D
Dan Smith
Researcher at University of East Anglia
Publications - 53
Citations - 1363
Dan Smith is an academic researcher from University of East Anglia. The author has contributed to research in topics: Information extraction & Context awareness. The author has an hindex of 20, co-authored 52 publications receiving 1268 citations. Previous affiliations of Dan Smith include Texas A&M University & Norwich University.
Papers
More filters
Journal ArticleDOI
Acoustic environment classification
Ling Ma,Ben Milner,Dan Smith +2 more
TL;DR: A prototype HMM-based acoustic environment classifier incorporating an adaptive learning mechanism and a hierarchical classification model is described, which can accurately classify a wide variety of everyday environments.
Journal ArticleDOI
A thermally stable gold(III) hydride: synthesis, reactivity, and reductive condensation as a route to gold(II) complexes.
TL;DR: The first thermally stable gold(III) hydride [(C N C)*AuH] is presented and undergoes regioselective insertions with allenes to give gold( III) vinyl complexes.
Journal ArticleDOI
Cyclometallated gold(III) hydroxides as versatile synthons for Au–N, Au–C complexes and luminescent compounds
TL;DR: The gold(III) hydroxide reacts with C-H and N-H compounds and arylboronic acids to produce a range of perfluoroaryls, N-heterocyclic and alkynyl compounds in high yields, some of which show unexpectedly strong modulation of their photoluminescence from yellow to blue.
Journal ArticleDOI
T-cell-independent granuloma formation in response to Mycobacterium avium: role of tumour necrosis factor-alpha and interferon-gamma.
TL;DR: Data show for the first time that secretion of IFN‐γ from NK cells can mediate a T‐cell‐independent pathway of granuloma formation and cellular infiltration in response to mycobacteria.
Proceedings Article
Context Awareness using Environmental Noise Classification
Ling Ma,Dan Smith,Ben Milner +2 more
TL;DR: The approach for automatically sensing and recognising noise from typical environments of daily life, such as office, car and city street, is described and the hidden Markov model based noise classifier is presented.