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1. ISO/IEC 8802-2:1998 ANSI/IEEE Std 802.2, 1998 edition (Incorporating ANSI/IEEE Stds 802.2c-1997, 802.2f-1997, and 802.2h-1997): IEEE International Standard for Information technology -- Telecommunications and information exchange between systems -- Local
出版情報: IEEE Electronic Library (IEL) Standards , IEEE, 1998
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2.

図書
図書
2. Dynamics : numerical explorations. 2nd, rev. and enl. ed
Helena E. Nusse, James A. Yorke
出版情報: New York : Springer, c1998  xvi, 608 p., [8] p. of plates ; 25 cm
シリーズ名: Applied mathematical sciences ; v. 101
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Preface
Getting the program running / 1：
The Dynamics program and hardware Smalldyn: a small version of Dynamics / 1.1：
Getting started with Dynamics Using the mouse Appendix: description of the interrupts / 1.2：
Questions / 1.3：
Samples of Dynamics: pictures you can make simply / 2：
Introduction Example / 2.1：
Plot a trajectory Example / 2-1a：
Draw a box Example / 2-1b：
Viewing the Parameter Menu Example / 2-1c：
Refresh the screen and continue plotting Example / 2-1d：
Clear the screen and continue plotting Example / 2-1e：
Single stepping through a trajectory Example / 2-1f：
Plot a cross at current position Example / 2-1g：
Draw axes and print picture Example / 2-1h：
Initializing Example / 2-1i：
Viewing the Y Vectors Example / 2-1j：
Find a fixed point Example / 2-1k：
Find a period 2 orbit Example / 2-1l：
Search for all periodic points of period 5 Example / 2-1m：
Change RHO Example / 2-1n：
Plotting permanent crosses Example / 2-1o：
Set storage vector y1 and initialize Example / 2-1p：
Change X Scale or Y Scale / 2-1q：
Complex pictures that are simple to make Example / 2.2：
Chaotic attractor Example / 2-2a：
Computing Lyapunov exponents Example / 2-2b：
Plotting trajectory versus time Example / 2-2c：
Graph of iterate of one dimensional map Example / 2-3a：
Cobweb plot of a trajectory Example / 2-3b：
The Henon attractor Example / 2-3c：
The first iterate of a quadrilateral Example / 2-5：
Plotting direction field and trajectories Example / 2-6：
Bifurcation diagram for the quadratic map Example / 2-7：
Bifurcation diagram with bubbles Example / 2-8：
All the Basins and Attractors Example / 2-9：
Metamorphoses in the basin of infinity Example / 2-10：
Search for all periodic points with period 10 Example / 2-11：
Search for all period 1 and period 2 points Example / 2-12：
Following orbits as a parameter is varied Example / 2-13：
The Mandelbrot set Example / 2-14：
3-Dimensional views on the Lorenz attractor Example / 2-15：
Unstable manifold of a fixed point Example / 2-17：
Stable and unstable manifolds Example / 2-18：
Plotting a Saddle Straddle Trajectory Example / 2-19a：
The unstable manifold of a fixed point Example / 2-19b：
The stable manifold of a fixed point Example / 2-19c：
Saddle Straddle Trajectory, and manifolds Example / 2-19d：
The basin of attraction of infinity Example / 2-20：
A trajectory on a basin boundary Example / 2-21：
A BST trajectory for the Tinkerbell map Example / 2-22：
Lyapunov exponent bifurcation diagram Example / 2-23：
Chaotic parameters Example / 2-24：
Box-counting dimension of an attractor Example / 2-25：
Zooming in on the Tinkerbell attractor Example / 2-26：
Period plot in the Mandelbrot set Appendix Commands for plotting a graph Commands from the Numerical / 2-27：
Explorations Menu Plotting multiple trajectories simultaneously
Screen utilities / 3：
Basic screen features (Screen Menu SM) / 3.1：
Commands for clearing the screen Commands for controlling the screen Level of Text output
Writing on pictures
The arrow keys and boxes (BoX Menu, BXM) / 3.2：
Initializing trajectories, plotting crosses, drawing circles and their iterates (Kruis Menu KM) / 3.3：
Drawing axes (AXes Menu AXM) / 3.4：
Windows and rescaling (Window Menu WM) Detailed view on the structure of an attractor / 3.5：
Zooming in or zooming out (ZOOm Menu ZOOM) / 3.6：
Setting colors (Color Menu CM and Color Table Menu CTM) Color screens Core copy of the picture / 3.7：
Color planes Commands for erasing colors
Utilities / 4：
Setting parameters (Parameter Menu PM) / 4.1：
Setting and replacing a vector (Vector Menu VM) Y Vectors "Own" and the coordinates of yÃââÇ ÃâÅô / 4.2：
Setting step size (Differential Equation Menu DEM) / 4.3：
Saving pictures and data (Disk Menu DM) Creating a batch file of commands Commands for reading disk files / 4.4：
Setting the size of the core (Size of Core Menu SCM) / 4.5：
Printing pictures (PriNter Menu PNM) Commands for specifying printer / 4.6：
Encapsulated PostScript Commands for printer options
Text to printer Printing color pictures
Printing pictures with any p
Preface
Getting the program running / 1：
The Dynamics program and hardware Smalldyn: a small version of Dynamics / 1.1：
3.

図書
図書
3. Graphical models for machine learning and digital communication
Brendan J. Frey
出版情報: Cambridge, Mass : The MIT Press, c1998  xiii, 195 p. ; 24 cm
シリーズ名: Adaptive computation and machine learning
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Preface
Introduction / 1：
A probabilistic perspective / 1.1：
Pattern classification / 1.1.1：
Unsupervised learning / 1.1.2：
Data compression / 1.1.3：
Channel coding / 1.1.4：
Probabilistic inference / 1.1.5：
Graphical models: Factor graphs, Markov random fields and Bayesian belief networks / 1.2：
Factor graphs / 1.2.1：
Markov random fields / 1.2.2：
Bayesian networks / 1.2.3：
Ancestral simulation in Bayesian networks / 1.2.4：
Dependency separation in Bayesian networks / 1.2.5：
Example 1: Recursive convolutional codes and turbocodes / 1.2.6：
Parameterized Bayesian networks / 1.2.7：
Example 2: The bars problem / 1.2.8：
Organization of this book / 1.3：
Probabilistic Inference in Graphical Models / 2：
Exact inference using probability propagation (the sum-product algorithm) / 2.1：
The generalized forward-backward algorithm / 2.1.1：
The burglar alarm problem / 2.1.2：
Probability propagation (the sum-product algorithm) / 2.1.3：
Grouping and duplicating variables in Bayesian networks / 2.1.4：
Exact inference in multiply-connected networks is NP-hard / 2.1.5：
Monte Carlo inference: Gibbs sampling and slice sampling / 2.2：
Inference by ancestral simulation in Bayesian networks / 2.2.1：
Gibbs sampling / 2.2.2：
Gibbs sampling for the burglar alarm problem / 2.2.3：
Slice sampling for continuous variables / 2.2.4：
Variational inference / 2.3：
Choosing the distance measure / 2.3.1：
Choosing the form of the variational distribution / 2.3.2：
Variational inference for the burglar alarm problem / 2.3.3：
Bounds and extended representations / 2.3.4：
Helmholtz machines / 2.4：
Factorial recognition networks / 2.4.1：
Nonfactorial recognition networks / 2.4.2：
The stochastic Helmholtz machine / 2.4.3：
A recognition network that solves the burglar alarm problem / 2.4.4：
Pattern Classification / 3：
Bayesian networks for pattern classification / 3.1：
Autoregressive networks / 3.2：
The logistic autoregressive network / 3.2.1：
MAP estimation for autoregressive networks / 3.2.2：
Scaled priors in logistic autoregressive networks / 3.2.3：
Ensembles of autoregressive networks / 3.2.4：
Estimating latent variable models using the EM algorithm / 3.3：
The expectation maximization (EM) algorithm / 3.3.1：
The generalized expectation maximization algorithm / 3.3.2：
Multiple-cause networks / 3.4：
Estimation by iterative probability propagation / 3.4.1：
Estimation by Gibbs sampling / 3.4.2：
Generalized EM using variational inference / 3.4.3：
Hierarchical networks / 3.4.4：
Ensembles of networks / 3.4.6：
Classification of handwritten digits / 3.5：
Logistic autoregressive classifiers: LARC-1,ELARC-1 / 3.5.1：
The Gibbs machine: GM-1 / 3.5.2：
The mean field Bayesian network: MFBN-1 / 3.5.3：
Stochastic Helmholtz machines: SHM-1, SHM-2, ESHM-1 / 3.5.4：
The classification and regression tree: CART-1 / 3.5.5：
The naive Bayes classifier: NBAYESC-1 / 3.5.6：
The k-nearest neighbor classifier: KNN-CLASS-1 / 3.5.7：
Results / 3.5.8：
Unsupervised Learning / 4：
Extracting structure from images using the wake-sleep algorithm / 4.1：
Wake-sleep parameter estimation / 4.1.1：
Automatic clean-up of noisy images / 4.1.2：
Wake-sleep estimation without positive parameter constraints / 4.1.3：
How hard is the bars problem? / 4.1.4：
Simultaneous extraction of continuous and categorical structure / 4.2：
Continuous sigmoidal Bayesian networks / 4.2.1：
Inference using slice sampling / 4.2.2：
Parameter estimation using slice sampling / 4.2.3：
Nonlinear Gaussian Bayesian networks (NLGBNs) / 4.3：
The model / 4.3.1：
Variational inference and learning / 4.3.2：
Results on the continuous stereo disparity problem / 4.3.3：
Pattern classification using the variational bound / 4.3.4：
Data Compression / 5：
Fast compression with Bayesian networks / 5.1：
Communicating extra information through the codeword choice / 5.2：
Example: A simple mixture model / 5.2.1：
The optimal bits-back coding rate / 5.2.2：
Suboptimal bits-back coding / 5.2.3：
Relationship to maximum likelihood estimation / 5.3：
The "bits-back" coding algorithm / 5.4：
The bits-back coding algorithm with feedback / 5.4.1：
Queue drought in feedback encoders / 5.4.2：
Experimental results / 5.5：
Bits-back coding with a multiple-cause model / 5.5.1：
Compressing handwritten digits / 5.5.2：
Integrating over model parameters using bits-back coding / 5.6：
Channel Coding / 6：
Review: Simplifying the playing field / 6.1：
Additive white Gaussian noise (AWGN) / 6.1.1：
Capacity of an AWGN channel / 6.1.2：
Signal constellations / 6.1.3：
Linear binary codes can get us to capacity / 6.1.4：
Bit error rate (BER) and signal-to-noise ratio (Eb/N0) / 6.1.5：
Capacity of an AWGN channel with binary signalling / 6.1.6：
Achievable BER for an AWGN channel with binary signalling / 6.1.7：
Graphical models for error correction: Turbocodes, low-density parity-check codes and more / 6.2：
Hamming codes / 6.2.1：
Convolutional codes / 6.2.2：
Decoding convolutional codes by probability propagation / 6.2.3：
Turbocodes: parallel concatenated convolutional codes / 6.2.4：
Serially-concatenated convolutional codes, low-density parity-check codes, and product codes / 6.2.5：
"A code by any other network would not decode as sweetly" / 6.3：
Trellis-constrained codes (TCCs) / 6.4：
Homogeneous trellis-constrained codes / 6.4.1：
Ring-connected trellis-constrained codes / 6.4.2：
Decoding complexity of iterative decoders / 6.5：
Parallel iterative decoding / 6.6：
Concurrent turbodecoding / 6.6.1：
Speeding up iterative decoding by detecting variables early / 6.6.2：
Early detection / 6.7.1：
Early detection for turbocodes: Trellis splicing / 6.7.2：
Future Research Directions / 6.7.3：
Modularity and abstraction / 7.1：
Faster inference and learning / 7.2：
Scaling up to the brain / 7.3：
Improving model structures / 7.4：
Iterative decoding / 7.5：
Iterative decoding in the real world / 7.6：
Unification / 7.7：
References
Index
Preface
Introduction / 1：
A probabilistic perspective / 1.1：
4.

図書
図書
4. Kinematics of human motion
Vladimir M. Zatsiorsky
出版情報: Champaign, Ill. : Human Kinetics, c1998  xi, 419 p. ; 24 cm
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Preface
Acknowledgments
Notations and Conventions
Kinematic Geometry of Human Motion: Body Position and Displacement / Chapter 1：
Defining body location / 1.1：
The coordinate method / 1.1.1：
Cartesian versus oblique coordinates / 1.1.2：
Defining body orientation / 1.2：
Fixation of a local system with a rigid body / 1.2.1：
Fixation of a somatic system with a human body / 1.2.2：
Indirect method of defining body orientation / 1.2.3：
What is ""body rotation""? / 1.2.4：
Describing position and displacement / 1.2.5：
Advantages and disadvantages of the various angular conventions / 1.2.6：
Determining body position from experimental recordings / 1.2.7：
Three-dimensional representation of human movement: Eye movement / 1.3：
Eye orientation / 1.3.1：
Motions actually made by the human eye (Donders' law and Listing's law) / 1.3.2：
Rotation surfaces. The laws obeyed by the pointing head and arm movements / 1.3.3：
Summary / 1.4：
Questions for Review / 1.5：
Bibliography / 1.6：
Kinematic Geometry of Human Motion: Body Posture / Chapter 2：
Joint configuration / 2.1：
Technical and somatic systems / 2.1.1：
The clinical reference system / 2.1.2：
Globographic representation / 2.1.3：
Segment coordinate systems / 2.1.4：
Joint rotation convention / 2.1.5：
Kinematic chains / 2.2：
Degrees of freedom. Mobility of kinematic chains / 2.2.1：
Open kinematic chains: The end-effector mobility / 2.2.2：
Kinematics models and mobility of the human body / 2.2.3：
Constraints on human movements / 2.2.4：
Position analysis of kinematic chains / 2.2.5：
Biological solutions to kinematic problems / 2.3：
Internal representation of the immediate extrapersonal space / 2.3.1：
Internal representation of the body posture / 2.3.2：
Differential Kinematics of Human Movement / 2.4：
Velocity of a kinematic chain / 3.1：
Planar movement / 3.1.1：
Motion in three dimensions / 3.1.2：
Acceleration of a kinematic chain / 3.2：
Acceleration of a planar two-link chain / 3.2.1：
Acceleration of a two-link chain in three dimensions / 3.2.2：
Acceleration of a multi-link chain / 3.2.3：
Jerk and snap / 3.2.4：
Biological solutions to the problems of differential kinematics: Control of movement velocity / 3.3：
Control of approach: The tau hypothesis / 3.3.1：
Control of velocity in reaching movement / 3.3.2：
Joint Geometry and Joint Kinematics / 3.4：
Intrajoint kinematics / 4.1：
Articular surfaces and types of joints / 4.1.1：
Movement of articular surfaces / 4.1.2：
Geometry and algebra of intra-articular motion / 4.1.3：
Ligaments and joint motion: A joint as a mechanical linkage / 4.1.4：
Centers and axes of rotation / 4.2：
Planar joint movement / 4.2.1：
Three-dimensional joint movement / 4.2.2：
Kinematics of Individual Joints / 4.3：
Nominal joint axes / 5.1：
The joints of the foot / 5.2：
Metatarsophalangeal joints. The foot as a two-speed construction / 5.2.1：
The joints of the midfoot / 5.2.2：
The ankle joint complex / 5.3：
The talocrural joint / 5.3.1：
The subtalar joint / 5.3.2：
The knee / 5.4：
The tibiofemoral joint / 5.4.1：
The patellofemoral joint / 5.4.2：
The hip joint and the pelvic girdle / 5.5：
The spine / 5.6：
Movement in synarthroses / 5.6.1：
The lumbar and thoracic spine / 5.6.2：
The cervical region: Head and neck movement / 5.6.3：
The rib cage / 5.6.4：
The shoulder complex / 5.7：
Individual joints / 5.7.1：
Movement of the shoulder complex: The scapulohumeral rhythm / 5.7.2：
The elbow complex / 5.8：
Flexion and extension / 5.8.1：
Supination and pronation / 5.8.2：
The wrist / 5.9：
The joints of the hand / 5.10：
The joints of the thumb / 5.10.1：
The joints of the fingers / 5.10.2：
The temporomandibular joint / 5.11：
Glossary / 5.12：
Index
About the Author"
Preface
Acknowledgments
Notations and Conventions
5.

図書
図書
5. Parallel database techniques
[edited by] Mahdi Abdelguerfi, Kam-Fai Wong
出版情報: Los Alamitos, CA : IEEE Computer Society, c1998  vii, 222 p. ; 26 cm
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Introduction / 1：
Background / 1.1：
Parallel Database Systems / 1.2：
Computation Model / 1.2.1：
Engineering Model / 1.2.2：
About this Manuscript / 1.3：
Bibliography
Request Manager / I：
Designing an Optimizer for Parallel Relational Systems / 2：
Overall Design Issues / 2.1：
Design a Simple Parallel Execution Model / 2.2.1：
The Two-Phase Approach / 2.2.2：
Parallelizing is Adding Information! / 2.2.3：
Two-Phase versus Parallel Approaches / 2.2.4：
Parallelization / 2.3：
Kinds of Parallelism / 2.3.1：
Specifying Parallel Execution / 2.3.2：
Search Space / 2.4：
Slicing Hash Join Trees / 2.4.1：
Search Space Size / 2.4.2：
Heuristics / 2.4.3：
The Two-Phase Heuristics / 2.4.4：
Cost Model / 2.5：
Exceptions to the Principle of Optimality / 2.5.1：
Resources / 2.5.2：
Skew and Size Model / 2.5.3：
The Cost Function / 2.5.4：
Search Strategies / 2.6：
Deterministic Search Strategies / 2.6.1：
Randomized Strategies / 2.6.2：
Conclusion / 2.7：
New Approaches to Parallel Join Utilizing Page Connectivity Information / 3：
The Environment and a Motivating Example / 3.1：
The Methodology / 3.3：
Definition of Parameters / 3.3.1：
The Balancing Algorithm / 3.3.2：
Schedules for Reading Join Components and Data Pages / 3.3.3：
Performance Analysis / 3.4：
The Evaluation Method / 3.4.1：
Evaluation Results / 3.4.2：
Concluding Remarks and Future Work / 3.5：
A Performance Evaluation Tool for Parallel Database Systems / 4：
Performance Evaluation Methods / 4.1：
Analytical Modeling / 4.2.1：
Benchmarks / 4.2.2：
Observations / 4.2.3：
The Software Testpilot / 4.3：
The Experiment Specification / 4.3.1：
The Performance Assessment Cycle / 4.3.2：
The System Interface / 4.3.3：
The Software Testpilot and Oracle/Ncube / 4.4：
Database System Performance Assessment / 4.4.1：
The Oracle/Ncube Interface / 4.4.2：
Preliminary Results / 4.5：
Load Placement in Distributed High-Performance Database Systems / 4.6：
Investigated System / 5.1：
System Architecture / 5.2.1：
Load Scenarios / 5.2.2：
Trace Analysis / 5.2.3：
Load Setup / 5.2.4：
Load Placement Strategies Investigated / 5.3：
Scheduling Strategies for Transactions / 5.4：
Simulation Results / 5.5：
Influence of Scheduling / 5.5.1：
Evaluation of the Load Placement Strategies / 5.5.2：
Lessons Learned / 5.5.3：
Decision Parameters Used / 5.5.4：
Conclusion and Open Issues / 5.6：
Parallel Machine Architecture / II：
Modeling Recovery in Client-Server Database Systems / 6：
Uniprocessor Recovery and Formal Approach to Modeling Recovery / 6.1：
Basic Formal Concepts / 6.2.1：
Logging Mechanisms / 6.2.2：
Runtime Policies for Ensuring Correctness / 6.2.3：
Data Structures Maintained for Efficient Recovery / 6.2.4：
Restart Recovery--The ARIES Approach / 6.2.5：
LSN Sequencing Techniques for Multinode Systems / 6.3：
Recovery in Client-Server Database Systems / 6.4：
Client-Server EXODUS (ESM-CS) / 6.4.1：
Client-Server ARIES (ARIES/CSA) / 6.4.2：
Shared Nothing Clients with Disks (CD) / 6.4.3：
Summary of Recovery Approaches in Client-Server Architectures / 6.4.4：
Parallel Strategies for a Petabyte Multimedia Database Computer / 6.5：
Multimedia Data Warehouse, Databases, and Applications / 7.1：
Three Waves of Multimedia Database Development / 7.2.1：
National Medical Practice Knowledge Bank Application / 7.2.2：
Massively Parallel Architecture, Infrastructure, and Technology / 7.3：
Parallelism / 7.3.1：
Teradata-MM Architecture, Framework, and New Concepts / 7.4：
Teradata-MM Architecture / 7.4.1：
Key New Concepts / 7.4.2：
SQL3 / 7.4.3：
Federated Coordinator / 7.4.4：
Teradata Multimedia Object Server / 7.4.5：
Parallel UDF Execution Analysis / 7.5：
UDF Optimizations / 7.5.1：
PRAGMA Facility / 7.5.2：
UDF Value Persistence Facility / 7.5.3：
Spatial Indices for Content-Based Querying / 7.5.4：
The MEDUSA Project / 7.6：
Indexing and Data Partitioning / 8.1：
Standard Systems / 8.2.1：
Grid Files / 8.2.2：
Dynamic Load Balancing / 8.3：
Data Access Frequency / 8.3.1：
Data Distribution / 8.3.2：
Query Partitioning / 8.3.3：
The MEDUSA Architecture / 8.4：
Software / 8.4.2：
Grid File Implementation / 8.4.3：
Load Balancing Strategy / 8.4.4：
MEDUSA Performance Results / 8.5：
Test Configuration / 8.5.1：
Transaction Throughput / 8.5.2：
Speedup / 8.5.3：
Load Balancing Test Results / 8.5.4：
Conclusions / 8.6：
Partitioned Data Store / III：
System Software of the Super Database Computer SDC-II / 9：
Architectural Overview of the SDC-II / 9.1：
Design and Organization of the SDC-II System Software / 9.3：
Parallel Execution Model / 9.3.1：
I/O Model and Buffer Management Strategy for Bulk Data Transfer / 9.3.2：
Process Model and Efficient Flow Control Mechanism / 9.3.3：
Structure of the System Software Components / 9.3.4：
Evaluation of the SDC-II System / 9.4：
Details of a Sample Query Processing / 9.4.1：
Comparison with Commercial Systems / 9.4.2：
Data Placement in Parallel Database Systems / 9.5：
Overview of Data Placement Strategies / 10.1：
Declustering and Redistribution / 10.2.1：
Placement / 10.2.2：
Effects of Data Placement / 10.3：
STEADY and TPC-C / 10.3.1：
Dependence on Number of Processing Elements / 10.3.2：
Dependence on Database Size / 10.3.3：
Contributors / 10.4：
Introduction / 1：
Background / 1.1：
Parallel Database Systems / 1.2：
6.

図書
図書
6. The software project manager's handbook : principles that work at work
Dwayne Phillips
出版情報: Los Alamitos, Calif. : IEEE Computer Society, c1998  xvi, 387 p. ; 26 cm
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Preface
Elements of Effective Software Management / Part 1：
What Makes a Good Software Manager? / Chapter 1：
People perspective / 1.1：
Business perspective / 1.2：
Process perspective / 1.3：
Successful process techniques / 1.3.1：
Best practices / 1.3.2：
Management "secrets" / 1.3.3：
Key thoughts in this chapter / 1.4：
References
Four Basics that Work / Chapter 2：
People, process, and product / 2.1：
People / 2.1.1：
Process / 2.1.2：
Product / 2.1.3：
Balancing the 3Ps / 2.1.4：
Visibility / 2.2：
Basic visibility techniques / 2.2.1：
Using the techniques / 2.2.2：
Configuration management / 2.3：
The CM plan / 2.3.1：
Basic baselines / 2.3.2：
Activities / 2.3.3：
CM people / 2.3.4：
CM sketch / 2.3.5：
Standards / 2.4：
What Doesn't Work and Why / 2.5：
When the 3Ps are out of balance / 3.1：
When there's not enough visibility / 3.2：
When configuration management is missing or abused / 3.3：
When standards are dismissed / 3.4：
Managing a Project Day by Day / 3.5：
Balancing the 3Ps to create a good environment / 4.1：
Emotional safety / 4.1.1：
Emphasis on team empowerment / 4.1.2：
High degree of personal interaction / 4.1.3：
Good balance of work and rest / 4.1.4：
Structure that promotes success / 4.1.5：
Visibility: Project control in a simple equation / 4.2：
Collecting status / 4.2.1：
Collection guidelines / 4.2.2：
Making status visible and undistorted / 4.2.3：
Analyzing the situation / 4.2.4：
Taking action / 4.2.5：
Making and communicating decisions / 4.2.6：
Making a decision visible / 4.2.7：
Keeping the environment good / 4.2.8：
Managing an external supplier / 4.2.9：
CM: Managing baselines with milestones / 4.3：
Looking to standards for help / 4.4：
The Development Life-Cycle: Early Stages / 4.5：
Requirements / Chapter 5：
Balancing the 3Ps: Requirements gathering and analysis / 5.1：
Selecting the requirements engineer / 5.1.1：
Interviewing customers / 5.1.2：
Conducting group meetings / 5.1.3：
Diffusing tense situations / 5.1.4：
Evolving requirements / 5.1.5：
Requirements vs. design / 5.1.6：
Visibility: Making requirements known / 5.2：
An overview of techniques / 5.2.1：
Joint Application Development / 5.2.2：
Design by Walking Around / 5.2.3：
System Storyboarding Technique / 5.2.4：
Concept of operations / 5.2.5：
Mind maps / 5.2.6：
Gilb charts / 5.2.7：
Method 315 / 5.2.8：
Rapid prototyping / 5.2.9：
Software diagrams / 5.2.10：
The software requirements specification / 5.2.11：
Database support / 5.2.12：
Using CM / 5.3：
Using standards / 5.4：
Planning / 5.5：
Elements of a good plan / 6.1：
Balancing the 3Ps: Selecting the process / 6.2：
Prototyping / 6.2.1：
Rapid application development / 6.2.2：
Microsoft process / 6.2.3：
Spiral process / 6.2.4：
Process improvement mechanisms / 6.2.5：
Making the project visible: Planning techniques / 6.3：
Project context / 6.3.1：
Creating a task network / 6.3.2：
Cards on the wall planning / 6.3.3：
Making the project visible: Estimating techniques / 6.4：
Rayleigh model / 6.4.1：
PSP's Probe / 6.4.2：
A technique for simple estimation / 6.4.3：
Judging an estimate / 6.4.4：
Tailoring techniques to the process model / 6.4.5：
All-in-one military and commercial standards / 6.5：
Documenting the plan / 6.6.2：
Risk Management / 6.7：
A task overview / 7.1：
Balancing the 3Ps: Uncertainty and choice / 7.2：
Risk identification / 7.2.1：
Risk planning / 7.2.2：
Risk control / 7.2.3：
Risk monitoring / 7.2.4：
Risk directing and staffing / 7.2.5：
Making risk visible / 7.3：
Risk estimating / 7.3.1：
Risk evaluation / 7.3.2：
Risk analysis products / 7.3.3：
The Development Life-Cycle: Middle to Late Stages / 7.4：
Design / Chapter 8：
The challenges of the 3Ps / 8.1：
Managing creativity / 8.1.1：
Reducing design frustration / 8.1.2：
Evaluating and selecting from design alternatives / 8.1.3：
Visibility--Expressing the design / 8.2：
Words / 8.2.1：
Pictures / 8.2.2：
Configuration control boards / 8.3：
Design documents / 8.3.2：
Tracing requirements / 8.3.3：
Standards: Writing the SDD / 8.4：
Contents / 8.4.1：
Organization / 8.4.2：
Integration and Testing / 8.5：
Some I&T myths / 9.1：
Managing the 3Ps: People / 9.2：
Managing the 3Ps: Process / 9.3：
Common testing problems / 9.3.1：
IDEA / 9.3.2：
Verification and validation / 9.3.3：
Visibility: Testing techniques and details / 9.4：
Elements of effective testing / 9.4.1：
Black box testing / 9.4.2：
White box testing / 9.4.3：
Combining white box and black box testing / 9.4.4：
Integration testing / 9.4.5：
Acceptance testing / 9.4.6：
Regression testing / 9.4.7：
Cleanroom testing / 9.4.8：
How testing relates to other activities / 9.5：
Controlling test artifacts / 9.5.2：
Using the requirements traceability matrix / 9.5.3：
Standards: Documenting the test plan / 9.6：
Software Maintenance / 9.7：
What is maintenance? / 10.1：
Maintenance or development? / 10.1.1：
Maintenance activities / 10.1.2：
Why use configuration management? / 10.1.3：
Why is it so expensive and difficult? / 10.1.4：
Balancing the 3Ps: Managing the maintainers / 10.2：
Balancing the 3Ps: Managing the process / 10.3：
Balancing the 3Ps: Making the most of the product / 10.4：
Visibility: Understanding the maintenance stages / 10.5：
Identification and classification / 10.5.1：
Analysis / 10.5.2：
Implementation / 10.5.3：
System test / 10.5.5：
Acceptance test / 10.5.6：
Delivery / 10.5.7：
Keeping baselines straight / 10.6：
Managing releases / 10.6.2：
Pacing the process / 10.6.3：
Applying the Principles / 10.7：
Cookbook / Chapter 11：
Essentials / 11.1：
Use journals and decision records / 11.1.1：
Perform all CM activities / 11.1.2：
Manage day by day / 11.1.3：
Use standards / 11.1.4：
Conduct post-mortems / 11.1.5：
OPT: A waterfall project / 11.2：
Context / 11.2.1：
Project details / 11.2.2：
System upgrade: An evolutionary project / 11.3：
CTRan: A spiral project / 11.3.1：
Risks / 11.4.1：
Quadrants of the spiral / 11.4.3：
Cycles of the spiral / 11.4.5：
Appendices / 11.5：
Documents for the OPT Project / Appendix A：
OPT Executive Sponsor Memorandum
OPT Project Context Document
OPT Configuration Management Plan
OPT Concept of Operations
OPT Software Requirements Specification
OPT Software Project Management Plan
OPT Software Design Description
Configuration Management / Appendix B：
Will the Real CM please stand up? / B.1：
The main ingredients / B.2：
Baselines / B.3：
Basics / B.3.1：
Applying baselines in a waterfall project / B.3.2：
Applying baselines in a non-waterfall project / B.3.3：
Documenting baselines / B.3.4：
Baseline contents / B.3.5：
Interface control documents / B.3.6：
CM Activities / B.4：
Identification / B.4.1：
Control / B.4.2：
Auditing / B.4.3：
Status accounting / B.4.4：
CM staff / B.5：
Project manager / B.5.3：
CM plan / B.6：
A CM sketch / B.7：
Summary / B.8：
Structured Analysis and Design / Appendix C：
Structured analysis / C.1：
Environmental model / C.1.2：
Preliminary behavioral model / C.1.3：
Final behavioral model / C.1.4：
Finished essential model / C.1.5：
Structured design / C.2：
User implementation model / C.2.1：
Systems implementation model / C.2.2：
Program implementation model / C.2.3：
Annotated Bibliography / Appendix D：
Index
About the Author
Preface
Elements of Effective Software Management / Part 1：
What Makes a Good Software Manager? / Chapter 1：
7.

図書
図書
7. Behavior-based robotics
Ronald C. Arkin
出版情報: Cambridge, Mass. ; London, England : MIT Press, c1998  xiv, 491 p. ; 24 cm
シリーズ名: Intelligent robotics and autonomous agents
所蔵情報: loading…
目次情報: 続きを見る
Foreword
Preface
Whence Behavior? / Chapter 1：
Toward Intelligent Robots / 1.1：
Precursors / 1.2：
Cybernetics / 1.2.1：
Artificial Intelligence / 1.2.2：
Robotics / 1.2.3：
The Spectrum Of Robot Control / 1.3：
Deliberative/Hierarchical Control / 1.3.1：
Reactive Systems / 1.3.2：
Related Issues / 1.4：
What's Ahead / 1.5：
Animal Behavior / Chapter 2：
What Does Animal Behavior Offer Robotics? / 2.1：
Neuroscientific Basis For Behavior / 2.2：
Neural Circuity / 2.2.1：
Brain Structure and Function / 2.2.2：
Abstract Neuroscientific Models / 2.2.3：
Schema-Theoretic Methods / 2.2.3.1：
Neural Networks / 2.2.3.2：
Psychological Basis For Behavior / 2.3：
Ethological Basis For Behavior / 2.4：
Representative Examples Of Bio-Robots / 2.5：
Ant Chemotaxis / 2.5.1：
Fly Vision / 2.5.2：
Cockroach Locomotion / 2.5.3：
Primate Brachiation / 2.5.4：
Robotic Honeybee / 2.5.5：
Chapter Summary / 2.6：
Robot Behavior / Chapter 3：
What Are Robotic Behaviors? / 3.1：
A Navigational Example / 3.1.1：
Basis for Robotic Behavior / 3.1.3：
Expression Of Behaviors / 3.2：
Stimulus-Response Diagrams / 3.2.1：
Functional Notation / 3.2.2：
Finite State Acceptor Diagrams / 3.2.3：
Formal Methods / 3.2.4：
RS / 3.2.4.1：
Situated Automata / 3.2.4.2：
Behavioral Encoding / 3.3：
Discrete Encoding / 3.3.1：
Continuous Functional Encoding / 3.3.2：
Assembling Behaviors / 3.4：
Emergent Behavior / 3.4.1：
Notation / 3.4.2：
Behavioral Coordination / 3.4.3：
Competitive Methods / 3.4.3.1：
Cooperative Methods / 3.4.3.2：
Behavioral Assemblages / 3.4.4：
Behavior-Based Architectures / 3.5：
What Is A Robotic Architecture? / 4.1：
Definitions / 4.1.1：
Computability / 4.1.2：
Evaluation Criteria / 4.1.3：
Organizing Principles / 4.1.4：
A Foraging Example / 4.2：
Subsumption Architecture / 4.3：
Behaviors in Subsumption / 4.3.1：
Coordination in Subsumption / 4.3.2：
Design in Subsumption-Based Reactive Systems / 4.3.3：
Foraging Example / 4.3.4：
Evaluation / 4.3.5：
Subsumption Robots / 4.3.6：
Motor Schemas / 4.4：
Schema-Based Behaviors / 4.4.1：
Schema-Based Coordination / 4.4.2：
Design in Motor Schema-Based Systems / 4.4.3：
Schema-Based Robots / 4.4.4：
Other Architectures / 4.5：
Circuit Architecture / 4.5.1：
Colony Architecture / 4.5.3：
Animate Agent Architecture / 4.5.4：
DAMN / 4.5.5：
Skill Network Architecture / 4.5.6：
Other Efforts / 4.5.7：
Architectural Design Issues / 4.6：
Representational Issues for Behavioral Systems / 4.7：
Representational Knowledge / 5.1：
What Is Knowledge? / 5.1.1：
Characteristics of Knowledge / 5.1.2：
Representational Knowledge For Behavior-Based Systems / 5.2：
Short-Term Behavioral Memory / 5.2.1：
Long-Term Memory Maps / 5.2.2：
Sensor-Derived Cognitive Maps / 5.2.2.1：
A Priori Map-Derived Representations / 5.2.2.2：
Perceptual Representations / 5.3：
Hybrid Deliberative/Reactive Architectures / 5.4：
Why Hybridize? / 6.1：
Biological Evidence In Support Of Hybrid Systems / 6.2：
Traditional Deliberative Planners / 6.3：
Deliberation: To Plan Or Not To Plan? / 6.4：
Layering / 6.5：
Representative Hybrid Architectures / 6.6：
AuRa / 6.6.1：
Atlantis / 6.6.2：
Planner-Reactor Architecture / 6.6.3：
The Procedural Reasoning System / 6.6.4：
Other Hybrid Architectures / 6.6.5：
Perceptual Basis for Behavior-Based Control / 6.7：
A Break From Tradition / 7.1：
What Does Biology Say? / 7.2：
The Nature of Perceptual Stimuli / 7.2.1：
Neuroscientific Evidence / 7.2.2：
Psychological Insights / 7.2.3：
Affordances / 7.2.3.1：
A Modified Action-Perception Cycle / 7.2.3.2：
Perception as Communication-An Ethological Stance / 7.2.4：
A Brief Survey Of Robotic Sensors / 7.3：
Dead Reckoning / 7.3.1：
Ultrasound / 7.3.2：
Computer Vision / 7.3.3：
Laser Scanners / 7.3.4：
Modular Perception / 7.4：
Perceptual Schemas / 7.4.1：
Visual Routines / 7.4.2：
Perceptual Classes / 7.4.3：
Lightweight Vision / 7.4.4：
Action And Perception / 7.5：
Action-Oriented Perception / 7.5.1：
Active Perception / 7.5.2：
The Role of Attention in Human Visual Processing / 7.5.5.1：
Hardware Methods for Focus of Attention / 7.5.5.2：
Knowledge-Based Focus-of-Attention Methods / 7.5.5.3：
Perceptual Sequencing / 7.5.6：
Sensor Fusion for Behavior-Based Systems / 7.5.7：
Representative Examples Of Behavior-Based Perception / 7.6：
Road Following / 7.6.1：
Visual Tracking / 7.6.2：
Adaptive Behavior / 7.7：
Why Should Robots Learn? / 8.1：
Opportunities For Learning In Behavior-Based Robotics / 8.2：
Reinforcement Learning / 8.3：
Learning to Walk / 8.3.1：
The Learning Algorithm / 8.3.1.1：
Robotic Results / 8.3.1.2：
Learning to Push / 8.3.2：
Learning to Shoot / 8.3.2.1：
Learning In Neural Networks / 8.3.3.1：
Classical Conditioning / 8.4.1：
Adaptive Heuristic Critic Learning / 8.4.2：
Learning New Behaviors Using an Associative Memory / 8.4.3：
Genetic Algorithms / 8.5：
What Are Genetic Algorithms? / 8.5.1：
Genetic Algorithms for Learning Behavioral Control / 8.5.2：
Classifier Systems / 8.5.3：
On-Line Evolution / 8.5.4：
Evolving Form Concurrently with Control / 8.5.5：
Hybrid Genetic/Neural Learning and Control / 8.5.6：
Fuzzy Behavioral Control / 8.6：
What Is Fuzzy Control? / 8.6.1：
Fuzzy Behavior-Based Robotic Systems / 8.6.2：
Flakey / 8.6.2.1：
Marge / 8.6.2.2：
Learning Fuzzy Rules / 8.6.3：
Other Types Of Learning / 8.7：
Case-Based Learning / 8.7.1：
Memory-based Learning / 8.7.2：
Explanation-Based Learning / 8.7.3：
Social Behavior / 8.8：
Are Two (Or N) Robots Better Than One? / 9.1：
Ethological Considerations / 9.2：
Characterization Of Social Behavior / 9.3：
Reliability / 9.3.1：
Social Organization / 9.3.2：
Communication / 9.3.3：
Spatial Distribution / 9.3.4：
Congregation / 9.3.5：
Performance / 9.3.6：
What Makes A Robotic Team? / 9.4：
Social Organization And Structure / 9.5：
The Nerd Herd / 9.5.1：
Alliance Architecture / 9.5.2：
Stagnation Behaviors / 9.5.3：
Societal Agents / 9.5.4：
Army Ant Project / 9.5.5：
Interrobot Communication / 9.6：
The Need for Communication / 9.6.1：
Communication Range / 9.6.2：
Communication Content / 9.6.3：
Guaranteeing Communication / 9.6.4：
Distributed Perception / 9.7：
Social Learning / 9.8：
L-Alliance / 9.8.1：
Tropism System Cognitive Architecture / 9.8.3：
Learning by Imitation / 9.8.4：
Case Study: Ugv Demo II / 9.9：
Formation Behaviors / 9.9.1：
Multiagent Mission Specification / 9.9.2：
Team Teleautonomy / 9.9.3：
Fringe Robotics: Beyond Behavior / 9.10：
Issues Of The Robot Mind / 10.1：
On Computational Thought / 10.1.1：
On Consciousness / 10.1.2：
On Emotions / 10.1.3：
On Imagination / 10.1.4：
Issues Of The Robot Body / 10.2：
Hormones and Homeostasis / 10.2.1：
The Homeostat / 10.2.1.1：
Subsumption-Based Hormonal Control / 10.2.1.3：
Immune Systems / 10.2.2：
Nanotechnology / 10.2.3：
On Equivalence (Or Better) / 10.3：
Opportunities / 10.4：
References / 10.5：
Name Index
Subject Index
Foreword
Preface
Whence Behavior? / Chapter 1：
8.

図書
図書
8. Programming concepts and methods : PROCOMET '98 : IFIP TC2/WG2.2, 2.3 International Conference on Programming Concepts and Methods (PROCOMET '98), 8-12 June 1998, Shelter Island, New York, USA
edited by David Gries, Willem-Paul de Roever
出版情報: London : Chapman & Hall, c1998  viii, 486 p. ; 24 cm
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9.

図書
図書
9. Natural Working Fluids '98, IIR - Gustav Lorentzen Conference : proceedings of the conference of Commission B2 with B1, E1 & E2, (June 2-5, 1998), Oslo, Norway = Fluides Actifs Naturels, Conference IIF - Gustav Lorentzen : compte rendu de la conférence de la Commission B2 with B1, E1 & E2
issued by International Institute of Refrigeration = edité par Institut International du Froid
出版情報: Paris : Institut International du Froid, [1998]  766 p ; 24 cm
シリーズ名: Science et technique du froid = Refrigeration science and technology ; 1998-4
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10.

図書
図書
10. Wavelength division multiple access optical networks
Andrea Borella, Giovanni Cancellieri, Franco Chiaraluce
出版情報: Boston : Artech House, c1998  x, 322 p. ; 24 cm
シリーズ名: The Artech House optoelectronics library
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目次情報: 続きを見る
Preface
Optical Networking / Chapter 1：
Brief History of Optical Communications / 1.1：
Main Features of WDMA Optical Networks / 1.2：
Classification / 1.3：
Practical Limits / 1.4：
Passive Components / 1.5：
Active Devices and Apparatuses / 1.6：
EDFAs / 1.7：
Traffic Aspects / 1.8：
References
Single-Hop Optical Networks / Chapter 2：
General Concepts / 2.1：
Transmission Protocols / 2.2：
Fixed and Semifixed Assignment Protocols / 2.2.1：
Random Access Protocols with No Pretransmission Coordination / 2.2.2：
Random Access Protocols with Pretansmission Coordination / 2.2.3：
Outline of Performance Comparison and Final Remarks on the Transmission Protocols / 2.2.4：
Experimental Broadcast-and-Select Single-Hop Networks / 2.3：
Lambdanet / 2.3.1：
Rainbow / 2.3.2：
Fox / 2.3.3：
Hypass / 2.3.4：
Bhypass / 2.3.5：
Photonic Knockout Switch / 2.3.6：
Passive Photonic Loop / 2.3.7：
Star-Track / 2.3.8：
Fiber Delay Line Switching Matrix / 2.3.9：
Symfonet / 2.3.10：
Mesh with Broadcast-and-Select / 2.3.11：
An Example of Wavelength-Routing WDMA Network: The Linear Lightwave Network / 2.4：
The LLN Architecture / 2.4.1：
Routing Constraints / 2.4.2：
Performance with Different Routing Schemes / 2.4.3：
Multihop Optical Networks / Chapter 3：
Preliminary Remarks / 3.1：
Basic Characteristics of Multihop Networks / 3.1.1：
Meaning and Importance of Some Performance Parameters / 3.1.2：
Manhattan Street Networks / 3.2：
Network Architecture / 3.2.1：
Some Topological Characteristics of a MSN / 3.2.2：
Distributed Routing Rules in MSNs / 3.2.3：
All-Optical Implementation of MSNs / 3.2.4：
Bidirectional MSNs / 3.2.5：
Characteristic Parameters of a BMSN / 3.2.6：
Bidirectional Manhattan Topology with Uplinks / 3.2.7：
Routing in BMSNs / 3.2.8：
Shuffle Networks / 3.3：
The Perfect Shuffle Topology / 3.3.1：
Shufflenets with Shared Channels / 3.3.2：
Size Modifications of Shuffle Networks Based on Multistar Architecture / 3.3.3：
Modular Expansion of Shufflenets / 3.3.4：
Channel Sharing in a Bidirectional Perfect Shuffle Topology / 3.3.5：
Routing in Shufflenets / 3.3.6：
Evolutions of the Shuffle Topology / 3.4：
Duplex Shufflenet / 3.4.1：
Gemnet / 3.4.2：
Enlarged Shufflenet Architecture / 3.4.3：
Modification of the Shufflenet Connectivity Graph / 3.4.4：
Banyan Net / 3.4.5：
De Bruijn Graph Topology / 3.5：
The de Bruijn Graph / 3.5.1：
Routing in de Brujin Networks / 3.5.2：
de Bruijn Versus Shufflenet / 3.5.3：
The Modified de Bruijn Topology / 3.5.4：
de Bruijn Network Variants / 3.5.5：
MATRIX Topology / 3.6：
Space Diversity to Avoid WDM Conversion / 3.6.1：
Network Parameters / 3.6.2：
SWIFT Architecture / 3.7：
The SWIFT Approach / 3.7.1：
The Data Link Layer / 3.7.2：
The Routing Layer / 3.7.3：
SWIFT Performance / 3.7.4：
Starnet Architecture / 3.8：
Starnet Basic Characteristics / 3.8.1：
Node Structure / 3.8.2：
The Circuit Switching and Packet Switching Subnetworks / 3.8.3：
Multihop Networks Supported by Starnet / 3.8.4：
Multilevel Optical Networks / Chapter 4：
Networks of Networks / 4.1：
Star-of-Stars Network / 4.2：
Hierarchical LLN / 4.3：
Combination of Single-Hop and Multihop Connection Modes in MONs / 4.4：
Basic Concepts for the Two-Level Case / 4.4.1：
Analysis and Optimization of MONs / 4.4.2：
Comparison with Shufflenet / 4.4.3：
Multiple Hierarchical Levels / 4.4.4：
About the Authors
Index
Preface
Optical Networking / Chapter 1：
Brief History of Optical Communications / 1.1：
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