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The student must attend three compulsory subjects and complete their credits with elective courses. The compulsory subjects are  three: Interdisciplinary Activities, Linear Algebra Computational Algorithms and software.

Nonlinear systems 

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Summary:

Introduction to nonlinear dynamical systems. Qualitative analysis of continuous dynamic systems. Attractors: balances, limits and aperiodic behavior cycles. approximate analysis methods. autonomous and non-autonomous systems: stability based on Lyapunov. Review of stability concepts. Feedback linearizante classical and robust. Design based on backstepping. Analysis and synthesis pathway absolute stability. Passivity in dynamic systems. Shaping techniques based on Energy. Examples of applications.

Bibliography:

KHALIL, HK, Nonlinear Systems, Prentice Hall, 2002.

Schaft, L2 AV-gain and nonlinear control techniques in passivity, Springer Verlag, 2000.

Isidori, AP Nonlinear Control Systems - Third Edition, Springer Verlag, 1995.

Slotine JJand LI, .W. Applied Nonlinear Control. Prentice Hall, 1991.

Sepulcre, RM and P. Kokotovic JANKOVIC, Constructive Nonlinear Control, Springer Verlag, 1997.

FANTONI I, Lozano R., Nonlinear Control for Underactuated Mechanical Systems, Springer Verlag, 2002.

Discrete mathematics

Level : Master Academic
Required : No
Hours :
Credits : 4.0
Area of Concentration : Multidisciplinary Area

Summary:

Review Sets. Relations. Functions and algorithms. Induction and Recursion. Graphs. Algorithms for graphs. Discrete models. Computational Complexity.

Bibliography:

N. Christofides Graph Theory. New York, Academic Press, 1975.

Jungnickel, D. (2005) Graphs Networks and Algorithms, 2nd edition, Springer, New York.

MANNA, Z. Mathematical Theory of Computacion. Dover, 2003.

Menezes, CP Discrete Mathematics for Computing and Information Technology. Artmed, 2008.

Sipser, M. Introduction to the Theory of Computation. Thomson, 2007.

Menezes, PB; TOSCANI, LV & LOPEZ, JG Learning Discrete Mathematics with Exercises. Bookman, 2009.

ROSEN, K. Discrete Mathematics and its Applications. McGraw-Hill, 2009.

Scheinerman ER Discrete Mathematics. Thomson, 2003.

STOLL, RR Set Theory and Logic. Dover, 1979.

Scheinerman, E. Discrete Mathematics: An Introduction. São Paulo, Thomson, 2006.

Chaos in Dynamical Systems

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

one-dimensional maps. Fractals. strange attractors. Chaos in Hamiltonian systems. Control chaos. Numerical simulation of chaotic systems.

Bibliography :

Ott, E. (1993) Chaos in Dynamical Systems, Cambridge University Press.
JOSE, N; Saletan, EJ (1998), Classical Dynamics, Cambridge University Press
Gutzwiller, MC (1991) Chaos in classical and quantum Mechanics, Springer-Verlag.

High Performance Computing

Level : Master Academic
Required : Yes
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

parallel and distributed architectures; trivially parallelizable algorithms; complexity of parallel algorithms; Programming with MPI and PVM;

Bibliography :

Tanenbaum, AS (2001) Computer Organization, Fourth Edition, LTC.

Hennessy, JL; PATTERSON, DA (2003) Computer Architecture - A Quantitative Approach, Campus.

GIBBONS, A .; Rytter, W. (1988) Efficiente Parallel Algorithms, Cambridge University Press.

BRASSARD, G .; Bratley, P. (1996) Fundamentals of Algorithmics, Prentice Hall.

Diverio, T.; Navaux, P. eds (2001) I Regional High School Performance, Proceedings, Gramado / RS.

Statistical inference

Level : Master Acadêmicon
Required : No
Hours : 30
Credits : 2.0
Area of Concentration : Multidisciplinary Area

Menu :

Inference based on the sampling design (classic), and inference-based model (likelihood theory). Estimation methods: moments, least squares and maximum likelihood. Sampling distribution: concept and applications using simulations. estimators properties: bias, precision, accuracy and consistency. estimators properties of maximum likelihood. Fisher information. Confidence intervals for maximum likelihood estimators: Normal approach and method of likelihood profile. confidence interval for simulation: bootstrap parametric and non-parametric. hypothesis tests: general procedure and applications. Errors type I and type II. Power of a test. likelihood ratio test. Estimation by the least squares method. Test regression and correlation coefficients (parametric and non-parametric). quality static control for variables.

Bibliography :

Bussab WO; MORETTIN, PA (2002) Basic Statistics (5 a. Edition). Editora Saraiva

HOEL, PG (1980) Mathematical Statistics (a.Ed. 4) Label 2 Guanabara

Mood The .M .; GRAYABILL, F., BOES, DC (1974) Introduction to the theory of statistics. McGraw-Hill

Nolan, D .; SPEED, T. (2000) Lab Stat: Mathematical Statistics Through Applications. Springer Verlag.

Siegel, S. (1975) Non-Parametric Statistics. McGraw-Hill

Soong TT (1986) Probabilistic Models in Engineering and Science. LTC Editora SA

Souza, GS (1998) Introduction to Linear Regression Models and Nonlinear. EMBRAPA. 489p.

Venables WN; SMITH, MD (2001) An Introduction to R. (pdf file to download)

Zar (1984) Biostatistical Analysis (2nd Ed.) - Prentice-Hall

Introduction to Inverse Problems

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Examples of Inverse Problems, ill-posed problems, problems hardly conditioners, least squares decomposition Values ​​Singles, Principle of discrepancy, regularization Tikhonov, regularization Entropy, Newton Method, Methods Quasi-Newton Landweber Method, Gradient Method Conjugate maximum decreases method, Levenberg-Marquardt method.

Bibliography :

GROETSCH, CW (1993) The Inverse Problems in Mathematical Science Braunschweig, Wiesbaden: Vieweg.

KIRSCH, A. (1996) An Introduction to The Mathematical Theory of Inverse Problems Applied Mathematical Sciences, 120 Springer-Verlag, New York.

Silva Neto, AJ; MOURA, FD (2000) Select models: Inverse Problems in Engineering (Mini-Course) CNMAC-SBMAC.

ENGL, HW; HANKE, M .; Neubauer A. (1996) regularization of Inverse Problems, Kluwer.

BECK, N; Blackwell, B .; St. Clair, CR (1985) Inverse Heat Conduction: Ill-Posed Problems John Wiley & Sons.

Tikhonov, AN; Arsenin (1977) Solution of Ill-Posed Problems, John Wiley & Sons

Mathematical Methods

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Differences equations Partial 1st order. Theorem classification. Method of separation of variables. Sturm-Liouville theorem. Special functions: Bessel, Legendre, Neuman. Function Green. Differences equations Partial higher order. Calculus of Variations. Hamilton principle. Differential Euler-Lagrange equation. Variational formulation for continuous systems. variational method of application for Eigenvalues ​​problems.

Bibliography :

GOOD, ML (1983) Mathematical Methods in the Physical Sciences, 2nd ed, J. Wiley.

ELSGOLTS, L. (1977) Differential Equations and the Calculus of Variations, Mir.

Goldstein, H. (1980) Classical Mechanics, 2nd ed, Addison-Wesley.

Lanczos, C. (1986). The Variational Principles of Mechanics, 4th ed., Dover.

GOULD, SH (1995) Variational Methods for eigenvalue Problems, Dover.

Numerical methods

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Interpolation approximation and numerical derivation functions. Zeros of algebraic and transcendental equations. numerical calculation of special functions (Bessel, elliptic integrals of functions, etc.). Systems of linear and nonlinear equations. Adjustment curves. numerical integration. Solutions of equations and partial differential common. Simulation of dynamic systems. Problems of eigenvalues ​​and eigenvectors. Fourier transforms: DFT and FFT.

Bibliography :

PRESS, WH et alli (1989), Numerical Recipes in Pascal: The Art of Scientific Computing. Cambridge University Press.

DEMIDOVITCH, B. et MARON, I. (1973). Eléments de Calcul Numérique, Mir.

Nougier, JP (1983) Méthodes de Calcul Numérique, Masson.

Lanzarini, C. and Franco, NMB (1980) Numerical calculation Topics, San Carlos, USP.

Climate modeling

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Climate System. Climate modeling. Energy Balance models. Radiative-convective models. Statistical-Dynamical models. General Circulation Models of the atmosphere. Studies on Climate Change.

Bibliography :

Houghton, JT; Meira Filho, LG; CALLANDER BA; Harris, N .; KATTEMBERG, MASKELL, A., K. eds. (1996) Climatic Change: The Science of Climate Change, Cambridge University Press.

GASH, JHC; NOBLE, CA; Roberts, JM; and Victoria, RL (1996) Amazonian deforestation and climate. New York: Wiley.

SCHLESINGER, ME (1988) Physically-Based Modeling and Simulation of Climate and Climatic Change. Part I and II, Kluwer.

TREMBERTH, K., (1995). Climate System Modeling, Cambridge University Press.

Robots Modeling

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Formalism Newtonian modeling formalism Euler-Lagrange, dynamic system modeling, examples, dynamic modeling of robots rigid manipulators, dynamic modeling of robot manipulators with flexible links, direct and inverse kinematic models, development of kinematic models of robot manipulators, kinematics rigid bodies in space motion, kinematic models of mobile robots, dynamic models of mobile robots, applications to underwater robotics.

bibliography :

Fossen, TI, 1994. Guidance and Control of Ocean Vehicles. Chichester: John Wiley & Sons

SPONG, MW and Vidyasagar, M., 1989. Robot dynamic and control. John Wiley and Sons.

MEIROVITCH, M., 1970. Methods of analytical dynamics. McGraw-Hill.

CRAIG, JJ, 1986. Introduction to robotics, mechanics and control. Addison Wesley.

FRANKLIN, GF and Powell, JD, 1995. Feedback control of dynamic systems. 3rd ed, New York, Addison-Wesley.

Probabilistic Models in Science and Engineering

Level : Master Academic Required : No Hours : 30 Credits : 2.0 Area of Concentration : Multidisciplinary Area

Menu :

Conceptualization Odds: classical, relative frequency subjective.
Axiom of Probability. Conditional probability and independence. Bayes Theorem. Random variables discrete and its representation: mass probability function; FUNCAP distribution. Measures Summary: Hope, variance, Quantile, Fashion, Asymmetry, Kurtosis. Probabiliísticos discrete models: Binomial, Poisson, Hypergeometric, Multinomial, Geometric and Negative Binomial. Continuous random variables and their representation: probability density; distribution function. Continuous probabilistic models: Normal, Log-Normal, Exponential, Gamma, Chi-square, Student and Fischer. Additional Topics.

Bibliography :

HOEL, PG; PORT SC; STONE, CJ (1978) Introduction to Probability Theory, Publisher Intersciência.

Soong TT (1986) Probabilistic Models and Engenahria Sciences, Editor CTL.

GRIMMET, DR; STIRZAKER, DR (1985) Probability and random process, Oxford University Press.

stochastic processes

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Time series and its analysis.
Chains and Markov processes, stochastic matrices.
Poisson processes and Ornstein-Uhlenbeck.
Wiener Formulation and Feynman-Kac (Hamiltonian formulation and thermodynamics).
Euclidean theory of fields in the network (functional integrals, corelação functions in n points, Approximação by average fields).
Gauge theories on the network (Abelian gauge theories and not Abelian, Wegner Wilson loops).

Bibliography :

Chung, KL (2000). A Course in Probability Theory Revised, Academic Press.

DOOB, JL (1990). Stochastic Processes (Wiley Classics Library) Wiley-Interscience.

LINDSEY, JK (2004). Statistical Analysis of Stochastic Processes in Time (Cambridge Series in Statistical and Probabilistic Mathematics), Cambridge University Press.

Roepstorff, G. (1996). Path Integral Approach to Quantum Physics: An Introduction (Texts and Monographs in Physics) Springer Verlag.

Theory Construtal

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

naturally, questions and theory; Mechanical structures; Thermal structures; Conductive trees; Trees fluids; Rivers and pipelines; Convective trees; Structures in Power Systems; Structures in time: rhythm; Structures Economy and Transport; Shapes with constant resistance.

Bibliography :

Bejan, A (2000) Shape and Structure, from Engineering to Nature, Cambridge University Press.

Bejan, A. (2003) Convection Heat Transfer, 2nd edition, Wiley.

Bejan, A. (1999) Thermodynamic Advanced Engineering, 2nd edition, Wiley.

TSATSARONIS, G .; Moran, M .; Bejan A. (1996) Thermal Design and Optimization, Wiley .

Modeling topics Applied Computational Physics

Level : Master Academic
Required : No
Hours : 60
Credits : 4.0
Area of Concentration : Multidisciplinary Area

Menu :

Special topics of computational modeling applied to description of physical systems. The course will address specific issues of each advisor.

Bibliography :
journal articles .

Topics in Applied Computing

Level : Master Academic
Required : No
Hours : 60
Credits : 4.0
Area of Concentration : Multidisciplinary Area

Menu :

Special topics of computational modeling applied to computing. The course will address specific issues of each advisor.

Bibliography :
journal articles.

Topics in Systems Modeling Thermofluids

Level : Master Academic
Required : No
Hours : 60
Credits : 4.0
Area of Concentration : Multidisciplinary Area

Menu :

Special computational modeling applied to topics termofluídicos systems. The course will address specific issues of each advisor.

Bibliography :
journal articles.

Heat Transfer and Fluid Mechanics Computational

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Introduction; conservation equations; Obtaining approximate conservation equations; interpolation functions; Solution diffusion problems; Solution of convection problems.

Bibliography :

Maliska, CR (2004) Heat transfer and computational fluid mechanics, LTC.

Patankar, SV (1980) Numerical Heat Transfer and Fluid Flow, McGraw-Hill Book Company.

Numerical modeling Applied Oceanography

Level : Master Academic
Required : No
Hours :
Credits : 3
Concentration Area : Multidisciplinary Area

Menu :

Introduction to numerical modeling applied to oceanography; equation of motion in oceanography; Series Raylor numerical methods, the finite difference method, boundary conditions, the finite element method the term element.

Intelligent systems

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Basic Concepts, Classification Methods, Clustering, Planning and Search. Baysianos filters. Applications.

Introduction to Climate Modeling

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

The course's aim is to discuss the predictability of atmosféficos systems and their consequences on climate modeling. uncertainties in the prediction will be evaluated, the application range of predictions and systematic errors. The course will be completed with the application of a global numerical model simulations of climate forecasts will be reviewed based on the concepts learned

Bibliography :

Buizza, R., 2000. Chaos and weather prediction, European Center for Medium-Range Weather.

CHANDLER, M. Educational Global Climate Model, http://edgcm.columbia.edu/.

Jung, T. and Tompkins A. 2000. Systematic errors in the ECMWF Forecasting System, European Center for Medium-Range Weather.

PALMER, TN, 1999. Predicting Uncertainty in climate and weather forecasts of, Technical Memorandum No. 294 ECMWF.

Heat transfer by convection Computational

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

Fundamental principles of heat transfer; fundamental principles of heat convection; laminar boundary layer; Laminar convection ducts interiro; Laminar convection over bodies; Internal convection; transition to turbulent flow; turbulent flow in pipes; free turbulent flow.

Bibliography :

Bejan, Convection Heat Transfer, Wyley Interscience

Bejan, Heat Transfer, Edgard Blücher Ltda

Incropera, FP and Witt, DP Transfer Fundamentals of Heat and Mass, LTC

Machine Learning Applied to Bioinformatics

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Summary:

Introduction to Molecular Biology. Introduction to probability and probabilistic models. pairs of alignment of biological sequences. Hidden Markov chains (HMMs). Alignment of pairs of sequences with biological HMMs. clustering algorithms for gene expression analysis. reverse engineering biological networks, relevant networks, Gaussian graphical models, Bayesian networks.

Bibliography :

HUSMEIER, D, dybowski, A. & Roberts, S. Probabilistic Modeling in Bioinformatics and Medical Informatics

JONES & Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press.

Hunter (1999). Artificial Intelligence and Molecular Biology Chapter 1.

BISHOP (2006) Pattern Recognition and Machine Learning. CM Bishop. Springer.

Durbin R Eddy, S., Krogh, A., Mitchison, G. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

Baldi, P., Brunak, S. Bioinformatics the Machine Learning Approach

Mathematical Elements

Level : Academic Dissertation
Required : No
Hours : 45

Credits : 3.0
Area of Concentration : Multidisciplinary Area

Summary:

Linear transformations, vectors, eigenvectors and eigenvalues, applications dynamical systems, ordinary differential equations (review), partial differential equations (review), dynamic models applications, Fourier and Laplace transforms, applications.

Elements of Artificial Intelligence

Summary:

History of Artificial Intelligence. Basic concepts. Languages and platforms to IA. Neural networks. Reasoning and knowledge under
uncertainty. Decision processes and Markov models. Multi-agent systems.

Bibliography:

RUSSEL, S. & Norvig, P. Artificial Intelligence. Campus, 2004.

BITTENCOURT, G. Artificial Intelligence - Tools and Theories. Publisher
of UFSC, 2006.

REZENDE, S. Intelligent Systems - Fundamentals and Applications. Manole, 2005.

Wooldridge, M. An Introduction to Multi-Agent Systems. Wiley, 2002.

Freeman JA and SKAPURA, MD 1991 Neural Networks: Algorithms, Applications, and Programming Techniques. Addison Wesley Longman Publishing Co., Inc.

Graphs and Networks

Summary:

Graphs: definitions and notation; connectivity; staining problems; rental centers and medians; generation of trees; shortest paths; Eulerian and Hamiltonian problems; Pairing problems. Network flow: formulation of models, primal-dual simplex methods channeled algorithm out-of-kilter and network flow problems with multiple products.

Bibliography:

Christofides, N. Graph An Algorithmic Approach Theory-; Academic Press, 1975

KENNINGTON, JL & HELGASON, RV; Algorithm for Network Programming; John Willey & Sons, 1980

BOLLOBOAS, B .; Advances in Graph Theory; Springer, 1981

Trudeau Richard J .; Introduction to Graph Theory; Dover; 1993

WEST, DB; Introduction to Graph Theory; Prentice Hall, 1996

Rabuske, MA; Introduction to Graph Theory; Ed. UFSC; 1992

Formal Modeling of Social Systems

Summary:

extensional and intensional approaches will modeling social systems. Invariant functional social systems: organization, regulation, adaptation. extensional aspects of social systems: modularity, interconnectivity, hierarchical structure, network causality, hierarchical causality functionality. intensional aspects of social systems: values, normative, institutional. Models of complex systems minimal Formia: population structure, organizational structure, extensional and intentional dimensions. Case studies, modeling societies, social systems, institutions, etc.

Bibliography:

SIMON, H. The Science of Artificial. MIT Press, 1996

SEARLE, J. The Construction of Social Reality. The Free Press, 1995

PIAGET, J. Biology and Knowledge 1996

WOUTERS, AG Explanation | Without a Cause. University of Utrecht, 1999 (PhD. Thesis)

DIGMUN, V. Multi Agent Systems: Semantics and Models of Organizational Dynamics. IGI Global, 2009

COSTA, ACR; Dimuro, GS Minimal Dynamical Model MAS Organization.

Chapter XVII of dignum, 2009, p. 419-445

Discrete systems

Summary:

Induction co-induction, recursion and co-recursion. Functional programming: functions, data structures, typing, lazy evaluation, monadic programming. discrete systems: difference equations, dynamics of discrete systems, discrete systems programmed, streams calculation. Simulation of discrete systems with functional programming.

Bibliography:

Luenberger, Introduction to D. Dynamic Systems-Theory and Applications Models. Wiley 1979

CULL, P .; Flahive, M & ROBSON, R. Difference Equations- From Rabbits to Chaos. Springer, 2005

SA, C., Smith, M. Haskell, a practical abrodagem. Novatec, 2006

Rutten M. Stream Elements of Calculus. CWI, 2001 (R0120 Report SEN)

Systems Modeling Discrete Event

Summary:

Introductory concepts and definition of discrete systems. Presentation templates for the design of discrete systems and events. automata theory of applying the discrete systems and events. automata theory of applying the discrete systems and events. Applications of discrete systems and events.

Bibliography:

Cassandras CG; Lafortune, S. Introduction to Discrete Event Systems. 2ns Ed, Springer. 2008

AGUIRRE, LA Encyclopedia Auto: Control and Automation. Vol 1, Editora Blucher, 2007.

CARDOSO, JVR Petri Nets, Publisher of UFSC. 1997

Moraes, C .; Castrucci, Plinio de Freitas. Engineering Industrial Automation. LTC 2007

MIYAGI, PE Programmable Control. Publisher Edgard Blucher. 1996

BIRTH, CL; Yoneyama, T. Artificial Intelligence in Control and Automation. Publisher Edgard Blucher, 2000

ARNOLD, A. Finite Transation Systems. Prentice Hall, 1994

JENSEN, K. Colored Petri Nets, Second Edition. Springer, 1996

Introduction to Mathematical Programming

Summary:

Linear Programming. integer programming. Programming semi set. Combinatorial optimization. Multi-objective optimization. Algorithms of search and optimization. Case studies: structural optimization; search for parameters; general problems of roateamento, partitioning and allocation.

Bibliography:

Murty, KG (1985) and Linear combinatorial programming. Robert E. Krieger P Company

Goldberg, DE (1989) Genetic Algorithms im Search, Optimization and Machine Learning. Kluwer Academic Publishers

Goldbarg, MC & LUNA, HPL (2000) Combinatorial Optimization and Linear Programming; Models and Algorithms "Campus

Koza, J. (1992) Genetic Programming On the Programming of Computers by means of natural selection. MIT Press

Russell, SJ; Norvig, P. (2003) Artificial Intelligence: a modern Aproach (2nd Ed), Prentice Hall

HOOS, HH, Stützle, T (2004) Stochastic Local Search: Foundations and Applications

Linear algebra

Level : Master Academic
Required : No
Hours : 45
Credits : 3.0
Area of Concentration : Multidisciplinary Area

Menu :

vector spaces; domestic and standard product. linear transformations. Algebra operators. unitary and orthogonal transformations. Quadratic forms; determinants. Eigenvalues ​​and eigenvectors. Diagonalization and canonical forms. Introduction to linear differential equations.

Bibliography :

LIMA, EL Linear Algebra

HOFFMAN-KUNZE, Algebra Linear