Browsing by Author "Onaran, I."
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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 Falling person detection using multisensor signal processing(SpringerOpen, 2008) Toreyin, B. U.; Soyer, E. B.; Onaran, I.; Cetin, E. E.Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR) and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov Models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision.Item Open Access Identification of damaged wheat kernels and cracked-shell hazelnuts with impact acoustics time-frequency patterns(American Society of Agricultural and Biological Engineers, 2008) Ince, N. F.; Onaran, I.; Pearson, T.; Tewfik, A. H.; Çetin, A. Enis; Kalkan, H.; Yardimci, Y.A new adaptive time-frequency (t-f) analysis and classification procedure is applied to impact acoustic signals for detecting hazelnuts with cracked shells and three types of damaged wheat kernels. Kernels were dropped onto a steel plate, and the resulting impact acoustic signals were recorded with a PC-based data acquisition system. These signals were segmented with a flexible local discriminant bases (F-LDB) procedure in the time-frequency plane to extract discriminative patterns between damaged and undamaged food kernels. The F-LDB procedure requires no prior knowledge of the relevant time or frequency indices of the impact acoustics signals for classification. The method automatically finds all crucial time-frequency indices from the training data by combining local cosine packet analysis and a frequency axis clustering approach, which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post-processed by principal component analysis, and fed to a linear discriminant classifier. Experimental results establish the superior performance of the proposed approach when compared to prior techniques reported in the literature or used in the field. The new approach separated damaged wheat kernels (IDK, pupal, and scab) from undamaged wheat kernels with 96%, 82%, and 94% accuracy, respectively. It also separated cracked-shell hazelnuts from those with undamaged shells with 97.1% accuracy. The adaptation capability of the algorithm to the time-frequency patterns of signals makes it a universal method for food kernel inspection that can resist the impact acoustic variability between different kernel and damage types.Item Open Access Image description using a multiplier-less operator(IEEE, 2009-06-05) Tuna, H.; Onaran, I.; Çetin, A. EnisA fast algorithm for image classification based on a computationally efficient operator forming a semigroup on real numbers is developed. The new operator does not require any multiplications. The co-difference matrix based on the new operator is defined and an image descriptor using the co-difference matrix is developed. In the proposed method, the multiplication operation of the well-known covariance method is replaced by the new operator. The proposed method is experimentally compared with the regular covariance matrix method. The proposed descriptor performs as well as the the regular covariance method without performing any multiplications. Texture recognition and licence plate identification examples are presented.Item Open Access Pulse doppler radar target recognition using a two-stage SVM procedure(IEEE, 2010-07-07) Eryildirim, A.; Onaran, I.It is possible to detect and classify moving and stationary targets using ground surveillance pulse-Doppler radars (PDRs). A two-stage support vector machine (SVM) based target classification scheme is described here. The first stage tries to estimate the most descriptive temporal segment of the radar echo signal and the target signal is classified using the selected temporal segment in the second stage. Mel-frequency cepstral coefficients of radar echo signals are used as feature vectors in both stages. The proposed system is compared with the covariance and Gaussian mixture model (GMM) based classifiers. The effects of the window duration and number of feature parameters over classification performance are also investigated. Experimental results are presented.Item Open Access Sparse spatial filter via a novel objective function minimization with smooth ℓ1 regularization(Elsevier, 2013-05) Onaran, I.; Ince, N. F.; Çetin, A. EnisCommon spatial pattern (CSP) method is widely used in brain machine interface (BMI) applications to extract features from the multichannel neural activity through a set of spatial projections. These spatial projections minimize the Rayleigh quotient (RQ) as the objective function, which is the variance ratio of the classes. The CSP method easily overfits the data when the number of training trials is not sufficiently large and itis sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited to some adequate number. We introduce a spatially sparse projection (SSP) method that exploits the unconstrained minimization of a new objective function with approximated 1 penalty. Unlike the RQ, this new objective function depends on the magnitude of the sparse filter. The SSP method is employed to classify the multiclass ECoG and two class EEG data sets. We compared our results with a recently introduced sparse CSP solution based on 0 norm. Our method outperforms the standard CSP method and provides comparable results to 0 norm based solution and it is associated with less computational complexity. We also conducted several simulation studies on the effect of noisy channel and intersession variability on the performance of the CSP and sparse filters.