An experiment in remote manufacturing using the advanced communications technology satellite

The goal of the completed project was to develop an experiment in remote manufacturing that would use the capabilities of the ACTS satellite. A set of possible experiments that could be performed using the Advanced Communications Technology Satellite (ACTS), and which would perform remote manufacturing using a laser cutter and an integrated circuit testing machine are described in detail. The proposed design is shown to be a feasible solution to the offered problem and it takes into consideration the constraints that were placed on the experiment. In addition, we have developed two more experiments that are included in this report: backup of rural telecommunication networks, and remote use of Synthetic Aperture Radar (SAR) data analysis for on-site collection of glacier scattering data in the Antarctic

Classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning

Article discussing research on classifying genes to the correct gene ontology slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning

Learning communication strategies in multiagent systems

In this paper we describe a dynamic, adaptive communication strategy for multiagent systems. We discuss the behavioral parameters of each agent that need to be computed, and provide a quantitative solution to the problem of controlling these parameters. We also describe the testbed we built and the experiments we performed to evaluate the effectiveness of our methodology. Several experiments using varying populations and varying organizations of agents were performed and are reported. A number of performance measurements were collected as each experiment was performed so the effectiveness of the adaptive communications strategy could be measured quantitatively. The adaptive communications strategy proved effective for fully connected networks of agents. The performance of these experiments improved for larger populations of agents and even approached optimal performance levels. Experiments with non-fully connected networks showed that the adaptive communications strategy is extremely effective, but does not approach optimality. Other experiments investigated the ability of the adaptive communications strategy to compensate for "distracting" agents, for systems where agents are required to assume the role of information routers, and for systems that must decide between routing paths based on cost information

Segmentation of satellite imagery of natural scenes using data mining

©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.In this paper, we describe a segmentation technique that integrates traditional image processing algorithms with techniques adapted from knowledge discovery in databases (KDD) and data mining to analyze and segment unstructured satellite images of natural scenes, We have divided our segmentation task into three major steps, First, an initial segmentation is achieved using dynamic local thresholding, producing a set of regions. Then, spectral, spatial, and textural features for each region are generated from the thresholded image. Finally, gi,en these features as attributes, an unsupervised machine learning methodology called conceptual clustering is used to cluster the regions found in the image into N classes-thus, determining the number of classes in the image automatically. We have applied the technique successfully to ERS-1 synthetic aperture radar (SAR), Landsat thematic mapper (TM), and NOAA advanced very high resolution radiometer (AVHRR) data of natural scenes

Information fusion for estimation of summer MIZ ice concentration from SAR imagery

©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.In this paper we define the concept of information fusion and show how we used it to estimate summer sea ice concentration in the marginal ice zone (MIZ) from single-channel SAR satellite imagery, We used data about melt stage, wind speed, and surface temperature to generate temporally-accumulated information, and fused this information with the SAR image, resulting in an interpretation of summer MIZ imagery, We also used the results of previous classifications of the same area to guide and correct future interpretations, thus fusing historical information with imagery and nonimagery data. We chose to study the summer MIZ since summer melt conditions cause classification based upon backscatter intensity to fail, as the backscatter of open water, thin ice, first-year ice, and multiyear ice overlap to a large degree. This makes it necessary to fuse various information and data to achieve proper segmentation and automated classification of the image. Our results were evaluated qualitatively and showed that our approach produces very good ice concentration estimates in the summer MIZ

ARKTOS: a knowledge engineering software tool for images

The goal of our ARKTOS project is to build an intelligent knowledge-based system to classify satellite sea ice images. It involves acquiring knowledge from sea ice experts, quantifying such knowledge as computational entities and ultimately building an intelligent classifier. In this paper we describe a two-stage knowledge engineering approach that facilitates explicit knowledge transfer, converting implicit visual cues and cognition of the experts to explicit attributes and rules implemented by the engineers. First, there is a prototyping stage that involves interviewing sea ice experts, transcribing the sessions, identifying descriptors and rules, designing and implementing the knowledge and delivering the prototype. The objective of this stage is to obtain a modestly accurate classification system quickly. Second, there is a refinement stage that involves evaluating the prototype, refining the knowledge base, modifying the design and re-evaluating the improved system. Since the refinement is evaluation-driven, the experts and the engineers are motivated explicitly to improve the knowledge base and are able to communicate with each other using a common, consistent platform. Moreover, since the classification result is immediately available, both sides are able to efficiently assess the correctness of the system. To facilitate the knowledge engineering of the second stage, we have designed and built three Java-based graphical user interfaces: arktosGUI, arktosViewer and arktosEditor. arktosGUI concentrates on feature-based refinement of specific attributes and rules. arktosViewer deals with regional evaluation. arktosEditor has a rule indexing and search mechanism and knowledge base editing capabilities. (C) 2002 Elsevier Science Ltd. All rights reserved

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. We used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture, We conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM by examining the effects textural descriptors such as entropy have in the representation of different sea ice textures, We showed that a complete gray-level representation of the image is not necessary for texture mapping, an eight-level quantization representation is undesirable for textural representation, and the displacement factor in texture measurements is more important than orientation. In addition, we developed three GLCM implementations and evaluated them by a supervised Bayesian classifier on sea ice textural contexts. This experiment concludes that the best GLCM implementation in representing sea ice texture is one that utilizes a range of displacement values such that both microtextures and macrotextures of sea ice can be adequately captured, These findings define the quantization, displacement, and orientation values that are the best for SAR sea ice texture analysis using GLCM

Analyzing lead information from SAR images

©1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Leads are relatively linear features in the sea ice cover, which are composed of open water or new, thin ice, Because of their composition, leads impact the ocean/air heat exchange, Automated analysis of leads from sea ice imagery may provide a means of gathering important information about the sea ice cover and its climatic influence, This paper describes: 1) a method for extracting and analyzing leads from ERS-1 synthetic aperture radar (SAR) images classified by ice type and 2) the results of using this method on images of the Beaufort Sea, The methodology consists of identifying potential lead features in the image and measuring their characteristics both before and after using a thinning or skeletonization technique on the features. The measurements obtained using this method include lead area, average width, number of leads in an area, amount of branching, and linearity of the lead, These measurements were analyzed with respect to the time of year and the latitude of the images. Results indicate that the measurements produced by the methodology are consistent with lead measurement distributions that others have found, The results of the study suggest that the methodology is appropriate to study lead characteristics on a large scale