages: 368
Published: Nov 1999
Copyright: 2000
Ding-Zhu Du
Ker-I Ko
Theory of Computational Complexity
This book offers complete, up-to-date coverage of computational complexity theory, an area that has grown dramatically over the past two decades due to technological advances and increased links with other disciplines. Written by two widely published authors of mathematics texts, the book promises to become the standard reference on computational complexity.
Contents
UNIFORM COMPLEXITY.
Models of Computation and Complexity Classes.
NP-Completeness.
The Polynomial-Time Hierarchy and Polynomial Space.
Structure of NP.
NONUNIFORM COMPLEXITY.
Decision Trees.
Circuit Complexity.
Polynomial-Time Isomorphism.
PROBABILISTIC COMPLEXITY.
Probabilistic Machines and Complexity Classes.
Complexity of Counting.
Interactive Proof Systems.
Probabilistically Checkable Proofs and NP-Hard Optimization Problems.
Bibliography.
Index.
Subject: Mathematics / Mathematics Special Topics /
Series Title: Wiley-Interscience Series in Discrete Mathematics
ISBN: 0-471-34506-7
Hardcover
Pages: 493
Published: Jan 2000
Copyright: 2000
edited by Ding-Zhu Du / University of Minnesota, Minneapolis, USA
J.M. Smith /Dept. of Industrial Engineering, University of Massachusetts, USA
J.H. Rubinstein /Mathematics Dept., Melbourne University, Australia
Advances in Steiner Trees
COMBINATORIAL OPTIMIZATION Volume 6
This book presents an up-to-date set of contributions by the most influential authors on the Steiner Tree problem. The authors address the latest concerns of Steiner Trees for their computational complexity, design of algorithms, performance guaranteed heuristics, computational experimentation, and range
of applications.
Audience: The book is intended for advanced undergraduates, graduates and research scientists in Combinational Optimization and Computer Science. It is divided into two sections: Part I includes papers on the general geometric Steiner Tree problem in the plane and higher dimensions; Part II includes
papers on the Steiner problem on graphs which has significant import to Steiner Tree applications.
Contents and Contributors
Preface. The Steiner Ratio of finite-dimensional Lp Spaces; J. Albrecht, D. Cieslik. Shortest Networks for One line and Two Points in Space; R. Booth, et al. Rectilinear Steiner Minimal Trees on Parallel Lines; M. Brazil, et al. Computing Shortest Networks with Fixed Topologies; T. Jiang, L. Wang. Steiner
Trees, Coordinate Systems, and NP-Hardness; J.F. Weng. Exact Algorithms for Plane Steiner Tree Problems: A Computational Study; D.M. Warme, et al. On Approximation of the Power-p and Bottleneck Steiner Trees; P. Bierman, A. Zelikovsky. Exact Steiner Trees in Graphs and Grid Graphs; S. Cheng.
Grade of Service Steiner Trees in Series-Parallel Networks; C. Colbourn, G. Xue. Preprocessing the Steiner Problem in Graphs; C. Duin. A Fully-Polynomial Approximation Scheme for the Euclidean Steiner
Augmentation Problem; J.C. Provan. Effective Local Search Techniques for the Steiner Tree Problem; A. Wade, V.J. Rayward-Smith. Modern Heuristic Search Methods for the Steiner Problem in Graphs; S. Voss.
Hardbound, ISBN 0-7923-6110-5
January 2000, 336 pp.
edited by Christodoulos A. Floudas Princeton University, NJ, USA
Panos M. Pardalos Dept. of Industrial & Systems Engineering, University of Florida, Gainesville, USA
Optimization in Computational Chemistry and Molecular Biology
Local and Global Approaches
NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 40
Optimization in Computational Chemistry and Molecular Biology: Local and Global Approaches covers recent developments in optimization techniques for addressing several computational chemistry and biology problems. A tantalizing problem that cuts across the fields of computational chemistry,
biology, medicine, engineering and applied mathematics is how proteins fold. Global and local optimization provide a systematic framework of conformational searches for the prediction of three-dimensional protein
structures that represent the global minimum free energy, as well as low-energy biomolecular conformations.
Each contribution in the book is essentially expository in nature, but of scholarly treatment. The topics covered include advances in local and global optimization approaches for molecular dynamics and modeling, distance geometry, protein folding, molecular structure refinement, protein and drug design, and molecular and peptide docking.
Audience: The book is addressed not only to researchers in mathematical programming, but to all scientists in various disciplines who use optimization methods in solving problems in computational chemistry and biology.
Contents and Contributors
Preface. Predicting Protein Tertiary Structure using a Global Optimization Algorithm with Smoothing; A. Azmi, et al. Methodology for Elucidating the Folding Dynamics of Peptides: Met-enkephalin Case Study; J.L. Klepeis, C.A. Floudas. Energy Landscape Projections of Molecular Potential Functions; A.T. Phillips, et al. Global Optimization and Sampling in the Context of Tertiary Structure Prediction: A Comparison of Two Algorithms; V.A. Eyrich, et al. Protein Folding Simulations by Monte Carlo Simulated Annealing and
Multicanonical Algorithm; Y. Okamoto. Thermodynamics of Protein Folding ・The Generalized-Ensemble Approach; U.H.E. Hansmann. An approach to detect the dominant folds of proteinlike heteropolymers from the statistics of a homopolymeric chain; E.D. Nelson, et al. Gene Sequences are Locally Optimized for Global mRNA Folding; W. Seffens, D. Digby. Structure Calculations of Symmetric Dimers using Molecular Dynamics/Simulated Annealing and NMR Restraints: The Case of the RIIa Subunit of Protein Kinase A; D. Morikis, et al. Structure Prediction of Binding States of MHC Class II Molecules based on the Crystal of HLA-DRB1 and Global Optimization; M.G. Ierapetritou, et al. A Coupled Scanning and Optimization
Scheme for Analyzing Molecular Interactions; J.C. Mitchell, et al. Improved Evolutionary Hybrids for Flexible Ligand Docking in AutoDock; W.E. Hart, et al. Electrostatic Optimization in Ligand Complementarity and Design; E. Kangas, B. Tidor. Exploring potential solvation sites of proteins by multistart local
minimization; S. Dennis, et al. On relative position of two biopolymer molecules minimizing the weighted sum of interatomic distances squared; A.B. Bogatyrev. Visualization of Chemical Databases Using the Singular Value Decomposition and Truncated-Newton Minimization; D. Xie, T. Schlick. Optimization of
Carbon and Silicon Cluster Geometry for Tersoff Potential using Differential Evolution; M.M. Ali, A. T?rn. D.C. Programming Approach for Large-Scale Molecular Optimization via the General Distance Geometry Problem; L.T.H. An, P.D. Tao.
Hardbound, ISBN 0-7923-6155-5
February 2000, 352 pp.
Vladimir Tsurkov :Computer Center, Russian Academy of Sciences, Moscow, Russia
Hierarchial Optimization and MathematicalPhysics
APPLIED OPTIMIZATION Volume 37
This book should be considered as an introduction to a special class of hierarchical systems of optimal control, where subsystems are described by partial differential equations of various types. Optimization is carried out by means of a two-level scheme, where the center optimizes coordination for the
upper level and subsystems find the optimal solutions for independent local problems. The main algorithm is a method of iterative aggregation. The coordinator solves the problem with macrovariables, whose number is less than the number of initial variables. On the lower level, we have the usual optimal control problems of mathematical physics, which are far simpler than the initial statements. Thus, we bridge the gap between two disciplines: optimization theory of large-scale systems and mathematical physics. The
first motivation was a special model of branch planning, where the final product obeys a precept assortment relation.
Audience: The monograph is addressed to specialists in operations research, optimization, optimal control, and mathematical physics.
Contents
Preface.
1. The Main Model and Constructions of the Decomposition Method.
2. Generalization of the Decomposition Approach to Mathematical
Programming and Classical Calculus of Variations.
3. Hierarchical Systems of Mathematical Physics.
4. Effectiveness of Decomposition.
5. Appendix. The Main Approaches in Hierarchical Optimization.
Index.
Hardbound, ISBN 0-7923-6175-X
January 2000, 320 pp.
Man Leung Wong /Lingnan University, Hong Kong
Kwong Sak Leung /The Chinese University of Hong Kong
Data Mining Using Grammar Based Genetic
Programming and Applications
GENETIC PROGRAMMING Volume 3
Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader,
including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced.
A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster
than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and
effortlessly.
Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases.
Contents
List of Figures. List of Tables. Preface. 1. Introduction. 2. An Overview of Data Mining. 3. An Overview on Evolutionary Algorithms. 4. Inductive Logic Programming. 5. The Logic Grammars Based Genetic Programming System (LOGENPRO). 6. Data Mining Applications Using LOGENPRO. 7. Applying
LOGENPRO for Rule Learning. 8. Medical Data Mining. 9. Conclusion and Future Work. Appendix A: The Rule Sets Discovered. Appendix B: The Grammar Used for the Fracture and Scoliosis Databases. References. Index.
Hardbound, ISBN 0-7923-7746-X
January 2000, 232 pp.