Networks at the Convergence of IT, BT, NT

포항공과대학교
지안나우렐리오 쿠니베르티

페이스북 트위터 카카오톡 네이버밴드 링크복사

주제분류

공학 >컴퓨터ㆍ통신 >정보통신공학
강의학기

2011년 1학기

조회수

3,610

Present a broad view of different network structures playing a role in computer, biological and physical sciences and engineering. Learning new advanced statistical methods for characterizing and predicting the behavior of such complex systems.Class Main Topic(1) Complex architectures: biological networks (immune networks, gene networks, methabolic networks, neural circuitry) and IT (artificial neural networks, internet, financial time series) networks.(2) Nanostructures and molecular assembly: (nanolithography, molecular selfassembly, molecular (DNA) computing).(3) Elements of stochastic processes. Non parametric statistics, Levy flights.(4) Scale-free networks.(5) Non linear time series analysis.(6) Simulation methods of complex random structures.

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Introduction to graph theory

차시별 강의

1.	Introduction to graph theory	1. Linear graphs: L(N) and L(N,S) 2. Reptation: SAW and RW 3. Minimal spanning tree 4. Bond percolation
2.	Examples of complex networks	1. Self-similarity, 2. Tress, 3. Mountains, rough surfaces, 4. Sierpinski triangle: fractal dimension, 5. Fractal Silica, 6. Tree-like structures in Biology, 7. DNA, 8. Percolation, 9. Finance, 10. Internet
3.	Mobile networks	1. Human mobile networks: crouds, 2. Mobile networks in Nature, 3. MST: the Dijkstra algorithm, 4. Examples of MST, 5. Further applications of mobile networks, 6. Forest fires.
4.	DNA	1. DNA, 2. Chromosomes, 3. Transcription of DNA, 4. DNA Nanotechnology, 5. DNA in the nucleus of cell, 6. RNA secondary strcutures, 7. Translation: Proteins formation, 8. Models of proteins.
5.	DNA	1. DNA, 2. Chromosomes, 3. Transcription of DNA, 4. DNA Nanotechnology, 5. DNA in the nucleus of cell, 6. RNA secondary strcutures, 7. Translation: Proteins formation, 8. Models of proteins.
6.	Exercises discussion. Protein models	1. Lattice dynamics for protein folding, 2. Lattice protein models, 3. Clusjatering coefficient, 4. Betweenness.
7.	Theory of networks	1. Random graphs, 2. Node degree distribution, 3. Scale-free networks. 4. Examples.
8.	Scale-free network models	1. Modeling scale-free networks with a given exponent, 2. Random models with given degree distribution, 3. Optimization algorithms.
9.	Fractal dimension and convergence networks.	1. Regular and fractal structures, 2. Random trees fractal dimensions, 3. Scale-free networks and clustering coefficients. 4. Networks on regular lattices: percolation. 5. Scale-free networks in biology. 6. Modules, 7. Human eye, 8. Alveoli.
10.	Student projects discussion	1. Network feature dependency, 2. Graph theory for anomalous traffic detection, 3. Protein-protein networks, 4. Mobility patterns in human networks, 5. Improved routing algorithm for random sensors, 6. Complex networks for Software engineering, 7. Graph theory for cancer therapy.
11.	Generic model of scale-free networks	1. Barabasi model: analytical solution, 2. Random nodes in space: optimization model, 3. Shrotest paths structures. 4. Continuum percolation: Theory and models.
12.	Stochastic processes and Levy flights.	1. Random walks and time series, 2. Central limit theorem, 3. Levy distribution, 4. Memory: short and long range. 5. Correlations: Hurst exponent.
13.	Levy flights statistics	1. Modeling Levy walks, 2. Distribution functions, 3. Correlations and stationarity, 4. Air temperature correlations in the atmosphere, 5. Correlation function and Hurst exponent.
14.	Fractal landscapes and profiles	1. Self-similarity and self-affinity, 2. Fractal decomposition for self-affine profiles, 3. Random fractal surfaces, 4. Temperature fluctuations and correlations, 5. Wavelets.
15.	Fractional derivates	1. Concept of fractional derivative, 2. Fractional random walks: Hurst exponent, 3. Distribution functions
16.	Student projects discussion: Mid-term presentation.	(see also 9)
17.	Stochastic models with long range memory	1. Autoregressive models, 2. Power-law distributions, 3. Financial time series: modeling stock markets, 4. Non-stationarity issues, 5. Fractional random walk model for stocks. 6. Handwriting process: long range memory versus short range behavior.
18.	Complex random networks	1. Complex system of chemical reactions, 2. Image denoising method based on wavelets, 3. Polymers in solution and attachment on a surface, 4. Glass sponges and multifunctional hybrid materials.