Data Science

Nonlinear phenomena are ubiquitous in our world. We develop new theories, methods and algorithms for analyzing and predicting nonlinear phenomena.

  1. Bootstrap nonlinear prediction
  2. Nonlinear DPCM for image signals
  3. Nonlinear analysis on complex movements of dishwasher
  4. Nonlinear models for headway of bus and nonlinear prediction for traffic flows
  5. Prediction systems of lightning
  6. Nonlinear analysis on mechanisms and prediction systems for seismic events
  7. Local weather prediction
  8. Nonlinear analysis on the Internet traffic
  9. Nonlinear analysis and prediction for the Nikkei Stock Average and exchange markets
  10. Statistical analysis on neuronal spikes
  11. Prediction and control for harvest of crops
  12. Nonlinear analysis on brain waves and pulse waves and for human status diagnostics
  13. Nonlinear analysis and synthesis of the audio signals

Neuro Science

In our brain, many neurons connect each other and construct huge neural networks. What is the basic principle of information processing in our brain? In this project, we will clarify new information processing principles used in our brain and apply it to resolve engineering issues.

  1. Analysis on synchronous firing phenomena using spike-timing-dependent plasticity (STDP) learning rules
    • Firing mechanism
    • Phase consistency (Consistency of firing timing)
  2. Analysis on information coding in the brain
    • Temporal coding (Depending on the timing when neuron fires)?
    • Rate coding (Depending on the firing rate)?
    • Dual information coding (Both temporal coding and rate coding)?
  3. Models for associative memory
    • What happens when we use chaotic dynamics?
    • What happens when we use chaos as noise?
    • Estimation of Lyapunov exponents by diagonalization of large-scale matrix using techniques of numerical calculations

Chaotic Optimization

In our world, we are often asked to solve combinatorial optimization problems. In this project, we develop a new framework for solving very large scale combinatorial optimization problems by chaotic dynamics.

  1. Traveling salesman problems
  2. Quadratic assignment problems
  3. Lin-Kernighan algorithms controlled by chaotic dynamics
  4. Dynamic shortest path search problems
    • Packets routing problems
    • Controlling traffics and signals
  5. Motif extraction problems in DNA sequence and amino acids
  6. Vehicle routing problems with the constrained time frame
  7. Timetable scheduling problems
  8. Variable-shape-block segmentation for image signals
  9. Graph partitioning problems
  10. Wiring problems
  11. Drilling problems
  12. VLSI design
  13. Chaotic ant system
  14. Computational theory with chaotic search and quantum computing

Network Science

Many complex networks exist in our world. Using the novel complex network theory, we analyse how complex networks are constructed in our real world.

  1. Analysis on spike-timing-dependent plasticity (STDP) learning rules using the complex network theory
    • Small-world?
    • Scale-free?
    • Developing quantification techniques as weighted digraph
  2. Relations between network structures and nonlinear dynamics
    • Estimation of network structures from multi-variable time series
    • Estimation of network structures from multi neuronal spike data
  3. Analysis on railway networks using the complex network theory
  4. Analysis on the number of infectious disease and prediction of epidemic
You can precede other research project! Please contact Prof. Ikeguchi.
The keywords are

Nonlinear dynamics



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