Adapting the User Context in Realtime: Tailoring Online Machine Learning Algorithms to Ambient Computing View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-10-03

AUTHORS

Johannes Schmitt, Matthias Hollick, Christoph Roos, Ralf Steinmetz

ABSTRACT

Ambient systems weave computing and communication aspects into everyday life. To provide self-adaptive services, it is necessary to acquire context information using sensors and to leverage the collected information for reasoning and classification of situations. To enable self-learning systems, we propose to depart from static rule-based decisions and first-order logic to define situations from basic context, but to build on machine-learning techniques. However, existing learning algorithms show substantial weaknesses if applied in highly dynamic environments, where we expect accurate decisions in realtime while the user is in-the-loop to give feedback to the system’s recommendations. To address ambient and pervasive computing environments, we propose the FLORA—multiple classification (FLORA-MC) online learning algorithm. In particular, we enhance the FLORA algorithm to allow for (1) multiple classification and (2) numerical input values, while improving its concept drift handling capabilities; thus, making it an excellent choice for use in the area of ambient computing. The multiple classification allows context-aware systems to differentiate between multiple categories instead of taking binary decisions. Support for numerical input values enables the processing of arbitrary sensor inputs beyond nominal data. To provide the capability of concept drift handling, we propose the use of an advanced window adjustment heuristic, which allows FLORA-MC to continuously adapt to the user’s behavior, even if her/his preferences change abruptly over time. In combination with the inherent characteristics of online learning algorithms, our scheme is very well suited for realtime application in the area of ambient and pervasive computing. We describe the design and implementation of FLORA-MC and evaluate its performance vs. state-of-the-art learning algorithms. We are able to show the superior performance of our algorithm with respect to reaction time and concept drift handling, while maintaining an excellent accuracy. Our implementation is available to the research community as a WEKA module. More... »

PAGES

583-598

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11036-008-0095-8

DOI

http://dx.doi.org/10.1007/s11036-008-0095-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041158970


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universit\u00e4t Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany", 
          "id": "http://www.grid.ac/institutes/grid.6546.1", 
          "name": [
            "Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universit\u00e4t Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schmitt", 
        "givenName": "Johannes", 
        "id": "sg:person.012513756775.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012513756775.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universit\u00e4t Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany", 
          "id": "http://www.grid.ac/institutes/grid.6546.1", 
          "name": [
            "Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universit\u00e4t Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hollick", 
        "givenName": "Matthias", 
        "id": "sg:person.010143067443.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010143067443.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universit\u00e4t Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany", 
          "id": "http://www.grid.ac/institutes/grid.6546.1", 
          "name": [
            "Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universit\u00e4t Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Roos", 
        "givenName": "Christoph", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universit\u00e4t Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany", 
          "id": "http://www.grid.ac/institutes/grid.6546.1", 
          "name": [
            "Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universit\u00e4t Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Steinmetz", 
        "givenName": "Ralf", 
        "id": "sg:person.014350724672.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014350724672.43"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-1-4471-0743-9_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018589532", 
          "https://doi.org/10.1007/978-1-4471-0743-9_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1007365809034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017758049", 
          "https://doi.org/10.1023/a:1007365809034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00058925", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003205548", 
          "https://doi.org/10.1007/bf00058925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1007661119649", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018650476", 
          "https://doi.org/10.1023/a:1007661119649"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00116900", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025237168", 
          "https://doi.org/10.1007/bf00116900"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-48157-5_10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010331620", 
          "https://doi.org/10.1007/3-540-48157-5_10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00116827", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004239284", 
          "https://doi.org/10.1007/bf00116827"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2008-10-03", 
    "datePublishedReg": "2008-10-03", 
    "description": "Ambient systems weave computing and communication aspects into everyday life. To provide self-adaptive services, it is necessary to acquire context information using sensors and to leverage the collected information for reasoning and classification of situations. To enable self-learning systems, we propose to depart from static rule-based decisions and first-order logic to define situations from basic context, but to build on machine-learning techniques. However, existing learning algorithms show substantial weaknesses if applied in highly dynamic environments, where we expect accurate decisions in realtime while the user is in-the-loop to give feedback to the system\u2019s recommendations. To address ambient and pervasive computing environments, we propose the FLORA\u2014multiple\u00a0classification (FLORA-MC) online learning algorithm. In particular, we enhance the FLORA algorithm to allow for (1) multiple classification and (2) numerical input values, while improving its concept drift handling capabilities; thus, making it an excellent choice for use in the area of ambient computing. The multiple classification allows context-aware systems to differentiate between multiple categories instead of taking binary decisions. Support for numerical input values enables the processing of arbitrary sensor inputs beyond nominal data. To provide the capability of concept drift handling, we propose the use of an advanced window adjustment heuristic, which allows FLORA-MC to continuously adapt to the user\u2019s behavior, even if her/his preferences change abruptly over time. In combination with the inherent characteristics of online learning algorithms, our scheme is very well suited for realtime application in the area of ambient and pervasive computing. We describe the design and implementation of FLORA-MC and evaluate its performance vs. state-of-the-art learning algorithms. We are able to show the superior performance of our algorithm with respect to reaction time and concept drift handling, while maintaining an excellent accuracy. Our implementation is available to the research community as a WEKA module.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11036-008-0095-8", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136741", 
        "issn": [
          "1383-469X", 
          "1572-8153"
        ], 
        "name": "Mobile Networks and Applications", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "6", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "13"
      }
    ], 
    "keywords": [
      "concept drift handling", 
      "ambient computing", 
      "learning algorithm", 
      "self-adaptive services", 
      "context-aware systems", 
      "pervasive computing environments", 
      "machine-learning techniques", 
      "self-learning system", 
      "numerical input values", 
      "online learning algorithm", 
      "area of ambient", 
      "multiple classifications", 
      "first-order logic", 
      "classification of situations", 
      "rule-based decision", 
      "computing environment", 
      "pervasive computing", 
      "user context", 
      "online machine", 
      "context information", 
      "realtime applications", 
      "input values", 
      "user behavior", 
      "sensor inputs", 
      "ambient systems", 
      "dynamic environment", 
      "computing", 
      "communication aspects", 
      "algorithm", 
      "system recommendations", 
      "accurate decisions", 
      "research community", 
      "binary decision", 
      "realtime", 
      "superior performance", 
      "inherent characteristics", 
      "multiple categories", 
      "implementation", 
      "nominal data", 
      "classification", 
      "capability", 
      "basic context", 
      "information", 
      "users", 
      "heuristics", 
      "environment", 
      "system", 
      "machine", 
      "performance", 
      "excellent accuracy", 
      "decisions", 
      "reasoning", 
      "adjustment heuristic", 
      "logic", 
      "handling", 
      "substantial weaknesses", 
      "handling capability", 
      "scheme", 
      "module", 
      "services", 
      "everyday life", 
      "processing", 
      "sensors", 
      "accuracy", 
      "context", 
      "situation", 
      "applications", 
      "feedback", 
      "art", 
      "input", 
      "excellent choice", 
      "design", 
      "technique", 
      "time", 
      "use", 
      "recommendations", 
      "support", 
      "data", 
      "weakness", 
      "aspects", 
      "area", 
      "community", 
      "loop", 
      "preferences", 
      "behavior", 
      "categories", 
      "state", 
      "choice", 
      "respect", 
      "combination", 
      "characteristics", 
      "values", 
      "life", 
      "ambient", 
      "reaction time", 
      "flora", 
      "static rule-based decisions", 
      "classification (FLORA-MC) online learning algorithm", 
      "FLORA algorithm", 
      "concept drift handling capabilities", 
      "drift handling capabilities", 
      "arbitrary sensor inputs", 
      "drift handling", 
      "advanced window adjustment heuristic", 
      "window adjustment heuristic", 
      "FLORA-MC", 
      "WEKA module"
    ], 
    "name": "Adapting the User Context in Realtime: Tailoring Online Machine Learning Algorithms to Ambient Computing", 
    "pagination": "583-598", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1041158970"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11036-008-0095-8"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11036-008-0095-8", 
      "https://app.dimensions.ai/details/publication/pub.1041158970"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-12-01T19:19", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/article/article_457.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11036-008-0095-8"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11036-008-0095-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11036-008-0095-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11036-008-0095-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11036-008-0095-8'


 

This table displays all metadata directly associated to this object as RDF triples.

217 TRIPLES      22 PREDICATES      140 URIs      124 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11036-008-0095-8 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 anzsrc-for:0806
4 schema:author N4b3a1a349b6f41f9aa5eb2c7e30818e7
5 schema:citation sg:pub.10.1007/3-540-48157-5_10
6 sg:pub.10.1007/978-1-4471-0743-9_11
7 sg:pub.10.1007/bf00058925
8 sg:pub.10.1007/bf00116827
9 sg:pub.10.1007/bf00116900
10 sg:pub.10.1023/a:1007365809034
11 sg:pub.10.1023/a:1007661119649
12 schema:datePublished 2008-10-03
13 schema:datePublishedReg 2008-10-03
14 schema:description Ambient systems weave computing and communication aspects into everyday life. To provide self-adaptive services, it is necessary to acquire context information using sensors and to leverage the collected information for reasoning and classification of situations. To enable self-learning systems, we propose to depart from static rule-based decisions and first-order logic to define situations from basic context, but to build on machine-learning techniques. However, existing learning algorithms show substantial weaknesses if applied in highly dynamic environments, where we expect accurate decisions in realtime while the user is in-the-loop to give feedback to the system’s recommendations. To address ambient and pervasive computing environments, we propose the FLORA—multiple classification (FLORA-MC) online learning algorithm. In particular, we enhance the FLORA algorithm to allow for (1) multiple classification and (2) numerical input values, while improving its concept drift handling capabilities; thus, making it an excellent choice for use in the area of ambient computing. The multiple classification allows context-aware systems to differentiate between multiple categories instead of taking binary decisions. Support for numerical input values enables the processing of arbitrary sensor inputs beyond nominal data. To provide the capability of concept drift handling, we propose the use of an advanced window adjustment heuristic, which allows FLORA-MC to continuously adapt to the user’s behavior, even if her/his preferences change abruptly over time. In combination with the inherent characteristics of online learning algorithms, our scheme is very well suited for realtime application in the area of ambient and pervasive computing. We describe the design and implementation of FLORA-MC and evaluate its performance vs. state-of-the-art learning algorithms. We are able to show the superior performance of our algorithm with respect to reaction time and concept drift handling, while maintaining an excellent accuracy. Our implementation is available to the research community as a WEKA module.
15 schema:genre article
16 schema:inLanguage en
17 schema:isAccessibleForFree false
18 schema:isPartOf N24e8b2da6beb45f49fea098f9e78f322
19 N838b1e92a65340069b88eb695a9ed412
20 sg:journal.1136741
21 schema:keywords FLORA algorithm
22 FLORA-MC
23 WEKA module
24 accuracy
25 accurate decisions
26 adjustment heuristic
27 advanced window adjustment heuristic
28 algorithm
29 ambient
30 ambient computing
31 ambient systems
32 applications
33 arbitrary sensor inputs
34 area
35 area of ambient
36 art
37 aspects
38 basic context
39 behavior
40 binary decision
41 capability
42 categories
43 characteristics
44 choice
45 classification
46 classification (FLORA-MC) online learning algorithm
47 classification of situations
48 combination
49 communication aspects
50 community
51 computing
52 computing environment
53 concept drift handling
54 concept drift handling capabilities
55 context
56 context information
57 context-aware systems
58 data
59 decisions
60 design
61 drift handling
62 drift handling capabilities
63 dynamic environment
64 environment
65 everyday life
66 excellent accuracy
67 excellent choice
68 feedback
69 first-order logic
70 flora
71 handling
72 handling capability
73 heuristics
74 implementation
75 information
76 inherent characteristics
77 input
78 input values
79 learning algorithm
80 life
81 logic
82 loop
83 machine
84 machine-learning techniques
85 module
86 multiple categories
87 multiple classifications
88 nominal data
89 numerical input values
90 online learning algorithm
91 online machine
92 performance
93 pervasive computing
94 pervasive computing environments
95 preferences
96 processing
97 reaction time
98 realtime
99 realtime applications
100 reasoning
101 recommendations
102 research community
103 respect
104 rule-based decision
105 scheme
106 self-adaptive services
107 self-learning system
108 sensor inputs
109 sensors
110 services
111 situation
112 state
113 static rule-based decisions
114 substantial weaknesses
115 superior performance
116 support
117 system
118 system recommendations
119 technique
120 time
121 use
122 user behavior
123 user context
124 users
125 values
126 weakness
127 window adjustment heuristic
128 schema:name Adapting the User Context in Realtime: Tailoring Online Machine Learning Algorithms to Ambient Computing
129 schema:pagination 583-598
130 schema:productId N041379922be64a84b44ba21feaf9b742
131 Nf6b713a588d14f44bc190dac6d2aff67
132 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041158970
133 https://doi.org/10.1007/s11036-008-0095-8
134 schema:sdDatePublished 2021-12-01T19:19
135 schema:sdLicense https://scigraph.springernature.com/explorer/license/
136 schema:sdPublisher N9a2a2ea3d7e040d59abe8f7253ec71ee
137 schema:url https://doi.org/10.1007/s11036-008-0095-8
138 sgo:license sg:explorer/license/
139 sgo:sdDataset articles
140 rdf:type schema:ScholarlyArticle
141 N041379922be64a84b44ba21feaf9b742 schema:name doi
142 schema:value 10.1007/s11036-008-0095-8
143 rdf:type schema:PropertyValue
144 N24e8b2da6beb45f49fea098f9e78f322 schema:volumeNumber 13
145 rdf:type schema:PublicationVolume
146 N34e5191ba5a64602a52f6c4215e3533b schema:affiliation grid-institutes:grid.6546.1
147 schema:familyName Roos
148 schema:givenName Christoph
149 rdf:type schema:Person
150 N4b3a1a349b6f41f9aa5eb2c7e30818e7 rdf:first sg:person.012513756775.52
151 rdf:rest Nef18ed5cdd9449dca3e8dc184e67bce4
152 N838b1e92a65340069b88eb695a9ed412 schema:issueNumber 6
153 rdf:type schema:PublicationIssue
154 N90dfee92d5a5455a942b34367194e91b rdf:first N34e5191ba5a64602a52f6c4215e3533b
155 rdf:rest Nfb6ed2a87da84f47b1ba599e00074187
156 N9a2a2ea3d7e040d59abe8f7253ec71ee schema:name Springer Nature - SN SciGraph project
157 rdf:type schema:Organization
158 Nef18ed5cdd9449dca3e8dc184e67bce4 rdf:first sg:person.010143067443.79
159 rdf:rest N90dfee92d5a5455a942b34367194e91b
160 Nf6b713a588d14f44bc190dac6d2aff67 schema:name dimensions_id
161 schema:value pub.1041158970
162 rdf:type schema:PropertyValue
163 Nfb6ed2a87da84f47b1ba599e00074187 rdf:first sg:person.014350724672.43
164 rdf:rest rdf:nil
165 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
166 schema:name Information and Computing Sciences
167 rdf:type schema:DefinedTerm
168 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
169 schema:name Artificial Intelligence and Image Processing
170 rdf:type schema:DefinedTerm
171 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
172 schema:name Information Systems
173 rdf:type schema:DefinedTerm
174 sg:journal.1136741 schema:issn 1383-469X
175 1572-8153
176 schema:name Mobile Networks and Applications
177 schema:publisher Springer Nature
178 rdf:type schema:Periodical
179 sg:person.010143067443.79 schema:affiliation grid-institutes:grid.6546.1
180 schema:familyName Hollick
181 schema:givenName Matthias
182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010143067443.79
183 rdf:type schema:Person
184 sg:person.012513756775.52 schema:affiliation grid-institutes:grid.6546.1
185 schema:familyName Schmitt
186 schema:givenName Johannes
187 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012513756775.52
188 rdf:type schema:Person
189 sg:person.014350724672.43 schema:affiliation grid-institutes:grid.6546.1
190 schema:familyName Steinmetz
191 schema:givenName Ralf
192 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014350724672.43
193 rdf:type schema:Person
194 sg:pub.10.1007/3-540-48157-5_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010331620
195 https://doi.org/10.1007/3-540-48157-5_10
196 rdf:type schema:CreativeWork
197 sg:pub.10.1007/978-1-4471-0743-9_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018589532
198 https://doi.org/10.1007/978-1-4471-0743-9_11
199 rdf:type schema:CreativeWork
200 sg:pub.10.1007/bf00058925 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003205548
201 https://doi.org/10.1007/bf00058925
202 rdf:type schema:CreativeWork
203 sg:pub.10.1007/bf00116827 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004239284
204 https://doi.org/10.1007/bf00116827
205 rdf:type schema:CreativeWork
206 sg:pub.10.1007/bf00116900 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025237168
207 https://doi.org/10.1007/bf00116900
208 rdf:type schema:CreativeWork
209 sg:pub.10.1023/a:1007365809034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017758049
210 https://doi.org/10.1023/a:1007365809034
211 rdf:type schema:CreativeWork
212 sg:pub.10.1023/a:1007661119649 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018650476
213 https://doi.org/10.1023/a:1007661119649
214 rdf:type schema:CreativeWork
215 grid-institutes:grid.6546.1 schema:alternateName Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany
216 schema:name Multimedia Communications Lab (KOM), Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Merckstr. 25, 64283, Darmstadt, Germany
217 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...