tailieunhanh - Báo cáo hóa học: " Research Article Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 247643 11 pages doi 2008 247643 Research Article Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns Habil Kalkan 1 Nuri Firat Ince 2 Ahmed H. Tewfik 2 Yasemin Yardimci 1 and Tom Pearson3 1 Informatics Institute Middle East Technical University 06531 Ankara Turkey 2 Department of Electrical and Computer Engineering University of Minnesota MN 55455 USA 3 Agricultural Research Service United States Department of Agriculture KS 66502 USA Correspondence should be addressed to Yasemin Yardimci yardimy@ Received 17 January 2007 Revised 7 July 2007 Accepted 8 October 2007 Recommended by Hugo Van hamme Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds Aspergillus flavus . These molds can cause cancer. In this study we introduce a new approach that separates damaged cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals. The proposed technique requires no prior knowledge of the relevant time and frequency locations. In an offline step the algorithm adaptively segments impact signals from a training data set in time using local cosine packet analysis and a Kullback-Leibler criterion to assess the discrimination power of different segmentations. In each resulting time segment the signal is further decomposed into subbands using an undecimated wavelet transform. The most discriminative subbands are selected according to the Euclidean distance between the cumulative probability distributions of the corresponding subband coefficients. The most discriminative subbands are fed into a linear discriminant analysis classifier. In the online classification step the algorithm simply computes the learned features from the observed signal and feeds them to the linear discriminant analysis LDA classifier. The algorithm achieved a