Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2009. / Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Mechanical Engineering, 2009. / Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2008. / Includes bibliographical references.

Thesis (M.S.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains xvii, 169 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 147-155).

Typescript. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2005. / Typescript. Includes bibliographical references (leaves 109-113).

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2007. / Dr. Martha Gallivan, Committee Member ; Dr. Chris Paredis, Committee Member ; Dr. Janet K. Allen, Committee Member ; Dr. Peter Hesketh, Committee Co-Chair ; Dr. Farrokh Mistree, Committee Co-Chair.

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 102-103).

Thesis (doctoral)--VTT Micronova, 2006. / Includes bibliographical references. Also available on the World Wide Web.

by Zhou, Shengli. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 70-75). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background of Gesture Recognition --- p.1 / Chapter 1.2 --- HCI System --- p.2 / Chapter 1.2.1 --- Vision Based HCI System --- p.2 / Chapter 1.2.2 --- Accelerometer Based HCI System --- p.4 / Chapter 1.3 --- Pattern Recognition Methods --- p.6 / Chapter 1.4 --- Thesis Outline --- p.7 / Chapter Chapter 2 --- 2D Hand-Written Character Recognition --- p.8 / Chapter 2.1 --- Introduction to Accelerometer Based Hand-Written Character Recognition --- p.8 / Chapter 2.1.1 --- Character Recognition Based on Trajectory Reconstruction --- p.9 / Chapter 2.1.2 --- Character Recognition Based on Classification --- p.10 / Chapter 2.2 --- Neural Network --- p.11 / Chapter 2.2.1 --- Mathematical Model of Neural Network (NN) --- p.11 / Chapter 2.2.2 --- Types of Neural Network Learning --- p.13 / Chapter 2.2.3 --- Self-Organizing Maps (SOMs) --- p.14 / Chapter 2.2.4 --- Properties of Neural Network --- p.16 / Chapter 2.3 --- Experimental Setup --- p.17 / Chapter 2.4 --- Configuration of Sensing Mote --- p.18 / Chapter 2.5 --- Data Acquisition Methods --- p.19 / Chapter 2.6 --- Data Preprocessing Methods --- p.20 / Chapter 2.6.1 --- Fast Fourier Transform (FFT) --- p.21 / Chapter 2.6.2 --- Discrete Cosine Transform (DCT) --- p.23 / Chapter 2.6.3 --- Problem Analysis --- p.25 / Chapter 2.7 --- Hand-written Character Classification using SOMs --- p.26 / Chapter 2.7.1 --- Recognition of All Characters in the Same Group --- p.27 / Chapter 2.7.2 --- Recognize the Numbers and Letters Respectively --- p.28 / Chapter 2.8 --- Conclusion --- p.29 / Chapter Chapter 3 --- Human Gesture Recognition --- p.32 / Chapter 3.1 --- Introduction to Human Gesture Recognition --- p.32 / Chapter 3.1.1 --- Dynamic Gesture Recognition --- p.32 / Chapter 3.1.2 --- Hidden Markov Models (HMMs) --- p.33 / Chapter 3.1.2.1 --- Applications of HMMs --- p.34 / Chapter 3.1.2.2 --- Training Algorithm --- p.35 / Chapter 3.1.2.3 --- Recognition Algorithm --- p.35 / Chapter 3.2 --- System Architecture --- p.36 / Chapter 3.2.1 --- Experimental Devices --- p.36 / Chapter 3.2.2 --- Data Acquisition Methods --- p.38 / Chapter 3.2.3 --- System Work Flow --- p.39 / Chapter 3.3 --- Real-Time Gesture Spotting --- p.40 / Chapter 3.3.1 --- Introduction --- p.40 / Chapter 3.3.2 --- Gesture Segmentation Based on Standard Deviation Calculation --- p.42 / Chapter 3.3.3 --- Evaluation of Gesture Spotting Program --- p.47 / Chapter 3.4 --- Comparison of Data Processing Methods --- p.48 / Chapter 3.4.1 --- Discrete Cosine Transform (DCT) --- p.48 / Chapter 3.4.2 --- Discrete Wavelet Transform (DWT) --- p.49 / Chapter 3.4.3 --- Zero Bias Compensation and Filtering (ZBC&F) --- p.51 / Chapter 3.4.4 --- Comparison of Experimental Results --- p.52 / Chapter 3.5 --- Data Base Setup --- p.53 / Chapter 3.6 --- Experimental Results Based on the Database Obtained from Ten Test Subjects --- p.53 / Chapter 3.6.1 --- Experimental Results when Gestures are Manually and Automatically “cut´ح --- p.54 / Chapter 3.6.2 --- The Influence of Number of Dominant Frequencies on Recognition --- p.55 / Chapter 3.6.3 --- The Influence of Sampling Frequencies on Recognition --- p.59 / Chapter 3.6.4 --- Influence of Number of Test Subjects on Recognition --- p.62 / Chapter 3.6.4.1 --- Experimental Results When Training and Testing Subjects Are Overlaped --- p.61 / Chapter 3.6.4.2 --- Experimental Results When Training and Testing Subjects Are Not Overlap --- p.62 / Chapter 3.6.4.3 --- Discussion --- p.65 / Chapter Chapter 4 --- Conclusion --- p.68 / Bibliography --- p.70