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El. knyga: Biometric Data in Smart Cities: Methods and Models of Collective Behavior [Taylor & Francis e-book]

(State University of Infrastructure and Technology, Ukraine.), , (Main Information and Computing Center, Ukraine.),
  • Formatas: 214 pages, 2 Tables, black and white; 114 Line drawings, black and white; 17 Halftones, black and white; 131 Illustrations, black and white
  • Serija: Sensors Communication for Urban Intelligence
  • Išleidimo metai: 19-Jul-2021
  • Leidėjas: CRC Press
  • ISBN-13: 9781003127468
Kitos knygos pagal šią temą:
  • Taylor & Francis e-book
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  • Standartinė kaina: 211,02 €
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Biometric Data in Smart Cities: Methods and Models of Collective BehaviorDidesnis paveikslėlis
  • Formatas: 214 pages, 2 Tables, black and white; 114 Line drawings, black and white; 17 Halftones, black and white; 131 Illustrations, black and white
  • Serija: Sensors Communication for Urban Intelligence
  • Išleidimo metai: 19-Jul-2021
  • Leidėjas: CRC Press
  • ISBN-13: 9781003127468
Kitos knygos pagal šią temą:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Partnerių interneto svetainė: https://www.taylorfrancis.com/books/9781003127468

In modern conditions of the development of intelligent systems to solve the problems of smart cities, more and more attention is paid to the construction of distributed intelligent systems, which, based on a network of sensors and specialized calculators, help residents and visitors of the city in real time to solve a whole range of complex problems that arise in an urban environment. In a smart city, much attention is paid to the processing of biometric information that comes from biometric sensors distributed throughout the city. Such biometric systems are multimodal and allow you to control the general condition of a person, and also help a person to move around the city and predict events within the city.

This book describes methods for processing biometric information in a smart city environment. The theoretical foundations of building a biometric multisensor network, which allows you to create a unified urban biometric community, are considered. The theoretical foundations of the parallel shift technology and the Radon transformation on cellular automata with a hexagonal covering are presented. On the basis of these technologies, methods of biometric identification by gait parameters and the geometric shape of the auricle are described, which are effectively used in a smart city. A method for tracking dynamic changes in the state of a smart city in real time is considered. Models of behavior of colonies of living organisms, their formation, movement and interaction are described on the basis of the technology of cellular automata with active cells. Models of behavior of active cells in meeting with unwanted cells and models of combining and destruction of active cell colonies are also described.

This book is intended for undergraduate, graduate students and specialists working and conducting research in the field of biometric information processing, as well as in the development and construction of distributed intelligent systems.


 

Preface ix
About the Authors xiii
1 Distributed Intelligent Systems and Natural Collective Intelligent Systems
1(8)
1.1 Introduction: Background and Driving Forces
1(1)
1.2 DIS Short Classification
2(2)
1.3 Natural Collective Intelligent Systems
4(1)
1.4 Ant Colony
4(2)
1.5 Bee Colony
6(1)
1.6 Collective Movements in Nature
6(3)
2 Multisensor Systems
9(16)
2.1 Interaction of the System with the External Environment
9(1)
2.2 Sensor Classification
10(7)
2.3 Multisensory Systems
17(6)
2.4 Conclusion
23(2)
3 Smart Cities Based on Multisensor Systems
25(6)
3.1 Introduction
25(1)
3.2 The Main Characteristics of the Smart City
26(3)
3.3 Biometric Sensors in a Smart City
29(2)
4 Biometric Characteristics
31(8)
4.1 Static Biometric Characteristics
31(2)
4.2 Dynamic Biometric Characteristics
33(2)
4.3 Multimodality of Biometric Identification
35(4)
5 Biometric Data Processing in Smart Cities Based on Multisensor Systems
39(12)
5.1 Uniform City Biometric Community
39(6)
5.2 Biometric Features in the Smart City
45(1)
5.3 Human Face Image Analysis
45(2)
5.4 Analysis of the Geometry of the Auricle
47(1)
5.5 Human Gait Analysis
48(3)
6 General Information About Parallel Shift Technology
51(20)
6.1 Theoretical Foundations of Parallel Shift Technology
51(1)
6.2 Data Generation and Storage
52(5)
6.3 Vector Formation of Functions of the Area of Intersection
57(3)
6.4 Formation of FAIs Sets for Non-Binary Images
60(5)
6.5 Noise Control with the Use of PST
65(6)
7 Image Recovery by Methods of Parallel Shift Technology
71(18)
7.1 The Method of Circumscribed Rectangles
71(5)
7.2 Determining the Parameters of Circumscribed Rectangles
76(6)
7.3 Formation of Reference Surfaces for Parts of the Existing Standard
82(7)
8 Scene Analysis for Two Objects
89(26)
8.1 Determining the Basic Parameters of the Scene
89(4)
8.2 Determining the Real Values of the Basic Parameters of the Scene Objects
93(8)
8.3 Determining the Shape of the Objects of Scene
101(10)
8.4 The Order for Determining the Basic Parameters of the Scene
111(4)
9 Radon Transformation Technology on Cellular Automata with Hexagonal Coating
115(10)
9.1 Introduction
115(1)
9.2 Images Description Technology Based on Cellular Automata with Hexagonal Coated Radon Transform
116(9)
10 Biometric Identification Methods Based on the Geometry of the Auricle Based on Parallel Shift Technology and Radon Transformation
125(24)
10.1 Technologies For Biometric Identification
125(1)
10.2 Biometric Identification Methods Based on the Analysis of the Auricle
126(11)
10.3 Determination of the Area of Recognition in Biometric Identification by the Shape of the Ear Based on the Parallel Shift Technology
137(1)
10.4 Determining the Cut-Off Points of the Ear Image
137(5)
10.5 The Method of Dividing the Image into Individual Objects
142(2)
10.6 The Actual Application of the Method of Determining the Location of the Image of the Ear
144(1)
10.7 The Procedure For Comparing the Elements of the Ear Image with the Reference Data
145(4)
11 Methods of Biometric Identification Based on Gait in the Smart City
149(20)
11.1 The Main Reasons for Using Human Gait for Biometric Identification in a Smart City
149(3)
11.2 Description of Gait Identification Method
152(5)
11.3 Search for the Motion Vector of the Object in the Visual Field and the Gait Period
157(6)
11.4 Defining the Area of Analysis in the Images of the Silhouette of the Object
163(3)
11.5 Comparison of the Obtained Data with the Reference Information
166(3)
12 Biometric Data Processing of Human and Animal Colonies in a Smart City
169(12)
12.1 Distributed Smart Sensor Network of Smart City
169(3)
12.2 A Method for Analyzing the Overall Urban Picture of the Behavior of People and Animals in a Smart City
172(2)
12.3 Selection and Description of Objects in the Visual Scene
174(2)
12.4 Time Impulse Description of the Visual Picture of a Smart City
176(5)
13 Behavioral Models of Human and Animal Colonies Based on the Technology of Cellular Automata with Active Cells
181(22)
13.1 Theoretical Positions of Cellular Automata Technology with Active Cells
181(2)
13.2 Interaction of Active Cells
183(7)
13.3 Colony Formation
190(3)
13.4 Cell Colony Movement
193(4)
13.5 Interaction of Active Cell Colonies
197(6)
References 203(6)
Index 209
Stepan Bilan works as an Associate Professor at the State University of Infrastructure and Technology (Kiev, Ukraine).

Mykola Bilan work as an informatics and physics teacher in a secondary school in the village of Mayak, Republic of Moldova.

Ruslan Motornyuk works as a leading engineer in the Production Unit Kiev Department branch of the Main Information and Computing Center of the JSC Ukrzaliznytsya.

Serhii Yuzhakov has been working in the information technology divisions of various state institutions of Ukraine.
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