The vapor classification performance of arrays of conducting polymer composite vapor detectors has been evaluated as a function of the number and type of detectors in an array. Quantitative performance comparisons were facilitated by challenging a collection of detector arrays with vapor discrimination tasks that were sufficiently difficult that at least some of the arrays did not exhibit perfect classification ability for all of the tasks of interest. For nearly all of the discrimination tasks investigated in this work, classification performance either increased or did not significantly decrease as the number of chemically different detectors in the array increased. Any given subset of the full array of detectors, selected because it yielded the best classification performance at a given array size for one particular task, was invariably outperformed by a different subset of detectors, and by the entire array, when used in at least one other vapor discrimination task. Arrays of detectors were nevertheless identified that yielded robust discrimination performance between compositionally close mixtures of 1-propanol and 2-propanol, n-hexane and n-heptane, and meta-xylene and para-xylene, attesting to the excellent analyte classification performance that can be obtained through the use of such semi-selective vapor detector arrays.
Thin films of carbon black-organic polymer composites have been deposited across two metallic leads, with swelling- induced resistance changes of the films signaling the presence of vapors. To identify and classify vapors, arrays of such vapor sensing elements have been constructed. Each element contained a different organic polymer as the insulating phase. The differing gas-solid partition coefficients for the various polymers of the detector array produced a pattern of resistance changes that was used to classify vapors and vapor mixtures. The performance of this system towards DNT, the predominant signature in the vapor phase above land miens, has been evaluated in detail, with robust detection demonstrated in the laboratory in less than 5 s in air at DNT levels in the low ppb range.
This paper describes a prototype visual discovery algorithm that is designed to identify regions of an image that differ significantly from the local background. Image regions are projected into a visually-relevant subspace using a set of multi-orientation, multi-scale Gabor filters that model the receptive field properties of simple cells in the human visual cortex. Within this filter response subspace, deviant areas are identified through an adaptive statistical test that compares the filter-space description of a region against a model derived from the local background. Deviant regions are then spatially agglomerated and grouped across scale. Experimentation on a variety of archived imagery collected by JPL spacecraft and ground-based telescopes shows that the algorithm is able to autonomously 're-discover' a number of important geological objects such as impact craters, volcanoes, sand dunes, and ice geysers that are known to be of interest to planetary scientists.
Diamond Eye is a distributed software architecture, which enables users (scientists) to analyze large image collections by interacting with one or more custom data mining servers via a Java applet interface. Each server is coupled with an object-oriented database and a computational engine, such as a network of high-performance workstations. The database provides persistent storage and supports querying of the 'mined' information. The computational engine provides parallel execution of expensive image processing, object recognition, and query-by-content operations. Key benefits of the Diamond Eye architecture are: (1) the design promotes trial evaluation of advanced data mining and machine learning techniques by potential new users (all that is required is to point a web browser to the appropriate URL), (2) software infrastructure that is common across a range of science mining applications is factored out and reused, and (3) the system facilitates closer collaborations between algorithm developers and domain experts.
One of the methods that can be used to enhance stationary target detection performance is to combine radar data from several looks at an area that may contain targets. This paper presents a study of several multilook techniques. The data used in the study were collected using the MIT Lincoln Laboratory 33.6 GHz Synthetic Aperture Radar (SAR) in the spotlight mode; this mode maintains the radar beam on the same area as the aircraft flies by. Consecutive 0.3 m by 0.3 m resolution images were registered to a single coordinate frame, and then combined in various ways. The processing techniques studied included some methods that combine the data prior to detection (such as noncoherent averaging, which reduces speckle), and others- that combine the detections from individual images (such as techniques that require m detections in n images).
This paper considers the problem of clutter segmentation in fully polarimetric, high-resolution, synthetic aperture radar (SAR) imagery. The goal of segmentation is to partition an image into regions of homogeneous terrain types (grass regions, tree regions, roads, etc.). Three approaches to segmentation are examined: (1) the optimal polarimetric classifier, (2) the optimal normalized polarimetric classifier, and (3) the polarimetric whitening filter (PWF) classifier. Segmentation performance results are presented for typical high-resolution, polarimetric SAR data gathered by the Lincoln Laboratory 35-GHz airborne sensor.
The Advanced Detection Technology Program has as one objective the application of fully polarimetric, high-resolution radar data to the detection, discrimination, and classification of stationary targets. In support of this program, the Advanced Detection Technology Sensor (ADTS), a fully polarimetric, 35-GHz SAR with 1 ft by 1 ft resolution was developed. In April of 1989, the ADTS gathered target and clutter data near Stockbridge, NY. Data from this collection is being used to investigate optimal polarimetric processing techniques. This paper summarizes the results of a recent study of an optimal polarimetric method for reducing speckle in SAR imagery.
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