Application of Real-Time Loudness Models Can Improve Speech Recognition for Cochlear Implant Users

The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation. Recent research using numerical models of loudness perception has identified that there are large differences between how loudness is encoded by...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on neural systems and rehabilitation engineering Ročník 21; číslo 1; s. 81 - 87
Hlavní autoři: Varsavsky, Andrea, McDermott, Hugh J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acoustics
Algorithms
Auditory system
Cochlear implant (CI)
Cochlear Implants
Computational modeling
Computer Simulation
Computer Systems
Ear
Electrodes
Implants
loudness encoding
loudness model
Models, Theoretical
Pattern Recognition, Automated - methods
Sound
sound processing
Sound Spectrography - methods
Speech
speech processing
Speech Production Measurement - methods
ISSN:1534-4320, 1558-0210, 1558-0210
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation. Recent research using numerical models of loudness perception has identified that there are large differences between how loudness is encoded by existing CI sound-processing strategies and how loudness is experienced by normally hearing listeners. In this paper, we present a new CI sound-coding algorithm aimed at addressing these discrepancies. This strategy, named SCORE, uses models of electric and acoustic loudness to modify the output of an existing CI sound-processing scheme in real time, so that the loudness changes are more accurately represented in the patterns of electrical stimulation. Five subjects (six implanted ears) were tested for understanding of speech presented at relatively low levels in quiet conditions. Using SCORE, subjects demonstrated an average 8.8 percentage-point statistically significant improvement ( p <; 0.02) in the number of words correctly identified relative to ACE, a commonly used stimulation strategy. These findings show that loudness changes over time are important for speech intelligibility, and that improving loudness coding in existing CI devices may lead to perceptual benefits.
AbstractList The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation. Recent research using numerical models of loudness perception has identified that there are large differences between how loudness is encoded by existing CI sound-processing strategies and how loudness is experienced by normally hearing listeners. In this paper, we present a new CI sound-coding algorithm aimed at addressing these discrepancies. This strategy, named SCORE, uses models of electric and acoustic loudness to modify the output of an existing CI sound-processing scheme in real time, so that the loudness changes are more accurately represented in the patterns of electrical stimulation. Five subjects (six implanted ears) were tested for understanding of speech presented at relatively low levels in quiet conditions. Using SCORE, subjects demonstrated an average 8.8 percentage-point statistically significant improvement in the number of words correctly identified relative to ACE, a commonly used stimulation strategy. These findings show that loudness changes over time are important for speech intelligibility, and that improving loudness coding in existing CI devices may lead to perceptual benefits.The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation. Recent research using numerical models of loudness perception has identified that there are large differences between how loudness is encoded by existing CI sound-processing strategies and how loudness is experienced by normally hearing listeners. In this paper, we present a new CI sound-coding algorithm aimed at addressing these discrepancies. This strategy, named SCORE, uses models of electric and acoustic loudness to modify the output of an existing CI sound-processing scheme in real time, so that the loudness changes are more accurately represented in the patterns of electrical stimulation. Five subjects (six implanted ears) were tested for understanding of speech presented at relatively low levels in quiet conditions. Using SCORE, subjects demonstrated an average 8.8 percentage-point statistically significant improvement in the number of words correctly identified relative to ACE, a commonly used stimulation strategy. These findings show that loudness changes over time are important for speech intelligibility, and that improving loudness coding in existing CI devices may lead to perceptual benefits.
The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation. Recent research using numerical models of loudness perception has identified that there are large differences between how loudness is encoded by existing CI sound-processing strategies and how loudness is experienced by normally hearing listeners. In this paper, we present a new CI sound-coding algorithm aimed at addressing these discrepancies. This strategy, named SCORE, uses models of electric and acoustic loudness to modify the output of an existing CI sound-processing scheme in real time, so that the loudness changes are more accurately represented in the patterns of electrical stimulation. Five subjects (six implanted ears) were tested for understanding of speech presented at relatively low levels in quiet conditions. Using SCORE, subjects demonstrated an average 8.8 percentage-point statistically significant improvement [Formula Omitted] in the number of words correctly identified relative to ACE, a commonly used stimulation strategy. These findings show that loudness changes over time are important for speech intelligibility, and that improving loudness coding in existing CI devices may lead to perceptual benefits.
The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation. Recent research using numerical models of loudness perception has identified that there are large differences between how loudness is encoded by existing CI sound-processing strategies and how loudness is experienced by normally hearing listeners. In this paper, we present a new CI sound-coding algorithm aimed at addressing these discrepancies. This strategy, named SCORE, uses models of electric and acoustic loudness to modify the output of an existing CI sound-processing scheme in real time, so that the loudness changes are more accurately represented in the patterns of electrical stimulation. Five subjects (six implanted ears) were tested for understanding of speech presented at relatively low levels in quiet conditions. Using SCORE, subjects demonstrated an average 8.8 percentage-point statistically significant improvement ( p < 0.02 ) in the number of words correctly identified relative to ACE, a commonly used stimulation strategy. These findings show that loudness changes over time are important for speech intelligibility, and that improving loudness coding in existing CI devices may lead to perceptual benefits.
The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation. Recent research using numerical models of loudness perception has identified that there are large differences between how loudness is encoded by existing CI sound-processing strategies and how loudness is experienced by normally hearing listeners. In this paper, we present a new CI sound-coding algorithm aimed at addressing these discrepancies. This strategy, named SCORE, uses models of electric and acoustic loudness to modify the output of an existing CI sound-processing scheme in real time, so that the loudness changes are more accurately represented in the patterns of electrical stimulation. Five subjects (six implanted ears) were tested for understanding of speech presented at relatively low levels in quiet conditions. Using SCORE, subjects demonstrated an average 8.8 percentage-point statistically significant improvement in the number of words correctly identified relative to ACE, a commonly used stimulation strategy. These findings show that loudness changes over time are important for speech intelligibility, and that improving loudness coding in existing CI devices may lead to perceptual benefits.
Author McDermott, Hugh J.
Varsavsky, Andrea
Author_xml – sequence: 1
  givenname: Andrea
  surname: Varsavsky
  fullname: Varsavsky, Andrea
  email: ava@unimelb.edu.au
  organization: The University of Melbourne, Australia
– sequence: 2
  givenname: Hugh J.
  surname: McDermott
  fullname: McDermott, Hugh J.
  email: hmcdermott@bionicsinstitute.org
  organization: Bionics Institute, East Melbourne, Australia
BackLink https://www.ncbi.nlm.nih.gov/pubmed/22961312$$D View this record in MEDLINE/PubMed
BookMark eNqNkU1v1DAQhi1URD_gD4CEInHhksUe27FzrFYFKi0gtduz5TgT6iqJg50g8e_xfpRDD8DJPjzvaN55zsnJGEYk5DWjK8Zo_WH79fbmagWUwQqAcS3YM3LGpNQlBUZPdn8uSsGBnpLzlB4oZaqS6gU5BagrxhmckeZymnrv7OzDWISuuEHbl1s_YLEJSztiSsWX0GKfirUdi-thiuEnFrcTorvPsAvfR7_PdiEW6-Due7Rxx_V2nIu7hDG9JM872yd8dXwvyN3Hq-36c7n59ul6fbkpnWBqLiU2zsmaCud4LQGx1dYBlx26RgDXuVMjQbWuoS1nkrqWOq5QNI1qJe06fkHeH-bmHX8smGYz-OSwz5tgWJJhggFwWmnxbxQUz_fUEv4HBSVEDXVG3z1BH8ISx9w5U5WmNVe6ytTbI7U0A7Zmin6w8Zd5dJIBfQBcDClF7Izz817QHK3vDaNmp9_s9ZudfnPUn6PwJPo4_a-hN4eQR8Q_gQpqIXL_37wtuU4
CODEN ITNSB3
CitedBy_id crossref_primary_10_1121_1_4874597
crossref_primary_10_1080_14992027_2017_1403655
crossref_primary_10_1016_j_heares_2020_107977
crossref_primary_10_1371_journal_pone_0045385
Cites_doi 10.1016/j.heares.2004.10.004
10.1055/s-0040-1715944
10.1016/j.heares.2008.06.005
10.1121/1.1809152
10.1121/1.1558378
10.1121/1.1612488
10.1016/j.heares.2008.11.003
10.1097/00003446-200012000-00008
10.1007/978-1-4419-6712-1
10.1121/1.423316
10.1121/1.410152
10.1016/S0378-5955(03)00347-2
10.1044/jslhr.4204.814
10.1088/1741-2560/6/6/065007
10.1177/108471380400800203
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jan 2013
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jan 2013
DBID 97E
RIA
RIE
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7QF
7QO
7QQ
7SC
7SE
7SP
7SR
7TA
7TB
7TK
7U5
8BQ
8FD
F28
FR3
H8D
JG9
JQ2
KR7
L7M
L~C
L~D
NAPCQ
P64
7X8
DOI 10.1109/TNSRE.2012.2213841
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
Aluminium Industry Abstracts
Biotechnology Research Abstracts
Ceramic Abstracts
Computer and Information Systems Abstracts
Corrosion Abstracts
Electronics & Communications Abstracts
Engineered Materials Abstracts
Materials Business File
Mechanical & Transportation Engineering Abstracts
Neurosciences Abstracts
Solid State and Superconductivity Abstracts
METADEX
Technology Research Database
ANTE: Abstracts in New Technology & Engineering
Engineering Research Database
Aerospace Database
Materials Research Database
ProQuest Computer Science Collection
Civil Engineering Abstracts
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
Nursing & Allied Health Premium
Biotechnology and BioEngineering Abstracts
MEDLINE - Academic
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Materials Research Database
Civil Engineering Abstracts
Aluminium Industry Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Mechanical & Transportation Engineering Abstracts
Electronics & Communications Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Ceramic Abstracts
Neurosciences Abstracts
Materials Business File
METADEX
Biotechnology and BioEngineering Abstracts
Computer and Information Systems Abstracts Professional
Aerospace Database
Nursing & Allied Health Premium
Engineered Materials Abstracts
Biotechnology Research Abstracts
Solid State and Superconductivity Abstracts
Engineering Research Database
Corrosion Abstracts
Advanced Technologies Database with Aerospace
ANTE: Abstracts in New Technology & Engineering
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic
Materials Research Database
Engineering Research Database

MEDLINE
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
– sequence: 3
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Occupational Therapy & Rehabilitation
EISSN 1558-0210
EndPage 87
ExternalDocumentID 2861537631
22961312
10_1109_TNSRE_2012_2213841
6294452
Genre orig-research
Research Support, Non-U.S. Gov't
Journal Article
GroupedDBID ---
-~X
0R~
29I
4.4
53G
5GY
5VS
6IK
97E
AAFWJ
AAJGR
AASAJ
AAWTH
ABAZT
ABVLG
ACGFO
ACGFS
ACIWK
ACPRK
AENEX
AETIX
AFPKN
AFRAH
AGSQL
AIBXA
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
ESBDL
F5P
GROUPED_DOAJ
HZ~
H~9
IFIPE
IPLJI
JAVBF
LAI
M43
O9-
OCL
OK1
P2P
RIA
RIE
RNS
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
RIG
7QF
7QO
7QQ
7SC
7SE
7SP
7SR
7TA
7TB
7TK
7U5
8BQ
8FD
F28
FR3
H8D
JG9
JQ2
KR7
L7M
L~C
L~D
NAPCQ
P64
7X8
ID FETCH-LOGICAL-c417t-5ebcc5904cc3952eed8ac235fecb4238021b527dcb0d3150cd0c37e4bb7d50ff3
IEDL.DBID RIE
ISICitedReferencesCount 7
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000313423200010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1534-4320
1558-0210
IngestDate Fri Sep 05 07:25:13 EDT 2025
Fri Sep 05 10:43:25 EDT 2025
Tue Oct 07 09:46:13 EDT 2025
Sun Nov 09 06:39:55 EST 2025
Thu Apr 03 07:03:32 EDT 2025
Tue Nov 18 22:17:44 EST 2025
Sat Nov 29 01:47:01 EST 2025
Wed Aug 27 02:51:14 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 1
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c417t-5ebcc5904cc3952eed8ac235fecb4238021b527dcb0d3150cd0c37e4bb7d50ff3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
PMID 22961312
PQID 1268093786
PQPubID 85423
PageCount 7
ParticipantIDs proquest_journals_1268093786
crossref_primary_10_1109_TNSRE_2012_2213841
proquest_miscellaneous_1412230684
proquest_miscellaneous_1273384852
ieee_primary_6294452
proquest_miscellaneous_1272744929
pubmed_primary_22961312
crossref_citationtrail_10_1109_TNSRE_2012_2213841
PublicationCentury 2000
PublicationDate 2013-Jan.
2013-1-00
2013-Jan
20130101
PublicationDateYYYYMMDD 2013-01-01
PublicationDate_xml – month: 01
  year: 2013
  text: 2013-Jan.
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: New York
PublicationTitle IEEE transactions on neural systems and rehabilitation engineering
PublicationTitleAbbrev TNSRE
PublicationTitleAlternate IEEE Trans Neural Syst Rehabil Eng
PublicationYear 2013
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref14
ref11
ref10
mcdermott (ref15) 2007
ref2
ref1
ref17
ref16
mcdermott (ref12) 2002; 13
ref8
ref9
ref4
ref3
moore (ref7) 1996; 82
ref6
ref5
References_xml – ident: ref4
  doi: 10.1016/j.heares.2004.10.004
– volume: 13
  start-page: 14
  year: 2002
  ident: ref12
  article-title: Benefits of syllabic input compression for users of cochlear implants
  publication-title: J Am Acad Audiol
  doi: 10.1055/s-0040-1715944
– ident: ref13
  doi: 10.1016/j.heares.2008.06.005
– volume: 82
  start-page: 335
  year: 1996
  ident: ref7
  article-title: A revision of zwicker's loudness model
  publication-title: ACUSTICAActa Acustica
– ident: ref11
  doi: 10.1121/1.1809152
– ident: ref9
  doi: 10.1121/1.1558378
– ident: ref10
  doi: 10.1121/1.1612488
– ident: ref3
  doi: 10.1016/j.heares.2008.11.003
– ident: ref14
  doi: 10.1097/00003446-200012000-00008
– ident: ref1
  doi: 10.1007/978-1-4419-6712-1
– year: 2007
  ident: ref15
  article-title: Speech processing for cochlear implants and bimodal stimulation
  publication-title: Joint 8th EFAS Congr 10th Congr German Soc Audiol
– ident: ref5
  doi: 10.1121/1.423316
– ident: ref17
  doi: 10.1121/1.410152
– ident: ref8
  doi: 10.1016/S0378-5955(03)00347-2
– ident: ref16
  doi: 10.1044/jslhr.4204.814
– ident: ref6
  doi: 10.1088/1741-2560/6/6/065007
– ident: ref2
  doi: 10.1177/108471380400800203
SSID ssj0017657
Score 2.0638719
Snippet The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation....
SourceID proquest
pubmed
crossref
ieee
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 81
SubjectTerms Acoustics
Algorithms
Auditory system
Cochlear implant (CI)
Cochlear Implants
Computational modeling
Computer Simulation
Computer Systems
Ear
Electrodes
Implants
loudness encoding
loudness model
Models, Theoretical
Pattern Recognition, Automated - methods
Sound
sound processing
Sound Spectrography - methods
Speech
speech processing
Speech Production Measurement - methods
Title Application of Real-Time Loudness Models Can Improve Speech Recognition for Cochlear Implant Users
URI https://ieeexplore.ieee.org/document/6294452
https://www.ncbi.nlm.nih.gov/pubmed/22961312
https://www.proquest.com/docview/1268093786
https://www.proquest.com/docview/1272744929
https://www.proquest.com/docview/1273384852
https://www.proquest.com/docview/1412230684
Volume 21
WOSCitedRecordID wos000313423200010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  customDbUrl:
  eissn: 1558-0210
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0017657
  issn: 1534-4320
  databaseCode: RIE
  dateStart: 20010101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La9wwEBZp6KGXvtLHJmlQoe2ldSLrYUnHsCT0EJay2ZS9GUuWSGCxQ3a3v78z8qMNNIHeDBpZsr8ZzUijmSHkk41Ci6IKmXNaZsAUPHO6EpmWRW608UGmCLmfF3o2M8ul_bFDvo2xMCGEdPksHONj8uXXrd_iUdlJwa2UChbcJ1oXXazW6DHQRcrqCQIMQwrOhgAZZk8Ws8v5Gd7i4sec58JILA_DuQVNlvN7-igVWHnY1kw65_zF_832JXne25b0tGOGV2QnNK_J57_zCNNFl0SAfqHzeym694g7_ePKpm2E9mqVYYAIvWi3NS6IFOumrdZ0WjW0O4sI9PI2BH9N58M1JOgLVjCdtv4a61Eg3Qqwo1cY1PmGXJ2fLabfs74AQ-ZlrjeZCs57ZZn0XljFQZ2aynOhYvAOzDAD9oFTXNfesVqAZelr5oUOEnCvFYtRvCW7TduE94R6VfNQGcWYc7KKGl4Km6FYWCdFjLmckHyAofT9p2ORjFWZdinMlgnFElEsexQn5OvY57bLzfEo9R5iNFL28EzI4YB22Yvvusx5YRgYbqaYkI9jMwgeelOqJrRbpNGYXRHMy0dpBIxtcJgHaWTOcR9o4B-867htnOPApPv_nvsBecZTdQ48EToku5u7bfhAnvpfm5v13RFIydIcJSn5DRNWC8Y
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Za9wwEBYhLbQvvdJjc7QqtH1pncg6LPkxLAkp3S5lsyl5M5YskcJih-xufn9m5KMNJIG-GTSyZH8zmpFGM0PIpzwILbLSJ9ZqmQBT8MTqUiRaZqnRxnkZI-R-T_R0as7P818b5NsQC-O9j5fP_D4-Rl9-1bg1HpUdZDyXUsGC-0hJyVkbrTX4DHQW83qCCMOggrM-RIblB_Pp6ewI73Hxfc5TYSQWiOE8B12W8lsaKZZYud_ajFrn-Pn_zfcFedZZl_SwZYeXZMPXr8jnfzMJ03mbRoB-obNbSbq3iD3868ymTYD2cpFgiAidNOsKl0SKldMWSzoua9qeRnh6eum9u6Cz_iIS9AU7mI4bd4EVKZBuAejRMwzrfE3Ojo_m45OkK8GQOJnqVaK8dU7lTDoncsVBoZrScaGCdxYMMQMWglVcV86ySoBt6SrmhPYSkK8UC0G8IZt1U_t3hDpVcV8axZi1sgwaXgrboZDlVooQUjkiaQ9D4bpPxzIZiyLuU1heRBQLRLHoUByRr0OfyzY7x4PUW4jRQNnBMyK7PdpFJ8DLIuWZYWC6mWxEPg7NIHroTylr36yRRmN-RTAwH6QRMLbBYe6lkSnHnaCBf_C25bZhjj2Tbt899w_kycn856SYfJ_-2CFPeazVgedDu2RzdbX2e-Sxu179WV69j7JyA88MDiU
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Application+of+real-time+loudness+models+can+improve+speech+recognition+for+cochlear+implant+users&rft.jtitle=IEEE+transactions+on+neural+systems+and+rehabilitation+engineering&rft.au=Varsavsky%2C+A&rft.au=McDermott+HJ&rft.date=2013-01-01&rft.issn=1534-4320&rft.volume=21&rft.issue=1&rft_id=info:doi/10.1109%2FTNSRE.2012.2213841&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1534-4320&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1534-4320&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1534-4320&client=summon