Browsing by Author "Pearson, T. C."
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Item Open Access Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics(IEEE, 2004-05) Çetin, A. Enis; Pearson, T. C.; Tewfik, A. H.An algorithm was developed to separate pistachio nuts with closed-shells from those with open-shells. It was observed that upon impact on a steel plate, nuts with closed-shells emit different sounds than nuts with open-shells. Two feature vectors extracted from the sound signals were melcepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals. Classification of a sound signal was done by linearly combining feature vectors from both mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable. During the training phase, sounds of the nuts with closed-shells and open-shells were used to obtain a representative vector of each class. The accuracy of closed-shell nuts was more than 99% on the test set.Item Open Access Classification of closed-and open-shell pistachio nuts using voice-recognition technology(American Society of Agricultural and Biological Engineers, 2004) Çetin, A. Enis; Pearson, T. C.; Tewfik, A. H.An algorithm using speech recognition technology was developed to distinguish pistachio nuts with closed shells from those with open shells. It was observed that upon impact with a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Features extracted from the sound signals consisted of mel-cepstrum coefficients and eigenvalues obtained from the principle component analysis (PCA) of the autocorrelation matrix of the sound signals. Classification of a sound signal was performed by linearly combining the mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable, as are most speech-recognition algorithms. During the training phase, sounds of nuts with closed shells and with open shells were used to obtain a representative vector of each class. During the recognition phase, the feature vector from the sample under question was compared with representative vectors. The classification accuracy of closed-shell nuts was more than 99% on the validation set, which did not include the training set.Item Open Access Detection of empty hazelnuts from fully developed nuts by impact acoustics(IEEE, 2005) Onaran, İbrahim; Dülek, Berkan; Pearson, T. C.; Yardımcı, Y.; Çetin, A. EnisShell-kernel weight ratio is the main determinate of quality and price of hazelnuts. Empty hazelnuts and nuts containing undeveloped kernels may also contain mycotoxin producing molds, which can cause cancer. A prototype system was set up to detect empty hazelnuts by dropping them onto a steel plate and processing the acoustic signal generated when kernels impact the plate. The acoustic signal was processed by five different methods: 1) modeling of the signal in the time domain, 2) computing time domain signal variances in short time windows, 3) analysis of the frequency spectra magnitudes, 4) maximum amplitude values in short time windows, and 5) line spectral frequencies (LSFs). Support Vector Machines (SVMs) were used to select a subset of features and perform classification. 98% of fully developed kernels and 97% of empty kernels were correctly classified.Item Open Access Detection of fungal damaged popcorn using image property covariance features(Elsevier, 2012) Yorulmaz, O.; Pearson, T. C.; Çetin, A.Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.Item Open Access Detection of insect damaged wheat kernels by impact acoustics(IEEE, 2005-03) Pearson, T. C.; Çetin, A. Enis; Tewfik, A. H.Insect damaged wheat kernels (IDK) are characterized by a small hole bored into the kernel by insect larvae. This damage decreases flour quality as insect proteins interfere with the bread-making biochemistry and insect fragments are very unsightly. A prototype system was set up to detect IDK by dropping them onto a steel plate and processing the acoustic signal generated when kernels impact the plate. The acoustic signal was processed by three different methods: 1) modeling of the signal in the time domain, 2) computing time domain signal variances in short time windows, and 3), analysis of the frequency spectra magnitudes. Linear discriminant analysis was used to select a subset of features and perform classification. 98% of un-damaged kernels and 84.4% of IDK were correctly classified.Item Open Access Detection of underdeveloped hazelnuts from fully developed nuts by impact acoustics(American Society of Agricultural and Biological Engineers, 2006) Onaran, I.; Pearson, T. C.; Yardimci, Y.; Çetin, A. EnisShell-to-kernel weight ratio is a vital measurement of quality in hazelnuts as it helps to identify nuts that have underdeveloped kernels. Nuts containing underdeveloped kernels may contain mycotoxin-producing molds, which are linked to cancer and are heavily regulated in international trade. A prototype system was set up to detect underdeveloped hazelnuts by dropping them onto a steel plate and recording the acoustic signal that was generated when a kernel hit the plate. A feature vector comprising line spectral frequencies and time-domain maxima that describes both the time and frequency nature of the impact sound was extracted from each sound signal and used to classify each nut by a support-vector machine. Experimental studies demonstrated accuracies as high as 97% in classifying hazelnuts with underdeveloped kernels.Item Open Access Discrimination between closed-and open-shell (Turkish) pistachio nuts using undecimated wavelet packet transform(American Society of Agricultural and Biological Engineers, 2008) Ince, N. F.; Goksu, F.; Tewfik, A. H.; Onaran, I.; Çetin, A. Enis; Pearson, T. C.Due to low consumer acceptance and the possibility of immature kernels, closed-shell pistachio nuts should be separated from open-shell nuts before reaching the consumer. A system using impact acoustics as a means of classifying closed-shell nuts from open-shell nuts has already been shown to be feasible and have better discrimination performance than a mechanical system. The accuracy of an impact acoustics based system is determined by the signal processing and feature extraction procedures. In this article, a new time-frequency plain feature extraction and classification algorithm was developed to discriminate between open- and closed-shell pistachio nuts produced in the Gaziantep region of Turkey. The proposed approach relies on the analysis of the impact acoustics signal of pistachio nuts, which are emitted from their impact with a steel plate after dropping from a certain height. Features are extracted by decomposing the acoustic signals into time and frequency components, using double-tree undecimated wavelet packet transform. The most discriminative features from the dual tree nodes are selected by a wrapper strategy that includes the structural pruning of the double-tree feature dictionary. The proposed approach requires no prior knowledge of the relevant time or frequency content of the acoustic signals. The algorithm used a small number of features and achieved a classification accuracy of 91.7% on the validation data set, while separating the closed shells from the open ones. A previously implemented algorithm, which uses maximum signal amplitude, absolute integration, and gradient features, achieved 82% classification accuracy on the same dataset. The results show that the time-frequency features extracted from impact acoustics can be used successfully for classification of open- and closed-shell Turkish pistachios.Item Open Access System for removing shell pieces hazelnut kernels using impact vibration analysis(Elsevier BV, 2014-02) Çetin, A. Enis; Pearson, T. C.; Sevimli, R. A.A system for removing shell pieces from hazelnut kernels using impact vibration analysis was developed in which nuts are dropped onto a steel plate and the vibration signals are captured and analyzed. The mel-cepstral feature parameters, line spectral frequency values, and Fourier-domain Lebesgue features were extracted from the vibration signals. The best experimental results were obtained using the melcepstral feature parameters. The feature parameters were classified using a support vector machine (SVM), which was trained a priori using a manually classified dataset. An average recognition rate of 98.2% was achieved. An important feature of the method is that it is easily trainable, enabling it to be applicable to other nuts, including walnuts and pistachio nuts. In addition, the system can be implemented in real time. 2013 Elsevier B.V. All rights reserved