Preface |
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ix | |
Acknowledgments |
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xi | |
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xiii | |
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Chapter 1 Overview of pervasive computing |
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1 | (58) |
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2.1 Overview of pervasive computing |
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1 | (2) |
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2.2 Distributed computing |
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3 | (1) |
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4 | (1) |
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5 | (9) |
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1.4.1 Pervasive computing examples |
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6 | (1) |
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1.4.2 Pervasive healthcare system |
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7 | (2) |
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1.4.3 Challenges of pervasive computing |
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9 | (3) |
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2.4.4 Issues in pervasive computing |
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12 | (1) |
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1.4.5 Advantages of pervasive computing |
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13 | (1) |
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1.4.6 Disadvantages of pervasive computing |
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14 | (1) |
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14 | (23) |
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1.5.1 Biometrics architecture |
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15 | (1) |
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1.5.2 Commonly used biometrics methods |
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16 | (1) |
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17 | (1) |
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1.5.2.2 Face recognition system |
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17 | (7) |
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24 | (1) |
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24 | (1) |
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2.5.2.5 Keystroke dynamics |
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25 | (1) |
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2.5.2.6 Dynamic signature verification |
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25 | (1) |
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2.5.2.7 Speech/voice recognition |
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25 | (5) |
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1.5.2.8 Facial thermograms |
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30 | (1) |
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31 | (1) |
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32 | (1) |
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33 | (1) |
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34 | (1) |
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1.5.5 Biometrics applications |
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34 | (3) |
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2.6 Sensor-based technology |
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37 | (2) |
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1.7 Individual care in pervasive environment |
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39 | (7) |
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41 | (3) |
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1.7.1.1 Network architectures |
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44 | (2) |
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46 | (11) |
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50 | (5) |
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1.8.2 Image processing in MATLAB |
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55 | (1) |
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2.8.3 Properties of image |
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55 | (1) |
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1.8.4 Signal processing in MATLAB |
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56 | (1) |
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57 | (2) |
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Chapter 2 Hybrid face recognition technology for individual's security at home |
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59 | (32) |
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2.2 Introduction to face recognition |
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59 | (3) |
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2.2 Face recognition algorithms |
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62 | (11) |
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2.2.2 Principle component analysis (PCA) |
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62 | (3) |
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2.2.2 Independent component analysis (ICA) |
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65 | (3) |
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2.2.3 Linear discriminant analysis (LDA) |
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68 | (2) |
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2.2.4 Discrete cosine transform (DCT) |
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70 | (1) |
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71 | (2) |
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2.3 Proposed hybrid PICA algorithm |
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73 | (2) |
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75 | (10) |
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85 | (3) |
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88 | (3) |
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Chapter 3 Hybrid MFRASTA voice recognition technology for individual's security at home |
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91 | (32) |
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3.2 Introduction to voice recognition |
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91 | (4) |
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3.2 Challenges of voice recognition system |
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95 | (1) |
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3.3 Voice recognition algorithms |
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95 | (10) |
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3.3.2 Mel frequency cepstral coefficient (MFCC) |
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95 | (4) |
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3.3.2 Perceptual linear prediction (PLP) |
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99 | (1) |
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3.3.3 Linear prediction code (LPC) |
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100 | (2) |
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3.3.4 RelAtive Spec TrA-perceptual linear prediction (RASTA-PLP) |
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102 | (1) |
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3.3.5 Zero-crossing peak amplitudes (ZCPA) |
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103 | (1) |
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3.3.6 Dynamic time warping (DTW) |
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103 | (1) |
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104 | (1) |
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3.4 Proposed hybrid MFRASTA technique |
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105 | (2) |
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107 | (1) |
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107 | (11) |
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3.7 Results and discussion |
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118 | (1) |
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119 | (4) |
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Chapter 4 Sensor-based health monitoring system using fingertip application |
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123 | (18) |
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4.1 Introduction to sensor-based services |
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123 | (6) |
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129 | (5) |
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4.2.1 Phonocardiograph application |
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129 | (1) |
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4.2.2 Mercury sphygmomanometer |
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130 | (1) |
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4.2.3 Automatic digital sphygmomanometer |
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131 | (1) |
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4.2.4 Electrocardiographs |
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132 | (1) |
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4.2.5 Accelerometer-based application |
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133 | (1) |
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4.3 Proposed technique: fingertip application |
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134 | (2) |
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136 | (3) |
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139 | (2) |
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Chapter 5 Hybrid PICA and MFRASTA technology with sensor-based fingertip application for individual's security at home |
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141 | (16) |
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5.1 Process of face and voice recognition |
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141 | (4) |
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5.2 Vital sign monitoring |
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145 | (1) |
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5.3 Conclusion and future work |
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145 | (12) |
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Chapter 6 Conclusion and future research |
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157 | (8) |
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157 | (3) |
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160 | (2) |
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162 | (3) |
References |
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165 | (8) |
Index |
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173 | |