US Army remotely piloted vehicle supporting technology program

Essential technology programs that lead to the full scale engineering development of the Aquila Remotely Piloted Vehicle system for U.S. Army are described. The Aquila system uses a small recoverable and reusable RPV to provide target acquisition, designation, and aerial reconnaissance mission support for artillery and smart munitions. Developments that will provide growth capabilities to the Aquila RPV system, as well as future RPV mission concepts being considered by the U.S. Army are presented

Ice/frost detection using millimeter wave radiometry

A series of ice detection tests was performed on the shuttle external tank (ET) and on ET target samples using a 35/95 GHz instrumentation radiometer. Ice was formed using liquid nitrogen and water spray inside a test enclosure containing ET spray on foam insulation samples. During cryogenic fueling operations prior to the shuttle orbiter engine firing tests, ice was formed with freon and water over a one meter square section of the ET LOX tank. Data analysis was performed on the ice signatures, collected by the radiometer, using Georgia Tech computing facilities. Data analysis technique developed include: ice signature images of scanned ET target; pixel temperature contour plots; time correlation of target data with ice present versus no ice formation; and ice signature radiometric temperature statistical data, i.e., mean, variance, and standard deviation

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

Remote sensing applications to forest vegetation classification and conifer vigor loss due to dwarf mistletoe

Criteria was established for practical remote sensing of vegetation stress and mortality caused by dwarf mistletoe infections in black spruce subboreal forest stands. The project was accomplished in two stages: (1) A fixed tower-tramway site in an infected black spruce stand was used for periodic multispectral photo coverage to establish basic film/filter/scale/season/weather parameters; (2) The photographic combinations suggested by the tower-tramway tests were used in low, medium, and high altitude aerial photography

Transforming Message Detection

The majority of existing spam filtering techniques suffers from several serious disadvantages. Some of them provide many false positives. The others are suitable only for email filtering and may not be used in IM and social networks. Therefore content methods seem to be more efficient. One of them is based on signature retrieval. However it is not change resistant. There are enhancements (e.g. checksums) but they are extremely time and resource consuming. That is why the main objective of this research is to develop a transforming message detection method. To this end we have compared spam in various languages, namely English, French, Russian and Italian. For each language the number of examined messages including spam and notspam was about 1000. 135 quantitative features have been retrieved. Almost all these features do not depend on the language. They underlie the first step of the algorithm based on support vector machine. The next stage is to test the obtained results applying N-gram approach. Special attention is paid to word distortion and text alteration. The obtaining results indicate the efficiency of the suggested approach

Pseudomonas aeruginosa can be detected in a polymicrobial competition model using impedance spectroscopy with a novel biosensor

Electrochemical Impedance Spectroscopy (EIS) is a powerful technique that can be used to elicit information about an electrode interface. In this article, we highlight six principal processes by which the presence of microorganisms can affect impedance and show how one of these - the production of electroactive metabolites - changes the impedance signature of culture media containing Pseudomonas aeruginosa. EIS, was used in conjunction with a low cost screen printed carbon sensor to detect the presence of P. aeruginosa when grown in isolation or as part of a polymicrobial infection with Staphylococcus aureus. By comparing the electrode to a starting measurement, we were able to identify an impedance signature characteristic of P. aeruginosa. Furthermore, we are able to show that one of the changes in the impedance signature is due to pyocyanin and associated phenazine compounds. The findings of this study indicate that it might be possible to develop a low cost sensor for the detection of P. aeruginosa in important point of care diagnostic applications. In particular, we suggest that a development of the device described here could be used in a polymicrobial clinical sample such as sputum from a CF patient to detect P. aeruginosa