Neural Network based Classification of Material Type and its Surface Properties

References

• S Aoshima, N Yoshizawa. T Yabuta & K Hanari, The object discrimination system using a neural network with inputs for distance and sensitivity information of an ultrasonic sensor, Proceedings of first IEEE international conference on Sensors, Orlando, Florida, vol 1, pp 532–536, June 12–14,2002.
   Google Scholar
• Y Tada, K Hosoda, Y Yamasaki & M Asada, Sensing the texture of surface by anthropomorphic soft fingertips with multi-modal sensors, Proceedings of the IEEE/RSJ international conference on intelligent robotics and systems, Las Vegas, Neveda, pp 31–35, Oct 2003.
   Google Scholar
• K Kyung, S Son, G Yang & D Kwon, How to effectively display surface properties using an integrated tactile display system, Proceedings of the IEEE international conference on robotics and automation, Barcelona, Spain, pp 1761–1766, April 2005.
   Google Scholar
• D Brenner, J C Principe & K L Doty, Neural network classification of metal surface properties using dynamic touch sensor, Proceedings of IJCNN, Seattle, vol 1, pp 189–194,1991.
   Google Scholar
• C P Hsieh & B T Khuri-Yakub, Single point contact measurements of mechanical resonances, Proceedings of Ultrasonic Symposium, Tucson, AZ, USA, vol 2, pp 787–789, Oct. 1992.
   Google Scholar
• W S McMath, M D Colven, S K Yeung, & E M Petriu, Tactile pattern recognition using neural networks, Proceedings of the international conference IECON, Maui, HI, USA, pp 1391–1394, Nov 1993.
   Google Scholar
• K Zhang, C Butler, Q Yang & Y Lu, A fibre optic sensor for the measurement of surface roughness and displacement using artificial neural networks, IEEE Trans, on Instrumentation and Measurement, vol 46, no. 4, pp 899–902, August 1997.
   Google Scholar
• J C L Wu, H Iksan, T J Huang, J D Wu & Kenny Lo, Impact of wafer surface profile on IC packaging, Proceedings of the 7th international symposium on Advanced Packaging Materials, Braselton GA, USA, pp 405–412, March 11–14,2001.
   Google Scholar
• T W Ng, The optical mouse as a two-dimensional displacement sensor, Sensors and Acluators-A, vol 107, pp 21–25,2003.
   Google Scholar
• G K Lott, M J Rosen & R R Hoy, An inexpensive submillisecond system for walking measurements of small animals based on optical computer mouse technology, Journal of Neuroscience Methods, vol 161, pp 55–61, 2007.
   Google Scholar
• M Akamatsu, Touch with a mouse: A mouse type interface device with tactile and force display, Proceedings of 3rd IEEE international workshop on Robot and Human Communication, Nagoya, Japan, pp 140–144,1994.
   Google Scholar
• L Sooyong, Mobile robot localization using optical mice, Proceedings of IEEE conference on Robotics, Automation and Mechatronics, Singapore, vol 2. pp 1192–1197, Dec 2004.
   Google Scholar
• A Bonarini, M Matteucci & M Restelli, Automatic error detection and reduction for an odometric sensor based on two optical mice, Proceedings of the IEEE international conference on Robotics and Automation, Barcelona, Spain, pp 1675–1680, April 2005.
   Google Scholar
• G J Donatell, D W Meister, J R O’Brien, J S Thurlow, J G Webster & F J Salvi, A Simple Device to Monitor Flexion and Lateral Bending of the Lumbar Spine, IEEE Trans on Neural Systems and Rehabilitation Engineering, vol 13, no 1, pp 18–23, March 2005.
   Google Scholar
• J V Draper, Hand acceleration impulse bandwidth during target acquisition: Implications for teleoperator bandwidth requirements, IEEE trans on Systems, Man, and Cybernetics, vol 24, no 6, pp 931–936, June 1994.
   Google Scholar
• Yu-Luen Chen, Application of Tilt Sensors in Human-Computer Mouse Interface for People With Disabilities, IEEE trans. on Neural Systems and Rehabilitation Engineering, vol 9, no 3, pp.289–294, Sept 2001.
   Google Scholar
• Z Yang & R Maeda, Automatic micro flow rate measurement using a modified computer mouse device, Proceedings of 1st annual international IEEE-EMBS special topic conference on Microtechnologies in Medicine and Biology, Lyon, France, pp 288–291, Oct 2000.
   Google Scholar
• F Tong, S K Tso & X M Xu, Tile-wall bonding integrity inspection based on time-domain features of impact acoustics, Sensors and Actuators-A, vol 132, pp 557–566, 2006.
   Google Scholar
• H Wu & M Siegel, Correlation of accelerometer and microphone data in the coin tap test, IEEE trans. on Instrumentation and Measurement, vol 49, no 3, pp 493–497, June 2000.
   Google Scholar
• P A Lynn & W Fuerst, Introductory digital signal processing, Wiley, New York, 1994.
   Google Scholar
• E O Brigham, The fast fourier transform and its applications, Englewood Cliffs, Prentice-Hall, 1988.
   Google Scholar
• J F Kalafut, Time frequency analysis of dynamic syringe friction - The benefits of an O-Ring plunger seal design, Proceedings of 29th IEEE annual conference on Bioengineering, pp 307–308, March 2003.
   Google Scholar
• E Krotkov, R Klatzky & N Zumel, Analysis and synthesis of the sounds of impact based on shape-invariant properties of materials, Proceedings of the 13th international conference on Pattern Recognition, Vienna, Austria, vol 1, pp 115–119,1996.
   Google Scholar
• J Principe, N Euliano & C Lefebvre, Neural and adaptive systems: fundamentals through simulations, John Wiley and Sons, 1999.
   Google Scholar
• J Lan, T Lan & S Nahavandi, A novel application of a microaccelerometer for target classification, IEEE Sensors Journal, vol 4, no 4, pp 519–524, August 2004.
   Google Scholar
• R P Lippmann, An introduction to computing with neural nets, IEEE ASSP Magazine, pp 4–22, April 1987.
   Google Scholar
• E B Baum, On the capabilities of multilayer perceptrons, Journal of Complexity, vol 4, pp 193–215, 1988.
   Google Scholar
• P Arpaia, P Daponte, D Grimaldi & L Michaeli, ANN -based error reduction for experimentally modeled sensors, IEEE Trans. on Instrumentation and Measurement, vol 51, no 1, pp 23–29, Feb 2002.
   Google Scholar
• S Haykin, Neural Network: A comprehensive foundation, McMillan, New York, 1994.
   Google Scholar
• I V Turchenko, Simulation modeling of multi-parameter sensor signal identification using neural networks, Proceedings of the 2nd IEEE international conference on Intelligent Systems, Sofia, Bulgaria, pp 48–53, June 2004.
   Google Scholar
• K Hornik, M Stinchcombe & H White, Multilayer feedforward networks are universal approximators, Neural Networh, vol 2, no 5, pp 359–366,1989.
   Google Scholar
• G Huang, Y Chen & H Babri, Classification ability of single hidden layer feedforward neural networks, IEEE trans on Neural Networks, vol 11, no 3, pp 799–801, May 2000.
   Google Scholar
• M Stinchcombe & H White, Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions, Proceedings of International Joint Conference on Neural Networks, Washington, DC, USA.vol 1,pp613–617, 1989.
   Google Scholar
• K W Lee & H N Lam, Optimal sizing of feedforward neural networks: Case studies, Proceedings of the 2nd New Zealand two-stream international conference on Artificial Neural Networks and Expert Systems, pp 79–82, 1995.
   Google Scholar
• R Fletcher, Practical methods of optimization, John Wiley, New York, 1987.
   Google Scholar