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Smelly Software

Smelly Software. Elizabeth Morin Penn State. Review of e-Noses. Schematic. Extracellular Frog. Cellular. Schematic. Algorithm ANN. Odors ANN (Artificial Neural Network). Trained to 27 odor 9 known, 18 ? Odor recognition harder as number of odors or noise increases.

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Smelly Software

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  1. Smelly Software Elizabeth Morin Penn State Review of e-Noses

  2. Schematic Extracellular Frog Cellular

  3. Schematic Algorithm ANN

  4. Odors ANN (Artificial Neural Network)

  5. Trained to 27 odor 9 known, 18 ? Odor recognition harder as number of odors or noise increases Number ANN= number odors= number sensors Noise is presence of background gas This isn’t practical.

  6. Olfactory Mucosa v. Gas Chromatography A T X X T A In single-component solution, brain identifies X distinctly; while X might not be differentiated given XAT Response XAT = Response X + A + T Nosingle spatial molecular predictor of response! Resolution is spatio-temporal LessExpensive, Sensors Start at $1 Each component of Solution B is separated; methanol peak from solution can only be named as methanol by comparison with methanol-containing solution. Strongcorrelations between molecular predictor of structure and response! Expensive Equipment Electronic Noses Complement Technology When Describing Multi-Component Solutions Holistically! Can GC complement e-nose?

  7. Spatial versus Spatio-TemporalE-nose + Gas Chromatography GC GC Sensor Sensor Ethanol or toulene concentration in ethanol-toluene solutions 15 Sensors (right) Error with respect to sensors (left) Peppermint and p’rmint+vanilla solution 15 Sensors (both) Researchers Sachez-Montanes; Gardner; Pearce

  8. Chemoresistor Sensors Metal Oxide Semiconductors Air Reducing Gas Grain Boundary R Chemocapacitor Sensors Conducting Polymer V ε C ΔVΔε ΔC Gravimetric (Acoustic) Sensors SAW, QCM and Optical Sensors Thin metal Film

  9. Hybrid Algorithms For Noisy Data Classification Genetic Algorithm (GA) :cluster rules within full search space Radial Basis Probabilistic NN (RNN) :random odor # ~ 1-9 :refined rules Fuzzy Subtractive Clustering (CA) : increases odor # : noise Hybrid Intelligence Classifier GARNNCA :Cost: Performance Pattern Data (Left) Noisy Pattern Data (right)

  10. Swarm Intelligence and E-noses CalTech

  11. Think About It • What applications can you think of, in addition to: • Fire and Explosives Detection • Perfume & Fragrances Industry • E-noses using linguistic descriptors (like conessour) • Body Odor Detection (Natural Disasters) • Lung Cancer Detection (Medicinal Uses) • Coffee (Food Industry, Components or Spoiling of Products, or Pest Control) • Vehicle Quality (Automotive, Military, Aerospace) • Natural Product Analysis (Chemical Synthesis, Pharmaceutical) • Smart Houses • Nanonose • Chemical Detection (Environmental, Occupational Safety) • Stationary Nose • Mobile Nose • There are so many papers discussing different types of algorithms, which a computerscientist could write, especially wind compensation for outdoor noses and background gas compensation for all noses • Bio-inspired noses incorporating spatial and temporal information, like a gas chromatograph with sensors… they just mapped the spatio-temporal aspect of the nose this year… what does this mean in terms of algorithms [Ref. C] • Cost matters… addition of sensors and more advanced algorithms increase costs

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