Ultrasonic Sensor Based Fluid Level Sensing Using Support Vector Machines

The characteristics, principles, and applications of ultrasonic type sensors, including some issues of the ultrasonic type level sensing applications in dynamic environments, were discussed in Chap. 2. In this chapter, first, the fundamental principles of signal classification and processing are discussed. Then the background and application of Support Vector Machines (SVM) in the context of this research are described. Finally, the use of SVM in providing solutions to the problems encountered in fluid-level measurement in dynamic environments is described.

[1]  Harald Hruschka,et al.  Comparing performance of feedforward neural nets and K-means for cluster-based market segmentation , 1999, Eur. J. Oper. Res..

[2]  Jenq-Neng Hwang,et al.  Handbook of Neural Network Signal Processing , 2000, IEEE Transactions on Neural Networks.

[3]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[4]  E. Brigham,et al.  The fast Fourier transform and its applications , 1988 .

[5]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[6]  Kazushi Ikeda,et al.  Effects of kernel function on Nu support vector machines in extreme cases , 2006, IEEE Transactions on Neural Networks.

[7]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[8]  M. Davenport The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM , 2005 .

[9]  Robert X. Gao,et al.  Discrete Wavelet Transform , 2011 .

[10]  C. Shavers,et al.  An SVM-based approach to face detection , 2006, 2006 Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory.

[11]  Bernhard Schölkopf,et al.  A tutorial on v-support vector machines , 2005 .

[12]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[13]  Chuan-Ying Jia,et al.  A new nu-support vector machine for training sets with duplicate samples , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[14]  Monson H. Hayes Schaum's Outline of Theory and Problems of Digital Signal Processing , 1998 .

[15]  Paulo S. R. Diniz,et al.  Adaptive Filtering: Algorithms and Practical Implementation , 1997 .

[16]  Vojin G. Oklobdzija The Computer Engineering Handbook , 2007 .

[17]  Gunnar Rätsch,et al.  Advanced lectures on machine learning : ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003 : revised lectures , 2004 .

[18]  Shigeo Abe Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.

[19]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[20]  Greg Humphreys,et al.  Physically Based Rendering: From Theory to Implementation , 2004 .

[21]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[22]  Ovidiu Ivanciuc,et al.  Applications of Support Vector Machines in Chemistry , 2007 .

[23]  Greg Humphreys,et al.  Sampling and Reconstruction , 2010 .

[24]  James C. Bezdek,et al.  Fuzzy mathematics in pattern classification , 1973 .

[25]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[26]  S. Mallat A wavelet tour of signal processing , 1998 .

[27]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[28]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[29]  Gerard V. Trunk,et al.  A Problem of Dimensionality: A Simple Example , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Chih-Jen Lin,et al.  A tutorial on?-support vector machines , 2005 .

[31]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[32]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[33]  Mark S. Nixon,et al.  Feature Extraction and Image Processing , 2002 .

[34]  David Salomon,et al.  Data Compression: The Complete Reference , 2006 .

[35]  Chih-Jen Lin,et al.  Training v-Support Vector Classifiers: Theory and Algorithms , 2001, Neural Computation.

[36]  Shigeo Abe,et al.  Two-Class Support Vector Machines , 2010 .

[37]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[38]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[39]  Joan L. Mitchell,et al.  Evolving JPEG color data compression standard , 1991, Other Conferences.

[40]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .