Business Cases

• Evaluating the stimulus response of spike information - software development

  We developed software that recorded population potentials and then used a sorting technique to separate each spike signal from the population potential, then clustered them by using their responsiveness to stimuli.

  In this example, we gave 11 types of oscillatory stimuli. The evaluation items of the neural response to each stimulus were (1) the presence or absence of a response, (2) the dominant frequency when a constant frequency response was observed, and (3) the time variation of the dominant frequency.

  The main frequency components were evaluated using the autocorrelation function. The response oscillatory characteristics were classified based on the time change of the autocorrelation function. These parameters were automatically extracted and an interface was created so that they could be passed to statistical processing software.

  

  Technologies

  • autocorrelation function
  • C
  • Visual Basic

• Medical support image processing system - development

  We have developed a system to automatically detect abnormal tissue areas from HE-stained tissue images.

  In this field, pattern matching methods have been mainly used, but instead of using this, we adopt a method that uses topological geometry to extract image features.
  Compared to similar systems, the processing speed is significantly improved.

  Technologies

  • C#
  • Histogram Processing
  • Peak Extraction Algorithm
  • Labeling Algorithm
  • Shape Extraction Process
  • Convolution Integral
  • Histogram Correction
  • Automatic White Balance Correction Algorithm
  • Color Deviation Automatic Correction Algorithm

• Bayesian networks - technical survey

  Bayesian Network is a technology of modeling causal relations between stochastic variables. In this work, we surveyed

  • Inference Algorithms
  • Parameter Learning Algorithms
  • Structure Learning Algorithms

  We made a report document which can be used until implementing as software program. We also surveyed concrete applied examples of Bayesian Networks to real world.

  Mainly surveyed the following methods:

  • Message Passing Algorithm (Probability Propagation)
  • SPI Algorithm (Symbolic Probability Inference)
  • Likelihood Weighted Sampling Method
  • Parameter Learning by EM Algorithm based on Dirichlet Distribution
  • K2 Structure Learning Algorithm

  Mainly surveyed the following application examples:

  • Abnormal Identification of Aircraft Gas Turbine Engine
  • Abnormal Diagnosis of Ship Engine Room
  • Abnormal Diagnosis of Turbine Power Generator
  • Anomaly Detection of Machines by Vibrations and Sounds
  • Anomaly Detection of Auto Door
  • Anomaly Detection of Space Appliances

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