Multivariable Control A Graph-Theoretic Approach

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Stirling, Todd K. Steven B. Other renewable energy systems : Fuel Cells, Biogas, Biomass etc Power electronic converters and control for Microgrids and Smart grids. Sharkh, Mohammad A. Abu-Sara, Georgios I. Transmitter and Receiver architectures: Review of modulation schemes, Receiver architectures, Transmitter architectures. Noise in electrical circuits and NF calculations, Two port noise theory. Frequency synthesizers : Integer N synthesizers, Dividers,.

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Abraham I.

Mark I. EE Multivariable Control Theory Mathematical Fundamentals: Invariant subspaces, Similarity transformations, Quotienting and equivalence classes; Canonical Representations and Feedback Laws:, Multivariable Observer and controller canonical representations, multivariable pole placement problem, multivariable observer design problem; System decomposition: Controllability indices and system invariants, Controllability subspaces and Observability subspaces, stabilizability and detectability, Disturbance decoupling and Output stabilization problems; Binary Systems:Introduction to linear modular systems.

Course Contents:. Introduction: Problem framing, feature selection, dimensionality reduction using PCA and other methods; Discriminative classifiers: LDA, Multi-layer perceptron, backpropagation, SVM; Unsupervised learning: Clustering, Vector Quantization, Kohonen Map, EM Algorithm; Generative models: Definition and characteristics, probabilistic graphical models, density estimation in learning; Combining classifiers: Advantages, boosting, hierarchical classifiers, and issues; Selected special topics such as manifold learning and case studies.

Review of ordinary differential equations. State space modeling of linear time invariant systems, Partial differential equations, State space modeling of time varying systems, Solution of state equations, matrix inversion, SVD, Difference equations, State space modeling of discrete time systems, Modeling of stochastic systems, Modeling examples of various practical systems. El Gamal, Y. Robert M. Grama, G. Karypis, V. Kumar, A. References: 1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Michael T. Heath, Abhiram Ranade, Robert S.

Karl Heinz Hoffmann, A. Kontoghiorghes E. Jacques M. Control Instrumentation and design, Component interconnection and signal conditioning, Performance and specification analysis, Classification of sensors and actuators, Theory and Analysis of Magnetic Sensors, Solid state sensors and their analysis, Linear Actuators, Fast acting actuators, Latching linear actuators, Stepper motors as actuators, Rotary sensors and actuators, Special magnetic devices, Digital Transducers.

Introduction to Fuzzy sets: Fuzzy relation, Approximate reasoning, Rules; Fuzzy control design parameters: Rule base, data base; Choice of fuzzification procedure; Choice of defuzzification procedure; Nonlinear fuzzy control; Adaptive fuzzy control; Introduction to Neural Networks: Biological Neurons, Artificial Neurons — various models, Artificial Neural Networks — various structures, Learning Strategies, Applications.

Ebook Multivariable Control A Graph Theoretic Approach

Introduction to Synchrophasor technology: basic architecture and communication requirement; Phasor and frequency estimation; Basic principles for Wide area monitoring and control in real-time; Dynamic modeling of synchronous generator; Transient stability monitoring and control; Small signal monitoring and control; Wide area power system stabilizers; Synchrophasor applications in power system protection and emergency control; Optimal placement of phasor measurement units; State estimation; Real-time monitoring and control of voltage stability.

Motors with continuous rotation, Electromagnetic Stepping Drives, Drives with limited motion, Piezoelectric drives, Open loop and closed loop control of fractional horse power motors, Magnetic bearings and their control, Integration and Control of Mechanical transfer units such as gears, pulleys, flexible drives etc.

References :. Review of random process, problem formulation and objective of signal detection and signal parameter estimation; Hypothesis testing: Neyman-Pearson, minimax, and Bayesian detection criteria; Randomized decision; Compound hypothesis testing; Locally and universally most powerful tests, generalized likelihood-ratio test; Chernoff bound, asymptotic relative efficiency; Sequential detection; Nonparametric detection, sign test, rank test. Parameter estimation: sufficient statistics, minimum statistics, complete statistics; Minimum variance unbiased estimation, Fisher information matrix, Cramer-Rao bound, Bhattacharya bound; Linear models; Best linear unbiased estimation; Maximum likelihood estimation, invariance principle; Estimation efficiency; Least squares, weighted least squares; Bayesian estimation: philosophy, nuisance parameters, risk functions, minimum mean square error estimation, maximum a posteriori estimation.

Groups Projects. Tech B. Hoffman and R. Balabanian and T. Cormen, C. Leiserson and R. EE Advanced Topics in Random Processes Course Contents Convergence of a sequence of random variables; Chernoff bound and large deviations theory; mean-square calculus- stochastic continuity derivatives and integrals; ergodicity; KarhunenLoeve expansion; Random walk process; Discrete time Markov chains: recurrence analysis, Foster's theorem; continuous time Markov Process; Poisson and birth and death processes; Wiener process and Brownian motion process.

Cox, D. Stark and J. Hlawatsch and F. Spanias, T. Painter and V. Gonzalez and R.

Network structure of multivariate time series | Scientific Reports

Gonzalez, R. Woods and S. Solari, Digital Video and Audio Compression, McGraw-Hill, EE Computer Vision Course Contents: Image formation and image models; Image filtering; Lines, Blobs, Edges and boundarydetection; Representation of 2-D and 3-D structures; Bayes decision theory for patternrecognition; Supervised and unsupervised classifications; Parametric and nonparametricschemes; Clustering for knowledge representation; Applications of neural networks andfuzzy logic in pattern recognition; Feature extraction in images; Texture analysis andclassification; Image segmentation; Optical character recognition; 2-D and 3-D objectrecognition; Surface extraction from monocular images; Stereo image pair analysis; Optical flow and 3-D motion analysis.

Forsyth and J. Ballard and C. Brown, Computer Vision, Prentice Hall, Duda and P. Jain, R. Kasturi and B. EE Biomedical Signal Processing Contents: Sources of bioelectric potential, resting potential, action potential, propagation of action potentials in nerves; rhythmic excitation of heart; ECG: Pre-processing, wave form recognition, morphological studies and rhythm analysis, automated diagnosis based on decision theory, ECG compression, Evoked potential estimation.

EEG: Evoked responses, averaging techniques, pattern recognition of alpha, beta, theta and delta waves in EEG waves, sleep stages, epilepsy detection. EMG: Wave pattern studies, biofeedback. With ill. Seller Inventory Book Description Springer-Verlag, Condition: Gut. Kurt Johannes Reinschke. Publisher: Springer Verlag , This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Great condition Learn more about this copy. Other Popular Editions of the Same Title.

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Search for all books with this author and title. Customers who bought this item also bought. By definition we have:. Symbolization is one of the standard requisites for the analysis of multivariate time series. There are several ways to construct such symbolised representations of multivariate signals, but one of the main problems which characterise symbolisation is the choice of the mapping function and the determination of the appropriate number of symbols to be used see SI for details. The resulting symbolized series is a symbol sequence. One can then estimate the mutual information between two symbol sequences x t and y t as.

Accordingly, an original multivariate time series is coarse-grained into a multivariate symbolic sequence. Then, we averaged the mutual information for each realization, as explained in the previous subsection, and finally. A similar computation is made in Fig. For the construction of Fig. For each realization , we constructed the corresponding multiplex visibility graph, by considering as layers the Horizontal Visibility Graphs of each of the five components.

Then, we computed the pairwise mutual information between any pair of layers , and the corresponding average. The value of I reported in the figure is the average over the realizations, i. How to cite this article : Lacasa, L. Network structure of multivariate time series. Kantz, H. Hastie, T. Elements of Statistical Learning Springer-Verlag, Albert, R. Statistical mechanics of complex networks. Boccaletti, S.

Complex networks: structure and dynamics. Newman, M. Networks: An Introduction. Oxford University Press, Oxford, Bullmore, E. Complex brain networks: graph theoretical analysis of structural and functional systems. Neurosci 10 3 , — Tumminello, M. Correlation, hierarchies, and networks in financial markets. Cai, S. Hierarchical organization and disassortative mixing of correlation-based weighted financial networks.

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C 21 3 , — Gao, Y. Influence network in the Chinese stock market. P Zhang, J. Complex network from pseudoperiodic time series: topology versus dynamics. Kyriakopoulos, F. Directed network representations of discrete dynamical maps. In Lecture Notes in Computer Science , — Xu, X. Superfamily phenomena and motifs of networks induced from time series.

USA , — Donner, R.


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Recurrence networks: a novel paradigm for nonlinear time series analysis. New J. B 84 , — Lacasa, L. From time series to complex networks: The visibility graph. USA , 13 Luque, B. Horizontal visibility graphs: Exact results for random time series. E 80 , On the degree distribution of horizontal visibility graphs associated to Markov processes and dynamical systems: diagrammatic and variational approaches. Nonlinearity 27 , — Gutin, G. A characterization of horizontal visibility graphs and combinatorics on words.

Physica A , 12 The Visibility Graph: a new method for estimating the Hurst exponent of fractional Brownian motion. Description of stochastic and chaotic series using visibility graphs. E 82 , Analytical properties of horizontal visibility graphs in the Feigenbaum scenario. Chaos 22 , Quasiperiodic graphs: structural design, scaling and entropic properties. Nunez, A. Horizontal Visibility graphs generated by type-I intermittency. E 87 , Ahmadlou, M. Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems.

Physica D 4 , — Aguilar-San Juan, B. Earthquake magnitude time series: scaling behavior of visibility networks. Visibility graph analysis of geophysical time series: Potentials and possible pitfalls. Acta Geophysica 60 , — Zou, Y. Complex network approach to characterize the statistical features of the sunspot series. Long-term changes in the north-south asymmetry of solar activity: a nonlinear dynamics characterization using visibility graphs.

Processes Geophys. Qian, M. Universal and nonuniversal allometric scaling behaviors in the visibility graphs of world stock market indices. A 43 , Global organization of functional brain connectivity in methamphetamine abusers. Clinical Neurophysiology , 6, Visibility algorithms: a short review in Graph Theory Intech Bianconi, G. Statistical mechanics of multiplex networks: Entropy and overlap. Nicosia, V. Growing multiplex networks. De Domenico, M. Mathematical formulation of multilayer networks.