Multimodal Olfactory Scence Analysis

Abstract

This describes our effort for understanding biological and artifical olfactory systems along three multi-disciplinary fronts: 1. Experimental characterization of biological olfactory systems in their speed and adaptiveness to novel odors; 2. Mathematical modeling of the effective of various olfactory search strategies; 3. Machine learning algorithms for analyzing olfactory sensor data.

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Document Details

Document Type
Technical Report
Publication Date
Jul 25, 2006
Accession Number
ADA455446

Entities

People

  • Alan Gelperin
  • Boris Shraiman
  • Daniel D. Lee

Organizations

  • University of Pennsylvania

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Bayesian Networks
  • Carbon Nanotubes
  • Computational Science
  • Detection
  • Discrimination
  • Firing Rate
  • Information Processing
  • Information Systems
  • Machine Learning
  • Pattern Recognition
  • Recognition
  • Robotics
  • Signal Processing
  • Students

Fields of Study

  • Biology

Readers

  • Computer Science.
  • Distributed Systems and Data Platform Development
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms