Advances in intelligent signal processing and data mining: theory and applications
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2013
|
| Series: | Studies in computational intelligence
410 |
| Subjects: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025291124&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Item Description: | Literaturangaben |
| Physical Description: | XIII, 349 S. graph. Darst. |
| ISBN: | 9783642286957 9783642286964 |
Staff View
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CONTENTS
1 INTRODUCTION TO INTELLIGENT SIGNAL PROCESSING AND DATA MINING 1
LYUDMILA MIHAYLOVA, PETIA GEORGIEVA, LAKHMI C. JAIN 1.1 INTRODUCTION 1
1.2 CHAPTERS INCLUDED IN THE BOOK 2
1.3 CONCLUSION 4
1.4 RESOURCES 5
REFERENCES 5
2 MONTE CARLO-BASED BAYESIAN GROUP OBJECT TRACKING AND CAUSAL REASONING
7
AVISHY Y. CARMI, LYUDMILA MIHAYLOVA, AMADOU GNING, PINI GURFIL, SIMON J.
GODSILL 2.1 OVERVIEW 7
2.1.1 REASONING ABOUT BEHAVIORAL TRAITS 8
2.1.2 NOVELTIES AND CONTRIBUTIONS 9
2.1.3 MULTIPLE GROUP TRACKING 9
2.2 GROUP TRACKING BY SEQUENTIAL MONTE CARLO (SMC) METHODS AND EVOLVING
NETWORKS 11
2.2.1 PROBLEM FORMULATION 12
2.2.2 A NEARLY CONSTANT VELOCITY MODEL FOR INDIVIDUAL TARGETS 13
2.2.3 OBSERVATION MODEL 14
2.2.4 PARTICLE FILTERING ALGORITHMS FOR GROUP MOTION ESTIMATION 14
2.3 THE CLUSTER TRACKING PROBLEM 18
2.4 BAYESIAN FORMULATION 19
2.4.1 LIKELIHOOD DERIVATION 19
2.4.2 MODELING CLUSTER EVOLUTION 2 0
2.4.3 MARKOV CHAIN MONTE CARLO (MCMC) PARTICLE ALGORITHM FOR CLUSTER
TRACKING 23
HTTP://D-NB.INFO/1019655275
IMAGE 2
VIII CONTENTS
2.5 BAYESIAN CAUSALITY DETECTION O F GROUP HIERARCHIES 2 6
2.5.1 G-CAUSALITY AND CAUSAL NETWORKS 29
2.5.2 INFERRING CAUSAL RELATIONS FROM EMPIRICAL DATA 31
2.5.3 STRUCTURAL DYNAMIC MODELING 33
2.5.4 DOMINANCE AND SIMILARITY 34
2.5.5 BAYESIAN ESTIMATION O F A.I~ 35
2.5.6 A UNIFIED CAUSAL REASONING AND TRACKING PARADIGM . . . 35 2.6
NUMERICAL STUDY 36
2.7 CONCLUDING REMARKS 4 4
2.8 APPENDIX: ALGORITHMS FOR THE EVOLVING GRAPHS 44
2.8.1 EVOLVING GRAPH MODELS 44
2.8.2 GRAPH INITIALIZATION - MODEL /) 4 5
2.8.3 EDGE UPDATING - MODEL / EU 4 5
2.8.4 NEW NODE INCORPORATION - M O D E L FYI 4 6
2.8.5 OLD NODE SUPPRESSION - MODEL FYS 48
REFERENCES 4 9
3 A SEQUENTIAL MONTE CARLO METHOD FOR MULTI-TARGET TRACKING WITH THE
INTENSITY FILTER 55
MAREK SCHIKORA, WOLFGANG KOCH, ROY STREIT, DANIEL CREMERS 3.1
INTRODUCTION 55
3.2 POISSON POINT PROCESSES (PPPS) 57
3.2.1 PPP SAMPLING PROCEDURE 57
3.2.2 PPPS FOR MULTI-TARGET TRACKING 58
3.3 THE INTENSITY FILTER 59
3.3.1 GENERAL OVERVIEW 59
3.3.2 THE SMC-IFILTER 61
3.3.3 RELATIONSHIP TO THE PROBABILITY HYPOTHESIS DENSITY (PHD) FILTER 67
3.4 NUMERICAL STUDIES 68
3.4.1 SCENARIO-1 68
3.4.2 SCENARIO - 2 7 5
3.5 APPLICATIONS 7 6
3.5.1 BEARINGS-ONLY TRACKING 77
3.5.2 VIDEO TRACKING 80
3.6 CONCLUSIONS 85
REFERENCES 85
4 SEQUENTIAL MONTE CARLO METHODS FOR LOCALIZATION IN WIRELESS NETWORKS
89
LYUDMILA MIHAYLOVA, DONKA ANGELOVA, ANNA ZVIKHACHEVSKAYA 4.1 MOTIVATION
89
4.1.1 METHODS FOR LOCALIZATION 9 0
4.2 LOCALIZATION O F MOBILE NODES 92
4.2.1 MOTION MODEL O F THE MOBILE NODES 92
4.2.2 OBSERVATION MODEL 9 3
IMAGE 3
CONTENTS I X
4.2.3 CORRELATED IN TIME MEASUREMENT NOISE 94
4.2.4 MOTION AND OBSERVATION MODELS FOR SIMULTANEOUS LOCALIZATION O F
MULTIPLE MOBILE NODES 95
4.3 SEQUENTIAL BAYESIAN FRAMEWORK 95
4.3.1 GENERAL FILTERING FRAMEWORK 95
4.3.2 AUXILIARY MULTIPLE MODEL PARTICLE FILTERING FOR LOCALIZATION 96
4.3.3 APPROACHES TO DEAL WITH THE TIME CORRELATED MEASUREMENT NOISE 98
4.4 ESTIMATION O F THE MEASUREMENT NOISE PARAMETERS 100
4.5 GIBBS SAMPLING FOR NOISE PARAMETER ESTIMATION 101
4.6 PERFORMANCE EVALUATION O F THE GIBBS SAMPLING ALGORITHM FOR
MEASUREMENT NOISE PARAMETER ESTIMATION 103
4.6.1 MEASUREMENT NOISE PARAMETER ESTIMATION WITH SIMULATED DATA 103
4.6.2 MEASUREMENT NOISE PARAMETER ESTIMATION WITH REAL DATA 106
4.7 PERFORMANCE EVALUATION OF THE MULTIPLE MODEL AUXILIARY PARTICLE
FILTER 108
4.7.1 RESULTS WITH SIMULATED DATA 109
4.7.2 RESULTS WITH REAL DATA 112
4.8 CONCLUSIONS 114
REFERENCES 115
5 A SEQUENTIAL MONTE CARLO APPROACH FOR BRAIN SOURCE LOCALIZATION 119
PETIA GEORGIEVA, LYUDMILA MIHAYLOVA, FILIPE SILVA, MARIOFANNA MILANOVA,
NUNO FIGUEIREDO, LAKHMI C. JAIN 5.1 INTRODUCTION 120
5.2 SEQUENTIAL MONTE CARLO PROBLEM FORMULATION 121
5.3 THE STATE SPACE ELECTROENCEPHALOGRAPHY (EEG) SOURCE LOCALIZATION
MODEL 125
5.4 BEAMFORMING AS A SPATIAL FILTER 128
5.5 EXPERIMENTAL RESULTS 130
5.6 CONCLUSIONS 136
REFERENCES 137
6 COMPUTATIONAL INTELLIGENCE IN AUTOMOTIVE APPLICATIONS 139
YIFEI WANG, NAIRN DAHNOUN, ALIN ACHIM 6.1 INTRODUCTION 139
6.1.1 LANE DETECTION 139
6.1.2 LANE TRACKING 141
6.1.3 CHAPTER STRUCTURE 142
6.2 LANE MODELLING 142
6.3 LANE FEATURE EXTRACTION 147
6.3.1 THEORETICAL PRELIMINARIES 149
IMAGE 4
X CONTENTS
6.3.2 VANISHING POINT DETECTION 150
6.3.3 FEATURE EXTRACTION 152
6.4 LANE MODEL PARAMETER ESTIMATION 155
6.5 LANE TRACKING 157
6.5.1 TIME UPDATE 159
6.5.2 MEASUREMENT UPDATE 160
6.5.3 PARAMETER SELECTION 162
6.6 EXPERIMENTAL RESULTS 162
6.6.1 LANE FEATURE EXTRACTION RESULTS 162
6.6.2 LANE MODEL PARAMETER ESTIMATION RESULTS 165
6.6.3 LANE TRACKING RESULTS 167
6.7 CONCLUSIONS 172
REFERENCES 173
7 DETECTING ANOMALIES IN SENSOR SIGNALS USING DATABASE TECHNOLOGY 175
GEREON SCHIILLER, ANDREAS BEHREND, WOLFGANG KOCH 7.1 INTRODUCTION 175
7.2 DRIVING FACTORS FOR A TRACKING AND AWARENESS SYSTEM 176
7.3 CRITERIA FOR ANOMALY DETECTION 178
7.3.1 PATTERN BASED FILTERING FOR IMPROVED CLASSIFICATION AND THREAT
DETECTION 178
7.3.2 VIOLATION O F SPACE-TIME REGULARITY PATTERNS 180
7.3.3 EXPLOITING POOR-RESOLUTION SENSOR ATTRIBUTES 180
7.3.4 VARYING CRITERIA AND THE NEED FOR FLEXIBILITY 181
7.4 RELATIONAL DBMSS FOR PROCESSING SENSOR DATA 181
7.4.1 RELATIONAL DATABASES AND RELATIONAL ALGEBRA 182
7.5 EXPRESSING ANOMALIES IN RELATIONAL ALGEBRA 184
7.5.1 VELOCITY/ACCELERATION CLASSIFICATION 184
7.5.2 CONTEXT INFORMATION AND SEVERAL SENSORS 184
7.5.3 INCREMENTAL EVALUATION O F RELATIONAL QUERIES 185
7.6 ANOMALY DETECTION FOR IMPROVING AIR TRAFFIC SAFETY 187
7.6.1 PROBLEM SETTING 187
7.6.2 VIEW-BASED FLIGHT ANALYSIS 190
7.6.3 ENHANCING ROBUSTNESS AND TRACK PRECISION 191
7.6.4 HISTORY MANAGEMENT 193
7.6.5 EXPERIENCES 194
7.7 FUTURE WORK AND CONCLUSION 194
REFERENCES 195
8 HIERARCHICAL CLUSTERING FOR LARGE DATA SETS 1 97
MARK J. EMBRECHTS, CHRISTOPHER J. GATTI, JONATHAN LINTON, BADRINATH
ROYSAM 8.1 INTRODUCTION 197
8.2 INTRODUCTION TO CLUSTERING 198
8.3 HIERARCHICAL CLUSTERING 202
IMAGE 5
CONTENTS X I
8.4 DISPLAYING HIERARCHICAL CLUSTERING WITH DENDROGRAMS 207
8.4.1 DATA REORDERING 209
8.4.2 LEAF REORDERING 210
8.5 DATA SETS 211
8.6 CLUSTER PLOTS 213
8.6.1 CARTOON CLUSTER PLOT 214
8.6.2 TIMELINE ANALYSIS WITH PRINCIPAL COMPONENTS ANALYSIS 215
8.6.3 BICLUSTER PLOTS 216
8.7 ASSESSING CLUSTER QUALITY WITH CLUSTER EVALUATION INDICES 216
8.7.1 INTERNAL CLUSTER VALIDATION INDICES 218
8.7.2 EXTERNAL CLUSTER VALIDATION INDICES 222
, 8.8 SPEEDING UP HIERARCHICAL CLUSTERING WITH CLUSTER SEEDING 224 8.8.1
SCALING O F HIERARCHICAL CLUSTERING IN MEMORY AND TIME 224
8.8.2 SPEEDING UP HIERARCHICAL CLUSTERING 225
8.8.3 IMPROVING THE SCALING O F COMPUTING TIME FOR THE SAHN ALGORITHM
WITH CLUSTER SEEDING 226
8.8.4 IMPROVING THE SCALING O F MEMORY FOR THE SAHN ALGORITHM WITH A
DIVIDE AND CONQUER APPROACH 228
8.9 CONCLUSIONS 229
REFERENCES 230
9 A NOVEL FRAMEWORK FOR OBJECT RECOGNITION UNDER SEVERE OCCLUSION 235
STAMATIA GIANNAROU, TARIIA STCITHAKI 9.1 INTRODUCTION 235
9.2 PRIOR WORK ON SHAPE ANALYSIS AND IDENTIFICATION 236
9.3 SHAPE CONTEXT REPRESENTATION AND MATCHING 238
9.3.1 SHAPE CONTEXT DESCRIPTOR 238
9.3.2 MANY-TO-ONE EDGE POINT MATCHING 240
9.4 CLUSTERING O F THE MATCHED POINTS ON THE COMPLEX SCENE 242
9.5 OBJECT IDENTIFICATION 244
9.5.1 CLUSTER ELIMINATION BASED ON CLUSTER-ACTIVITY ESTIMATION 244
9.5.2 CLUSTER SELECTION FOR THE IDENTIFICATION O F SUSPICIOUS REGIONS
247
9.6 EXPERIMENTAL RESULTS 251
9.7 DISCUSSION 257
REFERENCES 257
10 HISTORICAL CONSISTENT NEURAL NETWORKS: NEW PERSPECTIVES ON MARKET
MODELING, FORECASTING AND RISK ANALYSIS 259
HANS-GEORG ZIMMERMANN, CHRISTOPH TIETZ, RALPH GROTHMANN 10.1
INTRODUCTION 259
10.2 HISTORICAL CONSISTENT NEURAL NETWORKS (HCNN) 260
IMAGE 6
X I I C O N T E N T S
10.2.1 MODELING OPEN DYNAMIC SYSTEMS WITH RECURRENT
NEURAL NETWORKS (RNN) 261
10.2.2 MODELING OF CLOSED DYNAMIC SYSTEMS WITH HCNN . . . . 263 10.3
APPLICATIONS IN FINANCIAL MARKETS 269
10.3.1 PRICE FORECASTS FOR PROCUREMENT 269
10.3.2 RISK MANAGEMENT 271
10.4 SUMMARY AND OUTLOOK 273
REFERENCES 273
11 REINFORCEMENT LEARNING WITH NEURAL NETWORKS: TRICKS O F THE TRADE 275
CHRISTOPHER J. GATTI, MARK J. EMBRECHTS 11.1 INTRODUCTION 275
11.2 OVERVIEW O F REINFORCEMENT LEARNING 276
11.2.1 SEQUENTIAL DECISION PROCESSES 278
11.2.2 REINFORCEMENT LEARNING WITH A NEURAL NETWORK 280
11.3 IMPLEMENTING REINFORCEMENT LEARNING 281
11.3.1 ENVIRONMENT REPRESENTATION 281
11.3.2 AGENT REPRESENTATION 285
11.4 EXAMPLES O F REINFORCEMENT LEARNING 297
11.4.1 TIC-TAC-TOE 297
11.4.2 CHUNG TOI 301
11.4.3 APPLYING/EXTENDING TO OTHER GAMES/SCENARIOS 305
11.5 SUMMARY 308
REFERENCES 309
12 SLIDING EMPIRICAL MODE DECOMPOSITION-BRAIN STATUS DATA ANALYSIS AND
MODELING 311
A. ZEILER, R. FALTERMEIER, A.M. TOME, I.R. KECK, C. PUNTONET, A.
BRAWANSKI, E. W. LANG 12.1 INTRODUCTION 311
12.1.1 EMPIRICAL MODE DECOMPOSITION 311
12.1.2 NEUROMONITORING 312
12.1.3 DYNAMIC CEREBRAL AUTOREGULATION 313
12.1.4 MODELING O F CEREBRAL CIRCULATION AND OXYGEN SUPPLY 313
12.2 EMPIRICAL MODE DECOMPOSITION 315
12.2.1 THE STANDARD EMPIRICAL MODE DECOMPOSITION (EMD) ALGORITHM 316
12.2.2 THE HILBERT - HUANG TRANSFORM 317
12.2.3 ENSEMBLE EMPIRICAL MODE DECOMPOSITION 318
12.3 SLIDING EMPIRICAL MODE DECOMPOSITION 319
12.3.1 THE PRINCIPLE O F SLIDING EMPIRICAL MODE DECOMPOSITION (SEMD) 320
12.3.2 PROPERTIES OF SEMD 323
IMAGE 7
CONTENTS X I I I
12.3.3 APPLICATION O F SEMD TO TOY DATA 323
12.3.4 PERFORMANCE EVALUATION O F SEMD 327
12.4 WEIGHTED SLIDING EMD 329
12.4.1 ERROR RANGE O F EMD 330
12.4.2 THE PRINCIPLE O F WEIGHTED SEMD 332
12.4.3 PERFORMANCE EVALUATION OF WEIGHTED SEMD 332
12.4.4 COMPLETENESS 335
12.4.5 EXAMINATION OF THE INTRINSIC MODE FUNCTIONS (IMF) CRITERIA 335
12.5 ANALYSIS O F BRAIN STATUS DATA 337
12.5.1 EEMD APPLIED TO BRAIN STATUS DATA 337
12.5.2 SEMD APPLIED TO BRAIN STATUS DATA 342
REFERENCES 348
AUTHOR INDEX 35 1
|
| any_adam_object | 1 |
| author | Georgieva, Petia |
| author_facet | Georgieva, Petia |
| author_role | aut |
| author_sort | Georgieva, Petia |
| author_variant | p g pg |
| building | Verbundindex |
| bvnumber | BV040443333 |
| classification_rvk | ST 300 ZN 6040 |
| ctrlnum | (OCoLC)811762016 (DE-599)DNB1019655275 |
| dewey-full | 006.312 621.3822 |
| dewey-hundreds | 000 - Computer science, information, general works 600 - Technology (Applied sciences) |
| dewey-ones | 006 - Special computer methods 621 - Applied physics |
| dewey-raw | 006.312 621.3822 |
| dewey-search | 006.312 621.3822 |
| dewey-sort | 16.312 |
| dewey-tens | 000 - Computer science, information, general works 620 - Engineering and allied operations |
| discipline | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV040443333 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:15:06Z |
| institution | BVB |
| isbn | 9783642286957 9783642286964 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025291124 |
| oclc_num | 811762016 |
| open_access_boolean | |
| owner | DE-11 DE-859 |
| owner_facet | DE-11 DE-859 |
| physical | XIII, 349 S. graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Georgieva, Petia Advances in intelligent signal processing and data mining theory and applications Studies in computational intelligence Stochastisches Modell (DE-588)4057633-4 gnd Cluster-Analyse (DE-588)4070044-6 gnd Data Mining (DE-588)4428654-5 gnd Signalverarbeitung (DE-588)4054947-1 gnd Soft Computing (DE-588)4455833-8 gnd Datenanalyse (DE-588)4123037-1 gnd Visualisierung (DE-588)4188417-6 gnd |
| subject_GND | (DE-588)4057633-4 (DE-588)4070044-6 (DE-588)4428654-5 (DE-588)4054947-1 (DE-588)4455833-8 (DE-588)4123037-1 (DE-588)4188417-6 (DE-588)4143413-4 |
| title | Advances in intelligent signal processing and data mining theory and applications |
| title_auth | Advances in intelligent signal processing and data mining theory and applications |
| title_exact_search | Advances in intelligent signal processing and data mining theory and applications |
| title_full | Advances in intelligent signal processing and data mining theory and applications Petia Georgieva ... (eds.) |
| title_fullStr | Advances in intelligent signal processing and data mining theory and applications Petia Georgieva ... (eds.) |
| title_full_unstemmed | Advances in intelligent signal processing and data mining theory and applications Petia Georgieva ... (eds.) |
| title_short | Advances in intelligent signal processing and data mining |
| title_sort | advances in intelligent signal processing and data mining theory and applications |
| title_sub | theory and applications |
| topic | Stochastisches Modell (DE-588)4057633-4 gnd Cluster-Analyse (DE-588)4070044-6 gnd Data Mining (DE-588)4428654-5 gnd Signalverarbeitung (DE-588)4054947-1 gnd Soft Computing (DE-588)4455833-8 gnd Datenanalyse (DE-588)4123037-1 gnd Visualisierung (DE-588)4188417-6 gnd |
| topic_facet | Stochastisches Modell Cluster-Analyse Data Mining Signalverarbeitung Soft Computing Datenanalyse Visualisierung Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025291124&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT georgievapetia advancesinintelligentsignalprocessinganddataminingtheoryandapplications |