| Description |
1 online resource (191 p.) |
| Contents |
Intro -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- List of abbreviations -- Chapter 1: Overview of pervasive computing -- 1.1 Overview of Pervasive Computing -- 1.2 Distributed Computing -- 1.3 Mobile Computing -- 1.4 Pervasive Computing -- 1.4.1 Pervasive computing examples -- 1.4.2 Pervasive healthcare system -- 1.4.3 Challenges of pervasive computing -- 1.4.4 Issues in pervasive computing -- 1.4.5 Advantages of pervasive computing -- 1.4.6 Disadvantages of pervasive computing -- 1.5 Biometrics -- 1.5.1 Biometrics architecture |
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1.5.2 Commonly used biometrics methods -- 1.5.2.1 Fingerprint -- 1.5.2.2 Face recognition system -- 1.5.2.2.1 Challenges of face recognition -- 1.5.2.2.2 Face recognition algorithms -- 1.5.2.3 Hand geometry -- 1.5.2.4 Iris scan -- 1.5.2.5 Keystroke dynamics -- 1.5.2.6 Dynamic signature verification -- 1.5.2.7 Speech/voice recognition -- 1.5.2.7.1 Voice recognition system -- 1.5.2.7.2 Voice recognition techniques -- 1.5.2.8 Facial thermograms -- 1.5.2.9 Ear -- 1.5.2.10 DNA -- 1.5.3 Advantages -- 1.5.4 Disadvantages -- 1.5.5 Biometrics applications -- 1.6 Sensor-Based Technology |
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1.7 Individual Care in Pervasive Environment -- 1.7.1 Neural networks -- 1.7.1.1 Network architectures -- 1.8 MATLAB 2012a -- 1.8.1 Matrix concept -- 1.8.2 Image processing in MATLAB -- 1.8.3 Properties of image -- 1.8.4 Signal processing in MATLAB -- 1.9 Book Organization -- Chapter 2: Hybrid face recognition PICA algorithm for individual's security at home -- 2.1 Inroduction to Face Recognition -- 2.2 Face Recognition Algorithms -- 2.2.1 Principle component analysis (PCA) -- 2.2.2 Independent component analysis (ICA) -- 2.2.3 Linear discriminant analysis (LDA) |
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2.2.4 Discrete cosine transform (DCT) -- 2.2.5 Gabor wavelet -- 2.3 Proposed Hybrid PICA Algorithm -- 2.4 Implementation -- 2.5 Results -- 2.6 Summary -- Chapter 3: Hybrid MFRASTA voice recognition technology for individual's security at home -- 3.1 Introduction to Voice Recognition -- 3.2 Challenges of Voice Recognition System -- 3.3 Voice Recognition Algorithms -- 3.3.1 Mel frequency cepstral coefficient (MFCC) -- 3.3.2 Perceptual linear prediction (PLP) -- 3.3.3 Linear prediction code (LPC) -- 3.3.4 RelAtive SpecTrA-Perceptual Linear Prediction (RASTA-PLP) |
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3.3.5 Zero crossing peak amplitudes (ZCPA) -- 3.3.6 Dynamic time warping (DTW) -- 3.3.7 Wavelet -- 3.4 Proposed Hybrid MFRASTA Technique -- 3.5 Similarity Index -- 3.6 Implementation -- 3.7 Results and Discussion -- 3.8 Summary -- Chapter 4: Sensor-based health monitoring system uses fingertip application -- 4.1 Introduction to Sensors-Based Services -- 4.2 Previous Techniques -- 4.2.1 Phonocardiograph application -- 4.2.2 Mercury sphygmomanometer -- 4.2.3 Automatic digital sphygmomanometer -- 4.2.4 Electrocardiographs -- 4.2.5 Accelerometer-based application |
| Notes |
Description based upon print version of record |
|
4.3 Proposed Technique: Fingertip Application |
| Form |
Electronic book
|
| Author |
Batra, Neera
|
| ISBN |
9781000465198 |
|
1000465195 |
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