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

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Presentation on theme: "Smelly Software Elizabeth Morin Penn State Review of e-Noses."— Presentation transcript:

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

2 Cellular Extracellular Frog Schematic

3 Algorithm ANN Schematic

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 X ATX A T In single-component solution, brain identifies X distinctly; while X might not be differentiated given XAT Response XAT = Response X + A + T No single spatial molecular predictor of response! Resolution is spatio-temporal Less Expensive, 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. Strong correlations 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-Temporal E-nose + Gas Chromatography Peppermint and p’rmint+vanilla solution 15 Sensors (both) Ethanol or toulene concentration in ethanol-toluene solutions 15 Sensors (right) Error with respect to sensors (left) GC Sensor GC Sensor Researchers Sachez-Montanes; Gardner; Pearce



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

11 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 Pattern Data (Left) Noisy Pattern Data (right) Hybrid Intelligence Classifier GA RNN CA :Cost: Performance

12 If S(x,m) > t Then x belongs to pass subset Else x belongs to fail subset x is an unknown pattern S(x,m) is a similarity measure between the mask m and the unknown pattern x Cluster Analysis (CA) Hybrid Intelligence Classifier GA RNN CA


14 Swarm Intelligence and E-noses CalTech

15 Think About It 1)What applications can you think of, in addition to: A)Fire and Explosives Detection B)Perfume & Fragrances Industry C)E-noses using linguistic descriptors (like conessour) D)Body Odor Detection (Natural Disasters) E)Lung Cancer Detection (Medicinal Uses) F)Coffee (Food Industry, Components or Spoiling of Products, or Pest Control) G)Vehicle Quality (Automotive, Military, Aerospace) H)Natural Product Analysis (Chemical Synthesis, Pharmaceutical) I)Smart Houses J)Nanonose K)Chemical Detection (Environmental, Occupational Safety) i)Stationary Nose ii)Mobile Nose 2)There are so many papers discussing different types of algorithms, which a computer scientist could write, especially wind compensation for outdoor noses and background gas compensation for all noses 3)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] 4)Cost matters … addition of sensors and more advanced algorithms increase costs

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