@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix org: <http://www.w3.org/ns/org#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://data.nasa.gov/dataset/96e8d9f9-a20b-49a5-b1ea-ebd595bd5b54> a dcat:Dataset ;
    dct:description """In adaptive signal processing, joint process estimation plays\r
an important role in various estimation problems. It is well\r
known that a joint process estimator consists of two struc-\r
tures, namely the orthogonalizer and the regression filter. In\r
literature, orthogonalization step is performed either by or-\r
thogonal transformations or by linear predictors. While the\r
orthogonal transformations do not preserve entropy; the\r
predictors, such as the lattice, do preserve it. However, the\r
steady-state performance of such linear predictors is not as\r
good as those of the orthogonal transformations. Lattice\r
filters do not perform perfect orthogonalization when they\r
operate as gradient-based adaptive predictors. In this work,\r
adaptive escalator predictor is proposed to be used as the\r
orthogonalizer of the joint process estimator. The proposed\r
method preserves the entropy and achieves perfect orthogo-\r
nalization at all times. Moreover it has good steady-state\r
performance compared to those structures utilizing gradient\r
adaptive lattice filters.""" ;
    dct:identifier "DASHLINK_213" ;
    dct:issued "2010-09-22"^^xsd:date ;
    dct:modified "2020-01-29"^^xsd:date ;
    dct:publisher <https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> ;
    dct:title "On The Performance Comparison of Gradient Type Joint-Process Est" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "Deniz Gencaga" ;
            vcard:hasEmail <mailto:dgencaga@gmail.com> ] ;
    dcat:keyword "ames",
        "dashlink",
        "nasa" .

<https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> a org:Organization ;
    skos:prefLabel "NASA" ;
    foaf:name "NASA" .