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E-grāmata: Integrated Approach to Home Security and Safety Systems [Taylor & Francis e-book]

, , (MM University, India)
  • Formāts: 176 pages, 11 Tables, black and white; 57 Line drawings, black and white; 86 Halftones, black and white; 143 Illustrations, black and white
  • Izdošanas datums: 18-Oct-2021
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003120933
  • Taylor & Francis e-book
  • Cena: 164,53 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standarta cena: 235,05 €
  • Ietaupiet 30%
  • Formāts: 176 pages, 11 Tables, black and white; 57 Line drawings, black and white; 86 Halftones, black and white; 143 Illustrations, black and white
  • Izdošanas datums: 18-Oct-2021
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003120933

This book provides an integrated solution for security and safety in the home, covering both assistance in health monitoring and safety from strangers/intruders who want to enter home with harmful intentions. It defines a system whereby recognition of a person/stranger at the door is done using three modules: Face Recognition, Voice Recognition and Similarity Index. These three modules are taken together to provide a percentage likelihood that the individual is in the “known” or “unknown” category. The system can also continuously monitor the health parameters of a vulnerable person living alone at home and aid them in calling for help in an emergency.

The author has analysed a number of existing biometric techniques to provide security for an individual living alone at home. These biometric techniques have been tested using MATLAB image processing and signal processing toolboxes and results have been calculated on the basis of recognition rate. A major contribution in providing security is a hybrid algorithm proposed by the author named PICA which combines features of both PCA (Principle Component Analysis) and ICA (Independent Component Analysis) algorithms. This hybrid approach gives better performance recognition than either system alone. The second proposed hybrid algorithm for voice recognition is named as a MFRASTA algorithm by combining features of MFCC (Mel Frequency Cepstral Coefficient) and RASTA-PLP (Relative Spectral- Perceptual Linear Prediction) algorithm. After performing experiments, results are collected on the basis of recognition rate.

The author has also proposed a third technique named as a similarity index to provide trust-based security for an individual. This technique is text independent in which a person is recognized by pronunciation, frequency, tone, pitch etc. irrespective of the content spoken by the person. By combining these three techniques, high recognition rate is provided to the person at the door and high security to the individual living independently at home. In the final contribution, the author has proposed a fingertip-based application for health monitoring by using the concept of sensors. This application is developed using iPhone 6’s camera. When a person puts their fingertip on a camera lens, with the help of brightness of the skin, the person’s heartbeat will be monitored. This is possible even with a low-quality camera. In case of any emergency, text messages will be sent to the family members of the individual living alone by using 3G dongle and MATLAB tool. Results show that the proposed work outperforms all the existing techniques used in face recognition, voice recognition and health monitoring.



This book provides an integrated solution for security and safety in the home, covering both assistance in health monitoring and safety from strangers/intruders who want to enter home with harmful intentions. 

Preface ix
Acknowledgments xi
List of abbreviations
xiii
Chapter 1 Overview of pervasive computing
1(58)
2.1 Overview of pervasive computing
1(2)
2.2 Distributed computing
3(1)
1.3 Mobile computing
4(1)
1.4 Pervasive computing
5(9)
1.4.1 Pervasive computing examples
6(1)
1.4.2 Pervasive healthcare system
7(2)
1.4.3 Challenges of pervasive computing
9(3)
2.4.4 Issues in pervasive computing
12(1)
1.4.5 Advantages of pervasive computing
13(1)
1.4.6 Disadvantages of pervasive computing
14(1)
1.5 Biometrics
14(23)
1.5.1 Biometrics architecture
15(1)
1.5.2 Commonly used biometrics methods
16(1)
1.5.2.1 Fingerprint
17(1)
1.5.2.2 Face recognition system
17(7)
1.5.2.3 Hand geometry
24(1)
1.5.2.4 Iris scan
24(1)
2.5.2.5 Keystroke dynamics
25(1)
2.5.2.6 Dynamic signature verification
25(1)
2.5.2.7 Speech/voice recognition
25(5)
1.5.2.8 Facial thermograms
30(1)
2.5.2.9 Ear
31(1)
2.5.2.20 DNA
32(1)
1.5.3 Advantages
33(1)
1.5.4 Disadvantages
34(1)
1.5.5 Biometrics applications
34(3)
2.6 Sensor-based technology
37(2)
1.7 Individual care in pervasive environment
39(7)
1.7.1 Neural networks
41(3)
1.7.1.1 Network architectures
44(2)
1.8 Matlab 2012a
46(11)
1.8.1 Matrix concept
50(5)
1.8.2 Image processing in MATLAB
55(1)
2.8.3 Properties of image
55(1)
1.8.4 Signal processing in MATLAB
56(1)
2.9 Book organization
57(2)
Chapter 2 Hybrid face recognition technology for individual's security at home
59(32)
2.2 Introduction to face recognition
59(3)
2.2 Face recognition algorithms
62(11)
2.2.2 Principle component analysis (PCA)
62(3)
2.2.2 Independent component analysis (ICA)
65(3)
2.2.3 Linear discriminant analysis (LDA)
68(2)
2.2.4 Discrete cosine transform (DCT)
70(1)
2.2.5 Gabor wavelet
71(2)
2.3 Proposed hybrid PICA algorithm
73(2)
2.4 Implementation
75(10)
2.5 Results
85(3)
2.6 Summary
88(3)
Chapter 3 Hybrid MFRASTA voice recognition technology for individual's security at home
91(32)
3.2 Introduction to voice recognition
91(4)
3.2 Challenges of voice recognition system
95(1)
3.3 Voice recognition algorithms
95(10)
3.3.2 Mel frequency cepstral coefficient (MFCC)
95(4)
3.3.2 Perceptual linear prediction (PLP)
99(1)
3.3.3 Linear prediction code (LPC)
100(2)
3.3.4 RelAtive Spec TrA-perceptual linear prediction (RASTA-PLP)
102(1)
3.3.5 Zero-crossing peak amplitudes (ZCPA)
103(1)
3.3.6 Dynamic time warping (DTW)
103(1)
3.3.7 Wavelet
104(1)
3.4 Proposed hybrid MFRASTA technique
105(2)
3.5 Similarity index
107(1)
3.6 Implementation
107(11)
3.7 Results and discussion
118(1)
3.8 Summary
119(4)
Chapter 4 Sensor-based health monitoring system using fingertip application
123(18)
4.1 Introduction to sensor-based services
123(6)
4.2 Previous techniques
129(5)
4.2.1 Phonocardiograph application
129(1)
4.2.2 Mercury sphygmomanometer
130(1)
4.2.3 Automatic digital sphygmomanometer
131(1)
4.2.4 Electrocardiographs
132(1)
4.2.5 Accelerometer-based application
133(1)
4.3 Proposed technique: fingertip application
134(2)
4.4 Results
136(3)
4.5 Summary
139(2)
Chapter 5 Hybrid PICA and MFRASTA technology with sensor-based fingertip application for individual's security at home
141(16)
5.1 Process of face and voice recognition
141(4)
5.2 Vital sign monitoring
145(1)
5.3 Conclusion and future work
145(12)
Chapter 6 Conclusion and future research
157(8)
6.1 Introduction
157(3)
6.2 Summary and scope
160(2)
6.3 Future work
162(3)
References 165(8)
Index 173
Dr. Sonali Goyal is PhD in Computer Science & Engg. She has teaching experience of 8 years and is currently working as an Assistant Professor in the computer science department at MMDU. She has published more than 10 papers in national and international journals of repute. She has published two patents which are to be awarded soon. Her areas of interest in computer science are Pervasive Computing, Bio-metrics and Data Mining.

Dr. Neera Batra has a Phd in Computer Applications. She has a decade of teaching and research experience and is currently working as an Associate Professor in computer science department at MMDU. She has published more than 40 papers in national and international journals of repute. She has published six patents which are to be awarded soon. Her areas of interest in computer science are Pervasive computing, Internet of Things, Distributed Database and Security.