close
1.

図書

図書
edited by Daniel P. Palomar and Yonina C. Eldar
出版情報: Cambridge ; Tokyo : Cambridge University Press, 2010  xiv, 498 p. ; 26 cm
所蔵情報: loading…
目次情報: 続きを見る
Automatic code generation for real-time convex optimization / J. Mattingley ; S. Boyd1:
Gradient-based algorithms with applications to signal recovery problems / A. Beck ; M. Teboulle2:
Graphical models of autoregressive processes / J. Songsiri ; J. Dahl ; L. Vandenberghe3:
SDP relaxation of homogeneous quadratic optimization / Z. Q. Luo ; T. H. Chang4:
Probabilistic analysis of SDR detectors for MIMO systems / A. Man-Cho So ; Y. Ye5:
Semidefinite programming, matrix decomposition, and radar code design / Y. Huang ; A. De Maio ; S. Zhang6:
Convex analysis for non-negative blind source separation with application in imaging / W. K. Ma ; T. H. Chan ; C. Y. Chi ; Y. Wang7:
Optimization techniques in modern sampling theory / T. Michaeli ; Y. C. Eldar8:
Robust broadband adaptive beamforming using convex optimization / M. RÃ1/4bsamen ; A. El-Keyi ; A. B. Gershman ; T. Kirubarajan9:
Cooperative distributed multi-agent optimization / A. NenadiÄç ; A. Ozdaglar10:
Competitive optimization of cognitive radio MIMO systems via game theory / G. Scutari ; D. P. Palomar ; S. Barbarossa11:
Nash equilibria: the variational approach / F. Facchinei ; J. S. Pang12:
Automatic code generation for real-time convex optimization / J. Mattingley ; S. Boyd1:
Gradient-based algorithms with applications to signal recovery problems / A. Beck ; M. Teboulle2:
Graphical models of autoregressive processes / J. Songsiri ; J. Dahl ; L. Vandenberghe3:
2.

図書

図書
William A. Gardner
出版情報: New York, N.Y. : Macmillan Pub. Co., c1986  xiii, 434 p. ; 24 cm
所蔵情報: loading…
3.

図書

図書
A.V. Balakrishnan
出版情報: New York : Wiley, c1995  xiii, 402 p. ; 25 cm
所蔵情報: loading…
目次情報: 続きを見る
Preface
Review
Random Processes: Basic Concepts, Properties / 1:
Stationary Random Processes: Covariance and Spectrum / 2:
Response of Linear Systems to Random Inputs: Discrete-Time Models / 3:
Response of Linear Systems to Random Inputs: Continuous-Time Models / 4:
Time Averages and the Ergodic Principle / 5:
Sampling Principle and Interpolation / 6:
Simulation of Random Processes / 7:
Random Fields / 8:
Linear Filtering Theory / 9:
Index
Preface
Review
Random Processes: Basic Concepts, Properties / 1:
4.

図書

図書
Enders A. Robinson, Tariq S. Durrani, with a chapter by Lloyd G. Peardon
出版情報: Englewood Cliffs, N.J. : Prentice-Hall, c1986  xi, 481 p. ; 24 cm
所蔵情報: loading…
5.

図書

図書
edited by Ludwig D. J. Eggermont... [et al.]
出版情報: New York, NY : Institute of Electrical and Electronics Engineers, c1993  xv, 527 p. ; 23 cm
所蔵情報: loading…
6.

図書

図書
A. Cichocki, R. Unbehauen
出版情報: New York : J. Wiley, 1993  xvii, 526 p. ; 24 cm
所蔵情報: loading…
目次情報: 続きを見る
Mathematical Preliminaries of Neurocomputing
Architectures and Electronic Implementation of Neural Network Models
Unconstrained Optimization and Learning Algorithms
Neural Networks for Linear, Quadratic Programming and Linear Complementarity Problems
A Neural Network Approach to the On-Line Solution of a System of Linear Algebraic Equations and Related Problems
Neural Networks for Matrix Algebra Problems
Neural Networks for Continuous, Nonlinear, Constrained Optimization Problems
Neural Networks for Estimation, Identification and Prediction
Neural Networks for Discrete and Combinatorial Optimization Problems
Appendices
Subject Index
Mathematical Preliminaries of Neurocomputing
Architectures and Electronic Implementation of Neural Network Models
Unconstrained Optimization and Learning Algorithms
7.

図書

図書
D. Brook and R.J. Wynne
出版情報: London : Edward Arnold, c1988  ix, 308 p. ; 25 cm
所蔵情報: loading…
8.

図書

図書
Herbert Matthews, editor
出版情報: New York : Wiley, c1977  xi, 521 p. ; 24 cm
所蔵情報: loading…
9.

図書

図書
V.N. Yarmolik, S.N. Demidenko
出版情報: Chichester [West Sussex] ; New York : Wiley, c1988  167 p. ; 23 cm
所蔵情報: loading…
10.

図書

図書
Saeid Sanei and Jonathon Chambers
出版情報: Chichester : John Wiley & Sons, c2007  xxii, 289 p. ; 25 cm
所蔵情報: loading…
目次情報: 続きを見る
Preface
List of Abbreviations
List of Symbols
Introduction to EEG / 1:
History / 1.1:
Neural Activities / 1.2:
Action Potentials / 1.3:
EEG Generation / 1.4:
Brain Rhythms / 1.5:
EEG Recording and Measurement / 1.6:
Conventional Electrode Positioning / 1.6.1:
Conditioning the Signals / 1.6.2:
Abnormal EEG Patterns / 1.7:
Ageing / 1.8:
Mental Disorders / 1.9:
Dementia / 1.9.1:
Epileptic Seizure and Nonepileptic Attacks / 1.9.2:
Psychiatric Disorders / 1.9.3:
External Effects / 1.9.4:
Summary and Conclusions / 1.10:
References
Fundamentals of EEG Signal Processing / 2:
EEG Signal Modelling / 2.1:
Linear Models / 2.1.1:
Nonlinear Modelling / 2.1.2:
Generating EEG Signals Based on Modelling the Neuronal Activities / 2.1.3:
Nonlinearity of the Medium / 2.2:
Nonstationarity / 2.3:
Signal Segmentation / 2.4:
Signal Transforms and Joint Time-Frequency Analysis / 2.5:
Wavelet Transform / 2.5.1:
Ambiguity Function and the Wigner-Ville Distribution / 2.5.2:
Coherency, Multivariate Autoregressive (MVAR) Modelling, and Directed Transfer Function (DTF) / 2.6:
Chaos and Dynamical Analysis / 2.7:
Entropy / 2.7.1:
Kolmogorov Entropy / 2.7.2:
Lyapunov Exponents / 2.7.3:
Plotting the Attractor Dimensions from the Time Series / 2.7.4:
Estimation of Lyapunov Exponents from the Time Series / 2.7.5:
Approximate Entropy / 2.7.6:
Using the Prediction Order / 2.7.7:
Filtering and Denoising / 2.8:
Principal Component Analysis / 2.9:
Singular-Value Decomposition / 2.9.1:
Independent Component Analysis / 2.10:
Instantaneous BSS / 2.10.1:
Convolutive BSS / 2.10.2:
Sparse Component Analysis / 2.10.3:
Nonlinear BSS / 2.10.4:
Constrained BSS / 2.10.5:
Application of Constrained BSS: Example / 2.11:
Signal Parameter Estimation / 2.12:
Classification Algorithms / 2.13:
Support Vector Machines / 2.13.1:
The k-Means Algorithm / 2.13.2:
Matching Pursuits / 2.14:
Event-Related Potentials / 2.15:
Detection, Separation, Localization, and Classification of P300 Signals / 3.1:
Using ICA / 3.1.1:
Estimating Single Brain Potential Components by Modelling ERP Waveforms / 3.1.2:
Source Tracking / 3.1.3:
Localization of the ERP / 3.1.4:
Time-Frequency Domain Analysis / 3.1.5:
Adaptive Filtering Approach / 3.1.6:
Prony's Approach for Detection of P300 Signals / 3.1.7:
Adaptive Time-Frequency Methods / 3.1.8:
Brain Activity Assessment Using ERP / 3.2:
Application of P300 to BCI / 3.3:
Seizure Signal Analysis / 3.4:
Seizure Detection / 4.1:
Adult Seizure Detection / 4.1.1:
Detection of Neonate Seizure / 4.1.2:
Chaotic Behaviour of EEG Sources / 4.2:
Predictability of Seizure from the EEGs / 4.3:
Fusion of EEG-fMRI Data for Seizure Prediction / 4.4:
EEG Source Localization / 4.5:
Introduction / 5.1:
General Approaches to Source Localization / 5.1.1:
Dipole Assumption / 5.1.2:
Overview of the Traditional Approaches / 5.2:
ICA Method / 5.2.1:
MUSIC Algorithm / 5.2.2:
LORETA Algorithm / 5.2.3:
FOCUSS Algorithm / 5.2.4:
Standardized LORETA / 5.2.5:
Other Weighted Minimum Norm Solutions / 5.2.6:
Evaluation Indices / 5.2.7:
Joint ICA-LORETA Approach / 5.2.8:
Partially Constrained BSS Method / 5.2.9:
Determination of the Number of Sources / 5.3:
Sleep EEG / 5.4:
Stages of Sleep / 6.1:
NREM Sleep / 6.1.1:
REM Sleep / 6.1.2:
The Influence of Circadian Rhythms / 6.2:
Sleep Deprivation / 6.3:
Preface
List of Abbreviations
List of Symbols
文献の複写および貸借の依頼を行う
 文献複写・貸借依頼