21,046 research outputs found

    A Morphological and Multicolor Survey for Faint QSOs in the Groth-Westphal Strip

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    Quasars representative of the populous faint end of the luminosity function are frustratingly dim with m~24 at intermediate redshift; moreover groundbased surveys for such faint QSOs suffer substantial morphological contamination by compact galaxies having similar colors. In order to establish a more reliable ultrafaint QSO sample, we used the APO 3.5-m telescope to take deep groundbased U-band CCD images in fields previously imaged in V,I with WFPC2/HST. Our approach hence combines multicolor photometry with the 0.1" spatial resolution of HST, to establish a morphological and multicolor survey for QSOs extending about 2 magnitudes fainter than most extant groundbased surveys. We present results for the "Groth-Westphal Strip", in which we identify 10 high likelihood UV-excess candidates having stellar or stellar-nucleus+galaxy morphology in WFPC2. For m(606)<24.0 (roughly B<24.5) the surface density of such QSO candidates is 420 (+180,-130) per square degree, or a surface density of 290 (+160,-110) per square degree with an additional V-I cut that may further exclude compact emission line galaxies. Even pending confirming spectroscopy, the observed surface density of QSO candidates is already low enough to yield interesting comparisons: our measures agree extremely well with the predictions of several recent luminosity function models.Comment: 29 pages including 6 tables and 7 figures. As accepted for publication in The Astronomical Journal (minor revisions

    Representation of Functional Data in Neural Networks

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    Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data analysis.Comment: Also available online from: http://www.sciencedirect.com/science/journal/0925231

    Power-Assist Wheelchair Attachment

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    This senior design project sought to combine the best characteristics of manual and power wheelchairs by creating a battery-powered attachment to propel a manual wheelchair. The primary customer needs were determined to be affordability, portability, and travel on uneven surfaces. After the initial prototype, using a hub motor proved unsuccessful, so a second design was developed that consisted of a gear reduction motor and drive wheel connected to the back of the wheelchair by a trailing arm that could be easily attached/detached from the frame. The prototype of the second design succeeded in meeting most of the project goals related to cost, off-road capability, inclines, and range. Improvements can be made by reducing the attachment weight and improving user control of the device

    Further results on dissimilarity spaces for hyperspectral images RF-CBIR

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    Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user's feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions defined either on spectral and spatial features extracted by spectral unmixing techniques, or on dictionaries extracted by dictionary-based compressors. These dissimilarity functions were not suitable for direct application in common machine learning techniques. We propose to use a RF general approach based on dissimilarity spaces which is more appropriate for the application of machine learning algorithms to the hyperspectral RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over a real hyperspectral dataset.Comment: In Pattern Recognition Letters (2013

    An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

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    In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network)

    An overview of decision table literature 1982-1995.

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    This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

    Mass data exploration in oncology: An information synthesis approach

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    New technologies and equipment allow for mass treatment of samples and research teams share acquired data on an always larger scale. In this context scientists are facing a major data exploitation problem. More precisely, using these data sets through data mining tools or introducing them in a classical experimental approach require a preliminary understanding of the information space, in order to direct the process. But acquiring this grasp on the data is a complex activity, which is seldom supported by current software tools. The goal of this paper is to introduce a solution to this scientific data grasp problem. Illustrated in the Tissue MicroArrays application domain, the proposal is based on the synthesis notion, which is inspired by Information Retrieval paradigms. The envisioned synthesis model gives a central role to the study the researcher wants to conduct, through the task notion. It allows for the implementation of a task-oriented Information Retrieval prototype system. Cases studies and user studies were used to validate this prototype system. It opens interesting prospects for the extension of the model or extensions towards other application domains
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