Learning and using taxonomies for visual and olfactory classification

Humans are able of distinguishing more than 5000 visual categories even in complex environments using a variety of different visual systems all working in tandem. We seem to be capable of distinguishing thousands of different odors as well. In the machine learning community, many commonly used multi-class classifiers do not scale well to such large numbers of categories. This thesis demonstrates a method of automatically creating application-specific taxonomies to aid in scaling classification algorithms to more than 100 cate- gories using both visual and olfactory data. The visual data consists of images collected online and pollen slides scanned under a microscope. The olfactory data was acquired by constructing a small portable sniffing apparatus which draws air over 10 carbon black polymer composite sensors. We investigate performance when classifying 256 visual categories, 8 or more species of pollen and 130 olfactory categories sampled from common household items and a standardized scratch-and-sniff test. Taxonomies are employed in a divide-and-conquer classification framework which improves classification time while allowing the end user to trade performance for specificity as needed. Before classification can even take place, the pollen counter and electronic nose must filter out a high volume of background “clutter” to detect the categories of interest. In the case of pollen this is done with an efficient cascade of classifiers that rule out most non-pollen before invoking slower multi-class classifiers. In the case of the electronic nose, much of the extraneous noise encountered in outdoor environments can be filtered using a sniffing strategy which preferentially samples the visensor response at frequencies that are relatively immune to background contributions from ambient water vapor. This combination of efficient background rejection with scalable classification algorithms is tested in detail for three separate projects: 1) the Caltech-256 Image Dataset, 2) the Caltech Automated Pollen Identification and Counting System (CAPICS) and 3) a portable electronic nose specially constructed for outdoor use.

Strength of thin-walled elliptical cylinders supported at the minor axis

In this investigation it was found that the instability failure of curved sheet is nearly independent of the type of loading and is primarily a function of the maximum stress, radius-thickness ration and modulus of elasticity. A method of correlating the critical stress of thin sheet under several different types of loading is given. An explanation for the experimental critical stress of thin walled cylinders under bending being greater than that for pure compression is given. The strength of unstiffened thin walled circular nose sections under pure bending was found to be controlled by local instability of the section, rather than a large scale instability. The equation of local instability of curved sheet gives values which are in fair agreement with those found experimentally.

The strength of elliptical cylinders supported at the minor axis under bending plus shear loads is governed primarily by the bending strength, and is little effected by the sheer force unless the amount of shear is quite large with respect to the moment. The effect of increasing the amount of elliptically greatly reduces the bending and shear strength of nose sections. Under torsional loads the stress at buckling falls off as the ration of the major to minor axis increases but the failure stress decreases at a slower rate than the buckling stress. The length effect of semi-circular sections under torsion is similar to that of a circular tube, and can be obtained by Donnell's theoretical equation.